Reviewerly/exHarmony huggingface dataset:
Maximal Distance Discounted and Weighted Revisit Period: A Utility Approach to Persistent Unmanned Surveillance

Autonomous unmanned vehicles are well suited for long-endurance, persistent intelligence, surveillance and reconnaissance (PISR) missions. In order to conduct missions, vehicles must implement a method of task selection. We propose the Maximal Distance Discounted &amp; Weighted Revisit Period ([Formula: see text]) utility function as a solution. We derive [Formula: see text] as a zeroth-order approximation to an infinite horizon solution of PISR when formulated as a dynamic programming (DP) problem. We then use the DP solution to develop a heuristic utility function for autonomous task selections, with the goal of minimizing the prioritized revisit time to each task. Our function adapts to different task maps and task priorities, is scalable in the number of tasks, and is robust to the ad-hoc addition or removal of tasks. We demonstrate how the [Formula: see text] parameters influence vehicle behavior. We also prove that the policy results in steady-state task selections that are periodic and that such periodicity occurs regardless of initial conditions. We then demonstrate periodicity via numerical simulations on a set of test scenarios. We present a two-step heuristic methodology for selecting utility function parameters that deliver empirically good performance, which we demonstrate through a simulation-based comparison to a single-vehicle Traveling Salesman Problem (TSP) solution. The comparisons are based on four sample task maps designed to resemble operational scenarios.
Towards integration of user-centered designed tutorials for better virtual reality immersion

Virtual reality (VR) has been contributing to education, health sciences and entertainment of late. The technology has made itself flexible enough to cater to users of different ages. Additionally head-mounted displays (HMDs) has led to the increase of immersion in the VR environment. Yet, it is sometimes hard for the users to get engaged if not instructed properly to on how to interact with the environment, adding stress to what was expected to be an entertaining experience. Hands-on instructions about environment before VR exposure is common in VR commercial studios. Also, VR game developers after completing their game, put tutorial sessions for the users to understand how the game works. In this paper, we suggested that there is a need for immersive user-center designed (UCD) tutorials for the VR games, including other high interaction and engagement environments. This approach allows users to get hands-off experience and sense of achievement through well-descriptive and interactive tutorials before going into the full VR Immersion.
Stimulight

Prolonged sitting at the workplace is a growing public health concern. In this paper, we propose the activity-focused design framework which provides an overview of recent work in HCI to stimulate physcial activity or to reduce sedentary behavior. Next, based on this framework, we present Stimulight, an intelligent system designed to explore the effect of providing personal and/or social feedback on the activity pattern of office workers. To test the intuitiveness of the feedback modalities of our design, three different feedback conditions were explored in a lab study with 61 participants. Our results show a positive effect of visualizing and sharing physical activity patterns with co-workers. Based on our findings, we present design implications and offer perspectives for future work on how to use social feedback mechanisms to encourage social interaction in the workplace to enhance physically active behavior among office workers.
Conceptual Framework for Enhancing Payroll Management and Attendance Monitoring System through RFID and Biometric

Business is basically divided into many functional areas but one that is crucial and of utmost importance is people Management department i.e. Human Resource. Payroll Management in any organization is a very crucial task. Only if the employees get their salary on time, they shall be motivated to work. Though the attendance is Biometric, still there is human intervention in case of leave management that consumes a lot of time. Performance appraisal is the most difficult and most important aspect of HR. There is a lot of document review work which takes a lot of time and also very cumbersome. Our paper is an attempt to provide a model where Data Science can help to implement the process for data collection, data analysis, data cleaning, and data processing can be done.Our paper is an attempt to introduce the concept of RFID based communication; voice data based analysis with biometric-based properties. This will collectively help to improve payroll management system, leave management system and performance appraisal also. Although the proposed model in the paper has further improvement scope also, at the same time it provides an efficient approach for to smoothly implement the payroll management system with Biometric and RFID technique.
Can Privacy-Aware Lifelogs Alter Our Memories?

