Reviewerly/exHarmony huggingface dataset:
Boosted hunting-based fruit fly optimization and advances in real-world problems

Fruit fly optimization algorithm (FOA) is a well-established meta-heuristic method with a clear core concept, a straightforward computation, and a code framework that is easy to build. In the case of large scale and multifaceted practical problems, the optimization effect of FOA may be unsatisfactory, and it is prone to stagnation. In this paper, to enrich the exploration and exploitation capability of the classic FOA, an effective whale-inspired hunting strategy is introduced to replace the random search plan of the original FOA, which we named it as WFOA. The proposed WFOA is compared with 9 state-of-the-art FOA's variants on a comprehensive set of 23 benchmark set and 30 IEEE CEC 2014 functions and advanced algorithms on a set of 21 test functions to validate its effectiveness. In addition, the effectiveness of WFOA is also verified on 20 IEEE CEC 2011 benchmark problems for tackling real-world problems. The statistical data shows that developed components effectively expand the exploration and exploitation capacity of the original FOA.
Data-driven anticipatory governance. Emerging scenarios in<i>data for policy</i>practices

The contemporary technological advancements in information and communication technologies (ICT) enable the employment of non-traditional data sources (e.g. satellite data, sensors, cell phone networks data, social media, etc.) in different aspects of the public sphere. Datafication is changing the relationship between governments and citizens, and the way governments address policy problems. Nowadays, policy-makers are urged to harness data for policies and public service design, while answering at the same time the demand for citizen engagement; as a consequence, innovative government/governance models appeared to connect these two instances. Although it is not a new concept, the model of Anticipatory Governance is particularly worth considering in light of contemporary data availability. Predictive analytics based on data increasingly realizes predictions for public action, although it presents many controversial implications (e.g. the epistemology of data evidence, public trust and privacy). In this article, we address Anticipatory Governance models emerging from data used in futures thinking and policy-making. To understand this phenomenon, we will briefly retrace current paradigms of futures thinking and Anticipatory Governance concerning policy-making, specifying the contemporary perspective design has on these topics. Then, we identify the use of data in futures thinking practices through a systematic literature search. Finally, we will address the challenges and implications of designing data-driven Anticipatory Governance by portraying three scenarios supported by real cases of data for policy-making.
An IoT Analytics Approach for Weather Forecasting using Raspberry Pi 3 Model B+

Stations for observing the weather are built, to collect meaningful and quantifiable data from the weather conditions in an expanse. The ever-changing weather makes it a concern for us, to make certain, that the weather conditions in an environment today are monitored and is of much significance. In this paper, a conceivable solution for an IoT based climate forecast system. The design implemented, primarily makes use of a Raspberry Pi 3 Model B, some sensors, and a weather forecast algorithm. Air pressure and temperature recordings values are used to predict the weather. One of the major goals was to discover an economical and purposeful solution for online and instant weather observing and prediction system for an expanse, and were able to carry that out by using the sensors in combination with the Raspberry Pi.
SIKE’d Up: Fast Hardware Architectures for Supersingular Isogeny Key Encapsulation

In this work, we present a fast parallel architecture to perform supersingular isogeny key encapsulation (SIKE). We propose and implement a fast isogeny accelerator architecture that uses fast and parallelized isogeny formulas. On top of our isogeny accelerator, we build a novel architecture for the SIKE primitive, which provides both quantum and IND-CCA security. We synthesized this architecture on the Xilinx Artix-7, Virtex-7, and Kintex UltraScale+ FPGA families. Over Virtex-7 FPGA's, our constant-time implementations are roughly 14% faster than the state-of-the-art with a better area-time product. At the NIST security level 5 on a Kintex UltraScale+ FPGA, we can execute the entire SIKE protocol in 15.3 ms. This work continues to improve the speed of isogeny-based computations and also features all parameter sets of the SIKE round 2 specification, with results applicable to NIST's post-quantum standardization process.
Federated Generative Privacy

We propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw private artificial data samples and empirically assess the risk of information disclosure. Our experiments show that FedGP is able to generate labeled data of high quality to successfully train and validate supervised models. Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.
Rigorous engineering of collective adaptive systems: special section

Abstract An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on “Rigorous Engineering of Collective Adaptive Systems.” It presents the seven contributions of the section and gives a short overview of the field of rigorously engineering collective adaptive systems by structuring it according to three topics: systematic development, methods and theories for modelling and analysis, and techniques for programming and operating collective adaptive systems.
Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns

