Reviewerly/exHarmony huggingface dataset:
Simplifying and Optimizing the Convolution Encoding Algorithm in Error Control Codes

The simplicity and clarity behind every mathematical principle are the computing recipes that give acceptance to scientific methods and procedures. The greater the volume of the recipes, the greater the degree and rate of mastery, acceptance, and application. It was against this backdrop that this study was initiated, to simplify and optimize the convolution code encoder (CCE). Specifically, the study investigated most closely the convolution code encoding algorithm (CCEA), simplified the CCEA, optimized the CCEA, designed the simplified and optimized CCEA (SOCCEA), and evaluated the performance of the SOCCEA. The incremental model was used in the study. It supported the breaking down of the simplification process. The SOCCEA has five critical steps which could be repeated until the least significant bit (LSB) in the input bits is moved into the shift registers (SR). The input sequence is moved through the SR one at a time leading to the flushing of the last shift in the register in an instance of bit movement within the register. The movement of the input bits through the registers is from the most significant bit (MSB) to the LSB. N modulo-2 adder (on all the registers) is applied to achieve the leftmost bit (LMB) of the output, while the Boolean XOR logic operation is required to obtain the rightmost bit (RMB). The output of two digits is realized by multiplexing the LMB with the RMB. The SOCCEA is recommended for the computation of the start and final states and the output of the error control codes.
A design of high-Speed SMS4 cipher circuit

Abstract SMS4 cipher algorithm is a commercial cipher independently developed by china. This paper proposes a circuit design of high-speed SMS4 cipher algorithm, which adopts two-dimensional expansion and key path optimization technology. The round key generation module runs synchronously with the encryption module, and the running round is constant, which can effectively resist the side channel attack. Using Xilinx artix-7 for synthesis, the results show that the design function is correct, and achieves the balance of computing speed and resource occupation.
Finding repeated strings in code repositories and its applications to code-clone detection

Although researchers have created many advanced code-clone detection techniques, more effort is required to realize wide adaptation of these techniques in the industry. One of the reasons behind this is the reliance of these advanced techniques on lexing and parsing programs. Modern programming languages have complex lexical conventions and grammar, which evolve constantly. Therefore, using advanced code-clone detection techniques requires substantial and continuous effort. This paper proposes a lightweight language-independent method to detect code clones by simply finding repeated strings in a code repository, relying on neither lexing nor parsing. The proposed method is based on an efficient technique developed in a bio-informatics context to find repeated strings. We refer to the repeated strings in the source-code as weak Type-1 clones. Because the proposed technique normalizes newlines, tabs, and white spaces into a single white space, it can find clones in which newline positions or indentations are changed, as often in the case when copy-pasting occurs. Although the proposed method only finds verbatim copies, it also makes interesting observations regarding repository structures. Many developers may prefer the proposed simple approach because it is easier to understand than other advanced techniques that use heuristics, approximation, and machine learning.
Sonar Image Generation from Underwater Optic Images utilizing Sensor Models and Depth Estimation with Neural Network

This paper proposes a method to generate a sonar image from an optical image, called optic-to-sonar which is one direction of opti-acoustic translation. To convert an optical image into a sonar image having a different geometry, the proposed method first reconstructs a three-dimensional point cloud by estimating the depth information using a neural network from given optical images. Then the sonar image is generated through the sonar projection model. We verified the proposed method through water tank experiments. The proposed method can generate a sonar image of the same viewpoint as a given optical image. So, it can be utilized as a dataset augmentation technique to further develop opti-acoustics in an underwater environment where data acquisition is challenging.
Securing Business Processes Using Blockchain Technology: A Case Study of Hydrocarbon Accounting Processes.

