Reviewerly/exHarmony huggingface dataset:
Dimensionality Reduction Methods in Machine Learning

The goal of machine learning algorithm is to understand the basic features of a complex system. If the dataset is large and the number of features is large as well, it is possible that one can get features or input variables easily identified. In case the dataset is small, there may be a circumstance that one may miss some of the observations and then eventually ignore some features and find the minimal set of features to define the system adequately. In this chapter we will cover the feature selection methods that choose a subset of important features and skip the rest and the feature extraction methods that form the minimally accepted feature from original set of features.
One-Shot Decoupled Face Reenactment with Vision Transformer

Recent face reenactment paradigm involves estimating an optical flow to warp the source image or its feature maps such that pixel values can be sampled to generate the reenacted image. We propose a one-shot framework in which the reenactment of the overall face and individual landmarks are decoupled. We show that a shallow Vision Transformer can effectively estimate optical flow without much parameters and training data. When reenacting different identities, our method remedies previous conditional generator based method’s inability to preserve identities in reenacted images. To address the identity preserving problem in face reenactment, we model landmark coordinate transformation as a style transfer problem, yielding further improvement on preserving the source image’s identity in the reenacted image. Our method achieves the lower head pose error on the CelebV dataset while obtaining competitive results in identity preserving and expression accuracy.
Combined Spatial and Frequency Domains in Algorithm of RGB Color Image Security for Telescope Images

With the rapid progress in space technology development, a lot of digital images are transmitted back to ground telescope receiver. Thus, image security plays a significant role during the transmission process. Existing algorithms in image security were developed based on Latin squares, DNA sequence etc. However, the suitability of the techniques is questionable and high cost was needed. In this paper, Combined Spatial and Frequency Domains (CSFD) algorithm is proposed. It discusses the RGB color image security algorithm based on the images combined spatial and frequency domain. Fourier transform is applied to an RGB color telescope image before it is processed in spatial domain. Then, the R, G, and B components are extracted out before they are encrypted by two levels of encryption algorithm. The outputs were evaluated using NPCR, UACI, PSNR, and information entropy. The results showed that this algorithm has better performance compared to other recently developed algorithms.
Blockchain Technology in Application Development and Associated Challenges

Blockchain, the word itself, suggests a block of chain most useful for secure peer-to-peer transactions. It is most popular and in current trends to develop various security-related applications. Blockchain technology is developed using various advance technique of cryptography and economic models. It is also a public ledger of all cryptographic transaction done online. Blockchain network also provides various types of implementations like permission less or permission blockchain network. It also provides facility of making smart contract to collect information digitally using cryptographic function. It also provides facility to create framework which is backward application compatible. Applications developed using blockchain technology are tamper-resistant and evident of digital ledgers implemented in a distributed manner without having a central administrator that makes it more reliable. In this manuscript, introduction of the working of blockchain technology for application development and various advantages and challenges of blockchain technology is discussed.
The Phenomenon of Inclusion in a Modern Society

The study compares the essential theoretical background of the issue of inclusion as a phenomenon of the present. It presents the basic concepts related to the phenomenon of inclusion and inclusive education in our conditions. We look at inclusion in the study from a terminological, historical, and strategic point of view. Accepting inclusion as a standard part primarily requires a change in the thinking of all actors in the educational process and a difference in the way of organization, culture, and measures for everyday practice concerning all its participants.
Emerging Role of Artificial Intelligence and Internet of Things on Healthcare Management in COVID-19 Pandemic Situation

The tremors caused by the COVID-19 epidemic have ushered in a new era of healthcare problems for people all across the globe. Significant problems and challenges have been observed in areas such as victim assistance, remote monitoring, health resources, and healthcare staff, among others. The goal of this study is to provide a comprehensive perspective of digital healthcare during the COVID-19 pandemic, which is now underway. Various efforts such as mobile applications, digital sites, and sophisticated analytics are being used in order to improve early diagnosis and overall healthcare management. While briefly describing the major aspects leading to widespread implementation of e - healthcare concepts, This research also sheds light on some key aspects of artificial intelligence and the Internet of Things, as well as their practical applications such as clinical decision support systems and predictive risk modelling, which are particularly relevant in the context of combating the COVID-19 pandemic’s practical difficulties. The potential uses of artificial intelligence and Internet of Things technology in the battle against the COVID-19 pandemic are thoroughly examined in this paper. The present and future applications of artificial intelligence and the Internet of Things are covered in depth, as well as a thorough assessment of the supporting tools and methodologies. An in-depth analysis.
Countermeasures of Management Information Innovation and Development Under the Background of Big Data

