Reviewerly/exHarmony huggingface dataset:
A Study on Industrial Security Psychology

In accordance with the fourth industrial revolution, the importance of science and technology is increasing day by day. Therefore, companies and countries around the world are working to develop technologies and prevent industrial espionage crime in order to strengthen their competitiveness. This is more important in that it is not just a matter of business but a matter of national economy and security. In response, Korea has made efforts to prevent the industrial espionage crime through various legal systems, including the Industrial Technology Protection Act. But every year, industrial espionage crimes are on the rise, and internal leaks account for a high percentage of them. This shows the need for psychological research by internal technology leakers. This study used AHP analysis to identify priorities among psychological factors of technology leakage crime revealed in previous studies. The Key factor has organizational commitment, self-regulation and neutralization. The sub factors of organizational commitment are affective commitment, continuance commitment, normative commitment . The sub factors of self-regulation are standard, motivation and will power. The sub factors of neutralization denial of the victim, denial of injury, denial of responsibility, condemnation of the condemners and appeal to higher loyalties As a result, it was found that the neutralization had the greatest influence, and among the sub factors, Affective commitment, will power and denial of injury had the highest influence.
Efficient Hardware/Software Co-Design of Elliptic-Curve Cryptography for the Internet of Things

The Internet of Things (IoT) is connecting the world in a way humanity has never seen before. With applications in healthcare, agricultural, transportation, and more, IoT devices help in bridging the gap between the physical and the virtual worlds. These devices usually carry sensitive data which requires security and protection in transit and rest. However, the limited power and energy consumption make it harder and more challenging to implementing security protocols, especially Public-Key Cryptosystems (PKC). In this paper, we present a hardware/software co-design for Elliptic-Curve Cryptography (ECC) PKC suitable for lightweight devices. We present the implementation results for our design on an edge node to be used for indoor localization in a healthcare facilities.
Statistical Concurrent Non-Malleable Zero-Knowledge from One-Way Functions

Concurrent non-malleable zero-knowledge ($$\mathrm {CNMZK}$$) protocols are zero-knowledge protocols that provides security even when adversaries interact with multiple provers and verifiers simultaneously. It is known that $$\mathrm {CNMZK}$$ arguments for $$\mathcal {NP}$$ can be constructed in the plain model. Furthermore, it was recently shown that statistical$$\mathrm {CNMZK}$$ arguments for $$\mathcal {NP}$$ can also be constructed in the plain model. However, although the former requires only the existence of one-way functions, the latter requires the DDH assumption. In this paper, we construct a statistical $$\mathrm {CNMZK}$$ argument for $$\mathcal {NP}$$ assuming only the existence of one-way functions. The security is proven via black-box simulation, and the round complexity is $$\mathsf {poly}(n)$$. Under the existence of collision-resistant hash functions, the round complexity is reduced to $$\omega (\log n)$$, which is essentially optimal for black-box concurrent zero-knowledge protocols.
USING INTERACTIVE APPROACH IN ENHANCING STUDENTS’ READING COMPREHENSION

English is one of the compulsory subjects at schools in Indonesia, including at junior high schools. This study was conducted to investigate the effectiveness of implementing an interactive approach in teaching reading comprehension. It also investigated the students' responses toward the implementation of an interactive approach in teaching reading. The true-experimental design was carried out in this study along with the pretest and post-test. Two classes of the first-grade students of SMPIK Nurul Quran in Aceh Besar were chosen as the sample of the research with total population sampling technique. This sampling technique was employed because of the limited individuals to be randomized as samples. Each group consisted of 20 students for the experimental class and 20 students for the control class. The instruments used in this study were test and questionnaire. The data collected were analyzed using descriptive statistics through SPSS Version 20. The results show that the use of an interactive approach improved students’ reading scores and they also gave positive responses toward the use of interactive approach in teaching reading. Therefore, the alternate hypotheses of this research was accepted. It means that the implementation of interactive approach could increase students’ reading scores significantly as compared with the students who were taught by using the bottom-up approach. The students also had positive responses to the implementation of interactive approach during teaching and learning reading comprehension. It can be concluded that interactive approach is one of the effective approach for teaching reading comprehension.
Use of 3D Images to Evaluate Formation Damage Induced by Montmorillonite Fines in Porous Media Systems