Abstract This research addresses the problems associated with hydrocarbon accounting reconciliation and allocation from the production facility to the export terminal. This paper further discusses the security of the hydrocarbon accounting database and the overall automation of the production value chain, providing transparency to the joint venture partners involved in the crude oil export agreement to avoid revenue loss. It also provides a system that is not prone to malware or data alteration and promotes hydrocarbon allocation among the injectors, production data management and production data security. The existing system mostly in the sub-saharan Africa lacks trust and greater transparency. A new technology will be devolped using Blockchain Hyperledger which is a distributed ledger technology. For the purpose of this research Javascript, PHP, MySQL on Apache virtual machine was used to design and simulate the blockchain network. The web interface of the system was tested using katalon web test framework using Agile methodology. The system will have to integrate with the Lease Automation Custody Transfer (LACT) and Supervisory Control and Data Acquisition (SCADA), A LACT-SCADANode synchronized system will be implemented in the custody transfer point in the export pipeline, so all the oil and gas Exploration and Production (E&amp;Ps) companies injecting crude oil can view what each company in the network injected and also the quantity that got to the export terminal daily. This will be achieved by having all the custody transfer nodes of the E&amp;Ps and export terminal node in a network of consensus. Production data will be distributed at strategic points during the transportation among the nodes uniformly at the same time, making it impossible for cyber-invasion on all the nodes at the same time. The outcome of this research will achieve an advanced secured transparent system to store production data for accurate hydrocarbon allocation, in which the consensus attribute of the implemented blockchain technology will give each node autonomy. Hence, making the production data highly reliable and reconciliation will be achieved at real-time. This innovation further presents a new knowledge in the application of mechatronics engineering to the oil and gas sector, with its multidisciplinary focus on electronics, mechanical and computer systems. It is the integration of a software system to a LACT and sensors on the crude oil pipelines to acquire data, compute in a Hyperledger fabric network and display at real-time for hydrocarbon allocation settlement
From Project Plans and Backlogs to Strategic Roadmaps: The Evolution Toward Value-Oriented Thinking in Requirements Engineering

New product and service innovations are ideally developed and rolled out along so-called value increments. Value entails delivered desirability (customer value) as well as realized viability for the company (business value). Whereas Design Thinking inherently emphasizes value-oriented thinking, requirements engineering does not foster this notion per se. On the contrary, during requirements engineering, initiatives tend to lose sight of desirability and viability since their primary focus shifts toward internal feasibility. We argue that frameworks, which are industry standards, such as project plan and backlog, contribute to this problematic shift. We further argue that strategic roadmaps, as a complementary framework, come along with advantages to resolve this issue. Our standpoint emerged from practical experience in industry projects within startups and large corporations.
InsurTech aggregators Development in Russia: Psychological Aspect

In modern society, the economic digitalization is gaining momentum, covering almost all its spheres. The versatility and diversity of consumer behavior that exists in each society changes the previous, well-established business models radically. Global changes have also affected the sphere of insurance services. With the advent of insurance technologies, the insurance culture in modern Russia is in constant development. The purpose of this paper is to describe the essence of the InsurTech technology and the psychological grounds for its application in Russian practice. As a result, five bases of psychological analysis were identified when determining the types (or strategies) of insurance behavior, namely, benefit, rationality of choice, risk sensitivity, financial anxiety, and attitude to insurance policy. The psychotypes of policyholders depending on their psychological attitudes are given: "paternalists", "market suppoters" and "neutral". It is concluded that the peculiarities of the attitude of Russian citizens to insurance services in the virtual environment have not yet been sufficiently studied, and research on the transformation of insurance consumers thanks to InsurTech is practically not presented. Nevertheless, there are some trends in the transformation of the behavior of the consumers of insurance services under the influence of the Internet technologies. That is why the makers of both psychological characteristics and demographic variables (sex, age, income level, employment) should be taken into account when developing insurance scoring for aggregators InsurTech.
A Spectral Clustering Algorithm Based on Differential Privacy Preservation

Spectral clustering is a widely used clustering algorithm based on the advantages of simple implementation, small computational cost, and good adaptability to arbitrarily shaped data sets. However, due to the lack of data protection mechanism in spectral clustering algorithm and the fact that the processed data often contains a large amount of sensitive user information, thus an existing risk of privacy leakage. To address this potential risk, a spectral clustering algorithm based on differential privacy protection is proposed in this paper, which uses the Laplace mechanism to add noise to the input data perturbing the original data information, and then perform spectral clustering, so as to achieve the purpose of privacy protection. Experiments show that the algorithm has both stability and usability, can correctly complete the clustering task with a small loss of accuracy, and can prevent reconstruction attacks, greatly reduce the risk of sensitive information leakage, and effectively protect the model and the original data.
The Role of Face Embeddings in Classification of Personality Traits from Portrait Images

There are research works going on to correlate image features with personality traits. This work proposes a deep learning frame work for classifying personality traits from Portrait images. We used a dataset with 30,736 images of hetero-geneous persons for the experimentation purpose. The influence of state-of-the-art face recognition networks were investigated for extracting the facial features. The classification of personality traits was done by applying Support Vector Machine (SVM) classifier based on Big Five Personality model. The loss functions of the selected networks are more discriminative in nature, better than the conventional Mean Square Error (MSE) which is justified through the performance matrices. The proposed method could beat the state-of-the-art results in terms of accuracy and F1-score. We summarize that facial features from Portrait images could very well classify the personality traits, rather than relying on the psychometric tests or analysis of physiological parameters.
Common Attacks on Near Field Communication Technology

