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أعمال المؤتمر العلمي الدولي الافتراضي الثاني حول مساهمة العلوم البيوطبية في تطويرالأداء الرياضي النخبوي أيام 29-30 ماي2021
(جامعة أم البواقي, 2021) مالك, رضا
تهدف الدراسة إلى التعرف على أثر كل من التدريب المتقطع والتدريب الفتر ي على السرعة الهوائية القصوى وقدرة الاسترجاع لدى لاعب ي كر ة القدم أكابر.ولهذا الغ رض استخدمنا المنهج التجريبي، على عينة متكونة من 40 لاعبا، مقسمة إلى عينتي ن 20 لاعبا من كل فريق. وقد تم اختياره ا بشكل قصدي من نادي أمل شلغوم العبد ونادي 2020 . لجم ع / شباب ميلة للموسم الرياض ي 2019 Polar و جهاز GPS البيانات استخدمنا جهاز والاختبارات البدنية بعد جمع النتائج ومعالجته ا إحصائيا تم التوصل إلى أ ن التدريب المتقطع أكثر فعالية من التدريب الفتري في تطوير السرعة الهوائية القصوى قدرة الاسترجاع لدى لاعب ي كر ة القدم أكابر. The study aims to identify the effect of both intermittent training and interval training on the maximum air speed and recovery ability of senior football players. For this purpose, we used the experimental method, on a sample of 40 players, divided into two samples, 20 players from each team. She was intentionally selected from Amal Shalghoum Al-Abed Club and Mila Youth Club for the 2019/2020 sports season. To collect the data, we used GPS, Polar and physical tests. After collecting the results and processing them statistically, it was concluded that intermittent training is more effective than interval training in developing the maximum aerobic speed and recovery capacity of senior football player
Les villes petites et moyennes dans un monde globalisé
(Université d'Oum El Bouaghi, 2021) Bousmaha, Ahmed
Electronics, artifical intelligence and new technologies
(University of Oum El Bouaghi, 2021) Lashab, Mohamed
The Electronics and New Technologies Laboratory (LENT) is organizing the first international conference on electronics, artificial intelligence and new technologies (ICEAINT'21), this will be held at the University of Larbi Ben M’hidi, Oum El Bouaghi. The conference will cover all the research activities of the laboratory; moreover, the conference will include different axes of electronics, computer science and new technologies likely to interest all researchers in these fields. The ICEAINT'21 aims to bring together academic researchers and industrial scientists to exchange new ideas, original research results and practical development experiences on all aspects dealing with the different themes of the conference.
Achieving the quality of accounting learning outcomes: an Algerian perspective
(University of Oum El Bouaghi, 2021-05-10) Rimouche, Kaoutar; Himrane, Mohammed
This paper aims to demonstrate the efforts made by the International Accounting Education Standards Board (IAESB) in achieving the quality of the accounting learning outcomes and to explore the reality of accounting education in Algerian. We based on the comparative method in order to compare between the content of universities' accounting programs and the International Accounting Education Standard (2). Considering that the accounting educational system in universities should emphasize serving students and ensure that they will be able to practice the accounting profession in the future, and that accounting education should seek to adapt to the trend of professional accounting development. We have concluded that there is almost a perfect match between what Algerian universities apply and what the International Federation of accountant’s committee approved, but the problem lies in achieving the learning outcomes with the required quality as stated in the IES2 تهدف هذه الورقة إلى إظهار الجهود التي يبذلها المجلس الدولي لمعايير تعليم المحاسبة (IAESB) في تحقيق جودة مخرجات تعلم المحاسبة واستكشاف واقع تعليم المحاسبة في الج ا زئر. حيث اعتمدنا على المنهج المقارن لمقارنة بي ن محتوى برامج المحاسبة بالجامعات ومعيار تعليم المحاسبة الدولي (2). مع الأخذ في الاعتبار أن النظام التعليمي المحاسبي في الجامعات يجب أن يركز على خدمة الط لب والتأكد من أنهم سيكونون قادرين على ممارسة مهنة المحاسبة في المستقبل، وأن التعليم المحاسبي يجب أن يسعى للتكيف مع اتجاه تطوير المحاسبة المهنية. لقد توصلنا إلى أن هناك تطابقًا تامًا تقريبًا بين ما تقدمه الجامعات الجزائرية وما أقرته لجنة المحاسبين الفيدرالية الدولية، لكن المشكلة تكمن في تحقيق مخرجات التعلم بالجودة المطلوبة كما هو مذكور في .IES2
Accounting Education: a theoretical overview
(University of Oum El Bouaghi, 2021-05-10) Khalfallah, Zakarya
