Reviewerly/exHarmony huggingface dataset:
PATRIoT: A Data Sharing Platform for IoT Using a Service-Oriented Approach Based on Blockchain

The Internet of Things has emerged as a paradigm in a variety of application domains where several parties share data to tackle specific tasks. However, these IoT data can be sensitive and the data subject wish not share them with other competitor organizations without retaining some level of control. Thus, a privacy-preserving, user-centric, and transparent solution is needed to deal with the challenges of IoT data sharing, such as the loss of control over the shared data, the trust need in data consumer infrastructure, and the lack of transparency in terms of data handling. Therefore, we propose PATRIoT, a privacy-preserving PlATfoRm for IoT data sharing using a service-oriented approach. The latter is proposed based on the blockchain technology, which enforces privacy requirement compliance according to the General Data Protection Regulation. For validation purposes, we deploy the proposed solution on a private Ethereum blockchain and give the performance evaluation.
Reliable Oral Cancer Classification Framework with Bayesian Deep Learning

Standard deep learning algorithms for clinical image classification are unable to understand their confidence in a decision. We developed a Bayesian deep network could estimate uncertainty to assess the reliability of oral cancer image classification.
You can detect but you cannot hide: Fault Assisted Side Channel Analysis on Protected Software-based Block Ciphers

Cryptographic implementations are prune to Side Channel Analysis (SCA) attacks and Fault Injection (FI) attacks at the same time. Therefore, countermeasures protecting an implementation need to be evaluated against both attacks. The main contribution of this work is twofold. First, we propose an evaluation platform capable to perform emulated fault injection campaigns against modern MCUs and at the same time able to acquire experimental electromagnetic EM emissions and power traces of cryptographic computations to be used for SCA attacks. Second, we perform experimental evaluations of countermeasures protecting against both SCA and FI attacks which show that the injections of faults can dramatically reduce the effectiveness of SCA countermeasures. We evaluate two cryptographic algorithms, an AES and a PRESENT-Sbox implementation, which are protected employing different countermeasures protecting in parallel against FI and SCA attacks. The AES secure implementation is protected by hiding-based SCA countermeasures, while it uses a redundancy-based technique against FI attacks. On the other hand, the PRESENT Sbox is protected by a software implementation of a Dual-rail with Precharge Logic (DPL) countermeasure including fault detection capabilities. We present extensive experimental evaluations for the AES implementation and first results for PRESENT-Sbox showing that for both implementations the fault injections increase the efficiency of the SCA attacks and lead to very fast recoveries of the secret keys.
A Procedure for the Routinary Correction of Back-to-Front Degradations in Archival Manuscripts

Virtual restoration of the nowadays frequently available digital copies of the human documental heritage is crucial for facilitating both the traditional work of philologists and paleographers and the automatic analysis of the contents. Here we propose a fast procedure for the correction of the typically complex background of recto-verso historical manuscripts, which does not need for a preliminary registration of the images of the two page sides. The simplicity of the procedure makes it suitable for the routinary use in the archives. The method consists of two stages, degradation identification and degradation cancellation. The first step performs the detection of both the primary text and the spurious strokes via soft segmentation based on the statistical decorrelation of the two recto and verso images. In the second step, the noisy pattern in the background is substituted with pixels that simulate the texture of the clean surrounding background, through an efficient technique of image inpainting. As shown in the experimental results, the proposed procedure is able to perform a fine and selective removal of the degradation, while preserving the original appearance of the manuscript.
Convergence Behaviour of Population Size and Mutation Rate for NSGA-II in the Context of the Traveling Thief Problem

The so-called optimization problems are encountered by mathematicians and computer scientists everywhere. The probably simplest optimization problem is finding a minimum or maximum of an analytic one-dimensional function, which is usually accomplished by finding the roots of the first derivative. It is, however, not unusual that no efficient algorithm is known for a particular optimization problem. It gets even harder, when you combine two such problems to a multi-component optimization problem. Such multi-component optimization problems are difficult to solve not only because of the contained hard optimization problems, but in particular, because of the interdependencies between the different components. Interdependence complicates a decision making by forcing each sub-problem to influence the quality and feasibility of solutions of the other sub-problems. The subject of the investigation of this work is the multi-component optimization problem called “Traveling Thief Problem”, which combines two well-known optimization problems: The Knapsack Problem and the Traveling Salesman Problem. In particular, we want to examine how the mutation rate and population size affect the fitness achieved by the Non-dominated Sorting Genetic Algorithm II when applying it to the Traveling Thief Problem.
A Brief Discussion about Application of Artificial Intelligence in Computer Network Technology in the Era of Big Data

