Reviewerly/exHarmony huggingface dataset:
Enforcement of K-Step Opacity with Edit Functions

Opacity is an information flow property for dynamic systems describing plausible deniability, that is whether an eavesdropper can deduce that "secret" behavior has occurred. In particular, K-step opacity considers secret actions that have occurred within the last K-steps in the past. We consider the problem of K-step opacity enforcement over automata using obfuscation. We present a general framework for K-step opacity enforcement and transform the problem of enforcing K-step opacity to enforcing current-state opacity. We can then apply existing obfuscation synthesis methods for current-state opacity to K-step opacity. We demonstrate this approach by enforcing privacy in the context of a novel contact tracing model.
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge

Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics. Lately, we have witnessed the increasing use of it to make more performing the deployment of machine learning (ML) techniques such as federated learning (FL). FL was debuted to improve communication efficiency compared to conventional distributed machine learning (ML). The original FL assumes a central aggregation server to aggregate locally optimized parameters and might bring reliability and latency issues. In this paper, we conduct an in-depth study of strategies to replace this central server by a flying master that is dynamically selected based on the current participants and/or available resources at every FL round of optimization. Specifically, we compare different metrics to select this flying master and assess consensus algorithms to perform the selection. Our results demonstrate a significant reduction of runtime using our flying master FL framework compared to the original FL from measurements results conducted in our EdgeAI testbed and over real 5G networks using an operational edge testbed.
TKQ: Top-K Quantitative High Utility Itemset Mining

High utility itemset mining is a well-studied data mining task for analyzing customer transactions. It consists of finding the sets of items purchased together that yield a profit that is greater than a minutil threshold, set by the user. To find more precise patterns with purchase quantities, that task was recently generalized as high utility quantitative itemset mining. But an important drawback of current algorithms is that finding an appropriate minutil value is not intuitive and can greatly influence the output. A too small minutil value may lead to very long runtimes and finding millions of patterns, while a too high value, may result in missing many important patterns. To address this issue, this paper redefines the task as top-k quantitative high utility itemset mining and proposes a novel algorithm named TKQ (Top K Quantitative itemset miner), which let the user directly specify the number k of patterns to be found. The algorithm includes three strategies to improve its performance. Experiments on benchmark datasets show that TKQ has excellent performance.
The Role of Universities in the Formation of Students’ Spiritual Values

This paper aims to reveal the role of the university and the content aspects of the formation of students’ spiritual values. The spiritual values of the generation are a factor in the formation of the cultural environment of society, contributing to the formation of social consciousness of the social formation. Theoretical analysis of axiological manifestations of a particular individual is an indispensable attribute of modern scientific knowledge. The relevance of the research subject is determined by the applied significance of cultural and ethical manifestations of the modern social environment. The object of the study. Spiritual values as an inherent property of individual manifestations. The subject of the study. Formation of students’ spiritual values in social institutions of socialisation and knowledge acquisition. Objectives of the study. They consist of detecting the form and content of the university’s role in forming the individual’s personality in isomorphic social groups. The methodology of the study. We used theoretical and heuristic methods of an extraspectual nature, connected with an axiological approach: analysis, synthesis, analogy, deduction, abstraction, generalisation, etc., due to the phenomenological and systemic principles of research.
Recommendation for Higher Education Candidates: A Case Study on Engineering Programs

Over the years, there have been created new applications recurring to automatic discovery of information in educational data. The recommendation of undergraduate programs to high school students is one of these applications with little researching so far. This can be explained by the existence of a small data quantity in this context, and traditional recommendation systems demand a large number of items and users. In this paper, we propose a hybrid approach, combining a collaborative filtering and content-based architecture, focused on programs and students. Our system suggest programs to the candidates that guarantee a high successful academic path by predicting their grades.
Brain-Inspired Image Quality Assessment Method based on Electroencephalography Feature Learning

With the explosion of multimedia data, quality of experience (QoE) has become a critical metric in multimedia transmission, and therefore, QoE-oriented image quality assess-ment (IQA) turns more important and urgent. However, the performance of the traditional user-based assessment methods is limited by the deviation caused by human cognitive activities. In this paper, we propose a brain-inspired IQA method based on electroencephalography (EEG) feature learning, which is a psychophysiological method for studying human perception for IQA. We first establish the EEG dataset by collecting the corresponding EEG signals when subjects watch distorted facial images and then design a siamese network to extract the EEG features that can distinguish image quality levels and measure user scores. The siamese network establishes the relationship between image quality and QoE that is reflected by the EEG scores. The relationship is then embedded into a prediction network that directly obtains the EEG scores from images with different qualities. In this way, EEG scores can be predicted through end-to-end learning. Experiment results show that our proposed method can not only better evaluate the perceptual quality of facial images and reflect real human perceptions but also achieve better score prediction performance on the facial image datasets.
Time-Optimal Path Planning in an Evolving Ocean Wave Field based on Reachability Theory

