Reviewerly/exHarmony huggingface dataset:
A Support Vector Machine Application for the Detection of Pupillary Markers of Cognitive Workload

The visual system is a core processing system and an assessment of the cognitive workload generated by vision-related tasks is made possible through pupillometry. Pupillometry, the study of pupil dimensions and their reactivity to mental effort, proposes that the diameter of the pupil expands concomitantly with the amount of mental resources mobilized (Beatty, 1982). However, considering the pupil’s responsiveness to the brightness of the surrounding, an application of pupillometry in the absence of controlled illumination represents a challenge. Overcoming this challenge is the objective of the present study. Using a head-mounted eye-tracking device, the present investigation induces transient levels of brightness in the continuous performance of cognitive tasks. Most importantly, the novel implementation of Support Vector Machine algorithms suited for recognition across intermittent time series enables the discrimination of frequency-specific pupillary markers of cognition from frequency-specific pupillary markers of brightness.
Synthetic Content Detection in Deepfake Video using Deep Learning

In recent years, significant improvements in the field of deep learning have promoted the development of highly realistic AI-based video manipulation and forgery technologies commonly known as “DeepFake” using Autoencoders and Generative Adversarial Networks (GANs). Although this technology has its own set of beneficial applications, it decreases the integrity of digital visual media and can have serious sociopolitical implications. With new improving video manipulation tools, the detection of deepfake is a major challenge. According to the recent surveys that are reported, the deepfake systems are able to give a decision about whether a given video contains fake content or not. The objective of our project is to identify the presence of deepfake content in the digital video and report the same. Our system uses ResNext Convolution Neural Network for extracting the features of the video at frame level and Long Short-Term Memory (LSTM) for training a model to classify if a video is deepfake or pristine. We have trained the network using Deepfake Detection Challenge dataset. The result along with the confidence score of the model is shown to the user using a simple graphical user interface.
Secure private cloud using machine learning and cryptography

There is a continuous increase in the use of the private cloud due to its many and important features. There are increase security challenges that target these systems. One of the most important requirements of users in cloud storage is to protect their cloud from attacks, and keep data secret. Modern technologies of machine learning provide the ability to analyze and classify the data. This paper proposes a model that serves as a server (a third party) between private cloud and users and it is based on two phases. The first of which is to protect the cloud from different types of attacks and detect normal and abnormal flow. Whilst the second one is to categorize the users’ data to be stored in the cloud and then encrypt it based on the degree of its importance using different types of encryption algorithms. The results show the accuracy respectively as Naive Bays 74%, SGD 82%, LR 85%, KNN 89%, and Random Forest 100%, Decision Tree 100%. For the second phase of classifying the data, the algorithms used are the decision tree, KNN, Random Forest, Naive Bays, LR, and SGD. The results have respectively 82%, 93%, 96%, and 97%, 98% and 98% respectively. Finally, the Random Forest and SGD algorithms for the first and second phases were used. The encryption algorithms that have been adopted are RC4, 3DES, and AES for encryption of classified data according to the importance of the second phase to be stored in the cloud in a secure form.
Possibilistic Neighborhood Graph: A New Concept of Similarity Graph Learning

Adaptive graph-based representation and learning methods have received extensive attention due to their good performance in supervised and unsupervised learning tasks. These methods often involve probability constraint, i.e., the sum-to-one constraint, when learning a similarity graph. Whereas this constraint may degrade the ability to precisely measure the similarity between samples. In terms of clustering tasks, limited by the sum-to-one constraint, the similarity between some noisy points may tend to be large, which causes these abnormal points to be grouped into a separate cluster, and other close clusters with normal points are merged. To address this problem, this study proposes a novel notion of possibilistic neighbor graph (PNG). In PNG, the possibility of two samples being neighbors can be measured adaptively and precisely by removing the sum-to-one constraint and incorporating a new regularization term. By using the possibility rather than the probability in the similarity graph learning, the solution space is enlarged without increasing the complexity. The mathematical properties of PNG are discussed in detail, and a new graph-based clustering method (CPNG) is also developed based on the learned PNG. Extensive experimental results on several benchmark datasets demonstrate the superiority of CPNG in comparison with some state-of-the-art methods.
Attribute-Based Anonymous Credential: Optimization for Single-Use and Multi-Use