This research was made possible by the National Priorities Research Program (NPRP) grant #NPRP8-594-2-244 from Qatar national research fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. The SMT images were collected using the X-ray Operations and Research Beamline Station 13-BMD at Argonne Photon Source (APS), Argonne National Laboratory. The authors thank Dr. Mark Rivers of APS for help in performing the SMT scans. They also acknowledge the support of GeoSoilEnviroCARS (Sector 13), which is supported by the National Science Foundation, USA, Earth Sciences (EAR-1128799), and the US Department of Energy (DOE) and Geosciences (DE-FG02-94ER14466). Use of the Advanced Photon Source, an Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory, was supported by DOE, USA under contract no. DE-AC02-06CH11357.
EdgeDeup: an Edge-IoT Data Deduplication Scheme with Dynamic Ownership Management and Privacy-preserving

This paper designs an edge-IoT encrypted data deduplication scheme supporting dynamic ownership management and privacy protection, which achieves fine-grained access control by user-level key and update mechanism and decreases the communication overhead greatly because the ownership update is performed by the cloud server. In conclusion, we construct the experimental environment required by the performance evaluation of the proposed scheme, and prove with the detailed results that the proposed scheme has obvious advantages in efficiency and security.
Attribute-Driven Capsule Network for Entity Relation Prediction

Multi-attribute entity relation prediction is a novel data mining application about designing an intelligent system that supports inferencing across attributes information. However, most existing deep learning methods capture the inner structural information between different attributes are far more limited. In this paper, we propose an attribute-driven approach for entity relation prediction task based on capsule networks that have been shown to demonstrate good performance on relation mining. We develop a self-attention routing method to encapsulate multiple attributes semantic representation into relational semantic capsules and using dynamic routing method to generate class capsules for predicting relations. Due to the lack of multi-attribute entity relation data is a major obstacle in this task, we construct a new real-world multi-attribute entity relation dataset in this work. Experimental results show significant superiority of our model, as compared with other baselines.
Agile Islands in a Waterfall Environment: Challenges and Strategies in Automotive

Driven by the need for faster time-to-market and reduced development lead-time, large-scale systems engineering companies are adopting agile methods in their organizations. This agile transformation is challenging and it is common that adoption starts bottom-up with agile software teams within the context of traditional company structures. This creates the challenge of agile teams working within a document-centric and plan-driven (or waterfall) environment. While it may be desirable to take the best of both worlds, it is not clear how that can be achieved especially with respect to managing requirements in large-scale systems. This paper presents an exploratory case study focusing on two departments of a large-scale systems engineering company (automotive) that is in the process of company-wide agile adoption. We present challenges that agile teams face while working within a larger plan-driven context and propose potential strategies to mitigate the challenges. Challenges relate to, e.g., development teams not being aware of the high-level requirements, difficulties to manage change of these requirements as well as their relationship to backlog items such as user stories. While we found strategies for solving most of the challenges, they remain abstract and empirical research on their effectiveness is currently lacking.
Object Detection and Localization in Natural Scenes Through Single-Step and Two-Step Models

Object detectors are of two types in state-of-the-art (SOTA) approaches, i.e. the two-stage detectors (Mask-RCNN, Faster-RCNN, Fast-RCNN) and one stage detector (SSD and YOLO). In two-stage detectors, first, generate region proposals and extract deep features for bounding box regression and classification for object detection. These two-stage models achieve a higher accuracy rate but seem slow in performance. Hence, in one-stage detector takes the image as input with region proposal generations and object detection is performed through regression and classification only. Hence these methods show a lower accuracy, however, they are more robust than two-stage detectors. In our research, we examine both types of detectors including Mask RCNN, SSD, and Retina Net and compare them by varying back-bone CNN network architectures i.e. (Inception V2, ResNet 50). These methods are evaluated on a subset of challenging datasets PASCAL-VOC 2012 and MS-COCO.
Learning a deep fully connected neural network from a single image for edit propagation

We introduce a simple but effective deep fully connected neural network (FNN) to solve the edit propagation as a multiclass pixel-level classification task. We construct the feature space using three- (or one-) dimensional normalized RGB (or grayscale) vectors and spatial coordinates. Our deep FNN-based model consists of color feature extraction, spatial feature extraction, feature combination, and classifier estimation. We train our model only using the feature within the region labeled by the user with strokes in a single image. Then our method directly outputs the edit propagation results after the forward pass without any refinement process. Our method automatically determines the importance of each image feature across the whole image jointly considering feature vectors from user strokes. Extensive experiments demonstrate that the proposed algorithm achieves superior performance over state-of-the-art methods, yet it remains simple and efficient.
Deep Learning Fusion of RGB and Depth Images for Pedestrian Detection.