We consider the navigation of surface vessels in an evolving ocean wave field, where the detrimental extreme waves are treated as moving and deforming obstacles. We propose a new time-optimal path planning algorithm with avoidance of such obstacles based on the reachability theory. In our framework, the mathematical optimization problem is boiled down to (1) forward propagation of reachable set described by a variational inequation, which is numerically solved through a prediction-correction scheme; and (2) a backtracking procedure to find the optimal path. The new path planner is tested in a number of cases including one with realistic ocean waves. We demonstrate that our algorithm is able to provide correct paths avoiding high waves, and achieve multiple optimal (equivalent in time) paths when available. Finally, the framework can be straightforwardly extended to other problems involving collision avoidance with moving and deforming obstacles.
Data-driven Dimensional Expression Generation via Encapsulated Variational Auto-Encoders

Abstract Concerning facial expression generation, relying on the sheer volume of training data, recent advances on generative models allow high-quality generation of facial expressions free of the laborious facial expression annotating procedure. However, these generative processes have limited relevance to the psychological conceptualised dimensional plane, i.e., the Arousal-Valence two-dimensional plane, resulting in the generation of psychological uninterpretable facial expressions. For this, in this research, we seek to present a novel generative model, targeting learning the psychological compatible (low-dimensional) representations of facial expressions to permit the generation of facial expressions along the psychological conceptualised Arousal-Valence dimensions. To generate Arousal-Valence compatible facial expressions, we resort to a novel form of the data-driven generative model, i.e., the encapsulated variational auto-encoders (EVAE), which is consisted of two connected variational auto-encoders. Two harnessed variational auto-encoders in our EVAE model are concatenated with a tuneable continuous hyper-parameter, which bounds the learning of EVAE. Since this tuneable hyper-parameter, along with the linearly sampled inputs, largely determine the process of generating facial expressions, we hypothesise the correspondence between continuous scales on the hyper-parameter and sampled inputs, and the psychological conceptualised Arousal-Valence dimensions. For empirical validations, two public released facial expression datasets, e.g., the Frey faces and FERG-DB datasets, were employed here to evaluate the dimensional generative performance of our proposed EVAE. Across two datasets, the generated facial expressions along our two hypothesised continuous scales were observed in consistent with the psychological conceptualised Arousal-Valence dimensions. Applied our proposed EVAE model to the Frey faces and FERG-DB facial expression datasets, we demonstrate the feasibility of generating facial expressions along with the conceptualised Arousal-Valence dimensions. In conclusion, to generate facial expressions along the psychological conceptualised Arousal-Valance dimensions, we propose a novel type of generative model, i.e., encapsulated variational auto-encoders (EVAE), allowing the generation process to be disentangled into two tuneable continuous factors. Validated in two publicly available facial expression datasets, we demonstrate the association between these factors and Arousal-Valence dimensions in facial expression generation, deriving the data-driven Arousal-Valence plane in affective computing. Despite its embryonic stage, our research may shed light on the prospect of continuous, dimensional affective computing.
How Explainability Contributes to Trust in AI

We provide a philosophical explanation of the relation between artificial intelligence (AI) explainability and trust in AI, providing a case for expressions, such as “explainability fosters trust in AI,” that commonly appear in the literature. This explanation relates the justification of the trustworthiness of an AI with the need to monitor it during its use. We discuss the latter by referencing an account of trust, called “trust as anti-monitoring,” that different authors contributed developing. We focus our analysis on the case of medical AI systems, noting that our proposal is compatible with internalist and externalist justifications of trustworthiness of medical AI and recent accounts of warranted contractual trust. We propose that “explainability fosters trust in AI” if and only if it fosters justified and warranted paradigmatic trust in AI, i.e., trust in the presence of the justified belief that the AI is trustworthy, which, in turn, causally contributes to rely on the AI in the absence of monitoring. We argue that our proposed approach can intercept the complexity of the interactions between physicians and medical AI systems in clinical practice, as it can distinguish between cases where humans hold different beliefs on the trustworthiness of the medical AI and exercise varying degrees of monitoring on them. Finally, we apply our account to user’s trust in AI, where, we argue, explainability does not contribute to trust. By contrast, when considering public trust in AI as used by a human, we argue, it is possible for explainability to contribute to trust. Our account can explain the apparent paradox that in order to trust AI, we must trust AI users not to trust AI completely. Summing up, we can explain how explainability contributes to justified trust in AI, without leaving a reliabilist framework, but only by redefining the trusted entity as an AI-user dyad.
Eccentric-rotation-scanned ptychography and its application in multispectral watermarking