User attributes can be authenticated by an attribute-based anonymous credential while keeping the anonymity of the user. Most attribute-based anonymous credential schemes are designed specifically for either multi-use or single-use. In this paper, we propose a unified attribute-based anonymous credential system, in which users always obtain the same format of credential from the issuer. The user can choose to use it for an efficient multi-use or single-use show proof. It is a more user-centric approach than the existing schemes. Technically, we propose an interactive approach to the credential issuance protocol using a two-party computation with an additive homomorphic encryption. At the same time, it keeps the security property of impersonation resilience, anonymity, and unlinkability. Apart from the interactive protocol, we further design the show proofs for efficient single-use credentials which maintain the user anonymity.
Presence Variations for E&amp;E Transitions Explainability

The exploration and exploitation (E&E) of a search space are fundamental processes in many fields of artificial intelligence. Indeed, it is important to ensure that many regions of the search space are examined, so as not to get trapped in a local optimum, but also that the promising regions are examined more in depth in order to find good local optima. Metaheuristics (MHs) are popular to perform E&E tasks. Explaining the dynamics of both processes is a repeated demand of researchers and practitioners, but remains a challenge. This paper presents an approach giving information about how a MH explores and exploits the search space. This approach is based on a way of representing transitions through presence variation vectors and associated proposed indicators, used to make transitions between E&E explicit and intelligible. Experimentations show the relevance of the proposed indicators.
A novel fully homomorphic robust secret sharing scheme

As an enhancement to traditional secret sharing, robust secret sharing provides additional fault tolerance. The fault tolerance means that the original secret can be recovered even if some shares used in reconstruction are incorrect. So far, there is a lot of research work on robust secret sharing, however the current schemes are all based on Shamir's scheme. Because Shamir's scheme is constructed on polynomials, it has only additive homomorphism and has a narrow range of applications. In this article, we will analyze the existence of robust secret sharing scheme based on Chinese Remainder Theorem from the perspective of redundant residue number system. Based on the existence, we construct a new robust secret sharing scheme and we analyze the time complexity, share size, robustness and homomorphism of our scheme. Compared with the existing scheme, our new scheme has both additive and multiplicative homomorphisms under the premise of achieving the same robustness and share size.
Obstacle detection using Raspberry Pi for driving safety based on Hough transform method

The rapid development of digital image processing technology can be used to simplify human life. This technology can be used in automotive technology which is also growing rapidly. Automotive technology leading to driverless automation cars is in dire need of image processing technology. This research aims to detect obstacle objects based on the detection of line changes emitted by line lasers. The line laser beam is captured using a camera and then using a Raspberry Pi to determine whether there is an obstacle or not. This research uses the Python programming language with the Hough Transform method. The Hough Transform method is used to detect lines in an image that is processed by looking at the consistency of the line laser. This research uses a box and a ball as obstacle objects. Research data collection was carried out in the afternoon in a closed room with an Illumination intensity of 10 Lux with parameters such as distance, camera angle, and line laser angle. An object can be said to be an obstacle if an image there is a laser line that is broken or not at the same pixel position. However, if in the image there is a consistent line or there is no line position change, then in the image there is no obstacle object. Based on the evaluation results of the calculation of the accuracy of the obstacle distance between the actual distance and the distance calculated by the program, the accuracy is above 90%.
Deep Relevant Feature Focusing for Out-of-Distribution Generalization

Convolution Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains. Unlike human’s strong ability of abstraction and connection, CNNs learn everything relevant and irrelevant from their training data while humans can understand its essential features and form. In this paper, we propose a method to improve the cross-domain object recognition ability from the model feature level: Our method masks the partial values of the feature maps to force models to focus on potentially important features. Multiple experiments on the PACS and VLCS confirm our intuition and show that this simple method outperforms previous domain generalization solutions.
Proposed Framework to Improve Quality and Automation of Training and Placement Activities