Web Application Security (WAS) is the focus of the cybersecurity world. The users use the web and mobile applications to offer personal data for data exchange and money transactions which can be misused by the attacker to launch malicious attacks. Broken Access Control (BAC) attack is the attack that targeting the URL resource to manipulate the SQL query parameters with the purpose of exposing personal information the attacker shouldn&#x0027;t be able to see or tamper with the improper linkage of right access to a specific function trying to do the function as a legitimate user. Our purpose is to highlight BAC vulnerability in e-Pay services that support Wi-Fi money transactions specifically in Mada-pay that uses Near Field Communication (NFC) by reviewing attacks targeting NFC technology.
An Algorithm for Selecting a Data Mining Technique

Selecting a data mining technique is an important step in the data mining process. Techniques like association, clustering, regression, naïve Bayes, and time series may be used for data mining. However, the various tools available in the market do not award the user a chance to verify whether the tool is appropriate for their data. This study presents an algorithm that may be used to select a technique based on the structure of the data to be mined. Literature was reviewed to identify the factors that may be considered in selecting a technique. The spiral model was adopted for development of the algorithm. The algorithm compares the data source to the defined criteria which has weights assigned to determine the suitable technique. A score is allocated to each evaluated technique and the technique with the highest score is recommended. The scoring and weighting details are described in pseudocode and flowcharts while Java programming language was used to implement the algorithm. The resultant artefact suggests a data mining technique after analysing the structure of given a data set.
Study on motion sickness based on EEG power spectrum characteristics

In this study, electroencephalogram (EEG) signals were collected from 20 subjects with motion sickness (MS) induced by visual rotation stimulation. We processed and analyzed the electroencephalogram (EEG) before and after vision-induced motion sickness, and obtained the EEG power spectrum characteristics. The results showed that the power spectrum characteristics of C3 channel α, Θ and δ waves were significantly different before and after stimulation (p<0.05). This study indicated that EEG power spectrum could be used as a reference for quantitative detection and evaluation of vision-induced motion sickness. It provides a reference for the detection and evaluation of motion sickness, and has a certain reference value for the study of the inherent neural mechanism of motion sickness.
Possessive inflection in Chichimec inalienable nouns

Abstract Person and number of a possessor are expressed in Chichimec in one of two ways. Most nouns use possessive classifiers. A smaller class (typically inalienables) inflects for the possessor synthetically. This paper constitutes the first in-depth exploration of this latter class. These nouns are characterized by unparalleled levels of irregularity, with more than 100 different inflection classes and most nouns exhibiting completely unique exponence patterns. The morphology of these nouns is based on several orthogonal inflectional layers: prefixes, stem alternations, and tone, all of which exhibit only weak predictive relations to other subsystems or cells, and equally unpredictable mappings to the possessor values they instantiate. The system is also extremely challenging with respect to segmentation, as most of the segments within the word can change in inflection seemingly independently of the neighbouring ones. This paper surveys this baroque system in search of its organizational principles.
Doctoral Consortium of WSDM'22

Deep neural networks (DNNs) have achieved extraordinary accomplishments on various machine learning tasks. However, the existence of adversarial attacks still raise great concerns when they are adopted to safety-critical tasks. As countermeasures to protect DNN models against adversarial attacks, there are various defense strategies proposed. However, we find that the robustness ("safety'') provided by the robust training algorithms usually result unequal performance either among classes or sub-populations across the whole data distribution. For example, the model can achieve extremely low accuracy / robustness on certain groups of data. As a result, the safety of the model is still under great threats. As a summary, our project is about to study the bias problems of robust trained neural networks from different perspectives, which aims to build eventually reliable and safe deep learning models. We propose to present our research works in the Doctoral Consortium in WSDM'22 and gain opportunities to share our contribution to the relate problems.
Efficient training for the hybrid optical diffractive deep neural network