The main purpose of this paper is to highlight the Accounting Education as a real asset that must continually evolve in order to best prepare future professionals on accounting for the new requirements imposed by technology and globalization. To do this, a descriptive methodological approach has been deployed to present Accounting Education Standards (IESs), which are developed by the International Accounting Education Standards Board (IAESB). These standards are influencing Accounting Education and training worldwide. The goal of the IESs is to ensure that economic decision makers can rely on the competence of professional accountants regardless of the country where the accountants received their education and training. Two major lessons emerge from this study. On the one hand, that the organizational structure, globalization and technology impact the skills needs of the accountancy profession and, on the other hand, there is a gap between the training provided and the needs of the profession. تستهدف هذه الورقة البحثية تسليط الضوء على التعليم المحاسبي كأداة حقيقية يجب أن تتطور باستمرار من أجل إعداد أفضل المهنيين المحاسبين في المستقبل، وذلك بهدف الاستجابة للمتطلبات الجديدة التي تفرضها التكنولوجيا والعولمة؛ وقد اعتمدت الورقة البحثية في تحقيق أهدافها على المنهج الوصفي التحليلي من خلال تقديم والتي تم تطويرها من قبل مجلس معايير تعليم ،(IESs) معايير التعليم المحاسبي حيث تؤثر هذه المعايير على التعليم المحاسبي ، (IAESB) المحاسبة الدولي والتدريب في جميع أنحاء العالم، كما تهدف إلى ضمان أن صناع القرار الاقتصادي يمكنهم الاعتماد على كفاءة المحاسبين بغض النظر عن الدولة التي تلقوا فيها تعليمهم وتدريبهم.
Behavioural verification of limited resources systems under true concurrency semantics
(University of Oum El Bouaghi, 2021-05-25) Bouneb; Messaouda; Saidouni; Djamel Eddine
In this paper we propose a true concurrency semantics for limited resources systems using K-bounded Petri net as modeling formalism and maximality labeled transition system (MLTS) as semantics model. Indeed the model of MLTS expresses clearly the semantics of true parallelism of concurrent systems. The proposed operational maximality semantics for Kbounded Petri nets makes it possible to interpret any K-bounded Petri net in terms of MLTS. Through an example we show the interest of the proposed semantics in comparison with the interleaving semantics and the ST semantics. The comparison concerns the preservation of true concurrency and the reduction of the size of the semantics model. Furthermore, we will show that expected CTL properties may be verified on the corresponding maximality labeled transition system of a modeled system using our developed tool.
Evaluation and comparison study of video streaming routing protocols in vehicular ad-hoc networks
(University of Oum El Bouaghi, 2021-05-25) Zaidi, Sofiane; Ogab, Mostafa; Khamer, Lazhar
Video streaming is a challenging issue in Vehicular Ad-Hoc Networks (VANETs), due to the strict video streaming Quality of Service (QoS) requirements, such as throughput, delivery ratio, and transmission delay. Moreover, video streaming is influenced by VANET characteristics, such as the high dynamic topology, fluctuation of vehicle density, and environmental obstacles. In VANET, video streaming can be achieved through different VANET communication types, such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to Broadband cloud (V2B). Based on these communications, the vehicles can exchange between them the video stream over single or multi-hop link. When the video content is delivered over a multi-hop link, the vehicles have to use a routing protocol to disseminate the video stream through a path (s) between the sender (s) end the receiver (s) vehicles. In this paper, we have presented an overview of popular existing routing protocols for video streaming in VANET, such as AODV, AOMDV, DSR, and DSDV. Furthermore, we have evaluated and compared these protocols in terms of some QoS evaluation metrics, such as throughput, packet delivery ratio, and end-to-end delay in function with vehicles density in order to judge which one is outperforming for video streaming in VANET. The simulation results show that the reactive routing protocols (AODV, AOMDV, DSR) provide higher throughput and packet delivery ratio than DSDV proactive routing protocol. However, DSDV achieves lower end-to-end delay than AODV, AOMDV, DSR routing protocols
NECS-based Cache Management in the Named Data Networking
(University of Oum El Bouaghi, 2021-05-25) Fathallah, Nour Al Huda; Bouziane, Hafiza; Sharafiya, Abdullah
The Information-Centric Networking ICN architectures proposed to overcome the problems of the actual internet architecture. One of the main straight points of the ICN architectures is in-network caching. The effectiveness of the adopted caching strategy, which manages and decides where to store them, influences the performance of the ICN. However, the major issue that faces the caching strategies in the ICN architectures is the strategic selection of the cache routers to store the data through its delivery path. This will reduce congestion, optimize the distance between the consumers and the required data furthermore improve latency and alleviate the viral load on the servers. In this paper, we propose a new efficient caching strategy for the Named Data Networking architecture NDN named NECS, which is the most promising architecture between all the ICN architectures. The proposed strategy reduces the traffic redundancy, eliminates the useless replication of contents, and improves the replay time for users due to the strategic position of cache routers. Besides, we evaluate the performance of this proposed strategy and compare it with three other NDN caching strategies, using the simulator network environment NdnSIM. Based on the simulations carried out, we obtained interesting and convincing results.