Due to the uncertainty that may occur during one experiment, the repetition of the same sample experiment is particularly important. Here we propose a novel eccentric-rotation-scanned ptychography (eRSP), eccentric rotation scanning is used to collect diffraction patterns instead of traditional mesh scanning. In the process of collecting diffraction patterns, only the single variable of rotation angle needs to be controlled. The position error is reduced from two-dimensional to one-dimensional, and the difficulty of experimental operation is greatly reduced. The repeatability of the experiment has been significantly improved. Rotating scanning can also avoid the raster grid pathology. In this paper, the theory of eRSP is introduced, and the feasibility of this method has been analyzed by simulation and optical experiment. A multi-spectral information hiding system based on eRSP was proposed. The double random-phase encoding system with a color random phase mask is embedded into a retractable cylinder. A super-continuum white laser source is used as the incident beam, records the ciphertext diffraction pattern by rotating the cylinder and embedded them into the host image. Additional eccentric distance key, rotation angle key, spectrum key and telescopic distance key are introduced, which greatly enhance the system security. Numerical simulations are implemented to demonstrate the feasibility, security and robustness of the proposed scheme.
Digitalization of Engineering Education in Training for Industry 4.0

The modern development of the world economy in the direction of Industry 4.0, taking place against the background of natural resources depletion, requires the search for new engineering solutions. The accelerating rates of technological progress lead, on the one hand, to the need for a transition to the digital economy, and on the other hand, make it possible to implement such a transition. This is a challenge to the engineering education system, which should prepare engineers “for the future” with new digital competencies. The article outlines the directions of digital development of the engineering education system, the problems with which one has to face. An example of learning engineers for the automotive industry is given, taking into account the business requirements for the competence of engineers. It is shown that such a solution will give a good result for both business and students, guaranteeing their relevance in the labor market.
Automatic Text Summarization in Natural Language Processing

Text summarization is one of the challenging and engrossing problems in the field of Natural Language Processing. The goal of Text Summarization is to read, understand and extract meaningful information from the unstructured data. The idea behind summarization is to get concise and precise summary from the original document. We used Sentence Ranking method based on Word Frequency to determine the important sentences in original text. The Summarizer uses three features to determine important Sentence in a document. The Term Frequency, similarity with the Title and Sentence score are the features considered. Values are assigned to each word and a sentence in a document and create a score for each word and a sentence. Normalize the scores to avoid raw scores that skew the results. The Scores of top ‘N’ sentences are taken for summarization. Text cleaning, Sentence tokenization, Word tokenization, Normalized Word frequency table generation, and Summarization are steps used in the Sentence Ranking Method. The method is implemented on WikiHow dataset. The results of proposed model are evaluated with ROUGE toolkit which has three evaluation metrics- Precision, Recall and F-Score.
Multi-level Relational Knowledge Distillation for Low Resolution Image Recognition

In spite of great success in many visual recognition tasks achieved by recent deep models, they performed poorly at low resolution conditions. low-resolution image recognition is still a challenging problem. The massive loss of image detail information is the ultimate cause of this problem. However, the loss of individual instance details has little effect on the relationship between instances.Therefore, we propose a multi-level relational distillation method to solve the low resolution identification problem. Based on the teacher student framework of knowledge distillation, It transfer knowledge from teacher to student in two steps. The first step is to train the auxiliary network through the teacher network to remove the redundancy of the model structure. In the second step, the auxiliary network as a new teacher guided low-resolution student network. In order to better learn the behavior of the teacher network, we propose a multi-level instance relationship loss function. It is divided into three different levels: central angle relationship, inter-class angle relationship, and intra-class angle relationship. Respectively it transfer the relationship between instances on different scales, so as to minimize the loss of the image caused by the loss of detailed information. Finally, we carry out experiments on the low resolution image recognition data set CIFAR100 and CIFAR10. The results of experiments have proved that our method can effectively deal with low resolution recognition problems.
Masked &amp; Unmasked Face Recognition Using Support Vector Machine Classifier

