Reviewerly/exHarmony huggingface dataset:
‎ The Influence of Human Development, Institutional Quality and ‎ISIS Emergence on Foreign Direct Investment in Iraq ‎

This study investigates the influence of human development and institutional quality on the foreign direct investment net inflows on the gross domestic product in Iraq for the period (2004-2016) by using OLS regression. The empirical results show that governance sub-indicators do not actually matter much in attracting Foreign Direct Investment (FDI) inflows in Iraq. This variable shows fixed results with a different sign; the Political Stability (PS), Government Effectiveness (GE) and Rule of Law (RL) are the sub-indicators of the governance with a positive sign which is insignificant, while the Voice and Accountability (VA), Regulatory Quality (RQ) and Control of Corruption (CC) are the sub-indicators of the governance index with a negative sign and an insignificant impact on FDI inflows. The value of Worldwide Governance Indicators (WGI) coefficient is negative and significant at 10% level. Hence it has been concluded that the explanatory variables are negatively associated with the dependent variable FDI. In contrast, the Human Development Index (HDI) has a positive and significant impact on FDI inflows. This study found that Islamic State of Iraq and Syria (ISIS) emergence after (2013) had a significant coefficient, meaning that ISIS has affected the level of FDI inflows.
An Efficient BGV-type Encryption Scheme for IoT Systems

Internet of Thing (IoT) systems usually have less storage and computing power than desktop systems. This paper proposes an efficient BGV-type homomorphic encryption scheme in order fit for secure computing on IoT system. Our scheme reduces the storage space for switch keys and ciphertext evaluation time comparing with previous BGV-type cryptosystems. Specifically, the switch key in homomorphic computations can be a constant but no longer one for each level. Moreover, the product of two ciphertexts can be at the same sublayer as them and the multiplication operations can be repeated between two sublayers. As a result, the multiplication times will not be limited by L in an L-level circuit and, thus, the ciphertext evaluation time will decrease significantly. We implement the scheme with the C language. The performance test shows that the efficiency of the improved scheme is better than Helib in same configurations.
Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training

Mispronunciation detection is an essential component of the Computer-Assisted Pronunciation Training (CAPT) systems. State-of-the-art mispronunciation detection models use Deep Neural Networks (DNN) for acoustic modeling, and a Goodness of Pronunciation (GOP) based algorithm for pronunciation scoring. However, GOP based scoring models have two major limitations: i.e., (i) They depend on forced alignment which splits the speech into phonetic segments and independently use them for scoring, which neglects the transitions between phonemes within the segment; (ii) They only focus on phonetic segments, which fails to consider the context effects across phonemes (such as liaison, omission, incomplete plosive sound, etc.). In this work, we propose the Context-aware Goodness of Pronunciation (CaGOP) scoring model. Particularly, two factors namely the transition factor and the duration factor are injected into CaGOP scoring. The transition factor identifies the transitions between phonemes and applies them to weight the frame-wise GOP. Moreover, a self-attention based phonetic duration modeling is proposed to introduce the duration factor into the scoring model. The proposed scoring model significantly outperforms baselines, achieving 20% and 12% relative improvement over the GOP model on the phoneme-level and sentence-level mispronunciation detection respectively.
Shared multisensory sexual arousal in virtual reality (VR) environments

Abstract This research explores multisensory sexual arousal in men and women, and how it can be implemented and shared between multiple individuals in Virtual Reality (VR). This is achieved through the stimulation of human senses with immersive technology including visual, olfactory, auditory, and haptic triggers. Participants are invited to VR to test various sensory triggers and assess them as sexually arousing or not. A literature review on VR experiments related to sexuality, the concepts of perception and multisensory experiments, and data collected from self-reports was used to conclude. The goal of this research is to establish that sexual arousal is a multisensory event that may or may not be linked to the presence or thought of the intended object of desire (sexual partner). By examining what stimulates arousal, we better understand the multisensory capacity of humans, leading not only to richer sexual experiences but also to the further development of wearable sextech products, soft robotics, and multisensory learning machines. This understanding helps with other research related to human-robot interaction, affection, detection, and transmission in both physical and virtual realities, and how VR technology can help to design a new generation of sex robots.
A Multi-Image Encryption with Super-Lager-Capacity Based on Spherical Diffraction and Filtering Diffusion

