Reviewerly/exHarmony huggingface dataset:
Realization and countermeasures for current location spoofing attacks

Recently, many retailers have rapidly increased cashless services. Customer guidance technology by providing advertisements and premiums linked with location information is drawing attention in cashless services using smartphones. While such technology is highly convenient, incidents such as the exploitation of premium points by disguising location information have occurred. It has become a major issue that shakes the reliability of cashless services and, in turn, affects the trust of the advanced information society that involves future financial services. In particular, the point acquisition service by visiting retail stores using smartphones has emerged as a real problem, with reports of cases of exploiting points by falsifying the location information of users who hold smartphones. This research will confirm the current location information spoofing attack method using smartphones for specific services and propose countermeasures.
Autoencoder-Based Gradient Compression for Distributed Training

Large-scale distributed training has recently been proposed as a solution to speed-up the training of deep neural networks on huge datasets. Distributed training, however, entails high communication rates for gradient exchange among computing nodes and requires expensive high-bandwidth network infrastructure. Various gradient compression methods have been proposed to overcome this limitation, including sparsification, quantization, and entropy encoding of the gradients. However, most existing methods leverage only the intra-node information redundancy, that is, they compress gradients at each node independently. In contrast, we advocate that the gradients across the nodes are correlated and propose a method to leverage this inter-node redundancy to obtain higher compression rates. In this work, we propose the Learned Gradient Compression (LGC) framework to reduce communication rates within a distributed training with the parameter server communication protocol. Our framework leverages an autoencoder to capture the common information in the gradients of the distributed nodes and eliminate the transmission of redundant information. Our experiments show that the proposed approach achieves significantly higher gradient compression ratios than state-of-the-art approaches like DGC and ScaleCom.
Costs and Benefits of Conducting Voice-based Surveys Versus Keypress-based Surveys on Interactive Voice Response Systems

Recent improvements through machine learning in speech technologies and natural language processing has prompted active interest in the development of conversational agents for various tasks. We look at the area of data collection in low-resource settings among rural women in North India, and explore the feasibility of using voice-based surveys conducted through IVR (Interactive Voice Response) systems where users may speak their responses in a conversational manner through natural speech. Through an iterative design process and detailed user feedback, we describe several nuances in running voice-based surveys, and compare their accuracy of data collection through equivalent keypress-based surveys. We find strong user preferences for voice-based surveys, and comparable performance with keypress-based surveys for most types of questions. Our results suggest that voice-based conversational interfaces may hold significant potential to build interactive applications for low-income and less-literate populations. Our findings are likely to be useful for other researchers and practitioners using ICTs (Information and Communication Technologies) in developing regions.
Technological Development of Image Aesthetics Assessment

Quantitative research on aesthetics is a classic interdisciplinary research. With the rapid development of deep learning, various approaches have been made in image aesthetics assessment (IAA). Starting from the concept of image aesthetics, this report roughly follows the chronological sequence and first introduces the manual design of image aesthetic features. We divide IAA into generic image aesthetics assessment (GIAA) and personalized image aesthetics assessment (PIAA) to introduce separately in the deep learning part. Majority of approaches are GIAA, which purpose is to simulate general aesthetics. In this section, we separately reviewed representative studies of five assessment methods (aesthetic classification, aesthetic regression, aesthetic distribution, IAA with attributes, aesthetic description). Due to the subjectivity of aesthetics, human’s aesthetics will more or less deviate from the generic value. PIAA aims to model the aesthetic preferences of specific user, and the research is of great value. We introduced this novel research in the fifth section. Finally, image aesthetic datasets of different uses are summarized. We hope this comprehensive survey can be helpful to researchers in the field of image and enhance the connection between computer and art.
Design of Japanese Interactive Network Teaching Platform in Information Age

In the era of network information, the construction and application of Japanese teaching models are facing many difficulties and shortcomings. Internet-based Japanese interactive teaching is a brand-new teaching model, forming in-class and out-of-class, online and offline, independent learning and classroom teaching, which is the interactive complement of other teaching links. This article is designed based on the ideas and methods of software engineering, and provides solutions for the development of Japanese interactive network teaching platform. First, use the case diagram to analyze the requirements and determine the basic functions of the platform; then, use the module structure diagram to go through the functional design, and refine the software functions determined in the requirements analysis stage; finally, the Struts2 architecture is studied to describe the components’ responsibilities and collaborative behaviors between them to improve the efficiency of software development.
MSS: Lightweight network authentication for resource constrained devices via Mergeable Stateful Signatures