On the Drivers’ Behavior Evaluation using Vehicular Networks
(University of Oum El Bouaghi, 2021-05-25) Bendouma, Tahar; Tahari, Abdou El Karim; Kerrache, Chaker Abdelaziz; Boukhelkhaly, Mama Chima; Bendoumay, Rekaia; Lagraa, Nasreddine
With the emergence of connected and intelligent vehicles, various research projects aiming at reducing traffic accidents by detecting driver behavior have also emerged. These vehicles are generally equipped with cameras and sensors that can be used to detect driver’s fatigue, drowsiness, and distraction using different technologies and a multitude of classification techniques. In this work, we propose a new real-time driver behavior-detection technique based on vehicle-to-vehicle communication (V2V) and by exploiting the information carried by the periodically exchanged messages known as Cooperative Awareness Message (CAM) that are a part of the European ETSI-ITS standard (or Basic safety message BSM in the US standard). These information include the vehicle’s current speed, the average speed, the position, the acceleration, to name a few. In our proposal, each vehicle can classify its neighbors (normal, aggressive) according to its driver’s driving style. An audio or video message can be then generated to warn the driver of any vehicle presenting a danger. Simulations conduct in both rural and urban environments depict that our proposal called ”Vehicular Ad-Hoc Network Exchange Message (VanetExM)” can determine the state of the driver with a relatively high success rate and low overhead.
Biometric Image Encryption Scheme based on Modified Double Random Phase Encoding System
(University of Oum El Bouaghi, 2021-05-25) Yahi, Amina; Bekkouche, Tewfik; Diffellah, Nacira; Daachi, Mohamed El Hossine
In this paper, an opto-digital encryption scheme based on a modified Double Random Phase Encoding (DRPE) system is proposed. Two biometric modalities are used in this work which is the face and the corresponding finger print of the same person. Firstly the face biometric image is encrypted chaotically using the permutation-diffusion architecture. Then obtained encrypted face is multiplied element by element by a constructed mask formed by injecting the finger print image within the phase of this mask. The obtained result will be transformed into a frequency domain by the two-dimensional. Fourier transform or any of its derivatives, resulting complex image is exactly the encrypted biometric image. Experiment computer simulations confirm the efficiency of this work in terms of histogram analysis, loss data and sensitivity test when compared with existing works.
Deep Neural Transformer Model for Mono and Multi Lingual Machine Translation
(University of Oum El Bouaghi, 2021-05-25) Khaber, Mohamed Islam; Frahta, Nabila; Moussaoui, Abdelouahab; Saidi, Mohamed
In recent years, the Transformers have emerged as the most relevant deep architecture, especially machine translation. These models, which are based on attention mechanisms, outperformed previous neural machine translation architectures in several tasks. This paper proposes a new architecture based on the transformer model for the monolingual and multilingual translation system. The tests were carried out on the IWSLT 2015 and 2016 dataset. The Transformers attention mechanism increases the accuracy to more than 92% that we can quantify by more than 4 BLEU points (a performance metric used in machine translation systems).
