Reviewerly/exHarmony huggingface dataset:
Investigating Bad Smells with Feature Selection and Machine Learning Approaches

Code Smell is a piece of code that is designed and implemented poorly and it gives adverse effect on the software quality and maintenance. Now, a day’s machine learning based techniques have been extensively used towards code smell research. The main objective of this research is to optimise the features of Android code smells in terms of software metrics using feature selection technique based on Correlation on 2896 instances of open-source projects which are extracted from GitHub. Further, we have examined the performance measures like accuracy, precision, F-measure and execution time etc. with the reduced features data set of Android code smells. This paper also discussed about implementation of correlation-based feature selection algorithm to reduce the features of code smells. Then, the data has been analyzed with 4 machine learning algorithms that are Logistic Regression, Stochastic Gradient Descent (SGD), Simple Logistic and Sequential minimal optimization (SMO). The performance metrics for the above-mentioned machine learning algorithms with and without performing the feature selection have been compared. The computed outcome shows that the best accuracy and lesser execution time for all 3 considered Android code smells have been achieved using Logistic Regression algorithm. After feature selection the accuracy has increased up to 16%, 25% and 4.7% for NLMR, MIM and DTWC code smells respectively. Meanwhile, the other performance measures have also been increased.
Would-be specialists’ vocational training within the course “English for academic mobility”

This article addresses the issues of would-be specialists vocational training within the course English for Academic Mobility. The course is an important part of lifelong leaning vocational training, which is supposed to provide training for modern specialists who are ready to fulfil their potential and build up their learning curve utilizing up-to-date information, advanced technological platforms and company resources. The authors emphasize that, despite the current world situation, higher education institutions of Russia continue to implement programmes within the framework of international cooperation, and are also looking for new opportunities and directions for academic and scientific partnerships. Thus, academic mobility being a key aspect of any educational organisation activities has not lost its ground under the current circumstances. The article illustrates the implementation of the English for Academic Mobility course aiming at preparing the Nizhnevartovsk State University students (would-be specialists) for successful participation in both international academic mobility programmes and competitive labour market in future. The article provides a description of the course developed by the teachers of Nizhnevartovsk State University and defines its importance as one of the links in the professional training of NVSU students in the framework of continuing education.
Results on the Gowers U2 Norm of Generalized Boolean Functions

Recently, a framework for employing the Gowers [Formula: see text] norm in the context of (generalized) Boolean functions with cryptographic significance was introduced. In this paper, we first give tight bounds on the Gowers [Formula: see text] norm of generalized Boolean functions via the (generalized) sum-of-squares indicator. Secondly, we provide a framework for the generalized signal-to-noise ratio ([Formula: see text]) of generalized [Formula: see text]-functions. We characterize the [Formula: see text] in terms of the Gowers [Formula: see text] norm. In particular, we present a direct link between the [Formula: see text] of a class of generalized Boolean functions and the [Formula: see text] of its component Boolean functions. Finally, the expressions of the Gowers [Formula: see text] norm of generalized Boolean functions from some well-known secondary constructions (the concatenation and Carlet’s construction) are obtained.
Voice Interfaces for Underserved Communities

Abstract Internet technologies – like social media platforms and discussion forums – have transformed how people communicate with each other. In addition to improving access to information, news, and entertainment, they have impacted governance, politics, civil society movements, crisis response, marketplaces, and healthcare, among other parts of our lives. Although concerns regarding data misuse, privacy breaches, and overuse have grown recently, these technologies are continuing to soar, mostly among literate, urban, and connected communities, all across the world. However, despite their promises (and pitfalls), these technologies currently exclude billions of people worldwide who are too remote to access the Internet, too low-literate to navigate the mostly text-driven Internet, or too poor to afford Internet-enabled devices. This chapter presents the innovation, implementation, and adaptation of voice forums that have evolved over the last two decades, discusses challenges that plagued their growth, and highlights ingenious solutions that were used to address their pain points.
YORÙBÁ CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL RECURRENT NEURAL NETWORK

