Reviewerly/exHarmony huggingface dataset:
Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan

Taiwan has been successful in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic; however, without a vaccine the threat of a second outbreak remains. Young adults who show few to no symptoms when infected have been identified in many countries as driving the virus’ spread through unidentifiable community transmission. Mobile tracking technologies register nearby contacts of a user and notifies them if one later tests positive to the virus, potentially solving this issue; however, the effectiveness of these technologies depends on their acceptance by the public. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google’s Bluetooth exposure notification system) among four samples of young Taiwanese adults (aged 25 years or younger). Using Bayesian methods, we find high acceptance for all three tracking technologies (&gt;75%), with acceptance for each technology surpassing 90% if additional privacy measures were included. We consider the policy implications of these results for Taiwan and similar cultures.
Aligning Faithful Interpretations with their Social Attribution

Abstract We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case study, we present several failure cases. Borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and the attribution of human behavior to the interpretation (social attribution). We reformulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of aligned faithfulness: faithful causal chains that are aligned with their expected social behavior. The two steps of causal attribution and social attribution together complete the process of explaining behavior. With this formalization, we characterize various failures of misaligned faithful highlight interpretations, and propose an alternative causal chain to remedy the issues. Finally, we implement highlight explanations of the proposed causal format using contrastive explanations.
Improved YOLO Network for Free-Angle Remote Sensing Target Detection

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.
Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.
Simulating quantum materials with digital quantum computers

Quantum materials exhibit a wide array of exotic phenomena and practically useful properties. A better understanding of these materials can provide deeper insights into fundamental physics in the quantum realm as well as advance technology for entertainment, healthcare, and sustainability. The emergence of digital quantum computers (DQCs), which can efficiently perform quantum simulations that are otherwise intractable on classical computers, provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. Equipped with these new tools, scientists from diverse domains are racing towards achieving physical quantum advantage (i.e., using a quantum computer to learn new physics with a computation that cannot feasibly be run on any classical computer). The aim of this review, therefore, is to provide a summary of progress made towards this goal that is accessible to scientists across the physical sciences. We will first review the available technology and algorithms, and detail the myriad ways to represent materials on quantum computers. Next, we will showcase the simulations that have been successfully performed on currently available DQCs, emphasizing the variety of properties, both static and dynamic, that can be studied with this nascent technology. Finally, we work through two examples of how to map a materials problem onto a DQC, with full code included in the Supplementary Material. It is our hope that this review can serve as an organized overview of progress in the field for domain experts and an accessible introduction to scientists in related fields interested in beginning to perform their own simulations of quantum materials on DQCs.
Dynamics of online hate and misinformation

Abstract Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin’s law, online debates tend to degenerate towards increasingly toxic exchanges of views.
Hunger games search algorithm for global optimization of engineering design problems

Abstract The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.
Random Processes and Visual Perception

The aim of this chapter is to explore a classic stochastic problem using the tools of the graphics environment. Stochastic processes are associated with the concepts of uncertainty or chance. Major areas of research in mathematical and applied sciences, statistics, finance, and artificial intelligence/machine learning benefit from the knowledge gained studying this process. Visual Art also depends on elements of uncertainty and chance. To explore the commonality of concern between Science and Art and better understand stochastic processes, the author organizes his research according to the Knowledge Visualization framework, examines a graph theory reference model called the “shortest route problem,” and, adding additional elements specific to the art-making process, shares his results to highlight the relevance of interdisciplinary studies in the fields of randomness and visual perception.
Choice Disjunctive Queries in Logic Programming

One of the long-standing research problems on logic programming is to treat the cut predicate in a logical, high-level way. We argue that this problem can be solved by adopting linear logic and choice-disjunctive goal formulas of the form G0 ⊕ G1 where G0, G1 are goals. These goals have the following intended semantics: choose the true disjunct Gi and execute Gi where i (= 0 or 1), while discarding the unchosen disjunct. Note that only one goal can remain alive during execution. These goals thus allow us to specify mutually exclusive tasks in a high-level way. Note that there is another use of cut which is for breaking out of failure-driven loops and efficient heap management. Unfortunately, it is not possible to replace cut of this kind with use of choice-disjunctive goals.
Introducing Uninformed Search with Tangible Board Games

Researchers have established the value of hands-on learning with tangible artifacts in mathematics and related fields. Inspired by this work, an assignment was developed for an undergraduate/graduate Artificial Intelligence course to introduce students to the formal representation of search. Students analyzed a familiar board game — e.g., Rush Hour or peg solitaire — using the standard approach to modeling an uninformed search process. The assignment was well-received by students, and analysis of their work yielded unexpected insights into the challenges students face in understanding how the formal problem model interacts with search algorithms. This paper introduces the theoretical motivations for the work, analyzes student work products, and makes recommendations for future extensions.
A Graph Retrieval Architecture and System for Online Sign Language Dictionary- with an application to Taiwanese Sign Language

Emerging applications for an online sign language dictionary require that retrieval systems retrieve a target vocabulary through visual symbols. However, when people encounter an unknown vocabulary in sign language during communication, they require the online dictionary to retrieve the vocabulary with higher recall-rate and smaller-sized graph through a mobile device. Still, three situations show that the current online dictionary needs an extension. First, previous works lack of retrieving the target graph of a vocabulary through its complete visual symbol-portfolio. Secondly, they often respond a large number of possible images; however, their precisions and recall rates remain very low. Thirdly, previous works of sign language gloves can convert the visual symbols into the graphic features, but only part of the symbols, ignoring the symbols of expression and relative direction. Therefore, the aim of this study is, based on Taiwanese Sign Language, to design a new graph retrieval architecture for sign-language (GRAS), and to implement a new graph retrieval system for sign-language (GRSS) based on this architecture. Finally, we invite users to evaluate GRSS. The experimental results show that GRSS gets convincing performance. And, GRSS adopting RDF technology can improve the performance of GRSS without adopting RDF technology.
PubMed literacy: Developing skills for effective literature search

