Reviewerly/exHarmony huggingface dataset:
Framework and Value-Driven Process of Software Engineering for Business and Society (SE4BS)

This paper proposes a framework and a value-driven process for new software engineering, named “Software Engineering for Business and Society: SE4BS”, towards the digital transformation (DX) era. Successful DX requires not only technology, but also an understanding of the importance of business agility. In addition, without careful traceability, software engineering projects can be not based on business and social values. To address these issues, we categorize useful methods, practices, and models in software development and operations that make connections and traceability from business ideas incorporating business agility to software products, services, and user experiences. Then we propose a typical value-driven process stemming from business and social perspectives as new software engineering necessary for the DX era. We also categorized these items of the value-driven process through the mental factors of cognition, affective, and conative to analyze the process.
Photovoltaic Power Forecasting Using Cascade Forward Neural Network Based On Levenberg-Marquardt Algorithm

Renewable energy is useful energy collected from renewable resources, and renewable resources are naturally replenished within human time. Photovoltaic (PV) is a generator that converts solar energy into electrical energy. Depending on the weather, photovoltaic output is an occasional output. Therefore, this study will use the Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt algorithm to predict photovoltaic power plants one day in advance. The measure of accuracy error from the simulation result in this study is calculated using Mean Square Error (MSE). From the simulation results, it is obtained that The Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt could give the better MSE at the learning rate of 0.1 by mean MSE of 0,308% while the learning rate of 0.05 by mean MSE of 0,326% and learning rate of 0.01 by mean MSE of 0,322%. It is also obtained that Cascade Forward Neural Network (CFNN) method also eligible for solving photovoltaic power forecasting problem due to its accuracy and should be eligible for another renewable energy electricity source power forecasting.
Parallel strategies for SIDH: Towards computing SIDH twice as fast

We present novel strategies and concrete algorithms for the parallel computation of the Supersingular Isogeny-based Diffie-Hellman key exchange (SIDH) protocol when executed on multi-core platforms. The most relevant design idea exploited by our approach is that of concurrently computing scalar multiplication operations along with a parallelized version of the strategies required for constructing and evaluating large smooth degree isogenies. We report experimental results showing that a three-core implementation of our parallel approach achieves an acceleration factor of 1.45 compared against a sequential implementation of the Supersingular Isogeny Key Encapsulation (SIKE) protocol instantiated with the prime p<sub>751</sub>
Modeling and Application of Service Dynamic Extended Network Ontology

The rapid development of SDEN (Service Dynamic Extended Network) has led to an expansion of its related concepts and inter-concept relationships. How to formally and normatively express SDEN system from the knowledge level is of great significance to realize intelligent management of SDEN domain knowledge and support status monitoring and evaluation of SDEN system. Firstly, the characteristics of SDEN and its implementation principle are summarized; Then, SDEN domain knowledge is analyzed to extract ontology concepts and their attributes; Nextly, inter-concept relationships are analyzed to establish SDEN-Ontology; Finally, the specific application and effect of SDEN-Ontology are verified through two application scenarios based on customized inference rules.
Validity of science edupark e-book based on scientific approach on the national geopark of ranah minang silokek, Indonesia

Abstract The background of the design and manufacture of the e-book is a follow-up to the preliminary research. Based on preliminary research revealed: first, there is no learning tool related to regional potential. Second, learning tools are not based technology yet. Third, there are many facts, principles and concepts of science in The National Geopark of Ranah Minang Silokek. The purpose of this study is to produce a valid quality Science Edupark E-Book based on material substance, presentation feasibility, appearance feasibility, and language. This e-book is designed based on the 2013 curriculum by containing the scientific approach steps. This research uses the Plomp development model in the development phase and the Prototyping phase. Data collection is taken by a questionnaire technique. Validation is carried out through an e-book validation instrument sheet by three experts, containing material substance, feasibility of presentation, feasibility of display, and language. The data analysis technique used is the Likert scale and the validity value by using the Aiken’V formula. This research results in an e-book with valid criteria. The conclusion of this research is the e-book that is made is suitable as a source of learning science in junior high school.
Diagnosing Cancer Cells Using Histopathological Images with Deep Learning

