Reviewerly/exHarmony huggingface dataset:
Real-time State Recognition of Switches on Electrical Cabinet Panel Using Hybrid Visual Features

An automatic and accurate state recognition of switches on electrical cabinet control panels plays an increasingly important role in the routine inspection of power equipment. This paper presents a novel method for real-time cabinet panel switch state recognition using hybrid visual features. Compared to traditional methods, the proposed approach can ensure the rectangular object regions from images captured at arbitrary angles by applying the perspective transformation model. Besides, the switch regions are segmented and the corresponding visual features are extracted on HSV space, instead of raw RGB space, which overcomes the illumination variability issues. The morphological operations and the inherent geometrical constraints, are employed to group the switch regions. Eventually, the switch recognition is implemented on high-dimensional vector space using feature similarity discriminants. The proposed method has been evaluated on the image dataset collected from power station cabinets. The experimental results verify the effectiveness of the method.
Diversity Image Coding Using Irregular Interpolation

Diversity “multiple description” (MD) source coding promises graceful degradation in the presence of a priori unknown number of erased packets in the channel. A simple coding scheme for the case of two packets consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation and splitting into two descriptions are done in the discrete cosine transform (DCT) domain. Moreover, unlike the classical source-channel separation approach - which is designed for a predetermined number of erasures (say, K out of N), hence its distortion does not improve when the channel behaves better than expected - an MD coding scheme aims to achieve a better reconstruction quality when more or all the N descriptions are received at the decoder side. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions form a non-uniform sampling pattern. In this work, we examine inter- and intra-block interpolation methods, and show how noise amplification can be reduced by redesigning the interpolation filter at the encoder. Specifically, for a given total coding rate, we demonstrate that an “irregular” interpolation filter is robust to the pattern of received packets over all ( K out of N) patterns, with some degradation relative to low-pass (LP) interpolation in the case where all N packets arrived. We provide experimental results comparing LP and irregular interpolation filters, and examine the effect of noise shaping on the trade-off between the central distortion (receiving all packets) and side distortion (receiving K packets).
Automated Extraction and Classification of Slot Machine Requirements from Gaming Regulations

In well-regulated industries, important technical requirements can often be found in state and federal laws and regulations. This paper examines how natural language processing can be used during requirements analysis to analyze government regulations. The examples used in this paper are drawn from casino industry regulations for slot machine development, but are applicable to analyses of government regulations in other industries as well. More specifically, this paper analyzes South Dakota and Nevada regulations for slot machines and applies natural language processing to extract and analyze technical requirements derived from them using four techniques. First, key words and key phrases are drawn from the regulations using the Rapid Automatic Keyword Extraction algorithm so that they can be imported into a program glossary. Second, requirements are extracted from the regulations. Many of these requirements do not have the word “shall”, so a 12-rule transformation algorithm is used to convert the text into “shall” or “may” statements. Third, a Naive Bayes model is developed from the South Dakota regulations to predict which of the extracted Nevada requirements are functional, and which are not. Finally, a Dice similarity metric weighted with term frequency-inverse document frequency scores is used to identify related and equivalent requirements between the South Dakota and Nevada regulation sets.
Towards Measuring Test Coverage of Attack Simulations

Designing secure and reliable systems is a difficult task. Threat modeling is a process that supports the secure design of systems by easing the understanding of the system’s complexity, as well as identifying and modeling potential threats. These threat models can serve as input for attack simulations, which are used to analyze the behavior of attackers within the system. To ensure the correct functionality of these attack simulations, automated tests are designed that check if an attacker can reach a certain point in the threat model. Currently, there is no way for developers to estimate the degree to which their tests cover the attack simulations and, thus, they cannot the determine the quality of their tests. To resolve this shortcoming, we analyze structural testing methods from the software engineering domain and transfer them to the threat modeling domain by following an Action Design Research approach. Further, we develop a first prototype, which is able to assess the test coverage in an automated way. This will enable threat modeler to determine the quality of their tests and, simultaneously, increase the quality of the threat models.
Enriching Financial Software Requirements Concerning Privacy and Security Aspects: A Semiotics Based Approach

