Reviewerly/exHarmony huggingface dataset:
Data Loss and Reconstruction of Location Differential Privacy Protection Based on Edge Computing

With the development of the Internet of Things (IoT), the delay caused by network transmission has led to low data processing efficiency. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, at the same time, the IoT faces important challenges, and edge computing uses a large number of distributed devices, making it difficult to perform centralized control. When an edge node is attacked, the attacker can continue to invade its connected nodes, thereby mining and stealing a user's private data and causing losses. Once the edge layer communication link is attacked or accidentally interrupted, the user's private information is likely to be leaked. To solve these problems, this paper proposes to protect user privacy by using differential privacy. First, according to the three-layer communication link structure of edge computing, a data query model is proposed; the main function is to capture the structure information and the data center connection weight and to query the connection relationship between the edge node and the client. Second, the edge node is regarded as the central server, and the differential privacy theory is used to realize the protection of location privacy. Finally, to reduce the data loss caused in the process of location protection, linear programming is adopted to realize the selection of the optimal location fuzzy matrix, and data loss and reconstruction methods are used to minimize the data uncertainty. In comparison to the existing differential privacy method, the method in this paper can achieve better privacy protection and can effectively reduce data loss.
Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors

This article presents an implementation of a denoising algorithm based on High-Order Singular Value Decomposition (HOSVD) of tensors.It belongs to the class of patch-based methods such as BM3D and NL-Bayes.It exploits the grouping of similar patches in a local neighborhood into a 3D matrix also called a third order tensor.Instead of performing a different processing in each dimension, as in BM3D for instance, it is based on the decomposition of a tensor simultaneously in all dimensions reducing it to a core tensor in a similar way as SVD does for matrices in computing the diagonal matrix of singular values.The core tensor is filtered and a tensor is reconstructed by inverting the HOSVD.As it is common in patch-based algorithms, all tensors containing a pixel are then merged to produce an output image.
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation

A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries.This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation.In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain.Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.
Value Development as an Indicator of Personal Readiness for Self-Realization Among Starting Researchers

Introduction. The article for the first time presents the results of the study concerned with the starting scientists’ value development in relation to their dominant experiences during their research activity. An increasing interest of young people in science motivates studying value-semantic and activity aspects of the starting researchers’ milieu to determine conditions for developing their research competence, resources for coping with arising objective and subjective difficulties that can reduce their motivation for research and lead to the formation of negative mental states and renunciation of scientific activity. The aim of the study is to identify specific features of post-graduate students’ value development in relation to their emotions and meanings of dominant experiences while they do their resear ch. Materials and Methods. M. Rokeach’s Value Survey (extended interpretation by M. S. Yanitskiy, A. V. Seryi); the semantic differential technique (Ch. Osgood); classification of emotions by E. P. Ilyin; and content analysis were used in this study. The data underwent statistical processing, including frequency analysis and the Kruskal-Wallis H and Jonckheere-Terpstra nonparametric tests. Results. Three groups of post-graduate students with different varieties of value development were identified. Dominant terminal and instrumental values among doctoral students with different variants of value development were specified. Semantic categories of post-graduate students’ dominant experiences during their research are highlighted. Discussion and Conclusion. The study results can be used in the educational process during professional and personal development of modern youth. They make it possible to identify high-educated young people with the research potential at the stage of post-graduate programs, dissertation writing and implementation of their scientific products. These issues may be further developed from the cross-cultural p erspective.
THEMATIC PROGRESSION IN STUDENTS’ DESCRIPTIVE TEXTS

This research aims to find out types of thematic progression pattern used by students in writing descriptive text and to know the dominant thematic progression used in students’ writing descriptive text. This research employed a descriptive-qualitative research design. The respondents of the research were eleven grade students of SMKN 1 Cimahi. The data collection was documentation. The source of the data was descriptive texts written by students of Instrumentasi Otomatisasi Process-D. There were 9 texts written by the respondents. The collected data was analyzed by using the theory from Bloor &amp; Bloor (2013) as the theoretical to analyze the data. The result of this research found that students used three types of Thematic Progression in students’ writing descriptive text, they are Constant Theme, Linear Theme, Split Rheme. Furthermore, Constant Theme is the most frequent themes used in students’ writing descriptive text.
Penggunaan Fuzzy Logic & Metode Mamdani untuk Menghitung Pembelian, Penjualan dan Persediaan

