Reviewerly/exHarmony huggingface dataset:
Relational data paradigms: What do we learn by taking the materiality of databases seriously?

Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities— and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms.
Review on the Application of Artificial Neural Networks in Real Estate Valuation

Real estate appraisal is needed in assessment of the value of properties and contribute the regional economy of any country.Real estate valuation is thus an important subject, which has to be studied carefully as there are many individual subjective criteria, which often results in variations, which makes the traditional valuation methods to be inadequate.This work aimed at coping with the complexity of real estate valuation, by putting forth the advantages of artificial neural network, one of the novel model of real estate appraisal.The review of literature has helped to establish that the neural network model is better than the traditional model previously.This work opens up a new and better understanding to the principle of real estate evaluation.
Contactless U: Higher Education in the Postcoronavirus World

The profound impact of the coronavirus COVID- has tragically affected the world, including undergraduate and graduate university education. These impacts will be very long lasting, significantly changing the nature of higher education forever. A contemporary account of what is happening and predictions on how this situation will transform higher education are offered.
Video Content Analysis for Compliance Audit in Finance and Security Industry

The quality and accessibility of modern financial service have been quickly and dramatically improved, which benefits from the fast development of information technology.It has also witnessed the trend for applying artificial intelligence related technology, especially machine learning to the finance and security industry ranging from face recognition to fraud detection.In particular, deep neural networks have proven to be far superior to traditional algorithms in various application scenarios of computer vision.In this paper, we propose a deep learning-based video analysis system for automated compliance audit in stock brokerage, which in general consists of five modules here: 1) Video tampering and integrity detection; 2) Objects of interest localization and association; 3) Analysis of presence and departure of personnel in a video; 4) Face image quality assessment; and 5) Signature action positioning.To the best of our knowledge, this is the first work that introduces remote automated compliance audit system for the dual-recorded video in finance and security industry.The experimental results suggest our system can identify most of the potential non-compliant videos and has greatly improved the working efficiency of the auditors and reduced human labor costs.The collected dataset in our experiment will be released with this paper.
Revisiting Back-Translation for Low-Resource Machine Translation Between Chinese and Vietnamese

Back-translation (BT) has been widely used and become one of standard techniques for data augmentation in Neural Machine Translation (NMT), BT has proven to be beneficial for improving the performance of translation effectively, especially for low-resource scenarios.While most previous works related to BT mainly focus on European languages with high relatedness, few of them study less-related languages in other areas around the world.In this paper, we choose the language pair with less relatedness in Asia: Chinese and Vietnamese, to investigate the impacts of BT on extremely low-resource machine translation between them.We first discuss the similarities and differences between the two languages, then evaluate and compare the effects of different sizes of back-translated data on NMT and Statistical Machine Translation (SMT) models for Chinese-Vietnamese and Vietnamese-Chinese, with both characterbased and word-based settings, and conduct further analysis on the translation outputs from several aspects.Some conclusions from previous works are partially confirmed and we also draw some new findings and conclusions, which are beneficial to understand BT further and deeper for translation between less-related low-resource languages.
A General Differentiable Mesh Renderer for Image-Based 3D Reasoning

Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders.
Incorporating Extra Knowledge to Enhance Word Embedding

Word embedding, a process to automatically learn the mathematical representations of words from unlabeled text corpora, has gained a lot of attention recently. Since words are the basic units of a natural language, the more precisely we can represent the morphological, syntactic and semantic properties of words, the better we can support downstream Natural Language Processing (NLP) tasks. Since traditional word embeddings are mainly designed to capture the semantic relatedness between co-occurred words in a predefined context, it may not be effective in encoding other information that is important for different NLP applications. In this survey, we summarize the recent advances in incorporating extra knowledge to enhance word embedding. We will also identify the limitations of existing work as well as point out a few promising future directions.
Cyberbullying Detection, Based on the FastText and Word Similarity Schemes

With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect on mental health, especially for teenagers. In order to reduce or even stop cyberbullying, different machine learning techniques are applied and numerous studies have been conducted so far. However, conventional detection schemes still have challenges, such as low accuracy. Therefore, it is of significant importance to find an efficient detection solution in the natural language processing and machine learning communities. In the present study, characteristics of cyberbullying are initially analyzed from vocabulary and syntax points of view. Then a new detection algorithm is proposed based on FastText and word similarity schemes. Finally, experiments are carried out to evaluate the effectiveness and performance of the proposed method. Obtained results show that the proposed algorithm can effectively improve the detection accuracy and recall rate of cyberbullying detection.
Explanation Perspectives from the Cognitive Sciences---A Survey