Health science professionals need exhaustive and up-to-date information for patient care and research.They depend much on electronic resources for meeting their information needs.PubMed is a premier open access health science database of US National Library of Medicine (NLM).PubMed indexes more than 5600 health science related journals.PubMed literacy is the information literacy on PubMed database.It helps the health science professionals and researchers in understanding various features of the PubMed database and accessing the required health science information more efficiently and effectively.PubMed literacy means to how to use PubMed for conducting literature search and includes having training in literature search skills and techniques which are essential for searching in PubMed.The present article helps to get familiarity with search features and functionality of PubMed database and to know the importance of PubMed literacy in conducting health science literature search.
Critical Success Factors for an Effective Security Risk Management Program

This paper evaluates the perceived effectiveness of the security risk management (SRM) programs at two Fortune 500 firms using qualitative and quantitative methods. Layers of management and staff from both firms participated in the study. Perceived effectiveness of their SRM programs was based on nine critical success factors (CSFs). Six initial critical success factors (CSFs): executive management support, organizational maturity, open communication, risk management stakeholders, team member empowerment, and holistic view of an organization were extracted from organizational role theory. They were confirmed and synthesized with three additional CSFs (security maintenance, corporate security strategy, and human resource development). A survey based on the CSFs was implemented at the two firms. Although both firms are Fortune 500 technology companies, their perceptions of current perceived SRM effectiveness differ significantly.
Fault Tolerance Impact on Near Field Communication for Data Storage of Mobile Commerce Technology in Cloud Computing

Fault tolerance is a major concern to guarantee availability and reliability of critical services as well as application execution. Recently, due to rapid expansion of smart phone market and wireless communication environment, mobile devices are considered as resource for large scale distributed processing but there are several drawbacks, such as unstable wireless connection, limitation of data storage capacity, low communication bandwidth and frequent location changes. Cloud systems differ from these previous frameworks in that they are designed to support large numbers of customer-oriented applications, each with different quality of service (QoS) requirements and resource consumption characteristics. As resource providers, mobile devices can join and leave the distributed computing environment unpredictably. This interrupts the undergoing operation, and the delay or failure of completing the operation may cause a system failure. In this paper, we propose a monitoring technique based on Load Balancing.
Survey of Stochastic-Based Computation Paradigms

Effectively tackling the upcoming “zettabytes” data explosion requires a huge quantum leap in our computing power and energy efficiency. However, with the Moore's law dwindling quickly, the physical limits of CMOS technology make it almost intractable to achieve high energy efficiency if the traditional “deterministic and precise” computing model still dominates. Worse yet, the upcoming data explosion mostly comprises statistics gleaned from uncertain, imperfect real-world environment. As such, the traditional computing means of first principles modeling or explicit statistical modeling will very likely be ineffective to achieve flexibility, autonomy, and human interaction. The bottom line is clear: given where we are headed, the fundamental principle of modern computing-deterministic logic circuits can flawlessly emulate propositional logic deduction governed by Boolean algebra-has to be reexamined, and transformative changes in the foundation of modern computing must be made. This paper surveys some of the most important works on stochastic-based computing. We specifically focus on four important research areas: (1) random number generation, (2) stochastic computing, (3) stochastic electronics, and (4) emerging device technology and its potential application in stochastic computing. All these research works share two distinctive features. First, they embrace and exploit quantum-induced randomness as an invaluable information carrier, not the “villain of correct computation” to be suppressed. Second, the theoretical foundation underpinning most of these works are based on neither the Boolean algebra in digital circuit nor the nonlinear amplifying and filtering in analog circuit. Instead, it tightly brings together the algorithmatic essence of computing and the quantum-induced randomness through the powerful framework of stochastic-based computing transformation.
Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun's Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.
An Agent-Based Integrated Self-Evolving Service Composition Approach in Networked Environments

Service composition is an important research problem in service computing systems, which combines simple and individual services into composite services to fulfill users' complex requirements. Service composition usually consists of four stages, i.e., service discovery, candidate selection, service negotiation and task execution. In self-organising systems, there is the fifth stage of service composition: self-evolution. Most of existing works study only some of the five stages. However, these five stages should be systematically studied so as to develop an integrated and efficient service composition approach. Against this background, this paper proposes an agent-based integrated self-evolving service composition approach. This approach systematically takes the five stages of service composition into consideration. It is also decentralised and self-evolvable. Experimental results demonstrate that the proposed approach can achieve almost the same success rates while uses much less communication overhead and time consumption in comparison with three existing representative approaches.
Nonabelian Cohen–Lenstra moments

In this article, we give a conjecture for the average number of unramified G-extensions of a quadratic field for any finite group G. The Cohen–Lenstra heuristics are the specialization of our conjecture to the case in which G is abelian of odd order. We prove a theorem toward the function field analogue of our conjecture and give additional motivations for the conjecture, including the construction of a lifting invariant for the unramified G-extensions that takes the same number of values as the predicted average and an argument using the Malle–Bhargava principle. We note that, for even |G|, corrections for the roots of unity in Q are required, which cannot be seen when G is abelian.