Secure and Efficient Video Transmission in VANET

Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP.Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC

Harnessing a Refinement Theory to Compute Loop Functions

AbstractWe consider a while loop on some space S and we are interested in deriving the function that this loop defines between its initial states and its final states (when it terminates). Such a capability is useful in a wide range of applications, including reverse engineering, software maintenance, program comprehension, and program verification. In the absence of a general theoretical solution to the problem of deriving the function of a loop, we explore engineering solutions. In this paper we use a relational refinement calculus to approach this complex problem in a systematic manner. Our approach has many drawbacks, some surmountable and some not (being inherent to the approach); nevertheless, it offers a way to automatically derive the function of loops or an approximation thereof, under some conditions

Evaluation of Variability in Tunisian Olea europaea L. Accessions using Morphological Characters and Computational Approaches

The olive trees (Olea europaea L.) have been cultivated for millennia in the Mediterranean basin and its oil has been an important part of human nutrition in the region. In order to distinguish between olive accessions, morphological and biological characters have been widely and commonly used for descriptive purposes and have been used to characterize olive accessions. A comparative study of morphological characters of olive accessions grown in Tunisia was carried out and analyzed using Bayesian Networks (BN) and Principal Components Analysis (PCA). The obtained results showed that averages of fruit and kernel weights were 2.27 grams and 0.41 grams, respectively.  Besides, a relatively moderate level of variation (51.22%) being explained by four Principal components. BN revealed that geographical localisation plays a role in the increase of tree habit, size of lenticels and leaf shape. A dendrogram has been carried out in the aim to classify studied olive accessions. We proposed a novel method of analysis based on the three-step scheme, in which first the data set is clustered, then olive tree features are evaluated. The studied accessions can be divided into four main groups by cutting the dendrogram at a similarity value of 0.645. Different relationships are studied and highlighted, and finally the collected features are subjected to a global principal component analysis. Obtained results confirmed that core surface was negatively correlated with geographical location (r = -0.52, p<0.05) and maturation period r = -0.539, p<0.05). Number of lenticels was positively correlated to lenticels size (r = 0.632, p<0.05). Core shape had a negative correlation with fruit shape (r = -0.759, p<0.05). On the basis of these findings, this research confirmed that morphological markers are a preliminary tool to characterize olive oil accessions

Optimizations for real-time implementation of H264/AVC video encoder on DSP processor

International audienceReal-time H.264/AVC high definition video encoding represents a challenging workload to most existing programmable processors. The new technologies of programmable processors such as Graphic Processor Unit (GPU) and multicore Digital signal Processor (DSP) offer a very promising solution to overcome these constraints. In this paper, an optimized implementation of H264/AVC video encoder on a single core among the six cores of TMS320C6472 DSP for Common Intermediate Format (CIF) (352x288) resolution is presented in order to move afterwards to a multicore implementation for standard and high definitions (SD,HD).Algorithmic optimization is applied to the intra prediction module to reduce the computational time. Furthermore, based on the DSP architectural features, various structural and hardware optimizations are adopted to minimize external memory access. The parallelism between CPU processing and data transfers is fully exploited using an Enhanced Direct Memory Access controller (EDMA). Experimental results show that the whole proposed optimizations, on a single core running at 700 MHz for CIF resolution, improve the encoding speed by up to 42.91%. They allow reaching the real-time encoding 25 f/s without inducing any Peak Signal to Noise Ratio (PSNR) degradation or bit-rate increase and make possible to achieve real time implementation for SD and HD resolutions when exploiting multicore features

Leaderboards in Gamified Information Systems for Health Behavior Change: The Role of Positioning, Psychological Needs, and Gamification User Types

Leaderboards are widely used in gamified information systems (IS) for health behavior change (HBC) to evoke both instrumental and experiential outcomes within users. In literature, however, they are often discussed controversially as they are perceived positively by some users but discouraging by others. In this work, we investigate under which circumstances users’ position on the leaderboard influences their attitudes toward an mHealth app. Based on self-determination theory and the gamification user types hexad, we conducted an online experiment among 179 potential users. The results support our hypotheses that positioning influences perceived competence and relatedness, which alongside perceived autonomy positively impact users’ attitude. Yet, our findings do not support the assumption that the relationship between needs and attitude is moderated by gamification user type. This finding reinforces recent research which questions the effectiveness of user type-based gamification and calls to focus on general need satisfaction