Towards emotion recognition in immersive virtual environments: A method for Facial emotion recognition
(University of Oum El Bouaghi, 2021-05-25) Amara, Kahina; Ramzan, Naeem; Zenati, Nadia; Djekoune, Oualid; Larbes, Cherif; Guerroudji, Mohamed Amine; Aouam, Djamel
Virtual Reality (VR) is, thus, proposed as a powerful tool to simulate complex, real situations and environments, offering researchers unprecedented opportunities to investigate human behaviour in closely controlled designs in controlled laboratory conditions. Facial emotion recognition has attracted a great deal of interest for interaction in virtual reality, healthcare system: therapeutic applications, surveillance video application etc. In this paper, we propose a method for facial emotion recognition for immersive virtual environment based on 2D and 3D geometrical features. We used our collected dataset of 17 subjects’ performance of six basic facial emotions (anger, fear, happiness, surprise, sadness, and neutral) using three devices: Kinect (v1), Kinect (v2), and RGB HD camera. In addition, we present the performance results of the RGB data for facial emotion recognition using Bagged Trees algorithm. To assess the performance of the proposed system, we used leave-oneout- subject cross-validation. We compared the 2D and 3D data performance for facial expression recognition. The obtained results show the superior performance of the RGB-D features provided by Kinect (v2). Our findings highlight that the 2D images are not robust enough for facial emotion recognition. The built facial emotion models will animate virtual characters that can express emotions via facial expressions. This could be deployed for Chatting, Learning and Therapeutic Intervention.
Compressed VGG16 Auto-Encoder for Road Segmentation from Aerial Images with Few Data Training
(University of Oum El Bouaghi, 2021-05-25) Abdeldjalil, Kebir; Taibi, Mahmoud; Serradilla, Francisco; Spain, Madrid
Deep Learning methods have found many applications such as segmentation, recognition and classification. However, almost all of these methods require large data-set for the training step and a long training time. Indeed, in surveillance video domain, as for many real-world applications, samples are only accessible in limited amounts owing to acquisition and experiments complexity. In this work, we introduce compressed VGG Auto-Encoder system for road image segmentation in highresolution aerial imagery. The objective of our experiments is to improve the methodology of distinguishing the road network when only few Data is available. We propose an approach based on compressed Auto-encoder; focus on avoiding the over-fitting effect by generating new data augmentation, based on basic filter transformation to increase and enhance the quality of data training, in the aim of learn an appropriate and simplified representation of data from the original data set in order to obtain a deeper insight from large data-set, and to achieve a quick segmentation training time. Our model achieve a good result and is considered as the best network for fast and accurate segmentation of road images, compared to other models. Furthermore, we provide an explanation of these techniques and some recommendation for their use in the field of deep learning.
Distributed Secure Services Based on IoT and Blockchain for e-Health remote care
(University of Oum El Bouaghi, 2021-05-25) Reffad, Hamza; Djenaoui, Abdelatif; Alti, Adel
Nowadays, the Internet of Thing (IoT) is a potentially powerful solution for health applications. It is a smart technology that provides remote care in real time and requires low latency health data processing and transmission. The large number of connected objects to Cloud can be a problem for low-latency workloads, which is the case of several health mobile applications. To this end, Fog Computing, has emerged, where Cloud computing is extended to the edge of the network to reduce latency and network congestion. It provides a highly virtualized platform that provides health data storage on remote public Cloud servers to which users cannot be fully trusted, especially when we are dealing with sensitive data like health data. In fact, it becomes necessary to rethink a new more robust secure technique. To provide such technique, we proposed a new secure solution called IoToDChain for e-Health mobile application, based on cryptographic techniques especially Elliptic Curve Diffie Hellman-RSA and the Blockchain paradigm. They exchange of a secret key in confidential and robust manner and protect patients’ privacy in a mobile-Fog-Cloud environment. The experiments achieved promising results for good data protection against the most known attacks in healthcare systems.