Signature-based authentication is a core cryptographic primitive essential for most secure networking protocols. We introduce a new signature scheme, MSS, that allows a client to efficiently authenticate herself to a server. We model our new scheme in an offline/online model where client online time is premium. The offline component derives basis signatures that are then composed based on the data being signed to provide signatures efficiently and securely during run-time. MSS requires the server to maintain state and is suitable for applications where a device has long-term associations with the server. MSS allows direct comparison to hash chains-based authentication schemes used in similar settings, and is relevant to resource-constrained devices e.g., IoT. We derive MSS instantiations for two cryptographic families, assuming the hardness of RSA and decisional Diffie-Hellman (DDH) respectively, demonstrating the generality of the idea. We then use our new scheme to design an efficient time-based one-time password (TOTP) protocol. Specifically, we implement two TOTP authentication systems from our RSA and DDH instantiations. We evaluate the TOTP implementations on Raspberry Pis which demonstrate appealing gains: MSS reduces authentication latency and energy consumption by a factor of ~82 and 792, respectively, compared to a recent hash chain-based TOTP system.
Integrated Technology for Creating Quality Parallel Corpora

What determines the quality of parallel corpora? Firstly, it is determined by the quality of the translation. However, in this paper, we consider not the substantial quality of the translation, but the “technical” quality of parallel texts. Parallel texts are collected from different sources and often such texts have the following disadvantages: language mixing, font mixing, text alignment problems, the need for manual correction of parallel texts. All these problems require, firstly, their recognition, and secondly, they need to be resolved, and with large volumes of parallel texts, performing these operations manually is a very time-consuming process. Therefore, the work proposes an integrated technology for creating parallel corpora, which allows to minimize the number of manual operations. The authors present the technology as an example of a new linguistic resource - an open Kazakh-English parallel corpus.
Representation Results for Non-cumulative Logics

Most (if not all) nonmonotonic logics are assumed to be cumulative, which is often regarded as the minimum requirement for a logic. We argue that cumulativity, in particular, cumulative transitivity can be abandoned, in order to better characterize reasoning in uncertainty. But giving up cumulative transitivity makes it hard to obtain representation results for these logics. Borrowing the idea from strict-tolerant logics, we give some representation results for nonmonotonic logics that are not cumulatively transitive.
Dual Axis Smart Solar Tracker Using Arduino

This paper presents the plan and pragmatic execution of a basic dynamic dual-axis sun powered tracker (DAST) to follow the sun's development by utilizing less segments and low-cost also. A dual-axis instrument is created to tilt the PV board by Servo motor and rotates using mechanical motor confronting the most noteworthy power of daylight detected using light dependent resistors sensors, which are fixed in the four edges of the frame. The DAST model was developed essentially and tried utilizing a real-time virtual instrument dependent on Dominate to decide its proficiency. Also, a correlation dependent on trial results between the energy delivered by the proposed savvy DAST and a fixed board shows that the brilliant DAS <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> produces 36.26% more energy contrasted with the fixed board. The proposed dynamic DAS T can be effortlessly carried out without having profound information about sun oriented following innovations and electronic designing.
A Teaching Quality Evaluation System Based on J2EE Technology

To realize the effective quantification of the qualitative evaluation of the teaching quality of gold courses in colleges, this paper designs and implements a teaching quality evaluation system based on J2EE. We draw lessons from the existing experience of teaching quality evaluation, adopt B/S operation mode and SQL Server 2016 as the background database, and provide an analysis and design scheme based on three-tier architecture, including requirement analysis and design, as well as the specific implementation process of key modules. Finally, through the operation test analysis of the system, it shows that the evaluation system designed in this paper can implement diversified learning evaluation and provide a good scientific means for the comprehensive construction and quality evaluation of golden courses in colleges.
2D/3D US-TO-MRI RIGID REGISTRATION BY DEEP LEARNING

2D ultrasound (US) images to 3D magnetic resonance (MR) image registration is a crucial module in US-guided surgical navigation. US images need to be aligned with a preoperative image to provide good anatomy information guidance during interventions. However, the difference between the modality of US and MR makes the task challenging. To address this problem, we propose a learning-based rigid registration method between 2D US and 3D MR. The geodesic distance on the special Euclidean group SE(3) equipped with a left-invariant Riemannian metric is used as the loss function of a regression network. The registration result is optimized from the registration network by maximizing the similarity metric defined by a local structure orientation descriptor (LSOD). We achieve the angle and distance errors of 3.83 ± 0.39° and 0.017 ± 0.001 mm, outperforming the L2 norm loss function which results in 4.21 ± 0.19° angle error and 0.039 ± 0.001 mm distance error. Qualitative and quantitative evaluations confirm that the proposed method can achieve accurate 2DUS-3DMRI rigid registration.
DRLFNet: A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution

Light field records both spatial and angular information of light rays. By using light field cameras, 3D scenes can be reconstructed easily for further virtual reality applications. Limited by the sensor size, there is a trade-off between the spatial and angular resolution. To address this problem, we propose a dense-connection residual learning neural network, namely DRLFNet, to super resolve light field images in spatial domain. The dense-connection residual learning is implemented based on the proposed dense-connection residual block (DResBlock) that is used to efficiently exploit the joint spatial and angular features and the hierarchical features in different layers. Experimental results demonstrate that the proposed method out-performs other state-of-the-art methods by a large margin in both visual and numerical evaluations.
Face feature matching based on semantic Information

Abstract Face feature matching problems have always been a very important research topic in the field of image processing. Face feature matching is always the most important part of common face recognition algorithms, but how to match features quickly and accurately is a quite essential and urgent problem to be solved. Therefore, we propose a face feature matching method which can effectively improve the matching efficiency and accuracy. For each feature point in the input image, face feature matching based on nearest neighbor ratio method requires global search for the best matching in the reference image. However, we find that such matching method can achieve better matching results, but it costs a lot of computing resources and time. Therefore, we use the feature vectors constructed by CNN convolution feature map to provide the local matching region based on semantic information for handcrafted descriptors after feature matching. Although two feature matching processes are used in this paper, the matching efficiency is improved several times compared with the traditional global search scheme. Experiments show that our proposed can be used as an important module of face recognition system to improve the accuracy of face recognition.
Estimating Semantics Distance of Texts Based on Used Terms Analysis

Every day we are facing countless texts on various topics. With a growing number of resources, it is getting harder to find valuable content. There is a need for automated tools to evaluate texts and propose new reads based on their similarity to the original one. The paper aims at introducing a method for calculating the semantic distance between two texts. We use well-known morphological tools to disambiguate the meaning and function of each word in the text. Next, we create the similarity matrixes utilizing the weight of WordNet synset relations. Each term can be part of one of three sets, which visualize three levels of semantic distance. While calculating the distance between texts, we consider statistical characteristics that partly use the identification of terms. However, the primary stress is put on the meaning each word brings to the utterance.
Review of Security Aspects of 51 Percent Attack on Blockchain

Blockchain technology has emerged as a secure technology for performing financial transactions. It is a database which stores all the transactions into blocks. It has made the digital data decentralized and thus does not have the problem of single point failure. The newly created transaction is broadcasted to all other nodes in the network. Although blockchain has the property of immutability but it is always prone to the cyber attacks. The 51 percent is the predominant attack encountered over blockchain which is executed forming large mining pools. This is the attack over block addition process where maliciously mined blocks get added to blockchain. This paper encapsulates the critical review of existing solutions in the context of 51 percent attack so as to get the analysis of level of protection and the challenges in the existing methods.
Searching for ancient Aegean administrators: Computational experiments on identical seal impressions

Seals and sealings were widely used in Neopalatial Crete, primarily in community-wide administrative systems to manage the movement and storage of goods, but also to secure or control channels of intra-site communication using parchment documents. The main principle of this monitoring process was the use of the seal impression as an ‘indexical’ sign, that is, as a means of identifying a seal-user who was acting in an authorising or securing role. Given the fact that seal-users could be acting on their own behalf or, alternatively, as representatives of an institution, there is uncertainty in the literature still over whether the seal impression stood for a single identifiable individual or not. In this paper, we present a new computational methodology for identifying and quantifying similarity within sets of multiple seal impressions made by the same seal. Using a combination of visualisations derived from computational analysis of 3-D scans, experimental work to make seal impressions, and scrutiny of the relevant sealing casts within the Corpus of Minoan and Mycenaean Seals, Heidelberg, we are able to identify groups of sealings impressed in significantly similar ways. We consider it probable that seal impressions with a high degree of similarity were made by the same individual or by two or more people trained to work in a very specific way. This computational methodology is transferable and would prove valuable for other collections of material with identifiable repeated visual elements, such as coins. The implications of our research and the nuance it adds to reconstructions of Neopalatial sealing practices are of broader relevance for sphragistic studies, and provide a point of comparison with the contemporary sealing material of the Ancient Near East.
Continual Correction of Errors Using Smart Memory Replay