A multi-image encryption with super-large-capacity is proposed by using spherical diffraction and filtering diffusion. In the proposed method, initial images are processed sequentially by filtering diffusion and chaos scrambling. The images are combined into one image using XOR operation. The combined image is encrypted by improved equal modulus decomposition after spherical diffraction. There are three main contributions of the proposed method—(1) resisting phase-retrieval attack due to the asymmetry of spherical diffraction; (2) high flexibility of decrypting images individually; and (3) super-large encryption capacity of the product of image resolution and grayscale level, which is the most significant advantage. The feasibility and effectiveness of the proposed encryption are verified by numerical simulation results.
Disaster Preparedness of Academic Libraries: A Case Study From Indonesia

Abstract Disaster preparedness activities are important for libraries, as they collect and provide access to information and knowledge of human intellectual ideas and work. This study aims to identify potential disasters that may be experienced by academic libraries in Indonesia as well as their disaster preparedness activities. A quantitative approach was used to survey 85 academic library representatives who attended an academic librarian forum in Jakarta, Indonesia. The results identified the many types of natural and manmade disasters that have already and continue to threaten collections and services of respondent libraries. In addition to earthquakes and floods, threats include risks from human actions such as theft and vandalism, as well as problems arising from unstable electrical power supplies, and hardware and software malfunctions. This study also found that the majority of libraries participating in the study do not have a disaster preparedness plan.
Intellectualization of professional and pedagogical activity based on the integration of pedagogical and digital technologies

The article is dedicated to the issues of intellectualization of professional and pedagogical activity with the main purpose of continuously development of teachers’ competencies in the field of using digital technologies in the educational process, transformation of the teachers’ activities in accordance with the capabilities of modern information and communication technologies and the digital educational environment. The article highlights the potential of massive open online courses and open educational resources that contribute to the development of ways to effectively use Internet technologies in education. The article points out the role of the pedagogical technologies’ integration with the latest digital technologies in updating and developing the competencies of teachers. The article describes how to use online courses in the educational process, scenarios for integrating online courses into the educational process, technologies for placing educational materials, interaction tools in an electronic informational and educational environment. The article focuses on blended learning technology as a means of implementing an integrated learning model using Internet resources. The article highlights the main characteristics of blended learning technology allowing the use of modern methods and tools of online learning, combining the advantages of educational and digital technologies. Particular attention is paid to the “MOOC discipline support” model which uses massive open online courses as additional material. The article describes variants of methodological support for the process of implementing an online course, Internet services for organizing academic work and networking.
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable -- a counterfactual explanation tells the user how to gain the desired prediction with minimal changes to the input. Besides, counterfactual explanations can also serve as efficient probes to the models' decisions. In this work, we exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models. We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets, supporting users ranging from decision-subjects to model developers. DECE supports exploratory analysis of model decisions by combining the strengths of counterfactual explanations at instance- and subgroup-levels. We also introduce a set of interactions that enable users to customize the generation of counterfactual explanations to find more actionable ones that can suit their needs. Through three use cases and an expert interview, we demonstrate the effectiveness of DECE in supporting decision exploration tasks and instance explanations.
Human Action Recognition Algorithm Based on Multi-Feature Map Fusion

The emergence of the convolutional neural network greatly improves the accuracy of human action recognition. However, with the deepening of the network, fewer and fewer features are extracted, and in some datasets, due to the shooting angle, the size of the target to be recognized is different. To solve this problem, on the basis of resnext human action recognition method, we propose an improved resnext human action recognition method based on multi-feature map fusion. First, the video is uniformly sampled to generate training samples, and we generate samples with different frames as the input to the network. Second, we add n layers of up-sampling layers after layer 1 of resnext, to enlarge the feature maps and extract multiple feature maps, so that the extracted feature maps are clearer, and small targets can be better recognized. Finally, for the n results obtained, we use the weighted geometric means combination forecasting method based on L_1 norm to fuse and obtain the final result. In the process of experiment, using UCF-101 and HMDB-51 for verification, the accuracy of our model is 90.3% on UCF-101, which is higher than most of the state-of-art algorithms.
Modern Users of Libraries and the Linked Open Data Environment

