Reviewerly/exHarmony huggingface dataset:
Mobile application of BTS tower search build upon location based service (LBS)

Purpose Referring the phenomenon, the researchers create an application to deal with problems faced by the technician team and the vendor in searching the location of BTS tower. Design/methodology/approach This application uses the Software Development Life Cycle (SDLC) methodology which is the process of developing or changing a software system. One of the models owned by SDLC is the waterfall technique. It provides a sequential software flow approach starting from analysis, design, coding, testing and supporting stages. Findings There are some of the technician team and the vendor find difficulties in looking for the position of the BTS tower. As a solution, a facility which enables them to access the location in searching the BTS tower is needed. Originality/value The following is a display of the design results and the implementation of writing code in applications that have been made previously.
A Case Study on Computational Hermeneutics: E. J. Lowe’s Modal Ontological Argument

Computers may help us to better understand (not just verify) arguments. In this chapter we defend this claim by showcasing the application of a new, computer-assisted interpretive method to an exemplary natural-language argument with strong ties to metaphysics and religion: E. J. Lowe’s modern variant of St. Anselm’s ontological argument for the existence of God. Our new method, which we call computational hermeneutics, has been particularly conceived for use in interactive-automated proof assistants. It aims at shedding light on the meanings of words and sentences by framing their inferential role in a given argument. By employing automated theorem proving technology within interactive proof assistants, we are able to drastically reduce (by several orders of magnitude) the time needed to test the logical validity of an argument’s formalization. As a result, a new approach to logical analysis, inspired by Donald Davidson’s account of radical interpretation, has been enabled. In computational hermeneutics, the utilization of automated reasoning tools effectively boosts our capacity to expose the assumptions we indirectly commit ourselves to every time we engage in rational argumentation and it fosters the explicitation and revision of our concepts and commitments.
3D Convolutional Neural network for Home Monitoring using Low Resolution Thermal-sensor Array

The recognition of daily actions, such as walking, sitting or standing, in the home is informative for assisted living, smart homes and general health care. A variety of actions in complex scenes can be recognised using visual information. However cameras succumb to privacy concerns. In this paper, we present a home action recognition system using an 8×8 infared sensor array. This low spatial resolution retains user visual privacy, but is still a powerful representation of actions in a scene. Actions are recognised using a 3D convolutional neural network, extracting not only spatial but temporal information from video sequences. Experimental results obtained from a publicly available dataset Infra-ADL2018 demonstrate a better performance of the proposed approach compared to the state-of-the-art. We show that the sensor is considered better at detecting the occurrence of falls and actions of daily living. Our method achieves an overall accuracy of 97.22% across 7 actions with a fall detection sensitivity of 100% and specificity of 99.31%.
Formation by Younger Schoolchildren of the Ability to Learn in the Process of Performing Olympiad Mathematical Tasks

The pedagogical problem of the formation of the ability to learn as a set of universal skills in conjunction with objective actions is considered in the article based on the performance of olympiad mathematical tasks by younger schoolchildren. The requirements for the development and formulation of olympiad tasks, with the help of which a set of pedagogical activities are implemented, contributing to the development of the ability to learn by children of primary school age. The types of Olympiad tasks offered by the authors are connected with the main thematic sections of the initial course of mathematics.
Placing Conditional Disclosure of Secrets in the Communication Complexity Universe.

In the conditional disclosure of secrets (CDS) problem (Gertner et al. in J Comput Syst Sci, 2000) Alice and Bob, who hold n-bit inputs x and y respectively, wish to release a common secret z to Carol, who knows both x and y, if and only if the input (x, y) satisfies some predefined predicate f. Alice and Bob are allowed to send a single message to Carol which may depend on their inputs and some shared randomness, and the goal is to minimize the communication complexity while providing information-theoretic security. Despite the growing interest in this model, very few lower-bounds are known. In this paper, we relate the CDS complexity of a predicate f to its communication complexity under various communication games. For several basic predicates our results yield tight, or almost tight, lower-bounds of $$\Omega (n)$$ or $$\Omega (n^{1-\epsilon })$$ , providing an exponential improvement over previous logarithmic lower-bounds. We also define new communication complexity classes that correspond to different variants of the CDS model and study the relations between them and their complements. Notably, we show that allowing for imperfect correctness can significantly reduce communication—a seemingly new phenomenon in the context of information-theoretic cryptography. Finally, our results show that proving explicit super-logarithmic lower-bounds for imperfect CDS protocols is a necessary step towards proving explicit lower-bounds against the communication complexity class $$\text {AM}^{\text {cc}}$$ , or even $$\text {AM}^{\text {cc}}\cap \text {co-AM}^{\text {cc}}$$ —a well known open problem in the theory of communication complexity. Thus imperfect CDS forms a new minimal class which is placed just beyond the boundaries of the “civilized” part of the communication complexity world for which explicit lower-bounds are known.
Batching Location Cloaking Techniques for Location Privacy and Safety Protection

