Reviewerly/exHarmony huggingface dataset:
Post-quantum Cryptographic Hardware Primitives

The development and implementation of post-quantum cryptosystems have become a pressing issue in the design of secure computing systems, as general quantum computers have become more feasible in the last two years. In this work, we introduce a set of hardware post-quantum cryptographic primitives (PCPs) consisting of four frequently used security components, i.e., public-key cryptosystem (PKC), key exchange (KEX), oblivious transfer (OT), and zero-knowledge proof (ZKP). In addition, we design a high speed polynomial multiplier to accelerate these primitives. These primitives will aid researchers and designers in constructing quantum-proof secure computing systems in the post-quantum era.
The method of image alignment based on sharpness maximization

In this paper the method of image alignment based on average image sharpness maximization is proposed. The algorithm for global-shift model is investigated, its efficiency by applying FFT is shown. For projective model, an approach for image alignment using local shifts and RANSAC to obtain the final transform is considered. Experimental results for the system of document's reconstruction in a video stream increasing quality of output image are demonstrated.
An Intuition-Based Approach to Sustainable ICT: Insights from Eco-Ethica

In this chapter we draw on the Japanese philosopher Tomonobu Imamichi in order to further develop our understanding of the relationship between Information and Communications Technology (ICT) and ecological sustainability. Our main contribution is an intuition-based approach to design for sustainability, which, for instance, means to design ICT products in a way that invokes feelings of wastefulness or misuse if used in an unsustainable manner. In contrast to persuasive approaches – which have mainly focused on raising awareness, nudging decisions, or stimulating positive behavior through gamification – we rely on the human tendency to unconsciously and effortlessly formulate mental heuristics or intuitions, when exposed to consistent feedback. The claim is that people can learn to associate perceptual cues with environmental impact, and by that will be empowered to make more sustainable choices. Based on the implications of Imamichi’s Eco-ethica, we suggest that this approach can be more fruitful for encouraging sustainable choices than both awareness raising and behavior manipulation.
Unsupervised similarity learning from compressed representations via Siamese autoencoders

Many domain specific challenges for feature matching and similarity learning in computer vision have been relying on labelled data, either using heuristic or more recent approaches via deep learning. While aiming for precise solutions, we need to process larger number of features which may result in higher computational complexity. This paper proposes a novel method of similarity learning through two-part cost function as it could be done using heuristic approaches in original feature space in an unsupervised manner, while also reducing feature complexity. The features are encoded on the lower dimensionality manifold which preserve original structure of data. This approach takes advantage of siamese networks and autoencoders to obtain compressed features while maintaining same distance properties as in the original feature space. This is done by introducing new loss function with two terms, which aims for good reconstruction as well as learning the similar data point neighborhood from encoded and reconstructed feature space.
Breaking the Fourth Wall

Recommender algorithms deal with most of our contemporary culture consumption. Algorithmic Experience (AX) emerges in HCI to guide users' experience with algorithms. To the best of our knowledge, previous work on recommender systems does not consider tangible interfaces to support positive AX and better algorithmic awareness. The ongoing research proposes to expand the design space for the current AX debate by designing an embodied interface suited for movie recommender algorithms.
Simultaneous and automatic two surface detection of renal cortex in 3D CT images by enhanced sparse shape composition

Automatic organ localization plays a significant role in medical image segmentation. This paper introduces a novel approach for simultaneous and automatic two surface detection of renal cortex from contrast enhanced abdominal CT scans. The proposed framework is an integrated procedure consisting of three main parts: (i) cortex model training, both two shape variabilities are detected using principal components analysis from the manual annotation, and dual shape dictionaries and appearance dictionaries are constructed; (ii) outer mesh reconstruction, the initialized outer mesh is iteratively deformed to the target boundary; (iii) inner mesh reconstruction, the inner mesh can be reconstructed using the same deformation coefficients and similarity transformation of the outer mesh with the inner mesh shape dictionary. Our method was validated on a clinical data set of 37 CT scans using the leave-one-out cross validation strategy. The proposed method has improved the overall segmentation accuracy of Dice similarity coefficient to 91.95%&plusmn;3.15% for renal cortex segmentation.
Maximizing Refactoring Coverage in an Automated Maintenance Approach Using Multi-Objective Optimization