Fuzzy Logic is a development of Binary Logic that can be used to analyze data in research in both the exact and social fields. In this study, fuzzy logic is used to analyze data in a "Ayam Goreng Murah Rezeki" restaurant related to three linguistic attributes, each of which consists of two linguistic variables: Inventory (Little, Many), Sales (Down, Up), and Purchases (Reduced, Increased) using the Mamdani method. The purpose of the study is to provide an input value of Inventory and Sales to get a Purchase output value. The results of data analysis using a linear down graph and a linear upward graph on each linguistic variable with the Mamdani method yield results in the estimated area or range.
The Entropic Uncertainty Relation for Two Qubits in the Cavity‐Based Architecture

Abstract Based on a simple cavity‐engineered architecture, the dynamics of quantum memory–assisted entropic uncertainty relation (QMA‐EUR) for two qubits initially prepared in a generic Werner state is investigated. The effects of cavity decay rate, qubit–cavity couplings, and cavity–cavity couplings on the uncertainty are explored. It is found that the damped oscillation of uncertainty can be induced by the increase of two types of coupling strengths mentioned above. It is demonstrated that the maximum value of uncertainty is closely related to the purity of the initial state. The uncertainty can be either increased or decreased, depending on the threshold value of coupling strength between the two cavities. Finally, in agreement with a recent observation, an asynchronous relation between uncertainty and mixedness is found during the initial time evolution.
Organizational Conflicts During Universities Transformation: Destruam et Aedificabo

Introduction. The authors tackle the topical problem of assessing the conflict level between subidentical groups: “management academics” (professors and lectures staff) and “academic managers” (administrative managing staff). The aim of the paper is to construct a new method of quantitative assessment of the conflict level between professional groups generated by the organizational culture; to study the dynamics of the change in the conflict level between professional and age-based cohorts existing in Russian universities. Materials and Methods. In this study, the conflict level is defined as a difference of common cultural vectors found for each of target age-groups, demonstrating generations’ behavioral features and their professional competence, set up as the result of analysis of expert assessments with the help of organizational culture methods developed by the Organizational Cultural Assessment Instrument of K. Сameron and R. Quinn. Empirical data are taken from sociological research involving 384 respondents from 18 universities representing 12 regions of the Russian Federation. Results. It is shown that activity of the considered target bunches is under significant influence of hierarchical (bureaucratic) organizational culture. This objective circumstance of proceeding transformation processes forms the basis for fragmentation of existing academic identity into those intra-university bunches which are combined both in terms of valuable patterns of generations and patterns formed under the influence of social and economic conditions. Interference of various age-grade valuable patterns under permanent current transformation invokes escalation of strife, its minimum level is reached in identical age-grades of professors and lectures staff (“management academics”) and administrative managing staff (“academic managers”). A certain influence on the level of conflicts between the target groups under consideration is produced by the degree of goals adequacy and objectives of the university available resources (competence, material, technical, and financial base). Discussion and Conclusion The novelty of the conducted research lies in consideration of conflict problems in universities arisen from the increasing differentiation of general cultural vectors of university communities. Complex, sustainable and reproducible nature of this social phenomenon, linking together both value and behavioral aspects of university communities, requires a transformation of research tactics. The research materials might be useful in practical work of heads of universities as scientific and methodological recommendations for the study and prediction of conflict processes i n universities.
Challenges of Applying Blockchain Technology