Abstract With the rapid development of science and technology, the era of big data has come in full swing. A critical problem meriting reflection at present is how to take full advantage of this era in order to continuously improve the technological application level. As one of emerging technologies in modern society, artificial intelligence (AI) has achieved favorable effects when applied to different fields, e.g. AI computer network technology (CNT). The era of big data and AI were briefly expounded, the development of AI and its extensions in the era of big data were analyzed, and its application in CNT and development prospect were deeply explored, expecting to contribute to further application and development of AI in the era of big data.
Toward Fast Platform-Aware Neural Architecture Search for FPGA-Accelerated Edge AI Applications

Neural Architecture Search (NAS) is a technique for finding suitable neural network architecture models for given applications. Previously, such search methods are usually based on reinforcement learning, with a recurrent neural network to generate neural network models. However, most NAS methods aim to find a set of candidates with best cost-performance ratios, e.g. high accuracy and low computing time, based on rough estimates derived from the workload generically. As today's deep learning chips accelerate neural network operations with a variety of hardware tricks such as vectors and low-precision data formats, the estimated metrics derived from generic computing operations such as float-point operations (FLOPS) would be very different from the actual latency, throughput, power consumption, etc., which are highly sensitive to the hardware design and even the software optimization in edge AI applications. Thus, instead of taking a long time to pick and train so called good candidates repeatedly based on unreliable estimates, we propose a NAS framework which accelerates the search process by including the actual performance measurements in the search process. The inclusion of actual measurements enables the proposed NAS framework to find candidates based on correct information and reduce the possibility of selecting wrong candidates and wasting search time on wrong candidates. To illustrate the effectiveness of our framework, we prototyped the framework to work with Intel OpenVINO and Field Programmable Gate Arrays (FPGA) to meet the accuracy and latency required by the user. The framework takes the dataset, accuracy and latency requirements from the user and automatically search for candidates to meet the requirements. Case studies and experimental results are presented in this paper to evaluate the effectiveness of our framework for Edge AI applications in real-time image classification.
An Experimental Evaluation of a Teaching Approach for Statistical Process Control in Computer Courses

In organizations that are seeking a high degree of maturity, it is necessary to achieve a statistical control of software processes and to know their behavior and operational performance. The approach adopted for the research involves reading articles and experience performance reports, practical cases, discussion, the use of games and simulators, practical projects, and reflection by students on the knowledge learned and activities carried out. The evaluation was conducted with undergraduates enrolled in a Computer Science Bachelor's degree programme, who were divided into a control group and an experimental group. At the end, the two groups carried out a practical project to evaluate the learning effectiveness reached by the students. The results of the study suggest that there was a difference in the effectiveness of the learning resulting from the teaching approach and traditional instruction. The authors observed a mean gain of 30.06% in the experimental group, which is evidence of this rise in the learning effectiveness.
A temporal phase-truncated double random phase encoding cryptosystem in the temporal skew Fourier transform domain

A temporal phase-truncated double random phase encoding cryptosystem is proposed to be improved by replacing the temporal Fourier transformers (TFTs) of the dispersion-time lens-dispersion structure with temporal skew Fourier transformers (TSFTs). A TSFT is similar to a TFT except for the time lens (TL) replaced by a skew time lens (STL). A STL is a temporal analog of a spatial cylindrical lens with the in-plane rotational asymmetry mapping to a front–back asymmetry. The front–back asymmetry is achieved by adding the chirp factor C of a TL by +ϵ⋅C and −ϵ⋅C respectively along the positive and negative half axis of the local time coordinate. The skew coefficients ϵ1 and ϵ2 of the two TSFTs involved in the encryption stage are dimensionless and ideally unbounded, serving as additional secret keys. By mathematical analysis, STLs are proven to have complex-value modulations on low frequency components of input signals of TSFTs, which cannot be removed by phase truncations (PTs) in the legal decryption stage. The plain/cipher-text-independent and non-removability features of STLs make phase retrieval attacks unable to exempt eavesdroppers from brute-force trials of STLs. A STL is implemented by an electrical arbitrary waveform generator (AWG) driven electro-optic phase modulator (EOPM). By numerical simulations, the using of STLs is proven to increase the difficulty of phase retrieval attacks in terms of the time consuming required for convergence. Even if phase retrieval attacks converge, the plaintext signal cannot be recovered without the knowledge of ϵ1,2 in the encryption stage. The total key space of ϵ1,2 is also evaluated as 217 at AWG bandwidth Bm=20 GHz, and as 237 at Bm=100 GHz. The fidelity robustness of our optical cryptosystem against possible noise and occlusion penalties on the communication channels and networks of optical cipher-text signals is also proven.
Multi-agent Cooperation and Competition with Two-Level Attention Network

