Reviewerly/exHarmony huggingface dataset:
Dance and Movement-Led Research for Designing and Evaluating Wearable Human-Computer Interfaces

Movement research and practice in the context of wearable technologies and human-computer interaction (HCI) shifts the design paradigm to the lived body. Human movement is characterized by sense, intention and expressiveness. Designing HCI from this standpoint opens up new possibilities to make computational devices and applications more accessible and integrated. This work presents an iterative, collaborative, and cross-disciplinary approach using wearable sensor bands in an open-ended performative exploration in exchange with a professional dancer. The goal is to understand the benefits and challenges of using movement-centered tools originating from dance practice and movement research and how they might feed back into the design, development and evaluation process of embodied technologies to improve human-computer interactions. Movement analysis systems and motion computation models are reviewed and leveraged in an interactive audiovisual system, with focus on using force-sensing resistors for low-level motion descriptors and Laban Movement Analysis for higher-level movement features. The artistic methodology, which combines practice and research, results, discussion of the iterative and collaborative process, and the final system architecture are the main topics presented in the paper.
Dimensionality Reduction-Based Discriminatory Classification of Human Activity Recognition Using Machine Learning

Majority of work in activity recognition using different machine learning and deep learning has shown very challenging results to monitor daily activities. Different datasets available on Web have been used to improve the results, still model fitness need to be verified in terms of different characteristics of matrix and error analysis. Dimensionality reduction (DR) of datasets improves the results of models due to pruning of dataset features. In this paper, we have introduced seven different machine learning models to improve the results. Proposed framework has used principle components analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction of UCI-ML dataset. Results show that LDA is better than PCA. Kernel–SVM accuracy has increased from 95.39 to 96.23%. Naïve Bayes has shown 96.78% accuracy with dimensionality reduction. Simple dataset has shown low accuracy while dimensionality reduction has improved the performances of models. We have also introduced different challenges associated with machine learning models, fitness value, and future challenges. At the end of this work, we have done comparative study and error analysis of models.
Study and Develop a Convolutional Neural Network for MNIST Handwritten Digit Classification

The goal of this analysis has been on the development of handwritten digit recognition with the use of the MNIST dataset. In the latest days, the identification of handwritten digits has become a challenging research topic in machine learning. Due to physically formed digits having varying lengths, widths, orientations, and positions. It may be utilized in several ways, such as the amount and signature on bank checks, the location of postal and tax papers, and so on. This research used CNN for recognition. Total four steps followed by pre-processing, feature extraction, training CNN, classification, and recognition. Along with its great higher accuracy, CNN outperforms other methods in detecting essential characteristics without the need for human intervention. On top of that, it incorporates unique levels of convolution and pooling processes. Through CNN, 97.78% accuracy was obtained.
Detecting Affine Equivalence Of Boolean Functions And Circuit Transformation

Abstract Affine equivalence of Boolean functions has various applications in computer science and modern cryptography, such as circuit design and S-boxes. Existing methods for detecting affine equivalence of Boolean functions work in some cases but not when the truth table of a Boolean function is sparse. To improve previous methods and overcome this limitation, we propose a method by transforming the Boolean function to a function with the property that its function values at the orthonormal basis are all equal to 1 or 0, which narrows down the search space of affine transformations. Our first algorithm has the advantage of getting a smaller search space than previous methods and is especially useful for sparse functions. Specifically, when the Boolean functions are sparse, the search space can be reduced exponentially in average and experiments show the efficiency of our first algorithm. We then present another algorithm to transform one circuit into its equivalent affine circuit by synthesizing a reversible circuit and inserting it in front of the original circuit. To our knowledge, this is the first work to automatically synthesize an affine equivalent circuit for any given circuit and the first to do this by combining reversible circuit and non-reversible circuit.
MRI and CT image indexing and retrieval using steerable pyramid transform and local neighborhood difference pattern