Enriching software requirements with key security and privacy features requires professionals to have knowledge of requirements elicitation techniques, based on systematic processes and methods. We propose the Software Requirements Analysis Method for Improvement of Privacy and Security (SRAM-PS), which is based on concepts and techniques from Organizational Semiotics and on the analysis of information security and data privacy standards. SRAM-PS is a 7-steps systematic approach where an input set of software requirements is analyzed, processed, and then enriched with new security and privacy requirements. A case study with 4 experts was carried out, where SRAM-PS is used in a real world scenario: a bank sends a financial transaction receipt containing the customer’s personal data over the Internet. SRAM-PS is aimed at researchers and engineers who analyze and specify software requirements and need to systematize their methods and techniques.
HECC (Hyperelliptic Curve Cryptography)

Cybersecurity plays a very important role in our daily lives to protect our data against unauthorized access. Cryptography as the core functionality can provide key intelligence services such as protection, authentication, key setup, and integrity of data. We seek higher safety, efficiency, faster implementation, and low power consumption in the field of cryptography technology. There are already many cryptographic algorithms available that can meet these standards. Nevertheless, the new technological systems are smaller in size and have very limited capacity for processing and storage. The key size of RSA is somewhat large, and it takes much longer to handle and consumes a lot of power to implement RSA in small devices. Therefore, in practice, cryptosystems are favored which use smaller key sizes, including many of which depend on a discrete logarithm issue throughout a cumulative elliptical curve unit indicated in finite fields. For this, the concept of cryptography with the elliptic curve came. It is centered over the abelian group of curve points. The benefit of using the cryptography process of elliptic curve is that its key size is smaller than RSA. On the other hand, the complexity of the elliptic curve in mathematics is more involved than that of systems based at RSA. Then, the idea of a discrete logarithm problem with hyperelliptical curves and a cryptosystem was constructed over the Jacobian. Smaller base field size enables hyper-elliptical curves a better choice for lightweight cryptosystems. In this chapter, we have demonstrated the details of the cryptography of the hyper-elliptic curve and its level of security to protect data.
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions

With the advent of highly predictive but opaque deep learning models, it has become more important than ever to understand and explain the predictions of such models. Many popular approaches define interpretability as the inverse of complexity and achieve interpretability at the cost of accuracy. This introduces a risk of producing interpretable but misleading explanations. As humans, we are prone to engage in this kind of behavior. In this paper, we take the view that the complexity of the explanations should correlate with complexity of the decision. We propose to build a Treeview representation of the complex model using disentangled representations, which reveals the iterative rejection of unlikely class labels until the correct association is predicted.
Transfer Learning for Children Face Recognition Accuracy

Sumithra, R. Guru, D. S. Manjunath Aradhya, V. N. Anitha, RaghavendraIdentifying the missed and kidnapped children at the later age will be a quit challenging process. To overcome the challenge, this research work has proposed a new Children Face Recognition (CFR) application using Artificial Intelligence (AI) system. To the best of our knowledge, the existence of children face image dataset, which is created in a fruitful process has not been reported in the earlier literature. Hence, to this consequence, this research work has addressed the problem of developing the children face recognition model with a suitable dataset. A model has been proposed by using machine learning pipeline that consists of pre-processing, feature extraction, dimensionality reduction and learning model. To this end, an attempt has also been made to classify the face images of children by training a multi-classification algorithm with the ensemble techniques such as Bagging and Boosting. During the dataset creation, 40,828 longitudinal face images of 271 young children of age from 4 to 14 years are captured over the duration of 30 months. This extensive experimentation has analyzed that few projection vectors of k-NN classifier has achieved a high accuracy of about 93.05%.
An Information Retrieval-Based Approach to Activity Recognition in Smart Homes