Location-based services (LBSs) have become a profitable market because they offer real-time and local information to their users. Although several benefits are obtained from the usage of LBSs, they have opened up many privacy and safety challenges because a user needs to release his/her location. To tackle these challenges, many location-cloaking techniques have been proposed. Even though these solutions are effective in protecting either location privacy or location safety, they do not provide unified protection. Furthermore, most of them do not address the potential bottleneck in the anonymity server as a high demand of location and safety protection is requested. Finally, they do not take into account the potential impact of processing a large amount of location-cloaked queries. This paper deals with the efficient construction of location-cloaking areas for many users, who have both privacy and safety requirements. To achieve this goal, the construction of location-cloaking areas is carried out in batches. The LBSs’ batch processing takes advantage of users who are close to each other and who have similar requirements. Two batching techniques to build cloaking regions are analyzed using simulations. Empirical results show our techniques are able to balance the anonymizer workload, quality of location privacy and safety protection, and LBS workload.
A Precise and Stable Segmentation Algorithm of SAR Images Based on Random Weighting Method and Modified Level Set

Level set methods have been widely used for image segmentation due to their good boundary detection accuracy. In the context of synthetic aperture radar (SAR) image segmentation, the presence of speckles and the distribution estimation of SAR image remain important issues that may hinder the accuracy of any segmentation method based on level set methods. In this paper, we propose a more accurate and a stable segmentation method based on the random weighting method and modified threshold-based level set energy functional. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional, whose propagation function is based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ G}}^{0}$ </tex-math></inline-formula> statistical model, whose parameters are estimated by random weighting estimators, and the estimator is not affected by the hypothesized model and sampling number. In addition, a new regularization item and length term, which maintain the regularity of the level set function and contour respectively, were employed. The experimental results demonstrate that the proposed methodology has a good and stable capability of segmentation, both in homogeneous, heterogeneous, and extremely heterogeneous regions in SAR image.
A must for agencies or a candidate for deletion

Purpose This paper aims to report on a study that aimed at analyzing the relationships between information security and records management (RM), both as programs/functions established in organizations. Similar studies were not found in relevant literature. Design/methodology/approach The study used the classic grounded theory methodology. Pursuing the general curiosity about the information security-RM relationship in organizations, the study selected the United States (US) Federal Government as its field of entrance and followed the process of the classic grounded theory methodology that starts from the letting of the emergence of the research question to the formulation of a substantive theory that answered the question. Findings On the emergent question that why, despite the legislative establishment of agency RM programs and the use of the term records in their work, the US Federal Government information security community considered RM a candidate for deletion (CFD), the study coded the truncated application of the encompassing definition of records as the underlying reason. By this code, along with its three properties, i.e. limitations by the seemingly more encompassing coverage of information, insufficient legislative/regulatory support and the use of the terms of evidence and preservation in the records definition, the CFD consideration and the associated phenomena of unsound legislative/regulatory conceptualization, information shadow, information ignorance and archival shadow were explained. Research limitations/implications The study results suggested the data for subsequent theoretical sampling to be the operational situations of individual agency RM programs. Practical implications The rationale presented in the study regarding the encompassing nature of records and the comprehensive scope of RM program can be used for building strong RM business cases. Originality/value The study appears to be the first of its kind, which examined the RM–information security relationship in a very detailed setting.
Initiatives of Digital Humanities in Cantonese Studies: A Corpus of Mid-Twentieth-Century Hong Kong Cantonese