Early diagnosis of breast cancer for women can increase the survival opportunity with correct treatment in the clinic. Breast cancer can be diagnosed by detecting the malignancy of the cells of breast tissue. The microscopic images of breast cancer cells can be collected which can be used for detecting the presence of malignant (cancer) cells. The diagnosis process is tedious and the result may vary between pathologists. So, the Computer-Assisted Diagnosis (CAD) system is significant for improving diagnostic accuracy. The machine learning methods require export professional knowledge and experience to extract suitable features from histopathological images. A proposed deep learning-based approach can be used to classify breast cancer histopathological images in an automated way which utilizes different CNN models such as simple Convolutional Neural Network (CNN), dilated CNN, channel-wise separable CNN to extract image features and also uses Support Vector Machine (SVM) classifier and Softmax classifier to classify the histopathology images. This approach identifies the type of tumor and predicts the type of cancer cell as either benign (non-cancer) or malignant (cancer). This approach also identifies the best convolutional models which consume less time for training and also provide good training accuracy.
End-User Authentication Control in Cloud-based ERP Systems

Cloud Security is the use of latest technology and security techniques to safeguard data, applications and infrastructure associated with Cloud Computing. The set of policies, procedures, technologies, and controls that function jointly to safeguard cloud-based systems, infrastructures and data are known as the Cloud Computing Security Model. This paper reviews several Cloud Computing Security Models with a close look at the model that addresses data security challenges in cloud-based Enterprise Resource Planning (ERP) systems and proposes an End-User Authentication Control Model for Cloud-based ERP systems. This is a cloud computing security model that uses Enterprise Access Directory, Enterprise Data Fragmentation in cloud and End-user Access Queries, to ensure that End users share a greater security responsibility. The proposed model, when compared with other exiting models, will encourage more end-user participation in enterprise data security in the cloud. The proposed model also mitigates the impact that a malicious insider might have on the enterprise cloud data set, since no single user can gain access to the whole cloud-based enterprise database at the same time. The proposed model considers end-user role and responsibility within the enterprise to determine the level of access and to data in the cloud-based ERP system.
Correlation-based ConvNet for Small Object Detection in Videos

The detection of small objects is of particular interest in many real applications. In this paper, we propose STDnet-ST, a novel approach to small object detection in video using spatial information operating alongside temporal video information. STDnet-ST is an end-to-end spatio-temporal convolutional neural network that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing small objects. This architecture links the small objects across the time as tubelets, being able to dismiss unprofitable object links in order to provide high-quality tubelets. STDnet-ST achieves state-of-the-art results for small objects on the publicly available USC-GRAD-STDdb and UAVDT video datasets.
Blind Spot: An Interactive Gaze-aware Experience

Gaze in the context of interactive systems plays a vital role in Human-Computer Interaction (HCI). Gaze-aware systems provide support for users with physical disabilities, are employed as a game input device, or serve as an input form in public settings. The installation introduced in this work, called “Blind Spot”, reflects these developments by using gaze in an art context by partially obscuring the user’s field of view (i.e., the current gaze position). In doing so, the installation illustrates the potentials and characteristics of eye-tracking technology. It aims at fostering discussions about new design possibilities for gaze-based interactions in the arts and academia. Furthermore, it makes spectators aware of how visual information is processed by humans (i.e., only a small fraction with high visual acuity is perceived at a given moment). Thirdly, it addresses the human’s desire to retrieve a complete picture of a given scenery (i.e., revealing this desire by limiting the spectator’s perception capabilities).
An Attention Based Wavelet Convolutional Model for Visual Saliency Detection