Multi-agent reinforcement learning (MARL) has made significant advances in multi-agent systems. However, it is hard to learn a stable policy in complicated and changeable environment. To address these issues, a two-level attention network is proposed, which is composed of across-group observation attention network (AGONet) and intentional communication network (ICN). AGONet is designed to distinguish the different semantic meanings of observations (including friend group, foe group, and object/entity group) and extract different underlying information of different groups with across-group attention. Based AGONet, the proposed network framework is invariant to the number of agents existing in the system, which can be applied in large-scale multi-agent systems. Furthermore, to enhance the cooperation of the agents in the same group, ICN is used to aggregate the intentions of neighbors in the same group, which are extracted by AGONet. It obtains the understanding and intentions of their neighbors in the same group and enlarges the receptive filed of the agent. The simulation results demonstrate that the agents can learn complicated cooperative and competitive strategies and our method is superiority to existing methods.
An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks

Modularity provides advantages in neuroevolution by improving evolvability or efficiency of connections. Many techniques that leverage modularity, either by utilizing human knowledge or adding additional evolutionary objectives, have been studied. In this work, we reimplemented and explored an existing encoding based method, MENNAG, that appears promising but has received little attention. The algorithm is tested on four tasks that are expected to receive different levels of benefits from modularity. The results show that this method is able to produce modular neural networks without directly optimizing modularity, as long as the problem has some degree of modular nature.
A Survey on GAT-like Graph Neural Networks

The graph structure is one of the critical data structures in the real world, and its applications focus on graphs, where scholars study entity features and interactions among various entities. Recently, developments in graph neural networks (GNNs) have heightened the need for learning graph representations effectively. Simultaneously, graphs can be large and complex as well as noisy, posing obstacles for graph-related tasks. However, by incorporating the attention mechanism in graph neural networks, it is possible for GNNs to focus on the most important entities and interactions in graphs, contributing to better decisions. Therefore, this paper conducts a comprehensive survey about literature on GAT-like graph neural networks. According to inputs and outputs, types of attention mechanisms, tasks, this paper proposes a taxonomy to group recent works followed by detailed examples, aiming to overlook GAT-like GNNs from different perspectives. At last, this paper discusses the existing problems and challenges in this area, hoping to provide insights for future research directions.
EXPERIENCE IN USING DISTANCE LEARNING TECHNOLOGIES IN THE WORK OF A SIMULATION CENTER

Simulation training implies the direct work of the student with mannequins and simulators to hone, first of all, practical skills. However, the unfavorable epidemiological situation in the region in 2020 created the need to use new training methods for simulation training - using e-learning and distance learning technologies (EE and DOT).
Medical Image De-Noising Using Combined Bayes Shrink and Total Variation Techniques

Digital image processing brought a tremendous revolution in many fields; medical field being one of them. Medical images are used to detect abnormalities and diseases in the human body simply by analyzing them. During the acquisition process, noise signals may be introduced in these images which will impact negatively on the diagnostic process. Noise signals degrade the image quality by suppressing useful information present in the form of edges, fine structures, textures and so on. Hence it is essential to suppress noise signals because noisy images may lead to false interpretations by the radiologists. Suppression of noise signals from the medical images is called “Medical Image De-noising”. Some examples of medical images are Magnetic Resonance Images (MRI), Computed Tomography (CT) images, Ultra-Sound (US) images and so on. These images are corrupted by various noise signals; for example, MRI images are affected severely by noise which is known as Rician noise. CT images are corrupted by Gaussian noise, while US images are affected by multiplicative noise called speckle noise. To remove these unwanted noise signals various filters have been proposed, but none of these methods can be used as a global de-noising technique because any filter which can remove one noise effectively fails to remove others. Hence, it is necessary to develop a filter that can remove many noise signals because any image may be corrupted by more than one noise. This requirement motivates the researchers to push ahead to achieve such goal. In this chapter; a framework has been proposed to de-noise medical images, reducing the effect of additive white Gaussian noise. It consists of various spatial domain filters, specifically median filter and median modified Wiener filter. It also uses adaptive wavelet thresholding and total variation technique in parallel, the results of which are then fused together using wavelet based fusion technique. This process is named as shrinkage combined enhanced total variation technique as it enhances the quality of de-noised images.
Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling

Granting loans is one of the major concerns of financial institutions due to the risks of default borrowers. Default prediction by the neural networks is a popular technique for credit risk modeling. Neural networks generally offer the accurate predictions that help banks to prevent financial losses and grow their business by approving more creditworthy borrowers. Although neural networks are capable of capturing the complex, non-linear relationships between a large number of features and output, these models act as black boxes. This is a graduation project paper that is focused on loan default risk prediction by multi-layer perceptron neural network and building up explainability to some degree in the trained neural networks through sensitivity analysis. The architecture of a multi-layer perceptron neural network with the best result is used to help the credit-risk manager in explaining why an applicant is a defaulter or non-defaulter. The prediction of a trained multi-layer perceptron neural network is explained by mapping input features and target variables directly using a model-agnostic explanation as well as a model-specific explanation. Lastly, a comparison is performed between two explanation methods.
Here and hereafter: Preparing business students for a data-driven world

This case study describes activities developed by academic librarians to provide data literacy opportunities to graduate and undergraduate business students. These opportunities were provided by a data-related program in the libraries and in-class activities. Topic modules for the data program and steps for course-related data visualizations and are discussed. Conclusions reached as to the impact and acceptance of these programs and future directions are presented.
Adaptive Fixed-Lag Smoothing Algorithms Based on the Variational Bayesian Method

In this article, we address the adaptive fixed-lag smoothing (FLS) problem in the presence of unknown and slowly time-varying measurement noise covariance matrix (MNCM). Based on the variational Bayesian (VB) method, we use the VB inference to jointly estimate the system state and the unknown MNCM. According to different implementation methods, we propose two adaptive FLS algorithms: One is based on the augmented state-space models and another one is based on a sliding Rauch-Tung-Striebel smoothing. In addition, the evidence lower bound and the convergence criterion of the above two algorithms are given. The proposed adaptive algorithms are evaluated by a simulation example.
ML-Driven DASH Content Pre-Fetching in MEC-Enabled Mobile Networks

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.
Quantum Discord and Logarithmic Negativity in the Generalized n-qubit Werner State

Quantum Discord (QD) is a measure of the total quantum non-local correlations of a quantum system. The formalism of quantum discord has been applied to various two-qubit mixed states and it has been reported that there is a non-zero quantum discord even when the states are unentangled. To this end, we have calculated the Quantum Discord for a higher than two qubit mixed state, that is, the generalized n-qubit Werner state with a bipartite split. We found that the QD saturates to a straight line with a unit slope in the thermodynamic limit. Qualitative studies of entanglement between the two subsystems using logarithmic negativity revealed that the entanglement content between them increases non-uniformly with the number of qubits leading to its saturation. We have proved the above claims both analytically and numerically.
The Spectral Underpinning of word2vec

Word2vec introduced by Mikolov et al. is a word embedding method that is widely used in natural language processing. Despite its success and frequent use, a strong theoretical justification is still lacking. The main contribution of our paper is to propose a rigorous analysis of the highly nonlinear functional of word2vec. Our results suggest that word2vec may be primarily driven by an underlying spectral method. This insight may open the door to obtaining provable guarantees for word2vec. We support these findings by numerical simulations. One fascinating open question is whether the nonlinear properties of word2vec that are not captured by the spectral method are beneficial and, if so, by what mechanism.
Redundancy and Optimization of tANS Entropy Encoders

Nowadays entropy encoders are part of almost all data compression methods, with the Asymmetrical Numeral Systems (ANS) family of entropy encoders having recently risen in popularity. Entropy encoders based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tabled</i> variant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method that calculates encoder redundancies in almost linear time, which translates in practice to thousand-fold speedups in redundancy calculations for small automatons, and allows redundancy calculations for automatons with tens of millions of states that would be otherwise prohibitive. We also address the problem of improving tabled ANS encoder designs, by employing the aforementioned redundancy calculation method in conjunction with a stochastic hill climbing strategy. The proposed approach consistently outperforms state-of-the-art methods in tabled ANS encoder design. For automatons of twice the alphabet size, experimental results show redundancy reductions around 10% over the default initialization method and over 30% for random initialization.