One of the principal challenges in developing robust Machine Learning (ML) classification algorithms for Human Activity Recognition (HAR) from real-time smart home sensor data is how to account for variations in 1) the activity sequence length, 2) the contribution each sensor has to an activity, and 3) the amount of activity class imbalance. Such changes generate observations that do not conform to expected patterns potentially reducing the efficacy of classification models. Moreover the architecture of prior solutions have been quite complex which have resulted in large training times for these approaches to achieve acceptable classification accuracy. In this paper we address these three issues by 1) proposing a data structure representing the duration and frequency information of each sensor for an activity, 2) transforming this data structure into an Information Retrieval (IR)-based representation, and finally 3) compare and contrast the utility of this IR-based representation using four different supervised classifiers. Our proposed framework in combination with a state-of-the-art ensemble learner results in more accurate and scalable ML classification models that are better suited toward off-line HAR in a smart home setting.
Automatic modulation classification over MIMO amplify and forward (AF)-relay fading channels

Automatic Modulation Classification (AMC) plays an important role in many civilian applications such as spectrum monitoring (SM) and cognitive radio (CR). The majority of the AMC algorithms for MIMO systems are developed for rich scattering environments, which offer a full rank channel matrix. A rank deficient channel such as a keyhole channel severely affects the AMC performance. It is reported that cooperative relaying is an attractive solution to overcome the rank deficiency of multiple-input multiple-output (MIMO) channels. This work investigates the effect of cooperative relaying on the AMC performance. A higher-order cumulant (HOC)-based AMC method, namely the relay assisted RA-AMC method, is proposed to classify a pool of modulation types, namely BPSK, OQPSK, QPSK, π4-QPSK, 8-PSK and 16-QAM over an amplify and forward (AF) relay network. Simulation results show that the proposed method performs reasonably well over AF-relay fading channels.
Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity.However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus, there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.
Holistic word descriptor for lexicon reduction in handwritten arabic documents

Most of word recognition systems rely on a pre-defined lexicon in aims to achieve high performance. Recently, the availability of training /testing data allows to include a huge number of words in the lexicon to recognize. However, this leads to high computation cost as the lexicon is grown. In addition, including more and more word-classes may lead to increase the burden on classification methods and degrade the recognition rate. In this work, we propose a holistic word descriptor for word lexicon reduction in Arabic handwritten documents. The proposed descriptor represents geometrical features of word shape through three main feature sets, defined from multi-scale convexity concavity analysis. The first two sets are dedicated to defined the number of peaks and their intensity levels of convexity/concavity peaks, respectively. In contrast, the last set is dedicated to define a region codes of the peaks by analyzing their regions according to their spatial information. Given a query word and lexicon(reference dataset), the lexicon reduction system is applied by first defining the holistic word descriptor for both query word and each word in the lexicon. The lexicon is then indexed according to its distances to the query word descriptor. Finally, the reduced lexicon is formulated from the first kth entries of the indexed lexicon. The proposed system has been evaluated under two well-known Arabic datasets, namely Ibn Sina and IFN/ENIT. Reported results show superior performance compared to prior art, with 93.7% and 91.2% reduction efficacy for Ibn Sina and IFN/ENIT, respectively.
Sensor deviation anomaly trend prediction based on phase space reconstruction and Support Vector Regression

Abstract Ship combat systems are developing in the direction of integration and intelligence, and the integration also brings problems such as difficulty in predicting abnormal sensor trends in combat systems. The failure characteristics of sensors in combat systems are highly secretive, and it is difficult to detect and deal with system equipment failures by relying on empirical qualitative or single-indicator testing methods. In order to safeguard the ship’s combat capability and make reasonable maintenance decisions, it is important to study more accurate prediction methods. This article proposes a method for predicting sensor deviation anomaly trends based on phase space reconstruction and Support Vector Regression(SVR), taking sensors in naval combat systems as typical research objects. Firstly, a phase space reconstruction is performed on the sensor deviation time series data to increase the accuracy of subsequent prediction; then a SVR prediction model is built, and the model is trained and optimised using the reconstructed data; finally, the constructed prediction model is compared with the regular SVR model and BP neural network model to verify that the constructed model has higher prediction accuracy.
On the Work of the Samara Branch of the RAS Scientific Council on the Methodology of Artificial Intelligence and Cognitive Research