This article examines the connectedness between Bitcoin returns and returns of ten additional cryptocurrencies for several frequencies—daily, weekly, and monthly—over the period January 2015–March 2020 using a nonlinear autoregressive distributed lag (NARDL) approach. We find important and positive interdependencies among cryptocurrencies and significant long-run relationships among most of them. In addition, non-Bitcoin cryptocurrency returns seem to react in the same way to positive and negative changes in Bitcoin returns, obtaining strong evidence of asymmetry in the short run. Finally, our results show high persistence in the impact of both positive and negative changes in Bitcoin returns on most of the other cryptocurrency returns. Thus, our model explains about 50% of the other cryptocurrency returns with changes in Bitcoin returns.
GasFuzzer: Fuzzing Ethereum Smart Contract Binaries to Expose Gas-Oriented Exception Security Vulnerabilities

Ethereum is a kind of blockchain platform where developers may develop and run programs called smart contracts. It inherently relies on gas consumption within a specified allowance to constrain code execution, making every instruction along an execution path to be a location for raising an exception. In this paper, we present GasFuzzer, the first work in exploring the effects of gas allowance manipulation to expose gas-oriented exception security vulnerabilities. GasFuzzer consists of two phases. The first phase introduces a gas-greedy strategy to favor transactions having higher gas consumption for mutation to obtain test transactions with different gas consumptions. The second phase introduces a novel notion of fractional gas consumption coverage and a novel gas-leveling strategy. It applies them to mutate the gas allowances of some of these transactions resulting in the highest gas consumptions produced in the first phase followed by applying these allowance-mutated transactions together with those which remained non-mutated to fuzz test the smart contract. We report an evaluation of GasFuzzer via an experiment on 3170 real-world smart contracts deployed on the public Ethereum Blockchain between October 2017 and July 2019. The findings show that GasFuzzer with gas-greedy strategy can detect more Exceptions Disorder kind of security vulnerabilities (7 more cases) than the previous state-of-the-art black-box fuzzer, and GasFuzzer with gas-leveling strategy and gas coverage criterion can detect 6 additional cases of Exceptions Disorder security vulnerabilities, which is significant.
Verifiable Self-Aware Agent-Based Autonomous Systems

potentially have verifiable, self-aware behavior.
IoT Enabled Quality of Experience Measurement for Next Generation Networks in Smart Cities

Total internet traffic has witnessed unprecedented growth over the last two decades and this exponential trend is projected to continue. Future cities are expected to be smart and billions of devices will be connected forming a network. The emergence of the Internet of Things proffers to link sensors enabled digital devices with the internet thus offering new services in the context of smart cities. In this regard, it is crucial for the internet service providers (ISPs) to observe/monitor their end-to-end network performance and identify any possible service degradation(s) as the goal of smart cities can only be realized through good quality internet. In this paper, we demonstrate our hardware-software co-designed quality of experience (QoE) management solution for ISPs. Our system is capable of monitoring and reporting the web, video, VoIP, IPTV, and WiFi QoE at various network levels. We experimentally tested our solution at appropriate network locations and demonstrated QoE and latency results for one of the leading ISP of South Asia. Our solution enables the ISPs to remotely monitor the quality of their network and identify any issue(s) which can lower the customer's QoE. As the internet is the backbone of smart cities, we believe our proposed solution for QoE will help expedite the smart cities’ infrastructure and solutions.
Parrondo effect in quantum coin-toss simulations

Game A + Game B = Game C. Parrondo's games follow this basic structure where A and B are losing games and C is a winning game-a phenomenon called Parrondo's paradox. These games can take on a wider class of definitions and exhibit these paradoxical results. In this paper, we show three paradoxical cases. (i) The successive "tossing" of a single fair quantum coin gives a biased result, a previously known result. (ii) The random tossing of two quantum coins, each with successive biased expectations, gives an average random walk position of approximately zero. (iii) The sequential periodic tossing of two quantum coins, each with successive negative biased expectations, gives an average random walk with positive expectation. Using these results, we then propose a protocol for identifying and classifying quantum operations that span the same Hilbert space for a two-level quantum system.
Employees With Autism Spectrum Disorders in the Digitized Work Environment: Perspectives for the Future