The emergence of deep neural architectures greatly enhanced the accuracy of salient region detection algorithms that plays a vital role in computer vision applications. However, the accurate extraction of regions with fine boundaries still remains as a challenge. In this work, an attention based Wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. Further enhancement of the fine boundaries of the predicted map is made possible by the inclusion of a combinational loss function of balanced cross entropy loss, SSIM loss and edge loss. The effectiveness of the method is evaluated using three benchmark datasets and the results shows better performance achieving a minimum Mean Absolute Error (MAE) of 0.032 and maximum F-measure of 0.938.
Investigation of Fast and Efficient Methods for Multi-Speaker Modeling and Speaker Adaptation

In this paper, we propose a novel method for fast and efficient few-shot TTS task, which is able to disentangle linguistic and speaker representations. Specifically, an adversarial training strategy is firstly employed to wipe out speaker information from the linguistic representations. Then the speaker representations are extracted from audio signals by a speaker encoder with a random sampling mechanism and a speaker classifier, aiming to extract speaker embedding features that are independent of content information (such as prosody and style etc). Meanwhile, for faster and efficient adaptation, we further introduce the prior alignment knowledge between the text and audio pairs and propose a multi-alignment guided attention to help the attention learning. The Experimental results show the proposed method not only could generate higher speech quality and speaker similarity with an average absolute improvement of 0.26 and 0.30 in MOS respectively, when adapting to new speakers with 20 utterances, but also converge much faster and efficient. More-over, we can achieve a MOS of 4.45 for a premium voice, which outperforms a single speaker model of 4.23. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Inducing Rich Interaction Structures Between Words for Document-Level Event Argument Extraction

Event Argument Extraction (EAE) is the task of identifying roles of entity mentions/arguments in events evoked by trigger words. Most existing works have focused on sentence-level EAE, leaving document-level EAE (i.e., event triggers and arguments belong to different sentences in documents) an under-studied problem in the literature. This paper introduces a new deep learning model for document-level EAE where document structures/graphs are utilized to represent input documents and aid the representation learning. Our model employs different types of interactions between important context words in documents (i.e., syntax, semantic, and discourse) to enhance document representations. Extensive experiments are conducted to demonstratethe effectiveness of the proposed model, leading to the state-of-the-art performance for document-level EAE.
Omega: A Secure Event Ordering Service for the Edge

The edge computing paradigm extends cloud computing with storage and processing capacity close to the edge of the network, which can be materialized by using many fog nodes placed in multiple geographic locations. Fog nodes are likely to be vulnerable to tampering, so it is important to protect the functions they provide from attacks. A key building block of many distributed applications is an ordering service that keeps track of cause-effect dependencies among events and that allows events to be processed in an order that respects causality. This article presents the design and implementation of a secure event ordering service for fog nodes. Our service, named Omega, leverages the availability of a Trusted Execution Environment (TEE), based on SGX technology, to offer fog clients guarantees regarding the order in which events are applied and served, even when fog nodes are compromised. We have also built OmegaKV, a key-value store that uses Omega to offer causal consistency. Experimental results show that the ordering service can be secured without violating the latency constraints of time-sensitive edge applications, despite the overhead associated with using a TEE. Omega introduces an additional latency of approximately 4ms, that contrary to cloud based solutions, allows latency values in the 5ms-30ms range, as required by time-sensitive edge applications.
The Tao way to anti-fragile software architectures: the case of mobile applications

In an ever-changing world software architects need to embrace uncertainty and changes to exploit them in building better systems. We suggest that looking at the Taoist philosophy and its incarnation in the martial arts domain can help to promote this mental attitude and to get a better understanding of the potentialities of existing architectural approaches, using as a testbed the field of mobile applications design.
A Novel Design of Quantum 3:8 Decoder Circuit using Reversible Logic for Improvement in Key Quantum Circuit Design Parameters