The abundance of automatically-triggered lifelogging cameras is a privacy threat to bystanders. Countering this by deleting photos limits relevant memory cues and the informative content of lifelogs. An alternative is to obfuscate bystanders, but it is not clear how this impacts the lifelogger's recall of memories. We report on a study in which we compare viewing 1) unaltered photos, 2) photos with blurred people, and 3) a subset of the photos after deleting private ones, on memory recall. Findings show that obfuscated content helps users recall a lot of content, but it also results in recalling less accurate details, which can sometimes mislead the user. Our work informs the design of privacy-aware lifelogging systems that maximizes recall and steers discussion about ubiquitous technologies that could alter human memories.
Personalized POI recommendation based on check-in data and geographical-regional influence

Nowadays, many people like to share the places they visited to their friends in the location-based social networks (LBSNs). Therefore, LBSNs have accumulated large-scale user check-in data and the availability of these data enables many location-based services to users. As a location-based service, point-of-interests (POI) recommendation can provide services to people and promote merchants. Many researchers utilize user-based collaborative filtering and geographical influence for POI recommendation. However, existing studies have two limitations: (1) when modeling user-based CF, users' POI preferences are not fully considered; (2) when modeling geographical influence, geographical features have not been explored deeply. In this paper, we propose a POI recommendation approach by improved user-based CF and geographical-regional influence. Firstly, we construct the user-POI matrix by normalized check-in frequencies, which can effectively represent user preferences. Secondly, we find that each user's check-in POIs can be divided into several regions. Accordingly, we integrate geographical influence with the regional feature to produce recommendation. Finally, we utilize a unified framework to combine improved user-based CF with geographical-regional influence for POI recommendation. Our experimental results on real-world dataset show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.
High-Speed True Random Number Generator Based on Differential Current Starved Ring Oscillators with Improved Thermal Stability

True random number generator (TRNG) is a hardware security primitive that has been widely used in cryptography, Monte-Carlo simulation, video games, etc. with increasing significance in security solutions addressing emerging cyber-physical threats. This paper presents a new design of TRNG based on temperature insensitive frequency deviation extracted from a pair of matched current starved ring oscillators (CS-ROs). The proposed TRNG can maintain the entropy rate of the output bit stream over a broad range of temperatures. The CS-RO generates larger jitter noise than the regular ring oscillator, but consumes much less power for the same number of inverter stages. Monte Carlo simulated results based on a commercial 65 nm 1.2V CMOS process technology shows that it can output a random bit sequence at a rate of 485 Mbps with only 1.52 pJ of energy dissipation per bit. The proposed TRNG passes the NIST cryptographic randomness tests and autocorrelation test for the bit streams generated at temperature ranges from −40°C to 120°C.
Analysis of Bitcoin Exchange Using Relationship of Transactions and Addresses

This bitcoin system is a system made to support transactions between users without the help of any financial institution. The Bitcoin Exchange is a service that enables exchanges of bitcoins mined by others with cash. To use this service, the miner should send bitcoins from his bitcoin wallet to the deposit address provided by the Exchange. The bitcoins delivered to the deposit address are automatically moved to one of the Exchange's bitcoin addresses by the Exchange system. Since what is known to the user is only the deposit address for the user, the user can only guess that the Exchange has taken the bitcoins from his deposit address when he/she sees the transfer of his/her bitcoins to another address. We will present a method to find out a few bitcoin addresses through preliminary work and find out the associated Exchange addresses using the characteristics of the deposit addresses and system addresses of the Exchange through transactions with the relevant addresses on the blockchain.
Detecting Anomalous Network Traffic in IoT Networks