TFS-ViT: Token-Level Feature Stylization for Domain Generalization

Standard deep learning models such as convolutional neural networks (CNNs) lack the ability of generalizing to domains which have not been seen during training. This problem is mainly due to the common but often wrong assumption of such models that the source and target data come from the same i.i.d. distribution. Recently, Vision Transformers (ViTs) have shown outstanding performance for a broad range of computer vision tasks. However, very few studies have investigated their ability to generalize to new domains. This paper presents a first Token-level Feature Stylization (TFS-ViT) approach for domain generalization, which improves the performance of ViTs to unseen data by synthesizing new domains. Our approach transforms token features by mixing the normalization statistics of images from different domains. We further improve this approach with a novel strategy for attention-aware stylization, which uses the attention maps of class (CLS) tokens to compute and mix normalization statistics of tokens corresponding to different image regions. The proposed method is flexible to the choice of backbone model and can be easily applied to any ViT-based architecture with a negligible increase in computational complexity. Comprehensive experiments show that our approach is able to achieve state-of-the-art performance on five challenging benchmarks for domain generalization, and demonstrate its ability to deal with different types of domain shifts. The implementation is available at: https://github.com/Mehrdad-Noori/TFS-ViT_Token-level_Feature_Stylization

FANCA Gene Mutations in North African Fanconi Anemia Patients

Populations in North Africa (NA) are characterized by a high rate of consanguinity. Consequently, the proportion of founder mutations might be higher than expected and could be a major cause for the high prevalence of recessive genetic disorders like Fanconi anemia (FA). We report clinical, cytogenetic, and molecular characterization of FANCA in 29 North African FA patients from Tunisia, Libya, and Algeria. Cytogenetic tests revealed high rates of spontaneous chromosome breakages for all patients except two of them. FANCA molecular analysis was performed using three different molecular approaches which allowed us to identify causal mutations as homozygous or compound heterozygous forms. It included a nonsense mutation (c.2749C > T; p.Arg917Ter), one reported missense mutation (c.1304G > A; p.Arg435His), a novel missense variant (c.1258G > A; p.Asp409Glu), and the FANCA most common reported mutation (c.3788_3790delTCT; p.Phe1263del). Furthermore, three founder mutations were identified in 86.7% of the 22 Tunisian patients: (1) a deletion of exon 15, in 36.4% patients (8/22); (2), a deletion of exons 4 and 5 in 23% (5/22) and (3) an intronic mutation c.2222 + 166G > A, in 27.3% (6/22). Despite the relatively small number of patients studied, our results depict the mutational landscape of FA among NA populations and it should be taken into consideration for appropriate genetic counseling

Comparative Study of the Long-Term Impact of the COVID-19 Pandemic on Mental Health and Nutritional Practices Among International Elite and Sub-Elite Athletes: A Sample of 1420 Participants from 14 Countries

Background Although several studies have shown that the Coronavirus Disease 2019 (COVID-19) lockdown has had negative impacts on mental health and eating behaviors among the general population and athletes, few studies have examined the long-term effects on elite and sub-elite athletes. The present study aimed to investigate the long-term impact of COVID-19 lockdown on mental health and eating behaviors in elite versus sub-elite athletes two years into the pandemic. A cross-sectional comparative study was conducted between March and April 2022, involving athletes from 14 countries, using a convenient non-probabilistic and snowball sampling method. A total of 1420 athletes (24.5 ± 7.9 years old, 569 elites, 35% women, and 851 sub-elites, 45% women) completed an online survey-based questionnaire. The questionnaire included a sociodemographic survey, information about the COVID-19 pandemic, the Depression, Anxiety and Stress Scale—21 Items (DASS-21) for mental health assessment, and the Rapid Eating Assessment for Participants (REAP-S) for assessing eating behavior. Results The results showed that compared to sub-elite athletes, elite athletes had lower scores on the DASS-21 (p = .001) and its subscales of depression (p = .003), anxiety (p = .007), and stress (p < .001), as well as a lower REAP-S score indicating lower diet quality (p = .013). Conclusion In conclusion, two years into the pandemic, elite athletes were likelier to have better mental health profiles than sub-elite athletes but surprisingly had lower diet quality. Key Points Elite athletes had better mental health profiles compared to sub-elite athletes, with lower levels of depression, anxiety, and stress. Elite athletes reported greater psychological support and perceived themselves as more financially secure during the pandemic than sub-elite athletes do. Elite athletes were more likely to have poor eating habits compared to sub-elite athletes