Face masks become a need in epidemic scenarios such as the Corona virus pandemic of 2020-21. Most companies prefer face authentication instead of fingerprint, signature, and card verification. Face mask gives protection against Corona virus than other traditional methods used for identification. In the case of facial recognition, a machine must detect and recognise the face in a picture. In this paper used methods are supported by machine learning that permits a machine to evolve through a learning process and to perform recognition tasks. Caffe model of deep learning is used for face detection. The training dataset contains both masked and non-masked faces. This project and outcome has developed an approach to recognize faces in a real-time video stream that can also be used in the existing recognition systems to identify masked faces. Facial recognition has been done with a Support Vector Machine classifier. All are implemented in Python with OpenCv with tools modules and sub-modules.
Reduced Memory Viterbi Decoding for Hardware-accelerated Speech Recognition

Large Vocabulary Continuous Speech Recognition systems require Viterbi searching through a large state space to find the most probable sequence of phonemes that led to a given sound sample. This needs storing and updating of a large Active State List (ASL) in the on-chip memory (OCM) at regular intervals (called frames), which poses a major performance bottleneck for speech decoding. Most works use hash tables for OCM storage while beam-width pruning to restrict the ASL size. To achieve a decent accuracy and performance, a large OCM, numerous acoustic probability computations, and DRAM accesses are incurred. We propose to use a binary search tree for ASL storage and a max heap data structure to track the worst cost state and efficiently replace it when a better state is found. With this approach, the ASL size can be reduced from over 32K to 512 with minimal impact on recognition accuracy for a 7,000-word vocabulary model. This, combined with a caching technique for acoustic scores, reduced the DRAM data accessed by 31 \( \times \) and the acoustic probability computations by 26 \( \times \) . The approach has also been implemented in hardware on a Xilinx Zynq FPGA at 200 MHz using the Vivado SDS compiler. We study the tradeoffs among the amount of OCM used, word error rate, and decoding speed to show the effectiveness of the approach. The resulting implementation is capable of running faster than real time with 91% lesser block-RAMs.
Real-Time Threat Intelligence-Block Phising Attacks

Now-a-days there are different types of cybercrime, Phishing is one of cyber-attacks where attackers impersonate as a member of legitimate institutions or organizations through an email, text message, advertisements or through any means to steal sensitive information which results to loss of personal and sensitive information such as account no, social security no, credit card no etc.. Phishing attack has been increasing exponentially. In this attack mostly innocent users are comes to losses their sensitive, unique, personal, valuable and secure data and information’s. Many hackers are accomplished through phishing attacks where client are trapped into interacting with web-pages which looks like to be legitimate websites. The websites exactly seems to be semantically as well as visually to the original websites. The main idea of the phisher or hackers is to gain and purloin the critical information such as credential account, username, password and other private information related to any organization and company. According to phishing or web spoofing techniques is one examples of social engineering attack. Phishers are appears in many platform of communication such as in the form of VOIP, message and e-mails which is not real. Commonly users have many accounts on various websites including social media, email, and also in bank. So that innocent users are the most vulnerable targets for these types of attack. This happened because most of the peoples are unknown of the sensitive data, which helps them to get their information successfully. As of 2020, phishing is the most common attack performed by cyber criminals according to FBI’s Internet Crime Complaint Centre. First phishing Datasets are collected from phish tank and then legitimate websites are collected from University of New Brunswick and then dataset is preprocessed using wrapper and filter method so that it covers the dataset which gets missed, tampered and unstructured.
Image Encryption Scheme Using Combined Key Sequence of Logistic and Tent Map for Medical Applications

Securing the clinical records of the patient is very much important. Conventional encryption methods might not be robust for the applications of medical images due to their statistical properties. In this paper, the Combination of Logistic and Tent map based on image encryption method for medical image application is proposed. Initially, the Logistic and Tent map key sequence is combined by employing an XOR operation. The resulting sequence is XORed with medical image pixels. Hence the encrypted image is obtained. To evaluate the performance, ten different test images are chosen. The parameters like Entropy, Correlation and Mean Square Error (MSE) are used to analyse the security of the proposed scheme. Further, the proposed scheme is compared with schemes using Tent map and Logistic map alone. The results obtained demonstrate that the proposed encryption scheme provides better performance compared with a scheme using the sequence of Logistic map or Tent map alone.