The goal of any library is to ensure high quality and general availability of information retrieval tools. The paper describes the project implemented by the Russian State Library (RSL) to present Library Bibliographic Classification as a Networked Knowledge Organization System. The project goal is to support content and provide tools for ensuring system’s interoperability with other resources of the same nature (i.e. with Linked Data Vocabularies) in the global network environment. The project was partially supported by the Russian Foundation for Basic Research (RFBR). The RSL General Classified Catalogue (GCC) was selected as the main data source for the Classification system of knowledge organization. The meaning of each classification number is expressed by complete string of wordings (captions), rather than the last level caption alone. Data converted to the Resource Description Framework (RDF) files based on the standard set of properties defined in the Simple Knowledge Organization System (SKOS) model was loaded into the semantic storage for subsequent data processing using the SPARQL query language. In order to enrich user queries for search of resources, the RSL has published its Classification System in the form of Linked Open Data (https://lod.rsl.ru) for searching in the RSL electronic catalogue. Currently, the work is underway to enable its smooth integration with other LOD vocabularies. The SKOS mapping tags are used to differentiate the types of connections between SKOS elements (concepts) existing in different concept schemes, for example, UDC, MeSH, authority data. The conceptual schemes of the leading classifications are fundamentally different from each other. Establishing correspondence between concepts is possible only on the basis of lexical and structural analysis to compute the concept similarity as a combination of attributes. The authors are looking forward to working with libraries in Russia and other countries to create a common space of Linked Open Data vocabularies.
An improved two-stream CNN method for abnormal behavior detection

Abstract In recent years, abnormal behavior detection has become an active research field in computer vision and image processing. Several methods based on traditional two-stream CNN or 3D-CNN have been proposed and successfully applied in abnormal behavior detection. However, the abnormal behavior data is rarely less in the real scenario, in other words, the data is imbalanced, which may lead to overfitting problem and affect the final results. In this paper, an improved abnormal behavior detection method based on a two-stream CNN method was proposed to address the problem mentioned above. In the proposed method, DenseNet is adopted to extract both spatial and temporal features, and focal loss is employed to alleviate the influence of imbalanced data. The experiment results show that the proposed method provides a good performance in the real-world scenario.
A Three-valued Approach to Strategic Abilities under Imperfect Information

A major challenge for logics for strategies is represented by their verification in contexts of imperfect information. In this contribution we advance the state of the art by approximating the verification of Alternating-time Temporal Logic (ATL) under imperfect information by using perfect information and a three-valued semantics. In particular, we develop novel automata-theoretic techniques for the linear-time logic LTL, then apply these to finding “failure” states, where the ATL specification to be model checked is undefined. Such failure states can then be fed into a refinement procedure, thus providing a sound, albeit incomplete, verification procedure.
CFDSD: a Communication Framework for Distributed Software Development

Abstract Due to geographical and/or temporal dispersion, communication between teams in distributed software projects is a critical factor for success. Notably, distributed teams suffer adverse physical and temporal dispersion effects during an information exchange. To mitigate problems arising from interactions, it is important to understand the communication structure of teams during distributed projects. The objective of this work is to present the Communication Framework for Distributed Software Development. This framework groups a set of distributed projects communication concepts and enables a unified view of all stakeholders’ intercommunications that use many interaction technologies. The goal of this framework is to portray the dynamics of the interaction between distributed teams in a multi-tier structure, and each tier approaches a single function in the intercommunication process. We analyzed all interactions from distributed teams after developing an experimental IT Project, using the content analysis method to validate the Communication Framework for Distributed Software Development. The main contribution of this work is the framework specification, and the investigation, which has the potential to help mapping communication patterns in DSD. Moreover, this framework comprises interfaces for communication assessment and includes intercommunications from automated engineering tools as bots.
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (for example, neck type, dress length, collar type, print etc) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (for example, party wear, sports wear, casual wear etc). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept (POC) for our model is under pilot at Myntra (www.myntra.com) where it is exposed to some internal users for feedback.
A comparative performance evaluation of VP9, x265, SVT-AV1, VVC codecs leveraging the VMAF perceptual quality metric