Handwritten recognition systems enable automatic recognition of human handwritings, thereby increasing human-computer interaction. Despite enormous efforts in handwritten recognition, little progress has been made due to the variability of human handwriting, which presents numerous difficulties for machines to recognize. It was discovered that while tremendous progress has been made in handwritten recognition of English and Arabic languages, very little work has been done on Yorùbá handwritten characters. Those few works, in turn, made use of Hidden Markov Model (HMM), Support Vector Machine (SVM), Bayes theorem, and decision tree algorithms. To integrate and save one of Nigeria's indigenous languages from extinction, as well as to make Yorùbá documents accessible and available in the digital world, this research work was undertaken. The research presents a convolutional recurrent neural network (CRNN) for the recognition of Yorùbá handwritten characters. Data were collected from students of Kwara State University who were literate Yorùbá writers. The collected data were subjected to some level of preprocessing such as grayscale, binarization, and normalization in order to remove perturbations introduced during the digitization process. The convolutional recurrent neural network model was trained using the preprocessed images. The evaluation was conducted using the acquired Yorùbá characters, 87.5% of the acquired images were used for the training while 12.5% were used to evaluate the developed system. As there is currently no publicly available database of Yorùbá characters for validating Yorùbá recognition systems. The resulting recognition accuracy was 87.2% while the characters with under dot and diacritic signs has low recognition accuracy.
Content of modern social networks today and their psychological impact on spiritual and moral education of youth

The article describes the problems related to moral censorship, moral and ethical standards and their actions on the Internet. It also talks about the preservation and development of the morality of young people in the information society. But it must be emphasized that in order to solve existing problems, young people themselves must be active. Knowledge cannot be made information without losing something of value. Knowledge always has a personal aspect, it is a means of communication. And knowledge turned into information is alienated from the one who consumes this information. Therefore, for us real communication is more important than virtual communication, although the younger generation is increasingly striving for virtual communication.
A New Method of Disabling Face Detection by Drawing Lines between Eyes and Mouth

Face swapping technology is approaching maturity, and it is difficult to distinguish between real images and fake images. In order to prevent malicious face swapping and ensure the privacy and security of personal photos, we propose a new way to disable the face detector in the face detection stage, which is to add a black line structure to the face part. Using neural network visualization, we found that the black line structure can interrupt the continuity of facial features extracted by the face detector, thus making the three face detectors MTCNN, S3FD, and SSD fail simultaneously. By widening the width of the black line, MTCNN, S3FD, and SSD are able to reach probability of failure levels up to 95.7%. To reduce the amount of perturbation added and determine the effective range of perturbation addition, we firstly experimentally prove that adding perturbation to the background cannot interfere with the detector’s detection of faces.
Low-Light Image Enhancement Using Photometric Alignment with Hierarchy Pyramid Network

Low-light image enhancement can effectively assist high-level vision tasks that often fail in poor illumination conditions. Most previous data-driven methods, however, implemented enhancement directly from severely degraded low-light images that may provide undesirable enhancement results, including blurred detail, intensive noise, and distorted color. In this paper, inspired by a coarse-to-fine strategy, we propose an end-to-end image-level alignment with pixel-wise perceptual information enhancement pipeline for low-light image enhancement. A coarse adaptive global photometric alignment sub-network is constructed to reduce style differences, which facilitates improving illumination and revealing under-exposure area information. After the learned aligned image, a hierarchy pyramid enhancement sub-network is used to optimize image quality, which helps to remove amplified noise and enhance the local detail of low-light images. We also propose a multi-residual cascade attention block (MRCAB) that involves channel split and concatenation strategy, polarized self-attention mechanism, which leads to high-resolution reconstruction images in perceptual quality. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods in detail and color reproduction.
Representative Ring Signature Algorithm Based on Smart Contract

Traditional ring signature algorithms suffer from large signature data capacity and low speed of signature and verification during collective signing. In this work, we propose a representative ring signature algorithm based on smart contracts. By collecting the opinions of the signatory based on multiparty secure computation, the proposed technique protects the privacy of the signatory during the data interaction process in the consortium chain. Moreover, the proposed method uses smart contracts to organize the signature process and formulate a signature strategy of "one encryption per signature" to prevent signature forgery. It uses the Hyperledger Fabric framework as the signature test platform of the consortium chain to perform the experiments. We compare the results of the proposed method with the ECC ring signature scheme. The experimental results show that in the worst case, the signature volume of the proposed method decreases by more than two times, and the signature speed and verification speed increase by more than three times. Therefore, in the collective signature scenario of transaction verification in the consortium chain, the proposed method is verified to be innovative and practical.
Graded Rings in Lean’s Dependent Type Theory