With the advent of the era of big date (BD), management informatization has become an important step in management development and an important part of realizing management modernization. For management informatization in the context of BD, management positions and management objects are all innovating, and traditional management methods require continuous innovation and development. The purpose of this article is to study the countermeasures for the development of management informatization innovation under the background of BD. This paper adopts the method of questionnaire survey to investigate the current situation of the development of management informatization in H colleges in terms of infrastructure, resources and applications, and management informatization. Then through the statistics and analysis of the data, the main problems existing in the current university management informatization are summarized and the reasons are analyzed. Based on the actual situation, the countermeasures for the innovation and development of management informatization under the background of BD are proposed from five aspects. The survey data shows that universities have specialized informatization management organizations and those without specialized informatization management organizations account for 54.6% and 10.2% respectively, and 35.2% of the survey respondents choose not to be clear, which also explains informatization from the side.
DSV: Disassembly Soundness Validation Without Assuming a Ground Truth

Disassembly is a crucial step in binary security, reverse engineering, and binary verification. Various studies in these fields use disassembly tools and hypothesize that the reconstructed disassembly is correct. However, disassembly is a challenging and undecidable problem. Even state-of-the-art industrial disassemblers suffer from issues ranging from incorrectly recovered instructions to incorrectly assessing which addresses belong to instructions and which to data. We thus present DSV: a systematic and automated approach to validate whether the output of a disassembler is sound with respect to the binary. No source code, debugging information, or annotations are required. We apply DSV to 102 binaries of Coreutils with eight different state-of-the-art disassemblers from academia and industry. DSV is able to find soundness issues in the output of all these disassemblers. Using DSV to validate the output of a disassembler increases trust in any research effort built on top of it.
Best Practices for Requirements Validation Process

One of the most important phases in software development projects is the validation of requirements. Erroneous requirements, if not detected on time, can cause problems, such as additional costs, failure to meet expected objectives and delays in delivery. For these reasons, it is beneficial to invest efforts to this task. This paper aims to identify best practices that can help to carry out the Requirements Validation process. The best practices are determined by the analysis of software requirements validation approaches proposed in recent years, in order to evaluate their characteristics with the “Way-of” framework and the reference model for technical reviews.
Gaussian Processes and Functional Data Analyses

A stochastic process may be defined either as a sequence of random variables $$\{X_t\}_{t\in T}$$ , where T is a set of times, or as a function $$X_t(\omega ): T\rightarrow {\mathbb R}$$ of $$\omega \in \Omega $$ .
Random Feature Selection using Random Subspace Logistic Regression

Feature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we propose the random subspace logistic regression where features are randomly selected through bootstrap cycles. The random subspace regression method is applied to both standard and lasso logistic regression models. Using the simulated and empirical data, our proposed random subspace logistic regression shows favorable results and can be a promising alternative for flat feature selection.
Automatic Sentiment Analysis on Hotel Reviews in Bulgarian—Basic Approaches and Results

In recent years there is a lot of attention towards the web documents as a new source for people’s opinion and experience. This results in a growing interest within the automated extraction and sentiment analysis of online documents, like people’s feedback on products or services, blogs or comments on news. The aim of this paper is to explore the various methods for automatic sentiment analysis (ASA) on hotel reviews in Bulgarian language. It also introduces the motivation and objectives of the research work, together with a unified high level approach for ASA in Bulgarian language. This paper also gives a clear picture of the popularly used machine learning algorithms which could be applied on reviews in Bulgarian language. Aside from this is the creation of a data base of hotel reviews in Bulgarian language.
Space-Efficient B Trees via Load-Balancing

We study succinct variants of B trees in the word RAM model that require $$s + o(s)$$ bits of space, where s is the number of bits essentially needed for storing keys and possibly other satellite values. Assuming that elements are sorted by keys (not necessarily in the order of their integer representations), our B trees support standard operations such as searching, insertion and deletion of elements. In some applications it is useful to associate a satellite value to each element, and to support aggregate operations such as computing the sum of values, the minimum/maximum value in a given range, or search operations based on those values. We propose a B tree representation storing n elements in $$s + \mathop {}\mathopen {}\mathcal {O}\mathopen {}(s / \lg n)$$ bits of space and supporting all mentioned operations in $$\mathop {}\mathopen {}\mathcal {O}\mathopen {}(\lg n)$$ time.
Lateral Ego-Vehicle Control Without Supervision Using Point Clouds

Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to suffering from inadequate robustness in real world scenarios due to a lack of failure cases in the training data. In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed. The framework only requires an unlabeled sequence of RGB images. The trained model takes a point cloud as input and predicts the lateral offset to a subsequent frame from which the steering angle is inferred. The frame poses are in turn obtained from visual odometry. The point cloud is conceived by projecting dense depth maps into 3D. An arbitrary number of additional trajectories from this point cloud can be generated during training. This is to increase the robustness of the model. Online experiments conducted on a driving simulator show that the performance of our model is superior to that of a supervised model trained on the same initial data set and comparable to the same model but trained on data collected with noise injection.
Correction to: SpeechTyper: From Speech to Typographic Composition

In an older version of this paper, there was an error in reference no. 18: the names of the cited paper were incorrectly published. This has been corrected.
On Comprehensive English Classroom Model Based on PBL Theory

Success of foreign language acquisition depends on a variety of factors; the factors of language input and language output possess a very important position in foreign language learning. Positive and efficient input promote output whereas effective and accurate output further promote the input, the two promote each other. College English teachers should design a “learning-oriented” synthetic classroom teaching model based on PBL theory to promote students’ classroom language input and output as well as motivate deep learning, in order to motivate students to build knowledge initiatively, and the college English classroom should become a language input and output platform for the students, and ultimately improve the quality of the acquisition of EFL students.
Expected Value of Asymmetric Coordinated Search Technique for Detecting a Randomly Located Target on the Plane

This paper proposes Asymmetric coordination strategy, we study coordinated search technique for a lost located object on the plan, where there are two searchers beginning their search from the same initial point (0, 0), but both sides do not have the same area. Here we introduce a model of search plan and investigate the expected value of detecting the lost target in both sides to avoid wasting time. We present an algorithm to facilitate the search technique. An illustrative application from real life has been introduced to demonstrate the applicability of this search technique. The effectiveness of this model has been illustrated by numerical results
The Phenomenon of Reproducibility from the Point of View of Epistemology and Philosophy of Science

The article analyzes the phenomenon of reproducibility (repeatability, replica­tion), its terminological variations and grounds for assessing the current stage of development of science as a crisis in terms of reproducibility (verification) of scientific research results and their acquisition of intersubjective status. Attention is drawn to the fact that judgments about the crisis of reproducibility apply to al­most all areas of science, including such (often called exact) sciences such as physics and chemistry. We claim that understanding the phenomenon of repro­ducibility directly affects decision-making policy towards science (regulation of the activity of scientific directions and their funding). At the same time, the situ­ation in the social, humanitarian and biomedical sciences is of particular con­cern, since the level of reproducibility here is quite low. This applies to modern neuroscience as well. From the point of view of epistemological view, it is pro­posed to interpret reproducibility procedures as an attempt to overcome funda­mental limitations associated with measurement processes. It is shown that un­derstanding the meaning and significance of these procedures depends on the opinion of the disciplinary scientific communities, which differ in their ap­proaches to the criteria of reproducibility and follow different cognitive tradi­tions and attitudes. It is emphasized that in the social and humanitarian fields of knowledge it is necessary to take into account the “contextuality” of the mea­surement process, the constant and inevitable dynamics of the characteristics of society, culture and the person himself. All this push us to think about the prob­lem of defining the boundaries of de-anthropologization of scientific knowledge. An attempt is made to comprehend the phenomenon of reproducibility through the lens of the interval approach, and it is stated that reproducibility (ontologi­cally) is limited by the “pervasive inaccuracy of the real world” (L. Zadeh).
Safe Offline Reinforcement Learning Through Hierarchical Policies