With growing adoption of AI across fields such as healthcare, finance, and the justice system, explaining an AI decision has become more important than ever before. Development of human-centric explainable AI (XAI) systems necessitates an understanding of the requirements of the human-in-the-loop seeking the explanation. This includes the cognitive behavioral purpose that the explanation serves for its recipients, and the structure that the explanation uses to reach those ends. An understanding of the psychological foundations of explanations is thus vital for the development of effective human-centric XAI systems. Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations. We organize the insights gained therein by means of highlighting the advantages and shortcomings of various explanation structures and theories, discuss their applicability across different domains, and analyze their utility to various types of humans-in-the-loop. We summarize the key takeaways for human-centric design of XAI systems, and recommend strategies to bridge the existing gap between XAI research and practical needs. We hope this work will spark the development of novel human-centric XAI systems.
Economic Growth, Total Factor Productivity, and Institution Quality in Low-Middle Income Countries in Asia

The purpose of this paper is to investigate the impact of total factor productivity (TFP), institutional quality, and interactive variable between them on economic growth in 13 low-middle income countries in Asia for the period 2000-2018.The paper uses the difference Generalized Method of Moments (GMM) to explore the dataset provided by the World Bank.The empirical results show that TFP and the interactive variable positively impact on the economic growth, while the institutional determinants have a negative influence.The negative effect is explained by the weak institutions in these low-middle income countries.The findings of the study suggest two points.First, the government should continue to improve TFP, which is associated with the application of technical advances, technological innovations, improvement of management methods, and skilled workers.Second, far more important, is that the authorities should pay special attention to implement institutional reform and strengthen the governance in the future.The successful experiences from Japan, Korea and Singapore will help other governments in Asian low-middle income countries to build developmental state.Probably, the developmental state actively interfere in the market to promote and realize the development goals.By doing so, these economies might overcome the so-called "middle-income trap".
Custom Dilated Edge Detection Filters

Edge detection is a basic and fundamental function in image processing.Inspired by the new dilated convolution techniques which have impressive results in machine learning, we discuss here the idea of dilating the standard filters which are used to compute the gradient of an image.We compare the result of the proposed dilated filters with original filters and extend or custom variants of them.We also present the impact of dilatation on a complex edge detection algorithm, like Canny.
Intelligent cloud workflow management and scheduling method for big data applications

Abstract With the application and comprehensive development of big data technology, the need for effective research on cloud workflow management and scheduling is becoming increasingly urgent. However, there are currently suitable methods for effective analysis. To determine how to effectively manage and schedule smart cloud workflows, this article studies big data from various aspects and draws the following conclusions: Compared with the original JStorm system, the response time is shortened by a maximum of 58.26% and an average of 23.18%, CPU resource utilization is increased by a maximum of 17.96% and an average of 11.39%, and memory utilization increased by a maximum of 88.7% and an average of 71.16%. In terms of optimizing the dynamic combination of web services, the overall performance of both the MOACO and CCA algorithms is better than that of the GA algorithm, and the average performance of the MOACO algorithm is better than that of the CCA algorithm. This paper also proposes a cloud workflow scheduling strategy based on an intelligent algorithm and realizes the two-tier scheduling of cloud workflow tasks by adjusting the combination strategy for cloud service resources. We have studied three representative intelligent algorithms (ACO, PSO and GA) and improved them for scheduling optimization. It can be clearly seen that in the same scenario, the optimal values of the different algorithms vary greatly for different test cases. However, the optimal solution curve is substantially consistent with the trend of the mean curve.
Student Acceptance of Using Augmented Reality Applications for Learning in Pharmacy: A Pilot Study

Creating engaging learning experiences that are easy to use and support the different learning requirements of university students is challenging. However, improvements in simulation technologies, such as augmented reality (AR) and virtual reality (VR), are making such changes possible. The aim of this study is to use a mobile-based AR technology to develop an interactive learning module about contraceptive devices and medicines and to measure its acceptability and usability by undergraduate pharmacy students. The learning module comprising AR images of contraceptive medicines, case studies relating to their use and a series of directed questions was completed by 33 pharmacy students. Students answered a survey to collect information about the usability and acceptability of AR for learning. The results show that the majority of students reported that AR is a useful resource for learning about medicines compared to more traditional methods, such as didactic lectures and tutorials. Students indicated that the AR application was easy to use and improved their knowledge of medicines. These findings suggest that AR technology is a useful tool to create engaging and easy to use learning experiences for university students.
What If Bots Feel Moods?