The summary presents the main results of the work of the Samara branch of the RAS Scientific Council for the Methodology of Artificial Intelligence and Cognitive Research, created in 2007 on the basis of S.P. Korolev Samara National Research University (Samara University). The Samara branch of the Council and the Samara University held international conferences on information technology, information society, science fiction, established Artificial Intelligence Center as well as completed interdisciplinary technical and humanitarian research projects in the field of socio-humanitarian cybernetics, digital models of creative processes, computational aesthetics.
Progressive Semantic Reasoning for Image Inpainting

Image inpainting aims to reconstruct the missing or unknown region for a given image. As one of the most important topics from image processing, this task has attracted increasing research interest over the past few decades. Learning-based methods have been employed to solve this task, and achieved superior performance. Nevertheless, existing methods often produce artificial traces, due to the lack of constraints on image characterization under different semantics. To accommodate this issue, we propose a novel artistic Progressive Semantic Reasoning (PSR) network in this paper, which is composed of three shared parameters from the generation network superposition. More precisely, the proposed PSR algorithm follows a typical end-to-end training procedure, that learns low-level semantic features and further transfers them to a high-level semantic network for inpainting purposes. Furthermore, a simple but effective Cross Feature Reconstruction (CFR) strategy is proposed to tradeoff semantic information from different levels. Empirically, the proposed approach is evaluated via intensive experiments using a variety of real-world datasets. The results confirm the effectiveness of our algorithm compared with other state-of-the-art methods. The source code can be found from https://github.com/sfwyly/PSR-Net.
Power analysis attack resilient block cipher implementation based on 1‐of‐4 data encoding

Side-channel attacks pose an inevitable challenge to the implementation of cryptographic algorithms, and it is important to mitigate them. This work identifies a novel data encoding technique based on 1-of-4 codes to resist differential power analysis attacks, which is the most investigated category of side-channel attacks. The four code words of the 1-of-4 codes, namely (0001, 0010, 1000, and 0100), are split into two sets: set-0 and set-1. Using a select signal, the data processed in hardware is switched between the two encoding sets alternately such that the Hamming weight and Hamming distance are equalized. As a case study, the proposed technique is validated for the NIST standard AES-128 cipher. The proposed technique resists differential power analysis performed using statistical methods, namely correlation, mutual information, difference of means, and Welch's t-test based on the Hamming weight and distance models. The experimental results show that the proposed countermeasure has an area overhead of 2.3× with no performance degradation comparatively.
The Biometric Signature as a Blockchain Application

The Internet would be the largest one had it been a country of the world. The population and the produced and consumed data in this new country have been exponentially increasing, and the metrics with which one can measure all this growth are zettabytes, if not the yottabytes now. The issue of forgery detection on authentication and integrity and on the ownership of the data and the users grows incrementally along with this unprecedented growth of the Internet. With the advancements in hardware and software resulting in the Internet of Things (IoT), the checking against forgery will be even harder to perform since anything and everything will be interconnected as never seen before. Signing a document has always been a direct proof for belonging and authentication of the person. Thus, forgery has always been an equally likely issue to be encountered. While the journey of the signatures in the Internet era will be browsed in this chapter, the biometric signature as a complete and better-suited answer to the problem of forgery will be introduced within ironclad protection provided by a blockchain structure which is a new cybersecurity architecture in the domain of the IoT.
Efficient and Privacy-Preserving of COVID-19 Contact Tracing Scheme

The world has seen many pandemics in the past. COVID-19, SARS, H1N1, and Ebola are some of them. When these diseases break out, they spread very quickly, and tracing the source becomes a challenge to control the disease. In this paper, under the premise of not divulging the private information for infected persons and their potential contacts, we have proposed a privacy-preserving contact tracking schemes of infectious persons. In these schemes, if a user is diagnosed with COVID-19, potential contacts with the virus can be traced, so that Healthy Authority(HA) can better control the spread of the virus. The process is private and does not disclose any unnecessary information. In addition, we evaluate our schemes and compare them with existing similar schemes. Our scheme achieving all security goals and having lower communication and computation costs.