This paper proposes an effective system for indexing and retrieval of medical images, which uses steerable pyramid transform (SPT) and local neighborhood difference patterns (LNDP). In the first step of the proposed system, a medical image is subdivided into several sub-bands using SPT at different resolutions and orientations. Further, each sub-band is encoded using LNDP in a binary texture pattern to extract more complex texture features. Finally, LNDP histograms are computed for each SPT sub-band. Thereafter, all the sub-band histograms are combined to form a single histogram to produce the required feature vector. The proposed SPT-LNDP technique represents a medical image in an efficient way that contains many sources of information from various scales and orientations. Two open-source medical image datasets namely OASIS- MRI and VIA/I-ELCAP-CT images of human brain were considered for efficient evaluation of the proposed method. To validate the efficacy of the proposed method two parameters namely Average Retrieval Precision (ARP) and Average Retrieval Rate (ARR) were used. A detailed comparative study of the proposed method with other popular methods, which are described in this study, are also presented in this work. The results show improvement over other existing methods considered in this study.
A Comparative Analysis of Edge Detection Using Soft Computing Techniques

Detecting edges is one of the most significant aspects of computer vision. Typical methods for edge detection like Sobel and Canny are robust and fast, but they are sensitive to noise. Soft computing techniques such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithms (GA) and fuzzy logic system (FLS) have extensive application in edge detection of images because of their adaptive behavior. Edge detection is identifying the discontinuities in intensity of the pixel and grouping the contour of edges. The quality of edges in ACO-based edge detection majorly depends on the choice of constants, pheromone evaporation rate, number of iterations etc. In PSO-based edge detection, the quality of images depends on the values of acceleration coefficients and inertia weight. However, thresholding is major stakeholder in determining the fitness of the chromosomes. The population contains 2-D chromosomes. Fuzzy systems are most suitable for designing edge detection hardware. This paper presents a thorough comparative study of soft-computing-based edge detection techniques and highlights their key features. The factors affecting quality of edges are compared, and the actual outcomes of the approaches are systematically arranged for better understanding.
Fault detection for large scale indoor distributed antenna system based on time series similarity

With the advancement of the fifth-generation (5G) mobile communication networks, the number of subscribers in the interior environment continues to grow. The large-scale indoor distributed antenna system (DAS) is one of the critical approaches for bringing macro base station signals indoors. As the DAS becomes larger and the composition becomes more and more complex, the probability of system failure gradually increases. Therefore, it is very important to detect the failure of the DAS. Through actual research, limited by the user’s usage pattern, distribution, and regional functions, the daily power slave data of the room distribution system has a certain periodicity and similarity, but when a fault occurs, it will break this rule, and then be detected. However, the similarity and periodicity of the data are also affected by the randomness of users, which brings difficulties to fault detection. This paper will use the fault detection method based on N-dimensional Euclidean distance to mine the anomalies in the DAS detection data, and then carry out fault detection. To solve the influence of user randomness on the detection results, this paper will introduce a sliding window and a selection window. Although the filtering reduces the timeliness, it greatly reduces the false alarm rate. Finally, the simulation data and real data at DAS will be used to verify the method proposed in this paper.
A Qualitative Analysis of the Persuasive Properties of Argumentation Schemes

Argumentation schemes are generalised patterns that provide a way to (partially) dissociate the content from the reasoning structure of the argument. On the other hand, Cialdini's principles of persuasion provide a generic model to analyse the persuasive properties of human interaction (e.g., natural language). Establishing the relationship between principles of persuasion and argumentation schemes can contribute to the improvement of the argument-based human-computer interaction paradigm. In this work, we perform a qualitative analysis of the persuasive properties of argumentation schemes. For that purpose, we present a new study conducted on a population of over one hundred participants, where twelve different argumentation schemes are instanced into four different topics of discussion considering both stances (i.e., in favour and against). Participants are asked to relate these argumentation schemes with the perceived Cialdini's principles of persuasion. From the results of our study, it is possible to conclude that some of the most commonly used patterns of reasoning in human communication have an underlying persuasive focus, regardless of how they are instanced in natural language argumentation (i.e., their stance, the domain, or their content).
Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.
Recursive Deformable Image Registration Network with Mutual Attention

Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging. Many previous studies have used learning-based methods for multi-stage registration to perform 3D image registration to improve performance. The performance of the multi-stage approach, however, is limited by the size of the receptive field where complex motion does not occur at a single spatial scale. We propose a new registration network combining recursive network architecture and mutual attention mechanism to overcome these limitations. Compared with the state-of-the-art deep learning methods, our network based on the recursive structure achieves the highest accuracy in lung Computed Tomography (CT) data set (Dice score of 92% and average surface distance of 3.8 mm for lungs) and one of the most accurate results in abdominal CT data set with 9 organs of various sizes (Dice score of 55% and average surface distance of 7.8 mm). We also showed that adding 3 recursive networks is sufficient to achieve the state-of-the-art results without a significant increase in the inference time.
HateVersarial: Adversarial Attack Against Hate Speech Detection Algorithms on Twitter

Machine learning (ML) models are commonly used to detect hate speech, which is considered one of the main challenges of online social networks. However, ML models have been shown to be vulnerable to well-crafted input samples referred to as adversarial examples. In this paper, we present an adversarial attack against hate speech detection models and explore the attack’s ability to: (1) prevent the detection of a hateful user, which should result in termination of the user’s account, and (2) classify normal users as hateful, which may lead to the termination of a legitimate user’s account. The attack is targeted at ML models that are trained on tabular, heterogeneous datasets (such as the datasets used for hate speech detection) and attempts to determine the minimal number of the most influential mutable features that should be altered in order to create a successful adversarial example. To demonstrate and evaluate the attack, we used the open and publicly available “Hateful Users on Twitter” dataset. We show that under a black-box assumption (i.e., the attacker does not have any knowledge on the attacked model), the attack has a 75% success rate, whereas under a white-box assumption (i.e., the attacker has full knowledge on the attacked model), the attack has an 88% success rate.
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition

Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks. We intend to recognize our small-scale fine-grained Tai Chi action dataset using neural networks and propose a transfer-learning method using NTU RGB+D dataset to pre-train our network. More specifically, the proposed method first uses a large-scale NTU RGB+D dataset to pre-train the Transformer-based network for action recognition to extract common features among human motion. Then we freeze the network weights except for the fully connected (FC) layer and take our Tai Chi actions as inputs only to train the initialized FC weights. Experimental results show that our general model pipeline can reach a high accuracy of small-scale fine-grained Tai Chi action recognition with even few inputs and demonstrate that our method achieves the state-of-the-art performance compared with previous Tai Chi action recognition methods.
Two-attribute privacy protection method of MCS based on blockchain smart contract

In view of the problem of privacy leakage caused by the centralized location privacy protection method in mobile crowd sensing, a privacy protection method based on blockchain smart contract was proposed. Firstly, the trusted distributed environment blockchain is adopted to replace the third-party server. In order to avoid the leakage of personal information, users register an anonymous identity on the blockchain. In addition, in order to solve the problem of user's location privacy leakage caused by mutual deceiving behavior among users in distributed structure, a dummy location algorithm based on two-attribute decision is designed on smart contract. In this algorithm, the distance between each location and the user location and the historical query probability of each location are obtained by calculating the peripheral location. Then, different thresholds are set for each location and a cyclic filtering is carried out. As a result, an anonymous set is obtained that effectively protects the user's location. Finally, the simulation experiment proves that the method can not only improve the efficiency of protecting user's location privacy, but also has lower time and computation overhead.
WeScribe: An Intelligent Meeting Transcriber and Analyzer Application