The aim of this article is to examine the opportunities for employees with autism spectrum disorders (ASDs), arising from digital technology (DT) development. The author discusses assistive technology (AT) as a mean of creating a better work environment, making the digitized workplace more friendly for people with ASD. A possible solution of communication problems is replacing the interpersonal communication between employees with electronic (non-direct) forms of communication, such as online communicators or chatbots. Another solution is the implementation of wearable electronic systems, monitoring stress levels and facilitating effective stress control. In the future, the whole digitized workplace could be designed according to a “smart workplace” concept. Sensors recording various human body parameters could be connected to a network with sensors recording physical parameters of the work environment (temperature, humidity, noise, smell, sunlight exposure) and also with controllers of its values, adjusting it dynamically to reduce distracting factors. As a result, communication, stress management, and sensory sensitiveness problems could be limited, improving the work comfort of people with ASD, and their colleagues. The pertinence of solutions proposed was also confirmed by the experts interviewed in the field who were asked to assess it in the context of future implementation.
Deep Collaborative Discrete Hashing With Semantic-Invariant Structure Construction

While deep hashing has made great progress in large-scale multimedia retrieval, most of the existing approaches under-explore the semantic correlations and neglect the effect of context-aware visual learning. In this paper, we propose a dual-stream learning framework, termed as Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantics. Specifically, DCDH generates context-aware representations by employing the outer product of visual embeddings and semantic encodings. To further preserve the original semantics and alleviate the class imbalance problem, we introduce the focal loss to take advantage of frequent and rare concepts. Furthermore, a common binary code space is constructed based on the joint learning of the visual representations, the context-aware representations, and the label distribution calibration. Three losses, i.e., the pairwise similarity loss, the quantization loss, and the balanced classification loss, are collaboratively optimized in the general learning framework of DCDH. Extensive experiments conducted on three large-scale benchmark datasets demonstrate the superiority of the proposed method, yielding the state-of-the-art image retrieval performance.
Exploratory differential ant lion-based optimization

In this work, an improved alternative method of the ant lion optimizer (ALO), integrating opposition-based training with two practical operators on the basis of differential evolution, named MALO, is proposed to cope with the implied weaknesses of classical ALO. Firstly, opposition-based practice is adopted into the ALO to prevent it from the searching deflation and obtain a faster convergence rate. Besides, two more operators, mutation and crossover strategies are implemented to further improve the local searching efficiency of the agents. Additionally, to verify the effectiveness of the enhanced process, comparison with existing optimizers was conducted for different benchmark functions with different qualities likewise unimodal, multimodal, and fixed-dimensional multimodaltasks were also carried out. Moreover, the extensibility test is, undertaken to assess the dimensional influence on problem consistency and optimization quality. Furthermore, the enhanced method is exploited to crack three practical, well-known constrained optimization problems, including spring plan, the concern of the welded beam case and the subject of a pressure vessel. The findings show that the introduced strategies will significantly enhance ALO's capability in optimizing different tasks. Promisingly, the proposed approach can be viewed as an efficient and effective strategy for more optimization scenarios.
Blockchain-based two-party fair contract signing scheme

A fair contract signing scheme ensures that contract signing participants can fairly exchange the digital signatures of the contract, which has a wide range of applications on the Internet. Verifiable Encrypted Signature (VES) can be used as a fair exchange mechanism for digital signatures, in which it requires a centralized Trusted Third Party (TTP) as an adjudicator. However, there are many security challenges with the centralized TTP. For example, it may disclose the contents of the contract, collude with others, or even occurs a service interruption. To address those problems, in this paper, we propose a two-party fair contract signing scheme based on Ethereum smart contract, which allows participants to fairly perform the contract signing procedures on the blockchain. Specifically, we propose a modified VES scheme in which no centralized adjudicator is needed. Then we design the fair contract signing scheme based on the modified VES scheme. We leverage the Ethereum smart contract technology to ensure fairness, which has the characteristics of decentralization, verifiability, autonomy, efficiency, and tampering resistant. The theoretical analysis and experimental results show that our scheme is secure and feasible.
Improving Virtual Reality Ergonomics Through Reach-Bounded Non-Linear Input Amplification

Input amplification enables easier movement in virtual reality (VR) for users with mobility issues or in confined spaces. However, current techniques either do not focus on maintaining feelings of body ownership, or are not applicable to general VR tasks. We investigate a general purpose non-linear transfer function that keeps the user's reach within reasonable bounds to maintain body ownership. The technique amplifies smaller movements from a user-definable neutral point into the expected larger movements using a configurable Hermite curve. Two experiments evaluate the approach. The first establishes that the technique has comparable performance to the state-of-the-art, increasing physical comfort while maintaining task performance and body ownership. The second explores the characteristics of the technique over a wide range of amplification levels. Using the combined results, design and implementation recommendations are provided with potential applications to related VR transfer functions.
Callisto: Capturing the "Why" by Connecting Conversations with Computational Narratives