Industry Trainings are crucial for university students because they allow them to develop their technical abilities and obtain a deeper understanding of the business environment. Even while many students are already aware of how important training is, they nevertheless only participate in a limited number of these chances since they are unaware of many important facts about each one. In this article, we aim to study the development of a Training Management System (TMS) for Middle East College students. The main objective of this system is to inform the students about upcoming training opportunities and to provide them with enough details on previous students' training experiences and feedback. The Agile Dynamic Systems Development Method was used as a methodology. Accordingly, several design illustrations were created to describe the system's static and dynamic aspects. The final system has been produced and tested in light of the research-based design. Impressive feedback and compliance with the students' expectations were evident in the tests.
A Stochastic Approach to Determine the Optimal Number of Servers for Reliable and Energy Efficient Operation of Data Centers

The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers’ failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.
Probabilistic Inference Based Traversability Analysis for Autonomous Robot in Complex Environments

Autonomous navigation on unknown uneven terrain needs a reliable traversability map that indicates potential navigation hazards such as slipping down from a slope, colliding with an obstacle, etc. This paper focuses on generating a real-time traversability map using a 3D LiDAR. The proposed method leverages a probabilistic inference model to update the terrain map, detect static obstacles, and remove moving objects simultaneously. A robust travelability map is created by considering both uneven terrain conditions and obstacles. Our experiments demonstrate its suitability for real-time navigation over a variety type of real-world environments.
Facilitator Agent to Support Low-Resource Language Speakers in MT-Mediated Communication

Machine translation (MT) has become useful in intercultural collaboration. However, for low-resource language (LRL) speakers, the translation accuracy possible might still be a burden to them. Previous studies showed that it is difficult for the minority and LRL speakers to participate in conversions. To solve this problem and create equal chance for team members to communicate, we aim at creating a facilitator agent that helps in supporting the LRL speakers or team members who might have problems joining the conversation. We achieve this by proposing the concept of a virtual facilitation agent that responds to and puts questions to the team members to support the discussion. Experiments on different facilitation strategies for discussion groups are conducted using our multilingual chat system.
Dynamic Network Embedding by Using Sparse Deep Autoencoder

Learning network representation, which aims to capture various properties of networks in low-dimensional feature space, has recently attracted significant attention. Almost all existing static network embedding and dynamic network embedding methods that employ deep models adopt dense structures. Deep models can ensure that the network embedding achieves a good effect on the task (link prediction, network reconstruction, etc.); however, all works of this kind ignore the high complexity of the deep model training process. In this paper, we propose an embedding method that learns dynamic network embedding by using a sparse deep model. The general idea underpinning our approach involves greatly reducing the number of connections between layers in the deep model. Moreover, the sparse structure of the deep model evolved during the training process to achieve the purpose of fitting the network data. Experimental results on simulated benchmark networks and real-world networks prove that, compared with existing network embedding methods utilizing dense structures, our method is able to greatly reduce the number of training weights, while minimally affecting or sometimes even improving the effect of network tasks.
Adaptation problems during the transition to distance foreign language learning of technical university students

The paper deals with the problem of adaptation during the transition to a distance learning format of teaching a foreign language to students of a technical university. Based on the analysis of scientific, educational and methodological literature, the authors give key aspects that contribute to the effective adaptation of students to modern conditions of distance learning, and also point out the disadvantages of this training in the context of mastering the discipline “Foreign language”. The research part presents the data of a questionnaire survey of students of the Moscow Aviation Institute (National Research University), the results of which indicate the duration of the adaptation period among students with a positive formed attitude to the distance learning format. The practical application of the study lies in the fact that it not only allows to improve organizing and conducting “Foreign language” practical classes, but also shows the possibilities of developing and using electronic courses for students of technical universities.
User Expectations of Serendipitous Recommender Systems

Personalized recommender systems have been criticized for limiting opportunities to consume diverse content and reinforcing self-bias leading to negative side effects on society. To address these issues, serendipity has emerged as a design goal of recommender systems increasing the long-term satisfaction of the users. However, research on serendipity in recommender systems has focused on improving the performance of algorithms to predict surprising and diverse items without considering the user’s desired experience. To investigate user expectations of serendipitous recommender systems, we conducted an eight-day diary study with eight participants, using the YouTube recommender system. We found that users expect serendipitous recommender systems not only to provide surprising and diverse items based on existing definitions of serendipity but also to guide the desired impact in the long-term perspectives and help them discover unrealized needs which do not rely on their past behaviours. Users also expect serendipitous recommender systems to provide items that could relieve their burden posed by the novelty of serendipitous recommendations. Based on these findings, we discuss design implications for designing user-centred serendipitous recommender systems that can support users to experience fundamental values of serendipity.
Easily Overlooked Vulnerability in Implementation: Practical Fault Attack on ECDSA Round Counter