This paper describes a multi-objective genetic algorithm used to automate software refactoring. The approach is validated using a set of open source Java programs with a purpose built tool, MultiRefactor. The tool uses a metric function to measure quality in a software system and tests a second objective to measure the amount of code coverage of the applied refactorings by analyzing the code elements they have been applied to. The multi-objective setup will refactor the input program to improve its quality using the quality objective, while also maximizing the code coverage of the refactorings applied to the software. An experiment has been constructed to measure the multi-objective approach against the alternative mono-objective approach that does not use an objective to measure refactoring coverage. The two approaches are tested on six different open source Java programs. The multi-objective approach is found to give significantly better refactoring coverage scores across all inputs in a similar time, while also generating improvements in the quality scores.
Medical Data Security Tools and Techniques in E-Health Applications

The primary objective of this chapter is to analyze the existing tools and techniques for medical data security. Typically, medical data includes either medical signals such as electrocardiogram, electroencephalogram, electromyography, or medical imaging like digital imaging and communications in medicine, joint photographic experts group format. The medical data are sensitive, subject to privacy preservation, and data access rights. Security in e-health field is an integrated concept which includes robust combination of confidentiality, integrity, and availability of medical data. Confidentiality ensures the data is inaccessible to unauthorized access. Integrity restricts the alteration in data by the unauthorized user. Whereas availability provides the readiness of the data when needed by the authorized user. Additionally, confidentiality, integrity and availability, accountability parameter records the back action list which answers the why, when, what, and whom data is accessed. The selected tools and techniques used in medical data security in e-health applications is discussed.
Two-stream siamese network with contrastive-center losses for RGB-D action recognition

Many fusion methods have been developed to improve the performance of action recognition with RGB and depth data, where learning conjoint representation of heterogeneous modalities by a single network has not been paid enough attention. We present an associated representation method for RGB-D action recognition using the siamese network with contrastive-center loss. First, some samples of each class and modality data are selected as the references to construct positive and negative pairs. Each positive pair consists of a training sample and its class reference, whereas the negative pair only involves different classes reference. Then these pairs are inputted to a two-stream siamese network to learn the collaborative representation of RGB and depth data. Two ranking losses, namely intramodal and cross-modal contrastive-center loss, are developed to impose similarity/dissimilarity metric on those pairs. Specifically, the intramodal contrastive-center loss measures the relationship between samples and references from RGB or depth data. The cross-modal contrastive-center loss measures the relationship of visual and depth features in a same low-dimensional space. Finally, the ranking losses and a softmax loss are jointly optimized for action recognition. The proposed method is evaluated on two large action datasets, LAP IsoGD and NTU RGB+D, and a smaller dataset, Sheffield Kinect gesture. The experimental results demonstrate that the proposed method surpasses most of the state-of-the-art methods.
The Laboratory Stand Simulation for Programming Microprocessor Devices

Generally, in higher educational institutions there is no modern laboratory base, which includes microprocessor devices. As a result, students not only can't obtain practical experience with microprocessor devices, but even to get acquainted with them. Most of the time this is due to objective reasons: insufficient financial resources, lack of space for equipment layout, rapid equipment obsolescence. As a solution to these problems we can consider the creation of virtual stands with a full basic functions set of real physical objects using microprocessor devices. The paper discusses the creation of virtual laboratory stands in the environment of computer-aided design of electronic circuits Proteus. The advantages of working on a virtual stand: the speed of calculations, safety of researches, the ability to acquire practical skills with most modern microprocessor-based devices. As well students can carry out real project activities, creating devices for their own projects, connecting the required modules, and debug them.
Multiview mammographic mass detection based on a single shot detection system

Detection of suspicious breast cancer lesion in screening mammography images is an important step for the downstream diagnosis the of breast cancer. A trained radiologist can usually take advantage of multi-view correlation of suspicious lesions to locate abnormalities. In this work, we investigate the feasibility of using a random image pair of the same breast from the same exam for the detection of suspicious lesions. We present a novel approach to utilize a single shot detection system inspired by You only look once (YOLO) v1 to simultaneously process a primary detection view and a secondary view for the localization of lesion in the primary detection view. We used a combination of screening exams from Duke University Hospital and OPTIMAM to conduct our experiments. The Duke dataset includes 850 positive cases and around 10,000 negative cases. The OPTIMAM dataset includes around 350 cases. We observed a consistent left shift of the Free-Response Receiver Operating Characteristic (FROC) curve in the multi-view detection model compared to the single-view detection model. This result is promising for future development of automated lesion detection systems focusing on modern full-field digital mammography (FFDM).
Evaluating the effect of MIPM on vehicle detection performance