Catastrophic forgetting is an issue for all continual learning models with neural network architectures, however, research in this area often concentrates on prevention of catastrophic forgetting and not on the potential to improve existing knowledge with new information. In this paper, we explore using smart memory replay to augment a state-of-the-art regularization model. Our method continually corrects model errors for learned tasks, without compromising the learning of new tasks. We demonstrate how our method can achieve higher accuracy of predictions than a standard regularization model on multiple datasets, while incurring similar computational costs. In our experiments we show how using our method gains between five to ten percent accuracy on a large image classification dataset. We also illustrate how our method is able to regain task knowledge after catastrophic forgetting occurs.
A novel αDistance Borderline-ADASYN-SMOTE algorithm for imbalanced data and its application in Alzheimer’s disease classification based on Dense Convolutional Network

Abstract The classification problem of imbalanced data has become a very important issue in the fields of machine learning and data mining. At present, relatively effective oversampling methods for processing imbalanced data include SMOTE, Borderline-SMOTE, and ADASYN. These algorithms have their own advantages; however, they do not adequately consider the distance factor, which is an important factor for balancing data precisely and reducing the misclassification probability of a minority boundary sample. Therefore, a new algorithm, αDistance Borderline-ADASYN-SMOTE algorithm, is proposed in the paper by combining the optimized Borderline-SMOTE algorithm with the optimized ADASYN algorithm. In the new algorithm, both the amount and the distance distribution of the nearest neighbor samples are considered. A few formulas are created to realize the algorithm. After being balanced by the algorithm, the data obtained from ADNI data set is trained, verified and tested by the Dense Convolutional Network. The experimental results show that the new model improves the classification performance of the Alzheimer’s disease.
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters

When using arbitrarily oriented subspace clustering algorithms one obtains a partitioning of a given data set and for each partition its individual subspace. Since clustering is an unsupervised machine learning task, we may not have “ground truth” labels at our disposal or do not wish to rely on them. What is needed in such cases are internal measure which permits a label-less analysis of the obtained subspace clustering. In this work, we propose methods for revising clusters obtained from arbitrarily oriented correlation clustering algorithms. Initial experiments conducted reveal improvements in the clustering results compared to the original clustering outcome. Our proposed approach is simple and can be applied as a post-processing step on arbitrarily oriented correlation clusterings.
SR: Severely Defective Image Inpainting with Structural Reconstruction

Image inpainting especially inpainting large defects regions in images is a promising yet challenging task. However, existing approaches can suffer from generating distorted structures and blurry textures in large defects images. In this paper, a framework for severe defects of inpainting images has been presented by using image structure features. When a large defect in the image appears, the image structure is already severely damaged. Firstly, aiming at the problem of image structure defects, an iterative repair method based on large defect areas is proposed to guide the repair. Specifically, we integrally use depth encoding and decoding structures to generate models. Owing to deeper feature maps represent the structural features of the images in convolutional neural networks. We used deep features to guide the inpainting and repurposed deep features for the decoding process during the inpainting process. Utilizing feature map as feedback progressively fills the hole by trusting only the high confidence pixels within the hole at each iteration and focuses on the remaining pixels at the next iteration. Meanwhile, an AC block has been proposed to enhance contextual inference. Taking advantage of previously iterated partial as known pixels to predict other pixels, this process gradually improves the results. The evaluations for quantitative and qualitative are superior to existing methods by our experiments. Keywords-large defective image inpainting, deep learning, struct reconstruction
Ca se "My digital environment" in the theme "Informati on resources and services of internet"

The article discusses the use of case technology in an informatics lesson in the 9th grade in the theme "Information resources and servicesof Internet". Students can come to the conclusion about the effectiveness of the use of digital resources to solve everyday problems. They makea list of applications that allow them to find a solution in specific situations; analyze advantages and disadvantages of the selected applications;recommend one application for each situation. Students should present the results of the work in the form of a mind map or a table. The articleprovides an analysis of educational and methodological literature on the theme (first of all, the tasks proposed for solving on this theme areanalyzed); the content of the case for conducting a lesson on the specified theme is described; the stages of work, the time allotted for them aredescribed; the competencies that are formed during the realisation of the proposed case are listed.