Elliptic curve cryptographic is a widely used public-key cryptosystem. Though it has good theoretical security, it is still vulnerable to some physical attacks due to the implementation weakness. To resist the attacks, a number of physical countermeasures have been proposed. However, there are still some implementation vulnerabilities that may be overlooked, leading to more practical and effective attacks. In this article, we construct a new fault attack on round counter which is a component of scalar multiplications in ECDSA. The attack is divided into two parts. In the first part, the partial bits of nonce in each signature can be recovered by the fault injection on round counter. In the second part, an efficient lattice attack can be constructed to recover the private key by combining the recovered bits. Compared with other lattice-based fault attacks, our attack has the advantage of practicability and effectiveness. Especially, it has less requirement of moment precision and wide applicability of scalar multiplications, which is the critical factors for practicability and effectiveness. To verify the strength of our attack, we carry on the laser injection experiments, respectively, on an AVR MCU (ATmega163L) and a Kintex-7 FPGA (XC7K325T). The experimental results verify the practicability and effectiveness of the attack in both software and hardware platforms. Finally, we also propose two directions for efficient countermeasures against our attack.
Research on Unmanned Vehicle Detection Method Based on Improved YOLOv4_Tiny

In order to solve the problem of occluded the vehicle ahead during driving, pedestrians and other targets are small, and it is prone to problems such as missed detection, false detection. This paper proposes an improved network model based on YOLOv4_Tiny. Before training the model, data augmentation is performed by means of image segmentation. Increase the feature scale of the model, and determine the size of the final increased feature scale through experimental comparison. The attention mechanism SE module is introduced in the enhanced (Feature Pyramid Networks) FPN of YOLOv4_Tiny. The test results show that compared with YOLOv4_Tiny, the (average precision) AP of YOLONew_Tiny in large target trucks, cars, and small target pedestrians is improved by 0.73%, 3.85% and 12.95% respectively. The (mean Average Precision) mAP is improved by 5.48%. This shows that the improved YOLOv4_Tiny network model can effectively improve the detection ability of forward targets during driving.
A Discussion on Accessibility, Inclusivity, Cultural Design Considerations, and Health &amp; Safety Advisories for Virtual Reality Head Mounted Displays

With the exponential growth in availability of Virtual Reality (VR) head-mounted displays (HMDs), this study aims to discuss Health &amp; Safety advisory information provided by VR products and observe the extent to which each platform considers accessibility and inclusive and culturally informed design. To this end, five commercially available PC-based VR HMDs, and their corresponding setup applications were assessed in this study - the Oculus Quest 2, Oculus Rift S, Samsung Odyssey, HTC Vive Pro, and Varjo XR-3. Three proto personas were created to evaluate these platforms from various perspectives. VR HMDs are moving in the right direction to consider diverse populations in animation design, but there is room for improvement regarding individuals with visual, auditory, or mobility restrictions. Advancements in multimodal sensory cues and inclusive design considerations may increase adoption of VR technologies as well as compliance with Health &amp; Safety advisory information
Traffic Correlation for Deanonymizing Cryptocurrency Wallet Through Tor

Cryptocurrencies have increasingly become the preferred choice for private transactions due to their anonymity and decentralized features. When a user creates transactions using wallet software with built-in Tor module, their identity information is further protected. At the same time, however, this combination of Tor and cryptocurrency is misused to carry out illegal acts, while the perpetrators are difficult to detect. Therefore, it is important to study traffic correlation methods for cryptocurrencies over Tor to maintain a healthy blockchain ecosystem. In this paper, based on existing work, we propose CryptoCorr, a traffic analysis model for cryptocurrency wallets, which can screening the collected Tor traffic data based on time window and flow features, and implement traffic correlation for cryptocurrency wallets based on deep learning architecture. We validate the proposed model by constructing a dataset with 82077 collected packets of wallet, and the experiment results demonstrate the effectiveness of the CryptoCorr model.