In this paper, we propose an effective method based on the Faster-RCNN structureto combine RGB and depth images for pedestrian detection. During the training stage,we generate a semantic segmentation map from the depth image and use it to refine theconvolutional features extracted from the RGB images. In addition, we acquire moreaccurate region proposals by exploring the perspective projection with the help of depthinformation. Experimental results demonstrate that our proposed method achieves thestate-of-the-art RGBD pedestrian detection performance on KITTI [12] datase
A Preliminary Performance Comparison of Machine Learning Algorithms for Web Author Identification of Vietnamese Online Messages

With the rapid development of the Internet and accompanying technologies, communication between people has become easier than ever. Email, news sites, social networking applications become an indispensable connection tool. However, the Internet is also a favorable environment for cybercriminals with malicious activities. Therefore, it is necessary to develop a method to determine which user is the author of the online message. There has been a lot of researches with different corpora and various languages. In this article, we propose an approach to identify the authors of online messages in Vietnamese based on machine learning algorithms. Algorithms used include Naïve Bayes, SVM, Random Forest, and Logistic Regression. The algorithm that has yielded the best results in most cases is Random Forest.
Correlation Filter Tracking Algorithm Based on Multiple Features and Average Peak Correlation Energy

The traditional target tracking algorithm adopts artificial features. However, the artificial features are not strong enough to illustrate the appearance of the target. So it is difficult to apply to complex scenes; moreover, the traditional target tracking algorithm does not judge the confidence of the response. This paper proposes the Multiple Features and Average Peak Correlation Energy (MFAPCE) tracking algorithm, MFAPCE tracking algorithm combines deep features with color features and uses average peak correlation energy to measure confidence. Finally, according to the confidence to determine whether to update the model. Compared with the traditional tracking algorithm, MFAPCE algorithm can improve the tracking performance according to experiment.
Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the loss

The coefficients of regression are usually estimated for minimization problems with asymmetric loss functions. In this paper, we rather correct predictions so that the prediction error follows a generalized Gaussian distribution. In our method, we not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss. Predictions usually have errors. Therefore, it is necessary to use predictions in consideration of these errors. Our approach takes into account prediction errors. Furthermore, even if we do not understand the prediction method, which is a possible circumstance in, e.g. deep learning, we can use our method if we know the prediction error distribution and asymmetric loss function. Our method can be applied to procurement of electricity from electricity markets.
Agile new process for software product evaluation: Agile-MyFEPS

MyFEPS is a reference framework developed for the evaluation of software products based on the international standards series ISO/IEC 250xx. After using MyFEPS in 15 software product evaluation services, the need to define a more agile process was established while maintaining its effectiveness and reliability. The objective of this article is to present the new process of MyFEPS-Agile. For this, a process design has been carried out taking into account the agile manifesto. This new process was validated in two industry projects. As a result, MyFEPS-Agile activities were easily understood, and the evaluation and the results obtained remain reliable.
The Globalization Wave and Stock Market Return in Africa

The study investigated the effect of globalization on stock market returns in a panel of five (5) African countries (South Africa, Nigeria, Egypt, Morocco and Namibia) over the period of 2000 and 2018. The study used Pooled Mean Group/ARDL estimation with panel unit root and cointegration tests. After establishing cointegration, globalization index, foreign direct investments and exchange rate were found to have positive and significant effects on stock market returns in both in the long run, while trade openness had a negative and significant effect on stock market returns in the long-run. The error correction mechanism showed that the speed of adjustment between globalization, FDI, exchange rate and trade openness was high, that is, the aforementioned variables rapidly adjusted to any disequilibrium in the short-run. The short-run dynamics revealed that globalization index and trade openness had positive effect on stock market returns while FDI and exchange rate exerted negative effect on stock market returns in the short-run. However, only trade openness was significant in the short-run. Finally, the was one-way causal flow running from each of globalization index, exchange rate and trade openness to stock market returns among the selected African stock markets. Hence, it was concluded that globalization is a significant determinant of stock market returns in Africa.
LEXICAL AMBIGUITY COMPARED TO SYNTACTIC / OR STRUCTURAL AMBIGUITY