Image denoising algorithms using norm minimization techniques
(University of Oum El Bouaghi, 2021-05-25) Diffellah, Nacira; Bekkouche, Tewfik; Hamdini, Rabah
Image denoising is one of the fundamental image processing problems. Noise removal is an important step in the image restoration process. In this paper, firstly we develop and implement two different image denoising algorithms based on norm minimization, namely `1 and `2-regularization applied to images contaminated by gaussian noise. Then, after their discretization and implementation, we perform a comparison between the two methods using several test images. Through this study, the algorithm which minimizes `2-norm of gradient of image has a unique solution and it’s easy to implement, but it doesn’t accept contour discontinuities, causing the obtained solution to be smooth. The `2-norm will blur the edges of the image. In order to preserve sharp edges, `1-norm is introduced. There are different methods to solve the problem of energy minimization. In this work, we have chosen the discretization finite difference method before applying the gradient descent algorithm to optimize the signal (2D grayscale images) denoising functionality. Experiments results, show that `1 regularization encourages image smoothness while allowing for presence of jumps and discontinuities, a key feature for image processing because of the importance of edges in human vision.
Evaluation of ANN, ICA-ANN and PSO-ANN predicting ability in the prediction of CO2 emissions during the calcination of cement raw material
(University of Oum El Bouaghi, 2021-05-25) Boukhari, Yakoub
Cement industry releases large amounts of carbon dioxide CO2 as by-product to the atmosphere during the calcination of cement raw material. In fact, the calcination is a complex process and not completely understood. The amount of CO2 emitted varies with the grain size, chemical composition, burning temperature and time to pass through the kiln during calcination process. However, due to interaction of several parameters, it is not easy to establish accurate mathematic model to calculate the real amount of CO2 emission. Moreover, using the laboratory experiments to determine the amount of CO2 emissions are not usually easy, time-consuming, expensive and require good quality of reagents and equipments. To overcome the above problems, artificial neural network (ANN), ANN optimised by imperialist competitive algorithm (ICAANN), ANN optimised by particle swarm optimization (PSOANN) are applied to predict amount of CO2 emissions. A comparative accuracy of these tools is evaluated based on the coefficient of determination R2, R2 adjusted, mean absolute percentage error (MAPE) and scatter index (SI). The results obtained are promising and demonstrate that all proposed tools represent a good alternative for the prediction of CO2 emission with adequate accuracy. PSO and ICA are capable to improve the predicting accuracy of ANN. In addition, PSO-ANN can predict slightly better than ICA-ANN. Based on testing data, the results obtained show that 98.61%, 98.18% and 97.5% of experimental data are explained by PSO-ANN, ICAANN and ANN, respectively with average relative error less than 1.41%and SI less than 0.1.
An Improved Binary Particle Swarm Optimization of RFM’s for ALSAT2 Imagery
(University of Oum El Bouaghi, 2021-05-25) Mezouar , Oussama; Meskine, Fatiha; Boukerch, Issam
Rational function model (RFM) is commonly used in photogrammetric and remote sensing applications because it does not need sensor parameters. Therefore, the RFM terms or also rational polynomial coefficients (RPCs) have no physical significance but depends on many ground control points (GCPs) that make the model prone to the over parameterization problem. This paper proposes a new binary particle swarm optimization algorithm to surmount the issue of over parameterization and find the optimum combination of RPCs for the RFM by adding a new transfer function in binary PSO in order to increase the convergence speed and avoid the local minimum phenomenon. The results showed that the proposed method is compatible with different types of RFM, more stable, and gives higher accuracy than the traditional binary PSO.
2nd International Conference on Computer Science's Complex Systems and their Applications
(University of Oum El Bouaghi, 2021-05-25) Marir, Toufik; Bourouis, Abdelhabib; Benaboud, Rohallah
Modularity maximization to find community structure in complex networks
(University of Oum El Bouaghi, 2021-05-25) Saoud, Bilal
Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.
Lips Recognition for Biometric Identification Systems
(University of Oum El Bouaghi, 2021-05-25) Boucetta, Aldjia; Boussaad, Leila
In recent years, researches in biometric methods have gained much attention and they have advanced to a wide scope in security concepts. Therefore, many biometric technologies have been developed and enhanced with many of the most successful security applications. Lately, lip-based biometric identification becomes one of the most relevant emerging tools, which comes from criminal and forensic real-life applications. The main purpose of this paper is to prove the benefit of lips as a biometric modality, by using both handcraft and deeplearning based feature extraction methods. So, we consider three different techniques, Histogram of Oriented Gradients(HOG), Local Binary Pattern(LBP) and pretrained Deep-CNN. All results are confirmed by a ten-fold cross-validation method using two datasets, NITRLipV1 and database1. The mean accuracy is found to be very high in all the experiments carried out. Also the feature extraction using the Inceptionv3 model always achieve highest mean accuracy.

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