As a new emerging machine learning mechanism, optical diffractive deep neural network (OD2NN) has been intensively studied recently due to its incomparable advantages on speed and power efficiency. However, the training process of the OD2NN with traditional back-propagation (BP) method is always time-consuming. Here, we introduce the biologically plausible training methods without feedback to accelerate the training process of the hybrid OD2NN. Direct feedback alignment (DFA), error-sign-based DFA (sDFA) and direct random target projection (DRTP) are utilized and evaluated in the training process of the hybrid OD2NN respectively. For the hybrid OD<sup>2</sup>NN with 20 diffractive layers, about 160× (DFA; CPU), 30× (DFA; GPU), 170× (sDFA; CPU), 32× (sDFA; GPU), 158× (DRTP; CPU) and 32× (DRTP; GPU) accelerations are achieved respectively without significant loss of accuracy, compared with the training process using BP method on CPU or GPU.
An Analysis of Fireworks Algorithm Solving Problems With Shifts in the Decision Space and Objective Space

Fireworks algorithms for solving problems with the optima shifts in the decision space and/or objective space are analyzed. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single-objective problems. The optimum shift in decision space and/or objective space will increase the difficulty of problem-solving. Modular arithmetic mapping is utilized in the original fireworks algorithm to handle solutions out of the search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of the fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies are compared with different problems. The fireworks algorithms with mapping to the boundary or mapping to a limited stochastic region obtain good performance on problems with the optimum shift.
Semantic Learning and Understanding of multivehicle interaction patterns Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

The development of an automated vehicle that can handle complex driving scenarios and appropriately interact with other road users requires semantic learning and the ability to understand the driving environment, usually based on the analysis of a large amount of natural driving data. However, the explosive growth of driving data poses a huge challenge for extracting primitive driving patterns from long-term multi-dimensional time series traffic scene data, which involves multi-scale road users. In order to achieve this, a general framework to gain insights into intricate multi-vehicle interaction patterns in real-world driving was presented in this paper. A Bayesian nonparametric learning method based on a hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. Unlike previous articles, which only considers the interactive behavior of two vehicles, we consider the driving scenarios where the ego vehicle can sense all surrounding vehicles, including the front vehicle, the rear vehicle, the front left vehicle, the left vehicle, the rear left vehicle, the front right vehicle, the right vehicle, the rear right vehicle. Experimental results show that our proposed method can extract primitive driving patterns, thereby providing a semantic way to analyze multi-vehicle interaction patterns from multi-dimensional driving data and laying the foundation for the generation of coverage test cases for automated vehicles.
Human Identification Using Low-Resolution Thermal and High-Resolution RGB Images

Object detection and identification with great accuracy and speed in real time are a major issue in present growing applications, deep learning models, such as deep neural networks, convolutional neural networks, and modified models like YOLO, SSD are achieving sufficient performance in object identification with great computational power and better processing environment, and consequently this is the great drawback to run this deep learning (DL) models in low-end devices like embedded applications and portable applications. In this paper, we have used Tiny Yolo v3, K-means clustering algorithm of unsupervised learning and IR images has been used to address the issue of high-computational power and low-resolution human detection. We have used IR images instead of RGB images, and thermal images are great solution to the issue of low-illumination object identification. K-means clustering algorithm retrieves the object and classifies the object by segmenting the given image. The deep learning model Tiny Yolo extracts the object features and identifies the proper bounding box. Proposed model describes real-time low-resolution human identification in two steps, identifying anchor boxes by using K-means clustering algorithm of unsupervised learning model and predicting proper bounding boxes based on the anchor boxes by using Tiny Yolo of deep learning model.
Polynomial Phase Signal Denoising Connecting Semantic Information Based on Deep Neural Networks

Abstract This paper considers the problem of polynomial phase signal (PPS) denoising. To prove that proper use of semantic information can further improve the denoising performance based on deep neural networks, we propose an architecture combining the segmentation network and the denoising network. The vision semantic information is extracted from the segmentation network first. Then, that information connecting the time-frequency representation of noisy signal are fed into the denoising network for reconstructing signal. To effectively apply the semantic information, three connection strategies and the corresponding lower bound are presented and compared. The proposed method does not require the pre-identification of signal noise conditions and is suitable for a wide range of Signal-to-Noise-Ratio (SNR) scenarios. Simulation results demonstrate that the F1 scores of the spectrum segmentation results are over 0.98 and the proposed method connecting vision semantics for PPS denoising tasks outperforms the baseline and state-of-the-art architectures, when the SNR is larger than -8dB.
Conceptual Entailments

As text became trusted to screens rather than to paper, very soon the temptation emerged to escape from the chronology of the spoken words. Syntax is hard to abandon, but the macrostructure of text (the semantically linked keywords) became a favorite playground to experiment with. Reading a text was recognized as a saccadic movement where the jumping pattern not simply gives away the amount of needed cognitive load.