Language is an important tool for us to communicate and transfer information day by day. It is really hard to imagine a situation without language delivery. Furthermore, a natural language is not so perfectly designed to transfer all the information in an expected way. More or less, some gaps in a language cannot be avoided, such as the ambiguity of a language which causes confusion in people's common utterances. There are frequently ambiguous words and sentences appearing in our utterances which acquire more succinct clarification from speakers. Various interpretations of the same utterance may lead to various interpretations and to the difficulty of comprehension of listeners. Taken for granted, ambiguous sentences and words can be quite often interpreted in an accurate way when they are used in reality; moreover, writers and speakers use this characteristic of ambiguity to make an artistic impact on their language.Both syntactic and lexical ambiguity, are ruled by the same types of knowledge representations and processing mechanisms. Highly similarly empirical, theoretical and methodological issues have arisen in both the lexical and syntactic domains; the role of frequency information, the types of information involved in contextual constraints, the extent to which contextual information constrains the interpretation of ambiguities and whether the processing system is modular or interactive. Even though structural ambiguities are rarely noticed in ordinary language use, yet, they are extremely common like lexical ambiguities. They are a major contributor to the large number of parses produced by computational parsing systems. Syntactic and lexical ambiguity is very pervasive in linguistics and although the language intentions might have an objective sensationalistic interpretation, it can also be a misconstrued manipulative device. Recent types of theorizing eliminate the strong distinction between accessing a meaning and constructing a syntactic representation, which was central to previous accounts. These parallels between the domains are not coincidental; they reflect common underlying processes and types of knowledge representations. The parallels derive from the fact that the syntactic ambiguities in question are based on ambiguities at the lexical level. The same ambiguity resolution mechanisms apply in both domains because both involve ambiguities over various types of lexical representations.
QUANTUM ENTANGLEMENT RECOVERY USING SPEED GRADIENT OVER NOISY QUANTUM CHANNELS

Decoherence of entanglement is one of the main challenges in quantum communication. It is caused by quantum systems interacting with their environment and limiting the distribution of entangled qubits over quantum channels for a few kilo- meters. Preserving and recovering the initial entangled states is a critical issue to permit designed tasks (e.g. quantum key distribution, quantum teleportation, and quantum dense coding) to be fulfilled. This study investigates the adaptive control (speed gradient) method to recover initial entangled state after exposure to decoherence effects. The results show that the entanglement of the initial state can be maximized and recovered over the variant decoherence rates of the quantum channel noise.
Supervised learning for quantum maximum entropy estimation

For an unknown quantum state $\rho$, a set of measurement outcomes provides partial information. The principe of maximum entropy states that the density matrix which best represents the current knowlege is the one with the maximum entropy. However, the maximum entropy estimation (MEE) is hard to calculate in general. In this work, we develop a method that outputs an approximation to the MEE from limited measurement results by using supervised learning techniques. The method estimates the MEE of theoretical data of several different quantum systems with high fidelities. We also test our method experimentally with a photonic set-up, which exhibits high fidelities (all greater than 99.7%) and robustness against experimental errors. In comparison with previous known optimization algorithms for MEE, our method provides adequate fidelities with high efficiency.
IMPLEMENTATION OF E-VOTING SYSTEM USING BLOCK CHAIN TECHNOLOGY

Elections are a significant occasion in a cutting edge popular government yet enormous areas of society around the globe don't confide in their political race framework which is a significant worry for the vote based system. Indeed, even the world's biggest majority rules systems like India, United States, Japan experiences the ill effects of an imperfect discretionary framework. Vote fixing, hacking of the EVM (Electronic voting machine), political race control, and surveying stall catching are the significant issues in the present democratic framework. Right now, we are researching the issues in the political decision casting a ballot framework and attempting to propose the e-voting model which can resolve these issues. Additionally, this article expecting to assess the use of blockchain as administration to execute circulated electronic voting frameworks. The area of paper will feature a portion of the mainstream blockchain structures that offer blockchain as help and a related electronic voting system which depends on the blockchain that tends to all impediments separately; it additionally safeguards member's obscurity while as yet being available to open assessment.
Semantic-Enhanced Training Data Augmentation Methods for Long-Tail Entity Recognition Models

Named Entity Recognition (NER) is an essential information retrieval task. It enables a wide range of natural language processing applications such as semantic search, machine translation, etc. The NER can be formulated as the task of identifying and typing words or phrases in a text that refers to certain classes of interest (e.g., disease, Adverse Drug Reactions). There are different techniques to tackle NER, such as dictionary-based, rulebased, and machine learning-based. Machine learning-based NER techniques have shown to perform the best for entities with large amounts of human-labeled training datasets. However, their performance is limited when dealing with long-tail entities. Long-tail entities are entities that have a low frequency in the document collections and usually have no reference to existing Knowledge Bases. Obtaining human-labeled datasets is expensive and time-consuming, especially for long-tail entities that are scarcely available in document collections. This dissertation focuses on the problem of the lack of training data, arguably the largest bottleneck in training machine learning-based NER techniques. We investigated efficient and effective ways to augment training data by enhancing their size and quality automatically. Our work aimed at showing how, by enhancing the size and quality of the training data using different techniques, it will be possible to improve the performance of Long-tail Entity Recognition (L-tER).