In all existing organizations regardless of their type or size, meetings are conducted on the regular basis to invite discussions for organizational decision making. While many organizations, even large ones, still hire employees to perform these tasks, there is no doubt that the results are exposed to human error. Documenting meetings’ minutes is essential for its success and keeping track of the work progress and decisions flow, approvals, while keeping it complete, consistent, and coherent. This project idea was proposed by Aramco to develop a suitable solution for a hectic problem. The process of documenting and taking minutes can be tedious, so we aim to automate audio meeting transcription with the use of technologies that convert speech to text while recognizing the speaker and then process and analyze the most valuable information tagged based on persons in the meeting. This goal can be accomplished through the development of an app that uses speech recognition for conversion, voice recognition for identification of speakers, and natural language processing (NLP) for analysis and then combines them all in a transcription form with considerable accuracy. Further, the proposed system identifies potential events, deadlines, and follow-ups and adds them to the speaker’s calendar upon approval. In the future, we aspire to expand it with some features such as increasing the number of meeting members, creating special sections for each department in the company which adopt WeScribe, and feed our NLP model with more data to develop its performance and increase its accuracy.
Non-planar illumination deflectometry for axicon metrology

We introduce an on-axis deflectometry test configuration for axicon metrology. Axicons are challenging to measure due to their characteristically steep, convex geometry. However, if an axicon is coaxially aligned with a camera and a surrounding cylindrical illumination source, high-resolution surface measurements can be obtained via the principle of deflectometry. Emitted from the temporally modulated source, light deflects at the conical surface and into the entrance pupil of a camera, illuminating the full axicon aperture except the ø 0.5-mm rounded tip. Deflectometry measurements of a 100° and 140° axicon show holistic cone angle agreement within 0.035° against touch probe data and up to 7.93 root μm mean square difference from a best-fit cone. We discuss the non-planar illumination architecture, sensitivity, and experimental results of arbitrary apex angle axicons.
International cooperation as one of the directions for the development of the education system

One of the most important directions in the development of the system of secondary vocational education at the present stage is the need to constantly update the content and technologies of vocational education in accordance with current and future requirements for the qualifications of workers. Significant assistance in improving the quality of training of specialists can be provided by the development of international cooperation in the implementation of secondary vocational education programs. Establishing such cooperation will make it possible to take into account advanced foreign experience, increase the attractiveness of educational institutions of secondary vocational education among potential applicants, and bring programs of secondary vocational education to the international market of educational services. To date, international cooperation has received the greatest development in the fi eld of higher education, master’s and postgraduate programs. The experience of international cooperation of the Chelyabinsk Institute of Railways — a branch of the Ural State University of Railways is considered. Information is provided on the main areas of international cooperation, including the search for potential partners among foreign educational organizations, with the subsequent formalization of relationships within the framework of international cooperation agreements, as well as the experience of implementing events within the framework of signed agreements, including the organization of joint international events of educational, scientifi c and sports orientation, work to improve the professional competencies of the personnel potential of Chelyabinsk Institute of Railways — a branch of the Ural State University of Railways through the internships of teachers in educational institutions of the Republic of Kazakhstan.
SRPAR: anchor-free detector with aspect ratio priority for slender objects

Slender objects are more difficult to detect than conventional objects with regular shapes, and the rotation of these objects makes detecting them even more challenging. To address these challenges, we proposed SRPAR, an anchor-free detector with aspect ratio priority for slender objects. The aspect ratio priority factor is designed based on the object’s aspect ratio and rotation angle. The aspect ratio priority factor guides the regression of slender rotating objects and improves the regression accuracy. In addition, through the multi-level prediction of feature pyramid network, the range of bounding boxes and corresponding angular regressions at each level is limited, and the regression of overlapping object prediction boxes is accelerated. To better evaluate the SRPAR’s detection performance of slender rotating objects having an aspect ratio of at least 3:1, some images of sticks are supplemented into the baseball bat subset of the common objects in context (COCO) dataset to form a new self-made COCO-Stick dataset. Experimental results on the dataset of object detection in aerial images dataset and the self-made COCO-Stick dataset show that, compared with state-of-the-art detectors, the proposed method has some advantages in detection accuracy.