In principle, dependent type theory should provide an ideal foundation for formalizing graded rings, where each grade can be of a different type. However, the power of these foundations leaves a plethora of choices for how to proceed with such a formalization. This paper explores various different approaches to how formalization could proceed, and then demonstrates precisely how the authors formalized graded algebras in Lean’s mathlib. Notably, we show how this formalization was used as an API; allowing us to formalize various graded structures such as those on tuples, free monoids, tensor algebras, and Clifford algebras.
Formal Methods for NFA Equivalence: QBFs, Witness Extraction, and Encoding Verification

Nondeterministic finite automata (NFAs) are an important model of computation. The equivalence problem for NFAs is known to be PSPACE-complete, and several specialized algorithms have been developed to solve this problem. In this paper, we approach the equivalence problem for NFAs by means of another PSPACE-complete problem: quantified satisfiability (QSAT). QSAT asks whether a quantified Boolean formula (QBF) is true. We encode the NFA equivalence problem as a QBF which is true if and only if the input NFAs are not equivalent. In addition to determining the equivalence of NFAs, we are also able to decode strings that witness the inequivalence of two NFAs by looking into the solving certificate. This is a novel application of certified QSAT solving. Lastly, we formally verify key aspects of the encoding in the Isabelle/HOL theorem prover.
Learning to Reason Assisted by Automated Reasoning

We report on using logic software in a novel course-format for an undergraduate logic course for students in computer science or artificial intelligence. Although being designed as the students’ basic introduction to the field of logic, the course features a novel structure and it adds some modern content, such as SAT and SMT solving, to the traditional and established topics, such as propositional logic and first order predicate logic. The novel course design is characterized by, among others, the integration of existing logic software into the teaching of logic. In this paper we focus on the module on first-order predicate logic and the use of the Theorema system as a proof-tutor for the students. We report on statistical evaluation of data collected over two consecutive years of teaching this course. On the one hand, we asked for feedback of students on how helpful they felt the software support was. On the other hand, we evaluated their results in the exams during the course and their development over the entire teaching period. The performance in exams is then correlated with students’ own perception of the helpfulness of software.
Quality Assessment of Subtitles – Challenges and Strategies

This paper describes a novel approach for assessing the quality of machine-translated subtitles. Although machine translation (MT) is widely used for subtitling, in comparison to text translation, there is little research in this area. For our investigation, we are using the English to German machine translated subtitles from the SubCo corpus [11], a corpus consisting of human and machine-translated subtitles from English. In order to provide information about the quality of the machine-produced subtitles error annotation and evaluation is performed manually. Both the applied error annotation and evaluation schemes are covering the four dimensions content, language, format and semiotics allowing for a fine-grained detection of errors and weaknesses of the MT engine. Besides the human assessment of the subtitles, our approach comprises also the measurement of the inter-annotator agreement (IAA) of the human error annotation and evaluation, as well as the estimation of post-editing effort. The combination of these three steps represents a novel evaluation method that finds its use in both improving the subtitling quality assessment process and the machine translation systems.
Stay Focused - Enhancing Model Interpretability Through Guided Feature Training

In computer-assisted surgery, artificial intelligence (AI) methods need to be interpretable, as a clinician has to understand a model's decision. To improve the visual interpretability of convolutional neural network, we propose to indirectly guide the feature development process of the model with augmented training data in which unimportant regions in an image have been blurred. On a public dataset, we show that our proposed training workflow results in better visual interpretability of the model and improves the overall model performance. To numerically evaluate heat maps, produced by explainable AI methods, we propose a new metric evaluating the focus with regards to a mask of the region of interest. Further, we are able to show that the resulting model is more robust against changes in the background by focusing the features onto the important areas of the scene and therefore improve model generalization.
Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations

Morphological analysis of various cells is essential for understanding brain functions. However, the massive data volume of electronic microscopy (EM) images brings significant challenges for cell segmentation and analysis. While obtaining sufficient data annotation for supervised deep learning methods is laborious and tedious, we propose the first self-supervised approach for learning 3D morphology representations from ultra-scale EM segments without any data annotations. Our approach, MorphConNet, leverages contrastive learning in both instance level and cluster level to enforce similarity between two augmented versions of the same segment and the compactness of representation distributions within clusters. Through experiments on the dense segmentation of the full-brain EM volume of an adult fly FAFB-FFN1, our MorphConNet shows effectiveness in learning morphological representation for accurate classification of cellular subcompartments such as somas, neurites, and glia. The self-supervised morphological representation will also facilitate other morphological analysis tasks in neuroscience.
Accurate and Explainable Image-Based Prediction Using a Lightweight Generative Model

Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical alternatives that are easier to tune, train and interpret. The models we propose consist of (1) a causal forward model expressing the effect of variables of interest on brain morphology, and (2) a latent variable noise model, based on factor analysis, that is quick to learn and invert. In experiments estimating individuals' age and gender from the UK Biobank dataset, we demonstrate competitive prediction performance even when the number of training subjects is in the thousands – the typical scenario in many potential applications. The method is easy to use as it has only a single hyperparameter, and directly estimates interpretable spatial maps of the underlying structural changes that are driving the predictions.
Computational Approaches for Understanding Semantic Constraints on Two-termed Coordination Structures

Coordination is a linguistic phenomenon where two or more terms or phrases, called conjuncts, are conjoined by a coordinating conjunction, such as and, or, or but. Well-formed coordination structures seem to require that the conjuncts are semantically similar or related. In this paper, we utilize English corpus data to examine the semantic constraints on syntactically like coordinations, which link constituents with the same lexical or syntactic categories. We examine the extent to which these semantic constraints depend on the type of conjunction or on the lexical or syntactic category of the conjuncts. We employ two distinct, independent metrics to measure the semantic similarity of conjuncts: WordNet relations and semantic word embeddings. Our results indicate that both measures of similarity have varying distributions depending on the particular conjunction and the conjuncts’ lexical or syntactic categories.
An End-to-End GUI-Based Real-Time Attendance System by Training Annotated Facial Data on YOLO v3 Algorithm

Attendance plays a major role in every student’s life; parents and school/college administrators are really concerned about their student’s attendance. Lower attendance can also lead to bad grades in schools and colleges, and at the same time, taking attendance manually alleviates the allocated time of a particular subject. In counteraction to the issues which we are dealing, this paper initiates the “An End-to-End GUI-based Real-Time Attendance System by Training Annotated Facial Data on YOLO v3 Algorithm” which keeps traces the student/ward attending a certain class with the help of photos captured from a visual recording device like surveillance camera by means of utilizing CNN, max pooling, and YOLO algorithms. Identifying a student’s image was popularized through the media. Facial recognition system has a wide variety of uses in recent times on mobile platforms like unlocking the mobile phone with the help of face recognition and in other forms of technology, such as robotics. It is mostly used as authorization in monitoring systems and can be compared to other biometrics such as retina recognition, voice authentication, and fingerprint recognition systems. This designed system will be absolutely systematized and authentic as Siamese network which has proven to proffer high authenticity in the face recognition system. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and amount of students who are absent along with the amount of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial lately.
Comparative Analysis of Machine Learning Classification Algorithms for Predictive Models Using WEKA

The world is becoming progressively more dependent on technology. Artificial intelligence (AI), part of such technology is the simulation and emulation of human intelligence by machine and computer systems. Machine learning, one of the most significant branches of AI, makes it possible for machines to learn from experience and historical data using simple and complex algorithms. Predictive algorithm is a scientific idea of empirically establishing a relationship between the historical set of data which can then be used to make future decision in an attempt to solve some real-life problems. The action of predictive algorithm on big data results into predictive modeling which has been applied by various researchers to solve numerous problems including prediction of weather condition, rate of disease spread, birth rate, death rate, rate of road accident, and in population prediction too. In this paper, we have presented a comparative analysis on the performance of four major machine learning classification algorithms, namely K-nearest neighbor, naïve Bayes, random forest, and SVM on three case studies of predictive modeling using WEKA tool. The case studies selected for this paper are station-wise rainfall prediction, life time of a car prediction, and detection of breast cancer.
Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

Abstract In The hitchhiker’s guide to responsible machine learning , Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.