Reviewerly/exHarmony huggingface dataset:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life. In most cases, however, these systems and decisions are neither regulated nor certified. Given the potential harm that these algorithms can cause, their qualities such as fairness, accountability and transparency (FAT) are of paramount importance. To ensure high-quality, fair, transparent and reliable predictive systems, we developed an open source Python package called FAT Forensics. It can inspect important fairness, accountability and transparency aspects of predictive algorithms to automatically and objectively report them back to engineers and users of such systems. Our toolbox can evaluate all elements of a predictive pipeline: data (and their features), models and predictions. Published under the BSD 3-Clause open source licence, FAT Forensics is opened up for personal and commercial usage.
Cloud services application ways for preparation of future PhD

Currently, it is important in Ukraine to harmonize cloud technologies application with European and world scientific initiatives. Need to modernize preparation of future PhDs is caused by challenges of new information, globalized society and digital transformation of all spheres of life, including education and science. The research was aimed to determine features of cloud services application in preparation of future PhDs. Concepts of “cloud computing”, “cloud technologies”, “cloud learning technologies”, “cloud services”, “cloud oriented environment” were analyzed. Experience of cloud technologies and their services application in educational and scientific space in researches of foreign and Ukrainian students was considered. Ukrainian experience in preparation of future PhD of various specialties with cloud services application was analyzed. It was emphasized that approaches improving to preparation of future PhDs based on cloud services application would increase their level of digital competence. It is recommended to include a separate course or module of specific discipline on work with cloud technologies and services during preparation of future PhDs. It is important to improve disciplines and tools content to support education process. It can be learning of disciplines using cloud technologies or services by future PhD’s. Also, cloud services application to support scientific and scientific-organizational activities will increase level of organization and implementation of scientific research. It is important to create cloud-oriented environment for preparation of future PhDs in higher education and research institutions. Making cloud-oriented educational and scientific environment should be based on principles of open education. It is recommended to use cloud-based platforms and services (G Suite for Education; Microsoft Office 365; specialized SaaS (CoCalc or other)).
Video Steganography Using Knight Tour Algorithm and LSB Method for Encrypted Data

Abstract This paper aims to propose a method for data hiding in video by utilizing the least significant bit (LSB) method and improving it by utilizing the knight tour algorithm for concealing the data inside the AVI video file and using a key function encryption method for encrypting the secret message. First, the secret message is encrypted by utilizing a mathematical equation. The key used in the equation is a set of random numbers. These numbers differ in each implementation to warrant the safety of the hidden message and to increase the security of the secret message. Then, the cover video was converted from a set of frames into separated images to take the advantage of the large size of video file. Afterward, the knight tour algorithm is utilized for random selecting of the pixels inside the frame utilized for embedding the secret message inside it to overcome the shortcoming of the conventional LSB method that utilized the serial selection of pixel and to increase the robustness and security of the proposed method. Afterward, the encrypted secret message is embedded inside the selected pixels by utilizing the LSB method in bits (7 and 8). The observational results have drawn that the proposed method has a superior performance compared to the previous steganography method in terms of quality by a high PSNR of 67.3638 dB and the lowest MSE of 0.2578. Furthermore, this method preserves the security where the secret message cannot be drawn out without knowing the decoding rules.
A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators

Online reviews are crucial for evaluating product features and supporting consumers' purchase decisions. However, as a result of online buying behaviors, consumer habits, and discrete dynamic distribution characteristics of online reviews, and consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers' purchase decisions using the intuitionistic fuzzy scoring function and the “vertical projection distance” method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods.
Attention-Based Multi-Modal Fusion Network for Semantic Scene Completion

This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spatial dimension. It is achieved by employing a multi-modal fusion architecture boosted from 2D semantic segmentation and a 3D semantic completion network empowered by residual attention blocks. We validate our method on both the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results show that our method respectively achieves the gains of 2.5% and 2.6% on the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the state-of-the-art method.
Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.
Using machine learning to predict extreme events in the Hénon map

Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional H\'enon map at the classical parameters. The task is to determine whether a trajectory will exceed a threshold after a set number of time steps into the future. This task has a geometric interpretation within the dynamics of the H\'enon map, which we use to gauge the performance of the neural networks that are used in this work. We analyze the dependence of the success rate of the ML models on the prediction time $T$ , the number of training samples $N_T$ and the size of the network $N_p$. We observe that in order to maintain a certain accuracy, $N_T \propto exp(2 h T)$ and $N_p \propto exp(hT)$, where $h$ is the topological entropy. Similar relations between the intrinsic chaotic properties of the dynamics and ML parameters might be observable in other systems as well.
A comparative review on deep learning models for text classification

&lt;p&gt;Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.&lt;/p&gt;
Entanglement characterization using quantum designs

We present in detail a statistical approach for the reference-frame-independent detection and characterization of multipartite entanglement based on moments of randomly measured correlation functions. We start by discussing how the corresponding moments can be evaluated with designs, linking methods from group and entanglement theory. Then, we illustrate the strengths of the presented framework with a focus on the multipartite scenario. We discuss a condition for characterizing genuine multipartite entanglement for three qubits, and we prove criteria that allow for a discrimination of $W$-type entanglement for an arbitrary number of qubits.
A Study on Students’ Perceptions for Online Zoom-app based Flipped Class Sessions on Anatomy Organised during the Lockdown Period of COVID-19 Epoch

Introduction: In the present situation of containment for COVID-19 epoch, the physical classroom sessions in medical colleges were suspended by different Government advisories. Faculties have been guided by the university as well as institution to carry on the online teaching to the medical students. As the students were not present physically so flipped class model has been implemented using the Zoom cloud app for teaching Anatomy. Aim: To explore the perception of undergraduate students of Anatomy regarding online Zoom-app based flipped class sessions and to obtain the suggestions for improvement of such classes organised during the lockdown period of COVID-19. Materials and Methods: Ten gross Anatomy topics and 10 histology slides (total 15 sessions; one session for each gross anatomy topics and five sessions covering two slides for each day) were discussed in flipped class mode. For each session the text materials were served to the students two days before. On the third day, face to face interactive classes were undertaken using the Zoom platform; for the entire 199 students of Anatomy. After completion of one month, students' perceptions were obtained by semi-structured questionnaire made with Google form. Results: Although the department has organised almost daily Zoom sessions with pre-shared study material; in flipped classroom mode, but majority of the students opined for maximum three days per week Zoom sessions instead of every day's classes. Total 92% preferred the current strategy of advanced sharing of study material instead of concurrent sharing of text. Almost 93.5% students felt the study material helpful to them, 79% students found the Zoom sessions helpful for their doubt clearance. Strikingly, 53.36% students confessed that they failed to keep up with the progress of the classes in daily mode. There was a mixed reaction for continuing such mode of teaching in the post-lockdown era. The network connectivity became a broad issue as constrain to almost all of them to participate in online discussion platform. Conclusion: As the students and teachers were new in the online mode of teaching; so students feedbacks were felt need for future planning. The students had an opinion to lessen the number of classes so that they can cope up with the study material. However, majority of them wanted to move back to their classrooms rather than remain in the online mode of learning.
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern architectures. In this paper, we adapt RBF networks as a classifier on top of CNNs by modifying the training process and introducing a new activation function to train modern vision architectures end-to-end for image classification. The specific architecture of RBFs enables the learning of a similarity distance metric to compare and find similar and dissimilar images. Furthermore, we demonstrate that using an RBF classifier on top of any CNN architecture provides new human-interpretable insights about the decision-making process of the models. Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets.
Four-class emotion classification in virtual reality using pupillometry