The introduction of new techniques to improve the robustness and accuracy of vehicle detection is always important for the intelligent transportation system as it may face different problems and challenges. Conventional image-based vehicle detection methods have presented difficulties in acquiring good images due to perspective and background noise, poor lighting and weather conditions. We propose a high-accurate, vehicle detection method by using Modified Inverse Perspective Mapping. Thus, the perspective effect is removed, and then the Hough transform was applied to extract road lines and lanes. Gaussian Mixture Model and chromaticity-based strategy were applied to segment the moving vehicles and tackle shadow effects, respectively. We evaluated the performance of the proposed method under recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas). Results indicate that the proposed approach is feasible, and more accurate compared to others, especially when facing bad weather conditions and lighting variations in different environments.
A new cluster validity index for overlapping datasets

The choice of the optimal clustering number (Kopt) is very important for forming the clustering results. As clustering is a kind of unsupervised learning method, it is still difficult to determine the Kopt. The clustering validity index (CVI) is an effective method for determining the Kopt and evaluating the clustering results generated by clustering algorithms. However, many of the existing CVIs cannot efficiently with overlapping datasets. In this paper, we propose WCH, a new cluster validity index for overlapping datasets. The WCH index is constructed based on features of inner-cluster tightness, inter-cluster separation and inter-cluster overlapping of datasets. The WCH index can effectively process datasets with high overlapping and can accurately find the corresponding Kopt. The comparison experiments are carried between our new WCH index and four existing CVIs on five simulation datasets. The experimental results have shown that the new WCH index is stable in forming the Kopt for many kinds of overlapping datasets.
User Research And Persona Building Method In Mobile Games

This research aims to find the determinant of user research and persona building :mobile games to design questionnaire. The method presented in this paper is comparing tlsimilarities and differences of mobile games and PC games to conclude the characteristics of mobile games. Besides, we formulate the principles in cognitive psychology that might guiethe design of questionnaire of mobile games user persona. In this study, we conclude thmobile games are easier to operate, interrupted and have limited development of hardwadevices and storage comparing with the PC games. In addition, mobile games are easier 1begin and need more SNS support. Moreover, this research analyzes the cognition fields for thmobile games in the five sense of people, human ecological perception and human aesthetperception.
Repairing Broken Links Using Naive Bayes Classifier

The Internet is an extremely useful resource for education and research. The Internet has been experiencing broken connections issue in spite of its concurrent services. Broken links are common issues stirring in the area of the web. Sometimes the page which was pointing from another page has been disappeared forever or moved to some other location. There can be many reasons for broken links such as the target website is for all time not available, the target website page has been detaching, the target web page was changed or altered and also has misspellings in the link. The broken link itself contains a lot of information such as URL, mark content, encompassing content close to naming content and the content in the page. Every one of these assets of information is valuable for recovering the candidate pages relevance for broken links. The system returns the ranked lists of highly relevant candidate pages on submitting a query which has been extracted from different sources. In this paper, we explore the expression that is used for the proximity (position) connection in the terms of the label and full text in order to extract relative (good and bad) terms through Naïve Bayes classification model. This solves the problem by providing non-identical terms to inquire multiple broken connections and also enrich the accomplishment as the terms that are closely identical show relevancy.
Depth in the visual attention modelling from the egocentric perspective of view

An extensive research has been held in the field of the visual attention modelling throughout the past years. However, the egocentric visual attention in real environments has still not been thoroughly studied. We introduce a method proposal for conducting automated user studies on the egocentric visual attention in a laboratory. Goal of our method is to study distance of the objects from the observer (their depth) and its influence on the egocentric visual attention. The user studies based on the method proposal were conducted on a sample of 37 participants and our own egocentric dataset was created. The whole experimental and evaluation process was designed and realized using advanced methods of computer vision. Results of our research are ground-truth values of the egocentric visual attention and their relation to the depth of the scene approximated as a depth-weighting saliency function. The depth-weighting function was applied on the state-of-the-art models and evaluated. Our enhanced models provided better results than the current depthweighting saliency models.
Cybersecurity and Mobile Device Forensic

Issues relating to cybersecurity and its challenges in Nigeria can be traced as far back as the provision of Internet services to Nigerians in 1996. Nigerians are known for the huge frauds they commit using the Internet. This chapter treats cybersecurity challenges/conflicts history in Nigeria. Furthermore, it looks at the relevant laws guiding against cyber crimes in Nigeria. Lastly, the need for embracing cybersecurity and mobile device forensics is encouraged.
Take Me Out