Due to the fact that modern computers have “perfect memory” and can process a huge number of transactions through a computer network very quickly, the idea of a decentralized currency system is not a new thing and it has long been a dream of advocates of the concept of anonymous digital money. Satoshi Nakamoto has come up with a genius idea to create a system that is a digital abstraction book of the balance and make that system public for all practical transactions related to accounts and transactions, which, when simplified, are reduced to mere addition and subtraction operations. He used the digital media feature to make perfect own copies, so he distributed this copy of the balance book to every computer in the network (decentralized system) and obtained a unique (structurally unchangeable) and global (public) book of balance. Thus, a revolutionary blockchain technology was created, which initially enabled and supported the functioning of the first digital crypto currency - bitcoin. Blockchain technology is an amalgam of several different and equally revolutionary technological achievements in the field of cryptography and computer networks. The aim of this paper is, using Bitcoin as example, to briefly explain the functioning of this technology and its suitability for use in many other areas of human activity, especially those that have a problem of distrust and the possibility of fraud.
Design of an Ontology for Decision Support in VR Exposure Therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare.Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy.However, adapting the VR environment to the needs of the patient is time-consuming.Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy.In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR.The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology.Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data.The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy. CCS CONCEPTS• Theory of computation → Semantics and reasoning; • Information systems → Expert systems; • General and reference → Design.
PTB Graph Parsing with Tree Approximation

The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies.This paper proposes a method that approximates PTB graph-structured representations by trees.By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single treebased parsing.An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.
Study on Non-Commutativity Measure of Quantum Discord

In this paper, we are concerned with the non-commutativity measure of quantum discord. We first present an explicit expression of the non-commutativity measure of quantum discord in the two-qubit case. Then we compare the geometric quantum discords for two dynamic models with their non-commutativity measure of quantum discords. Furthermore, we show that the results conducted by the non-commutativity measure of quantum discord are different from those conducted by both or one of the Hilbert-Schmidt distance discord and trace distance discord. These intrinsic differences indicate that the non-commutativity measure of quantum discord is incompatible with at least one of the well-known geometric quantum discords in the quantitative and qualitative representation of quantum correlations.
Automatizing chromatic quality assessment for cultural heritage image digitization

In the context of digitization of photographs and other documents with graphical value, cultural heritage organizations need to give a guarantee that the stored digital image is a faithful representation of the physical image both at the physical level and the perceptual level. On the physical level, image quality can be measured objectively in a simple way by applying certain physical attributes to the image, as well as by measuring how distorting images affects the performance of the attributes. However, on the perceptual level, image quality should correspond to the perception that a human expert would experience when observing the physical image under certain determined and controlled conditions. In this paper we address the problem of image quality assessment (IQA) in the context of cultural heritage digitization by applying machine learning (ML). In particular, we explore the possibility of creating a decision tree that mimics the response of an expert on cultural heritage when observing cultural heritage images.
Chinese Zero Pronoun Resolution

Chinese zero pronoun (ZP) resolution plays a critical role in discourse analysis. Different from traditional mention-to-mention approaches, this article proposes a chain-to-chain approach to improve the performance of ZP resolution in three aspects. First, consecutive ZPs are clustered into coreferential chains, each working as one independent anaphor as a whole. In this way, those ZPs far away from their overt antecedents can be bridged via other consecutive ZPs in the same coreferential chains and thus better resolved. Second, common noun phrases (NPs) are automatically grouped into coreferential chains using traditional approaches, each working as one independent antecedent candidate as a whole. That is, those NPs occurring in the same coreferential chain are viewed as one antecedent candidate as a whole, and ZP resolution is made between ZP coreferential chains and common NP coreferential chains. In this way, the performance can be much improved due to the effective reduction of the search space by pruning singletons and negative instances. Third and finally, additional features from ZP and common NP coreferential chains are employed to better represent anaphors and their antecedent candidates, respectively. Comprehensive experiments on the OntoNotes V5.0 corpus show that our chain-to-chain approach significantly outperforms the state-of-the-art mention-to-mention approaches. To our knowledge, this is the first work to resolve zero pronouns in a chain-to-chain way.
Denoising Recurrent Neural Networks for Classifying Crash-Related Events