Abstract Background Emotion classification remains a challenging problem in affective computing. The large majority of emotion classification studies rely on electroencephalography (EEG) and/or electrocardiography (ECG) signals and only classifies the emotions into two or three classes. Moreover, the stimuli used in most emotion classification studies utilize either music or visual stimuli that are presented through conventional displays such as computer display screens or television screens. This study reports on a novel approach to recognizing emotions using pupillometry alone in the form of pupil diameter data to classify emotions into four distinct classes according to Russell’s Circumplex Model of Emotions, utilizing emotional stimuli that are presented in a virtual reality (VR) environment. The stimuli used in this experiment are 360° videos presented using a VR headset. Using an eye-tracker, pupil diameter is acquired as the sole classification feature. Three classifiers were used for the emotion classification which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and Random Forest (RF). Findings SVM achieved the best performance for the four-class intra-subject classification task at an average of 57.05% accuracy, which is more than twice the accuracy of a random classifier. Although the accuracy can still be significantly improved, this study reports on the first systematic study on the use of eye-tracking data alone without any other supplementary sensor modalities to perform human emotion classification and demonstrates that even with a single feature of pupil diameter alone, emotions could be classified into four distinct classes to a certain level of accuracy. Moreover, the best performance for recognizing a particular class was 70.83%, which was achieved by the KNN classifier for Quadrant 3 emotions. Conclusion This study presents the first systematic investigation on the use of pupillometry as the sole feature to classify emotions into four distinct classes using VR stimuli. The ability to conduct emotion classification using pupil data alone represents a promising new approach to affective computing as new applications could be developed using readily-available webcams on laptops and other mobile devices that are equipped with cameras without the need for specialized and costly equipment such as EEG and/or ECG as the sensor modality.
RHAS: robust hybrid auto-scaling for web applications in cloud computing

The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current resource requirement. The proposed auto-scaling technique is designed with the threshold-based rules and queuing model. The security mechanism is used to secure the user's request and response to the web-applications deployed in cloud environment. The designed approach has been tested with two real-time web application workloads of ClarkNet and NASA. The proposed technique achieves $$14\%$$ reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.
Wave-function positivization via automatic differentiation

We introduce a procedure to systematically search for a local unitary transformation that maps a wavefunction with a non-trivial sign structure into a positive-real form. The transformation is parametrized as a quantum circuit compiled into a set of one and two qubit gates. We design a cost function that maximizes the average sign of the output state and removes its complex phases. The optimization of the gates is performed through automatic differentiation algorithms, widely used in the machine learning community. We provide numerical evidence for significant improvements in the average sign for a two-leg triangular Heisenberg ladder with next-to-nearest neighbour and ring-exchange interactions. This model exhibits phases where the sign structure can be removed by simple local one-qubit unitaries, but also an exotic Bose-metal phase whose sign structure induces "Bose surfaces" with a fermionic character and a higher entanglement that requires deeper circuits.
An SMT-Based Approach for Verifying Binarized Neural Networks

Abstract Deep learning has emerged as an effective approach for creating modern software systems, with neural networks often surpassing hand-crafted systems. Unfortunately, neural networks are known to suffer from various safety and security issues. Formal verification is a promising avenue for tackling this difficulty, by formally certifying that networks are correct. We propose an SMT-based technique for verifying binarized neural networks — a popular kind of neural network, where some weights have been binarized in order to render the neural network more memory and energy efficient, and quicker to evaluate. One novelty of our technique is that it allows the verification of neural networks that include both binarized and non-binarized components. Neural network verification is computationally very difficult, and so we propose here various optimizations, integrated into our SMT procedure as deduction steps, as well as an approach for parallelizing verification queries. We implement our technique as an extension to the Marabou framework, and use it to evaluate the approach on popular binarized neural network architectures.
Witnessing quantum memory in non-Markovian processes

We present a method to detect quantum memory in a non-Markovian process. We call a process Markovian when the environment does not provide a memory that retains correlations across different system-environment interactions. We define two types of non-Markovian processes, depending on the required memory being classical or quantum. We formalise this distinction using the process matrix formalism, through which a process is represented as a multipartite state. Within this formalism, a test for entanglement in a state can be mapped to a test for quantum memory in the corresponding process. This allows us to apply separability criteria and entanglement witnesses to the detection of quantum memory. We demonstrate the method in a simple model where both system and environment are single interacting qubits and map the parameters that lead to quantum memory. As with entanglement witnesses, our method of witnessing quantum memory provides a versatile experimental tool for open quantum systems.