For social bots, smooth emotional transitions are essential for delivering a genuine conversation experience to users. Yet, the task is challenging because emotion is too implicit and complicated to understand. Among previous studies in the domain of retrieval-based conversational model, they only consider the factors of semantic and functional dependencies of utterances. In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion. Extensive experiments on two benchmark datasets indicate that the proposed model can effectively track the flow of emotions throughout a human-machine conversation and significantly improve response selection in dialogues over the state-of-the-art baselines. We also empirically validate the emotion-control effects of our proposed model on three different emotional aspects. Finally, we apply such functionalities to a real IoT application.
Entropic Uncertainty in Spin XY Model with Long-Range Interactions

The behavior of the uncertainty relations and their tightness for a system, consisting of two qubits interacting thermally with a magnetic field in the presence of Dzyaloshinskii–Moriya interaction, is discussed, where different types of interaction strengths are considered. It is shown that both coupling and the magnetic field parameters decay the degree of entanglement, and increasing the uncertainty relations and the degree of mixedness. The phenomena of the sudden changes in the investigated quantities are depicted at large values of the field and coupling parameters. Concerning the type of the coupling parameters, distance and the trigonometric coupling have a clear effect on the behavior of the studied physical quantities.
How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead

In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions.In this work, we present Session-Path, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path.We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.
Understanding Screaming Channels: From a Detailed Analysis to Improved Attacks

Recently, some wireless devices have been found vulnerable to a novel class of side-channel attacks, called Screaming Channels. These leaks might appear if the sensitive leaks from the processor are unintentionally broadcast by a radio transmitter placed on the same chip. Previous work focuses on identifying the root causes, and on mounting an attack at a distance considerably larger than the one achievable with conventional electromagnetic side channels, which was demonstrated in the low-noise environment of an anechoic chamber. However, a detailed understanding of the leak, attacks that take full advantage of the novel vector, and security evaluations in more practical scenarios are still missing. In this paper, we conduct a thorough experimental analysis of the peculiar properties of Screaming Channels. For example, we learn about the coexistence of intended and unintended data, the role of distance and other parameters on the strength of the leak, the distortion of the leakmodel, and the portability of the profiles. With such insights, we build better attacks. We profile a device connected via cable with 10000·500 traces. Then, 5 months later, we attack a different instance at 15m in an office environment. We recover the AES-128 key with 5000·1000 traces and key enumeration up to 223. Leveraging spatial diversity, we mount some attacks in the presence of obstacles. As a first example of application to a real system, we show a proof-of-concept attack against the authentication method of Google Eddystone beacons. On the one side, this work lowers the bar for more realistic attacks, highlighting the importance of the novel attack vector. On the other side, it provides a broader security evaluation of the leaks, helping the defender and radio designers to evaluate risk, and the need of countermeasures.
Fixslicing: A New GIFT Representation

The GIFT family of lightweight block ciphers, published at CHES 2017, offers excellent hardware performance figures and has been used, in full or in part, in several candidates of the ongoing NIST lightweight cryptography competition. However, implementation of GIFT in software seems complex and not efficient due to the bit permutation composing its linear layer (a feature shared with PRESENT cipher). In this article, we exhibit a new non-trivial representation of the GIFT family of block ciphers over several rounds. This new representation, that we call fixslicing, allows extremely efficient software bitsliced implementations of GIFT, using only a few rotations, surprisingly placing GIFT as a very efficient candidate on micro-controllers. Our constant time implementations show that, on ARM Cortex-M3, 128-bit data can be ciphered with only about 800 cycles for GIFT-64 and about 1300 cycles for GIFT-128 (assuming pre-computed round keys). In particular, this is much faster than the impressive PRESENT implementation published at CHES 2017 that requires 2116 cycles in the same setting, or the current best AES constant time implementation reported that requires 1617 cycles. This work impacts GIFT, but also improves software implementations of all other cryptographic primitives directly based on it or strongly related to it.
TOAD-GAN: Coherent Style Level Generation from a Single Example

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.
Genetic Algorithms for the Multiple Travelling Salesman Problem

We consider the multiple travelling salesman Problem (MTSP) that is one of the generalization of the travelling salesman problem (TSP).For solving this problem genetic algorithms (GAs) based on numerous crossover operators have been described in the literature.Choosing effective crossover operator can give effective GA.Generally, the crossover operators that are developed for the TSP are applied to the MTSP.We propose to develop simple and effective GAs using sequential constructive crossover (SCX), adaptive SCX, greedy SCX, reverse greedy SCX and comprehensive SCX for solving the MTSP.The effectiveness of the crossover operators is demonstrated by comparing among them and with another crossover operator on some instances from TSPLIB of various sizes with different number of salesmen.The experimental study shows the promising results by the crossover operators, especially CSCX, for the MTSP.