This paper reports on one of the new initiatives in digital humanities on Cantonese studies undertaken in the author’s department at the Education University of Hong Kong: A Corpus of Mid-twentieth-century Hong Kong Cantonese. The data of the corpus were collected by transcribing some of the dialogue of Cantonese movies produced between the 1950s and the 1970s. There are two phases of the corpus. This paper focuses on the second phase of the corpus which, when compared with the first phase of the corpus, includes lexical semantics information and media technology which can facilitate users to undertake Cantonese linguistic studies beyond the traditional approach such as discourse and pragmatics, multimodality, and ontology.
A Novel Approach of K-SVD-Based Algorithm for Image Denoising

In recent years, denoising has played an important role in medical image analysis. Image denoising is still accepted as a challenge for researchers and image application developers in medical image applications. The idea is to denoise a microscopic image through over-complete dictionary learning using a k-means algorithm and singular value decomposition (K-SVD) based on pursuit methods. This approach is good in performance on the quality improvement of the medical images, but it has low computational speed with high computational complexity. In view of the above limitations, this chapter proposes a novel strategy for denoising insight phenomena of the K-SVD algorithm. In addition, the authors utilize the technology of improved dictionary learning of the image patches using heap sort mechanism followed by dictionary updating process. The experimental results validate that the proposed approach successfully reduced noise levels on various test image datasets. This has been found to be more accurate than the best in class denoising approaches.
Image enhancement in lifting wavelet transform domain

In this paper, the scheme of image enhancement in lifting wavelet transform (LWT) domain using the singular value decomposition (SVD) and artificial bee colony (ABC) algorithm is proposed. The proposed algorithm uses 1-level LWT to break up the original image into different subbands and the LL subband is utilized for adaptive thresholding whereas ABC is used for optimizing the threshold value. The modified LL sub-band is further decomposed using SVD and the correction parameter is obtained that is used for image enhancement process. To verify the strength of the proposed algorithm, the experiment is performed over various low-quality images. The results thus obtained are used to compare with other existing schemes and the outcome depicts that the results obtained by the proposed scheme are better in most of the cases.
Mixing Different Realities in a Single Shared Space

The concept of internet of everything involves an intelligent connection of people, processes, data, and things. In this sense, shared spaces aimed to connect different users that collaborate following a common purpose are of relevance to the field. Many computer-based collaborative environments have been proposed in recent years. However, the design of mixed-platform collaborative spaces, in which different paradigms—such as augmented reality (AR) and virtual reality (VR)—are blended, is still uncommon. This chapter aims to analyze the benefits and features of these systems, reviewing existing related works and proposing a series of features for the design of effective mixed-platform collaborative shared spaces. In particular, the authors propose five setups with different levels of immersion/interaction, which are aligned to the current state of the art. These systems will be analyzed with respect to navigation, user representation, interaction, and annotation, among others. Finally, some applications are proposed within the given framework.
Evaluation of (power) side-channels in cryptographic implementations

Abstract Side-channel attacks enable powerful adversarial strategies against cryptographic devices and encounter an ever-growing attack surface in today’s world of digitalization and the internet of things. While the employment of provably secure side-channel countermeasures like masking have become increasingly popular in recent years, great care must be taken when implementing these in actual devices. The reasons for this are two-fold: The models on which these countermeasures rely do not fully capture the physical reality and compliance with the requirements of the countermeasures is non-trivial in complex implementations. Therefore, it is imperative to validate the SCA-security of concrete instantiations of cryptographic devices using measurements on the actual device. In this article we propose a side-channel evaluation framework that combines an efficient data acquisition process with state-of-the-art confidence interval based leakage assessment. Our approach allows a sound assessment of the potential susceptibility of cryptographic implementations to side-channel attacks and is robust against noise in the evaluation system. We illustrate the steps in the evaluation process by applying them to a protected implementation of AES.
BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services

Location-based services present an inherent challenge of finding the delicate balance between efficiency when answering queries and maintaining user privacy. Inevitable security issues arise as the server needs to be informed of the query location to provide accurate responses. Despite the many advancements in localization security in wireless sensor networks, servers can still be infected with malicious software. It is now possible to ensure queries do not generate any fake responses that may appear real to users. When a fake response is used, there are mechanisms that can be employed so that the user can identify the authenticity of the query. For this reason, this paper proposes Bloom Filter 0 Knowledge (BL0K), which is novel phase privacy method that preserves the framework for location-based service (LBS) and combines a Bloom filter and the Zero knowledge protocol. The usefulness of these methods has been shown for securing private user information. Analysis of the results demonstrated that BL0K performance is decidedly better when compared to the referenced approaches using the privacy entropy metric.
Medical Image Segmentation and Analysis