quantum computing is the emerging technology in the new era and is very promising, not only quantum computers substantially accelerate what classical computers are able to do nowadays but they are also capable of providing answers that classical computers never could. Using reversible logic in designing quantum circuits has many advantages such as lowering power consumption, reducing heat dissemination, and decreasing quantum cost, ancilla inputs, and garbage outputs that lead to even higher performance in quantum computers. Decoders have many utilizations in digital circuits any function in form of SOP or POS can be implemented using decoders, counters, and ROMs have also used decoder modules in their designs in this article two novel designs for 2:4 and 3:8 decoder have been proposed that has been proved to have less quantum cost, unused outputs, and ancilla inputs when it comes to comparison with recent researches that have been done concerning this field.
Swarm and Evolutionary Optimization Algorithm

In this chapter, fundamental concepts of optimization process are explained. Types of optimization problems and algorithms have been explained. Designing of objective function, decision variables, and different solution approaches are also elaborated. Overview of swarm and evolutionary optimization algorithms has been provided with detailed explanation of genetic algorithm, artificial bee colony algorithm, and particle swarm optimization algorithm. Few benchmark objective functions are explained with their equations and MatLab codes. Complete process of optimization of these objective functions through genetic algorithm has been demonstrated with working MatLab codes. Comparison of the performance of optimization methods has also been explained with few performance measures. After reading this chapter and practicing the example codes given in this chapter, readers will be able to apply optimization on problems of their field of interest.
Cosine Transformed Chaos Function and Block Scrambling-Based Image Encryption

The authors propose an image encryption process based on chaos that uses block scrambling to reduce the correlation among the neighboring pixels and random order substitution for slightly changing the value of the pixel. The chaotic sequence for encrypting the image is generated by using two 3D logistic maps called enhanced logistic map and intertwining logistic map; the cos function helps in reducing linearity. The entire encryption process is composed of scrambling, image rotation, and random order substitution. Scrambling is used for permuting the pixels in the image so that we can reduce the correlation among the neighboring pixels, and this is followed by image rotation which can ensure that shuffling of pixels is done to the remaining pixels in the image, and at last the authors use random order substitution where they bring the small change in the pixel value. The proposed method has the capability of encrypting digital colored images into cipher form with high security, which allows only authorized ones who hold the correct secret key to decrypt the images back to original form.
EduCATS for Distance Learning

This paper discusses a new learning management system called EduCATS. The proposed system covers all the components of the educational process, monitor the progress and the process of studying the educational material by students, check the completed tasks for plagiarism. The EduCATS system has been introduced into the educational process at the Faculty of Information Technologies and Robotics of Belarusian National Technical University and is actively used to train software engineers. The intellectual component in the EduCATS learning management system creates a unique training program that is based on the existing knowledge and the level of perception of the training material by students. As mathematical methods, it is proposed to use the analysis of expert systems, as well as artificial neural networks. By formalizing the intellectual processes that are carried out by both the teacher and the student, it is possible to automate a certain part of the teacher's functions, reduce the cost of manual labor, which will make it easier to monitor the educational process, and make the learning process more efficient.
Authorship Attribution using Filtered N-grams as Features

Authorship attribution is the problem of assigning an author to a document of unknown authorship, given a candidate set of authors and their sample documents. As a text classification task, this requires features that can capture the writing styles of authors. In this work, we compare the filtered n-grams with the traditional or unfiltered n-grams as features for authorship attribution. Filtered n-grams are the n-grams formed after filtering out from the text certain kinds of tokens. We explore the filtered n-grams formed after the removal of noun groups and verb groups. We hypothesize that the remaining text should still be enough to capture the writing style. Moreover, this removal makes possible the construction of new n-grams which would have been missed otherwise. In our experiments, we find that filtered n-grams improve the performance. In the feature ablation study, we confirm that this improvement is due to the new n-grams which are possible only after filtering.
An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services

The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online. The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it. To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets, this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets. In this work, we have used part-of-speech (PoS) tagged features in conjunction with n-gram models to construct the feature set for the ensemble model. We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set. The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset. The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms. This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.