When teams of data scientists collaborate on computational notebooks, their discussions often contain valuable insight into their design decisions. These discussions not only explain analysis in the current notebook but also alternative paths, which are often poorly documented. However, these discussions are disconnected from the notebooks for which they could provide valuable context. We propose Callisto, an extension to computational notebooks that captures and stores contextual links between discussion messages and notebook elements with minimal effort from users. Callisto allows notebook readers to better understand the current notebook content and the overall problem-solving process that led to it, by making it possible to browse the discussions and code history relevant to any part of the notebook. This is particularly helpful for onboarding new notebook collaborators to avoid misinterpretations and duplicated work, as we found in a two-stage evaluation with 32 data science students.
It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community

While the majority of research in chatbot design has focused on creating chatbots that engage with users one-on-one, less work has focused on the design of conversational agents for online communities. In this paper we present results from a three week test of a social chatbot in an established online community. During this study, the chatbot "grew up" from "birth" through its teenage years, engaging with community members and "learning" vocabulary from their conversations. We discuss the design of this chatbot, how users' interactions with it evolved over the course of the study, and how it impacted the community as a whole. We discuss how we addressed challenges in developing a chatbot whose vocabulary could be shaped by users, and conclude with implications for the role of machine learning in social interactions in online communities and potential future directions for design of community-based chatbots.
Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

The simultaneous growth of textual data and the advancements within Text Analytics enables organisations to exploit this kind of unstructured data, and tap into previously hidden knowledge. However, the utilisation of this valuable resource is still insufficiently unveiled in terms of transport policy decision-making. This research aims to further examine the potential of textual big data analytics in transportation through a real-life case study. The case study, framed together with the Danish Road Directorate or Vejdirektoratet, was designed to assess public opinion towards the adoption of driverless cars in Denmark. Traditionally, the opinion of the public has often been captured by means of surveys for the problem owner. Our study provides demonstrations in which opinion towards the adoption of driverless cars is examined through the analysis of newspaper articles and tweets using topic modelling, document classification, and sentiment analysis. In this way, the research attends to the collective as well as individualised characteristics of public opinion. The analyses establish that Text Analytics may be used as a complement to surveys, in order to extract additional knowledge which may not be captured through the use of surveys. In this regard, the Danish Road Directorate could find the usefulness while understanding the barriers in the results generated from our study, for supplementing their future data collection strategies. However there are also some methodological limitations that need to be addressed before a broader adoption of textual big data analytics for transport policy decision-making may take place.
Patriarchy, Maternal Health and Spiritual Healing

We examine the opportunities and challenges in designing for maternal health in low-income, low-resource communities in patriarchal and religious contexts. Pakistan faces a crisis in maternal health with a maternal mortality ratio of 178 deaths per 100,000 live births, as compared to the developed-country average of just 12 deaths per 100,000. Through a 6-month long qualitative, empirical study we examine the prevalent beliefs and practices around maternal health in Pakistan, the access women have to health-care, the existing religious practices that influence them and the agency they exert in their own health-care decision making. We reveal the rampant misinformation among mothers and health workers, house-hold power dynamics that impact maternal health and the deep link between maternal health and religious beliefs. We also show how current maternal health care interventions fit poorly into this context and discuss alternate design recommendations for meeting the maternal health needs of these women.
Therminator: Understanding the Interdependency of Visual and On-Body Thermal Feedback in Virtual Reality

Recent advances have made Virtual Reality (VR) more realistic than ever before. This improved realism is attributed to today's ability to increasingly appeal to human sensations, such as visual, auditory or tactile. While research also examines temperature sensation as an important aspect, the interdependency of visual and thermal perception in VR is still underexplored. In this paper, we propose Therminator, a thermal display concept that provides warm and cold on-body feedback in VR through heat conduction of flowing liquids with different temperatures. Further, we systematically evaluate the interdependency of different visual and thermal stimuli on the temperature perception of arm and abdomen with 25 participants. As part of the results, we found varying temperature perception depending on the stimuli, as well as increasing involvement of users during conditions with matching stimuli.