With detailed sensor and visual data from automobiles, a data-driven model can learn to classify crash-related events during a drive. We propose a neural network model accepting time-series vehicle sensor data and forward-facing videos as input for learning classification of crash-related events and varying types of such events. To elaborate, a novel recurrent neural network structure is introduced, namely, denoising gated recurrent unit with decay, in order to deal with time-series automobile sensor data with missing value and noises. Our model detects crash and near-crash events based on a large set of time-series data collected from naturalistic driving behavior. Furthermore, the model classifies those events involving pedestrians, a vehicle in front, or a vehicle on either side. The effectiveness of our model is evaluated with more than two thousand 30-s clips from naturalistic driving behavior data. The results show that the model, including sensory encoder with denoising gated recurrent unit with decay, visual encoder, and attention mechanism, outperforms gated recurrent unit with decay, gated CNN, and other baselines not only in event classification and but also in event-type classification.
Robust visual tracking based on response stability

In this paper, a new approach of response stability based for visual object tracking is developed. This approach proposes a response stability criterion to measure the tracking quality and fuse tracking results of multiple layers of a convolutional neural network (CNN). Inspired by recent detection based methods for visual tracking, the detection capability of EdgeBoxes is investigated, and proposes to re-detect target when tracking failure occurs. In addition, 3D locally adaptive regression kernels (LARK) feature is employed in correlation filter based tracking framework. Extensive experimental results and performance compared with state-of-the-art tracking algorithms on challenging benchmark datasets show that our method is more accurate and robust.
Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

Inconstancy of the market prices can affect society's purchasing power.One effort to anticipate the price uncertainty is by conducting commodity price forecasting.In the concept of forecasting, the commodity prices can be predicted by studying sales data in the previous period.This study aims to implement a decision support system in predicting food commodity prices trend.In data collection, the authors used list of food commodities provided by Industry and Trade Service of Gowa Regency.For data analysis, we use Naive Bayes algorithm to predict the food commodity prices in the future and Simple Exponential Smoothing to find out the price trend in a certain period.As a result, both methods can predict commodity prices and market tendency in a given time completely.
Relationship Detection Based on Object Semantic Inference and Attention Mechanisms

Detecting relations among objects is a crucial task for image understanding. However, each relationship involves different objects pair combinations, and different objects pair combinations express diverse interactions. This makes the relationships, based just on visual features, a challenging task. In this paper, we propose a simple yet effective relationship detection model, which is based on object semantic inference and attention mechanisms. Our model is trained to detect relation triples, such as <man ride horse>, <horse, carry, bag>. To overcome the high diversity of visual appearances, the semantic inference module and the visual features are combined to complement each others. We also introduce two different attention mechanisms for object feature refinement and phrase feature refinement. In order to derive a more detailed and comprehensive representation for each object, the object feature refinement module refines the representation of each object by querying over all the other objects in the image. The phrase feature refinement module is proposed in order to make the phrase feature more effective, and to automatically focus on relative parts, to improve the visual relationship detection task. We validate our model on Visual Genome Relationship dataset. Our proposed model achieves competitive results compared to the state-of-the-art method MOTIFNET.
An Enhancement of TOE Model by Investigating the Influential Factors of Cloud Adoption Security Objectives

Cloud computing (CC) is a future technological trend for technological infrastructure development. And it is growing strongly as the backbone of industrial future technological infrastructure. As CC service has a lot to offer, it also has some major downside that clients cannot ignore. For CC service adoption, the potential candidates are SMEs but due to lack of resources, experience, expertise and low financial structure scenario CC can be most helpful. CC faces a major issue in term of cloud security, an organization doesn’t understand the cloud security factors in the organization and data owner doubts about their data. In the research paper, an investigation on the cloud security objectives to find out the influential factors for cloud adoption in SMEs by proposing an enhancement of Technology-Organization-Environment (TOE) model with some positive influential factor like cloud security, relative advantages, cost saving, availability, SLA, capability, top management, organizational readiness, IS knowledge, malicious insiders, government regulatory support, competitive pressure, size and type. Some negative influencing factors like technological readiness, cloud trust and lack of standards in cloud security. Data were collected by questionnaires from a selected IT company based on SaaS and public cloud. Case study method has been used for validating the enhance TOE model. The IBM Statistics SPSS v22 tool was used for data analysis. The results of data analysis support the enhancement as well as all the proposed hypotheses. In summary, the results of the analysis show that all the enhancement factors were found to have a significant cloud security influence on adoption of cloud computing for SMEs.