Recently, offline reinforcement learning has gained increasing attention. However, the safety of offline reinforcement learning has been ignored. It poses a significant challenge to learn a safe and high-performance policy from a fixed dataset that contains unsafe or unexpected state-action pairs without interacting with the environment. Since the unsafe state-action pairs are usually sparse in the behavior data collected by humans, it is difficult to effectively model information about unsafe behaviors. This paper utilized the hierarchical reinforcement learning framework to alleviate the sparsity issue by modeling unsafe behaviors with hierarchical policies. Specifically, a high-level policy determines a prospective state, and a low-level policy takes action to reach the specified goal state. The training objective of the high-level policy is to improve the expected reward that the low-level policy collects when it moves toward the goal state and reduce the number of unsafe actions. We further develop data processing methods to provide training data for the high-level policy and the low-level policy. Evaluation experiments about performance and safety are conducted in simulation environments that return the rewards and unsafe costs obtained by agents during the interaction. Experimental results demonstrate that the proposed algorithm can choose safe actions while maintaining high performance.
A usability evaluation of the Google Home with non-native English speakers using the system usability scale

Advancements in multidisciplinary research have made voice user interfaces (VUIs) become a reality in both research and commercialisation. Although the existing research has indicated that VUIs are useful in people's daily lives (e.g., personal assistant), there is limited study on non-native English-speaking users' experiences in using commercial VUIs. Hence, in this study, the Google Home Smart Speaker was evaluated with 34 undergraduate students, who use English as a second language, to investigate its usability and user experiences. The SUS, PSSUQ, heuristics questionnaires, and interview questions were used to evaluate the Google Home's usability. The findings report that the Google Home is user-friendly, usable, and useful for non-native English-speaking users. Considering unsolved usability issues found in this study, some usability guidelines are recommended for the design and implementation of future VUIs. The findings also suggest that the SUS is suitable for the assessment of non-native English speakers' use of VUIs.
Performance Evaluation of Online Website Safeguarding Tools Against Phishing Attacks; a Comparative Assessment

Despite the security policies that organizations follow to defend against cyber crimes, phishing attacks are still among the most popular ways the criminals use to steal user’s credentials. Spear phishing, fake websites, fraudulent emails, smishing, and vishing all fall under the umbrella of phishing attacks. Recent procedures followed by many organizations tend to develop anti-phishing tools that identify fraudulent emails and websites, which are embedded either implicitly within the web browsers and email applications or explicitly as an online service. In this research, we have evaluated the effectiveness of six online checking tools in detecting potentially malicious websites. Six URL scanning engines from the best well-known engines in the VirusTotal website were tested on a list of legitimate and malicious URLs, which were collected from well-known anti-phishing frameworks, including PhishTank. In order to find the most efficient anti-phishing tool, the detection accuracy, precision, recall, and F1-Score were calculated for each engine. The results showed that among website checking tools, Sophos achieved a detection accuracy of 99.23% and a precision value of 100%.
PushID: A Pressure Control Interaction-Based Behavioral Biometric Authentication System for Smartwatches

Smartwatches support a wide range of functionality, including mediating access to sensitive data and services. However, securing access to them is difficult due to their small size—it is difficult, for example, to enter alphanumeric passwords accurately due to the limited space available to present a keyboard. Consequently, 4-digit PIN is commonly used to secure smartwatches, a technique widely acknowledged to be highly vulnerable to simple guessing attacks. To address these usability and security issues, we propose PushID, a new behavioral biometric technique for a smartwatch that combines four on-screen targets with five pressure levels to enable input of any one of 20 unique symbols from a single screen touch. In addition to this relatively large input space, PushID captures behavioral features during pressure input (e.g., finger touch profile, wrist motions) and uses this as a behavioral biometric. We report on a preliminary study of PushID and its security against random guessing attack: it achieves good usability for a single input (approximately 2 s) and high resistance to guessing (false-positive rates of 1.05%). We argue that pressure-based input can improve the security and maintain the usability of smartwatch lock systems.
Deep Convolutional Embedded Fuzzy Clustering with Wasserstein Loss

Deep clustering combines embedding and clustering to obtain the optimal nonlinear embedding space, which is more effective in real scenes than traditional clustering methods. However, for a class of deep embedding clustering represented by DCEC, the loss function is mostly KL divergence, and KL divergence will fail in some cases. To avoid KL failure, this paper proposes to use Wasserstein distance as the loss function of the clustering layer, and introduce the fuzzer into the clustering layer, and then develop a deep convolution embedded fuzzy clustering method with Wasserstein loss (DCEFC). Through comprehensive experiments on three popular image datasets with the comparison methods, the experimental results show that our proposed method is superior to the comparison methods, and the clustering results are effective.