An improved energy-based technique with a Lattice Boltzmann method organizes with the neighborhood and global energy terms, local term propels to pull the frame and constrain it to protest limit, decides noteworthy points of interest not confined to, snappy planning, automation, invariance of exact medical image segmentation, and analysis. Consequently, the worldwide vitality fitting term drives the advancement of the frame at a division of the question limit. The worldwide vitality term relies upon the worldwide division computation, which can better catch drive information of pictures than mixture area-based dynamic shape technique. Both neighborhood and worldwide terms are ordinarily acclimatized to construct a level set strategy to divide pictures with exactness. The level set technique with Boltzmann system uses neighborhood mean, a quality which engages it as far as possible. The proposed chapter gathers gainful purposes of intrigue not stuck just using expedient process, computerization, and right helpful picture partitions.
On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach

Using machine learning in high-speed networks for tasks such as flow classification typically requires either very resource efficient classification approaches, large amounts of computational resources, or specialized hardware. Here we provide a sketch of the discretize-optimize (DISCO) approach which can construct an extremely efficient classifier for low dimensional problems by combining feature selection, efficient discretization, novel bin placement, and lookup. As feature selection and discretization parameters are crucial, appropriate combinatorial optimization is an important aspect of the approach. A performance evaluation is performed for a YouTube classification task using a cellular traffic data set. The initial evaluation results show that the DISCO approach can move the Pareto boundary in the classification performance versus runtime trade-off by up to an order of magnitude compared to runtime optimized random forest and decision tree classifiers.
MultiLock: Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns.

In this paper we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals. We consider seven different data channels and their combination. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage of mobile user behavior across multiple sessions by updating a confidence value of the authentication score. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that different traits can be complementary and multimodal systems clearly increase the performance with accuracies ranging from 82.2% to 97.1% depending on the authentication scenario.
Simple phase shifting by polarizer rotations in a cube beam-splitter interferometer

A simple and low-cost method for phase-shifting interferometry by the rotation of a polarizer is presented. This proposal takes advantage of the polarization aberration in a cube beam splitter due to its geometry, to the angular dependence with the coating, and to the polarization angle of the input beam. The interferometric setup performs as a two-window common-path interferometer in which the added phase shifting is achieved by simply rotating a polarizer at the interferometer output. The viability of the proposal is sustained with experimental results in which the phase-shift value and the resulting wavefront are calculated with Farrel&#x2019;s technique and the three-step PSI algorithm, respectively.
Zero in and TimeFuzz: Detection and Mitigation of Cache Side-Channel Attacks

Cache Side Channel Attack (CSCA) works by monitoring security critical operations and recovering the secret or private information according to the accesses by the victim. Previous efforts on CSCA detection only rely on global statistics information, which leads to some drawbacks. To meet these challenges, Zero in and TimeFuzz (ZITF), a wide-coverage, high-accuracy mitigation scheme of CSCA based on Intel-PIN is presented here. The key point of ZITF is the combination of local features and global features, which can achieve a more accurate detection and mitigation to CSCA. To reduce the impact on other benign processes, a way to time fuzz suspicious processes is used by tampering with the time information required. The comparative experiments on benign processes and malicious processes show that ZITF really works and outperforms the previous work in several ways. In addition, the experiment also proves that ZITF can also be applied to the detection and mitigation of Flush-Flush and Meltdown attack.
Optimizing Body Region Classification with Deep Convolutional Activation Features

The goal of this work is to automatically apply generated image keywords as text representations, to optimize medical image classification accuracies of body regions. To create a keyword generative model, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted, which is trained with preprocessed biomedical image captions as text representation and visual features extracted using Convolutional Neural Networks (CNN). For image representation, deep convolutional activation features and Bag-of-Keypoints (BoK) features are extracted for each radiograph and combined with the automatically generated keywords. Random Forest models and Support Vector Machines are trained with these multimodal image representations, as well as just visual representation, to predict body regions. Adopting multimodal image features proves to be the better approach, as the prediction accuracy for body regions is increased.