The prevalence of video driven applications, leveraging over the top video on demand services as well as live video streaming applications, dominate network traffic over today’s internet landscape. As such, they necessitate efficient video compression methods to accommodate the desired quality of service and hence user experience. In this study, we compare the performances of the emerging versatile video coding (VVC) standard, the recently released AV1 encoder (using SVTAV1 instance), the established high efficiency video coding (HEVC) standard via its x265 implementation, and the earlier VP9 codec. We used selected videos coming from three different datasets, namely UT LIVE (432p), and HEVC test sequences (480p, 720p, 1080p) that provide diversity in video content, video resolutions, and frame rates. The experimental setup involved fixed quality encoding using four different rate points, and more specifically, QP values of 27, 35, 46, 55 for AV1 and VP9 and QP values of 22, 27, 32, 37 for VVC and x265 codecs. For estimating bitrate gains, we used the BD-RATE algorithm using both PSNR and VMAF for objective video quality assessment (VQA). We found that VVC achieved the best video coding performance, significantly outperforming all other codecs. AV1 consistently outperformed x265, but with narrow margins in some video sequences, suggesting that a cautious selection between the two codecs needs to based on application-specific criteria. Within the group of considered codecs, VP9 required the highest bitrates. Ongoing work involves extending the examined video datasets pool to different resolutions (e.g., 240p, 1600p) while investigating the correlation between subjective and objective VQA scores.
Study of 2D foveated video quality in virtual reality

In Virtual Reality (VR), the necessity of immersive videos leads to greater challenges in compression and communication owing to the much higher spatial resolution, rapid, and the often real-time changes in viewing direction. Foveation in displays exploits the space-variant density of the retinal photoreceptors, which decreases exponentially with increasing eccentricity, to reduce the amount of data from the visual periphery. Foveated compression is gaining relevance and popularity for Virtual Reality. Likewise, being able to predict the quality of displayed foveated and compressed content has become more important. Towards advancing the development of objective quality assessment algorithms for foveated and compressed measurements of VR video contents, we built a new VR database of foveated/compressed videos, and conducted a human study of perceptual quality on it. A foveated video player having low motion-to-photon latency (~50ms) was designed to meet the requirements of smooth playback, while an eye tracker was deployed to provide gaze direction in real time. We generated 180 distorted videos from 10 pristine 8K videos (30fps) having varying levels and combinations of foveation and compression distortions. These contents were viewed and quality-rated by 36 subjects in a controlled VR setting. Both the subject ratings and the eye tracking data are being made available along with the rest of the database.
Classical Coding Problem from Transversal T Gates

Universal quantum computation requires the implementation of a logical non-Clifford gate. In this paper, we characterize all stabilizer codes whose code subspaces are preserved under physical T and T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> gates. For example, this could enable magic state distillation with non-CSS codes and, thus, provide better parameters than CSS-based protocols. However, among non-degenerate stabilizer codes that support transversal T, we prove that CSS codes are optimal. We also show that triorthogonal codes are, essentially, the only family of CSS codes that realize logical transversal T via physical transversal T. Using our algebraic approach, we reveal new purely-classical coding problems that are intimately related to the realization of logical operations via transversal T. Decreasing monomial codes are also used to construct a code that realizes logical CCZ. Finally, we use Ax's theorem to characterize the logical operation realized on a family of quantum Reed-Muller codes. This result is generalized to finer angle Z-rotations in https://arxiv.org/abs/1910.09333.