Information interactions are strongly affected by the place where they occur. Specific locations are often associated with searches on particular topics, and individual users perform different tasks in habituated places. A classic example of habituated space is the commuter who regularly reads the news on the train. This paper investigates these associations through four user studies that examine different uses of place in information interaction. Through this, we reveal the ways in which the location of information interactions makes them effective or ineffective. This extends our interpretation of the role of place in information interaction beyond established foci such as location-based search.
High Bit-Depth Image Acquisition Framework Using Embedded Quantization Bias

In this paper, we present a novel image acquisition framework capable of reconstructing high bit-depth images using an array of low bit-depth scalar quantizers. Our key contribution is a codesign of pixel quantization and reconstruction in image acquisition pipeline. Different from the traditional image acquisition scheme, where each pixel value is quantized and reconstructed independently, the proposed framework is designed based on the interpixel correlations in local image regions. The interpixel correlations imply that quantized pixel values of adjacent locations can be interpreted as multiple descriptions of a common pixel value. Because combining multiple descriptions leads to reduced uncertainty, a pixel value with higher bit depth can be reconstructed by exploiting the interpixel correlations. In this paper, we propose to inject an embedded quantization bias (EQB) to the prequantized image signal and feed the sum to scalar quantizers. Injecting EQB has the same effect as shifting the quantizers by different amounts at different pixel locations, driving the quantized values of adjacent pixels to form informative descriptions of a common pixel value. We present comprehensive studies on this framework, including the optimal design of the EQB signal, the reconstruction strategies for noisy input, and generalized assumptions on interpixel correlations. We show in the experiments that the proposed image acquisition framework significantly outperforms the competing methods and effectively improves the quality of the reconstructed images.
Binary codes and partial permutation decoding sets from biadjacency matrices of bipartite graphs Γ(2<i>k, k, k</i> + 1<i>,</i> 1)

For a set Ω = {1, 2, . . . , n} where n = 2k ≥ 6, let Ω{k} denote the set of all subsets of Ω of size k. We examine the binary codes from the row span of biadjacency matrices of bipartite graphs with bipartition (Ω{k}, Ω{k+1}) and two vertices as k-subsets and (k+1)-subsets of Ω being adjacent if they have one element in common. We show that S2k is contained in the automorphism group of the graphs and the codes, respectively. In addition, we determine the duals of the codes, and by identifying suitable information sets, we construct 2-PD sets for the dual codes.
Mixed-Model Noise Removal in 3D MRI via Rotation-and-Scale Invariant Non-Local Means

Mixed noise is a major issue influencing quantitative analysis in different forms of magnetic resonance image (MRI), such as T1 and diffusion image like DWI and DTI. Using different filters sequentially to remove mixed noise will severely deteriorate such medical images. We present a novel algorithm called rotation-and-scale invariant nonlocal means filter (RSNLM) to simultaneously remove mixed noise from different kinds of three-dimensional (3D) MRI images. First, we design a new similarity weights, including rank-ordered absolute difference (ROAD), coming from a trilateral filter (TriF) that is obtained to detect the mixed and high-level noise. Then, we present a shape view to consider the MRI data as a 3D operator, with which the similarity between the patches is calculated with the rigid transformation. The translation, rotation and scale have no influence on the similarity. Finally, the adaptive parameter estimation method of ROAD is illustrated, and the effective proof that validates the proposed algorithm is presented. Experiments using synthetic data with impulse noise, Rician noise, and the real MRI data confirm that the proposed method yields superior performance compared with current state-of-the-art methods.
Automatic Folder Allocation System for Electronic Text Document Repositories Using Enhanced Bayesian Classification Approach

This article proposes a system equipped with the enhanced Bayesian classification techniques to automatically assign folders to store electronic text documents. Despite computer technology advancements in the information age where electronic text files are so pervasive in information exchange, almost every single document created or downloaded from the Internet requires manual classification by the users before being deposited into a folder in a computer. Not only does such a tedious task cause inconvenience to users, the time taken to repeatedly classify and allocate a folder for each text document impedes productivity, especially when dealing with a huge number of files and deep layers of folders. In order to overcome this, a prototype system is built to evaluate the performance of the enhanced Bayesian text classifier for automatic folder allocation, by categorizing text documents based on the existing types of text documents and folders present in user's hard drive. In this article, the authors deploy a High Relevance Keyword Extraction (HRKE) technique and an Automatic Computed Document Dependent (ACDD) Weighting Factor technique to a Bayesian classifier in order to obtain better classification accuracy, while maintaining the low training cost and simple classifying processes using the conventional Bayesian approach.