Network operators need effective tools to quickly detect anomalies in traffic data for identifying network attacks. In contrast to traditional Internet, detection of anomalous network traffic in IoT (Internet of Things) networks is becoming a challenge task due to limited network resources and performance. Comprehensive detection methods are no longer effective for IoT networks, calling for developing lightweight solutions. Principal Component Analysis (PCA) techniques can help to reduce computing complexity, thus, anomaly detection techniques based on PCA received a lot of attention in the past. However, PCA techniques could not be directly applied to IoT networks with constrained resources and limited performance. This paper investigates PCA techniques for detecting anomalous network traffic in IoT networks. We propose a novel detection scheme with two levels using PCA techniques. The first level is for quick detection with few principal components while the second level is for detailed detection with a number of principal components. We investigate the selection of parameters in a distance calculation formula using several experiments to show the feasibility of our proposed scheme.
Efficient Comparison and Swap on Fully Homomorphic Encrypted Data

Fully homomorphic encryption (FHE) allows arbitrary computations to be performed directly on encrypted data for ensuring the security of cloud computing. In contrast to encrypting the plaintext in bit level as done in existing works, this paper explores how to reduce the computation complexity of encrypted data by adopting the concept of aggregate plaintext and proposes an efficient scheme to handle the comparison and swap operation, which is commonly used for sorting and searching in cloud computing. Experimental results reveal that employing the proposed scheme can not only reduce the size of required FHE data, but also improve the total computation time of the chosen operation. For 32-bit data comparison, the proposed one can operate 2.3 times faster and achieve about 52 times reduction in the required FHE data size as well as the transmission bandwidth to the cloud in comparison to the related work.
National currency, world currency, cryptocurrency: A Fichtean approach to the Ethics of Bitcoin

Abstract I investigate ethical questions concerning a novel cryptocurrency, Bitcoin, using a Fichtean account of the ethics of currency. Fichte holds that currencies should fulfill an ethical purpose: providing access, in perpetuity, to the material welfare that underwrites citizens' basic rights. In his nineteenth‐century context, Fichte argues that currencies fulfill this purpose better when nations control them (i.e., when they are “national currencies”) than when foreigners freely trade them (as “world currencies”). After exploring conditions in which national currencies fail to secure material stability over time, e.g., in corrupt regimes, I develop a Fichtean model for ethically evaluating currencies and evaluate the extent to which Bitcoin meets its standards for ethical currency. I argue that Bitcoin undermines the (monetary) power of nations and, as such, threatens their ability to provide access to necessary material goods. While offering citizens a means of defending themselves against corrupt regimes, Bitcoin forsakes the general welfare and is, as such, unethical by Fichtean lights.
An industrial evaluation of data access techniques for the interoperability of engineering software tools

New industrial initiatives such as Industrie 4.0 rely on digital end-to-end engineering across the entire product lifecycle, which in turn depends on the ability of the supporting software tools to interoperate. A tool interoperability approach based on Linked Data, and the OASIS OSLC standard, has the potential to provide such integration, where each software tool can expose its information and services to other tools using the web as a common technology base. In this paper, we report on our negative findings when attempting to use existing ontology-based Linked Data access techniques to expose and manipulate the structured content managed in engineering tools. Such techniques typically target the Data Access Layer (DAL) of a tool to access and manipulate its content, with the assumption that sufficient information and control is available within this layer to automate the process. Based on a case study with the truck manufacturer Scania CV AB, our study finds that an engineering tool controls its artefacts using business logic that is not necessarily reflected at the data layer. This renders such ontology-based access techniques inadequate. Instead we propose an alternative Linked Data extraction architecture that can mitigate the identified shortcomings. While less automated compared to the existing solutions, the proposed architecture is realised as a standalone library that can still facilitate the extraction process.
Voice Control Device using Raspberry Pi

This paper shows the working of a device based on implementation of a voice command system as an intelligent personal assistant. The services provided by the device depends on the input given in the form of voice command by the user and ability to access information from a variety of online sources such as weather, telling time or accessing online applications to listen to music. This Voice driven device uses Raspberry Pi as its main hardware. Speech to text engine is used to convert the voice command to simple text. Query processing is then applied using natural language processing (NLP) onto this text to interpret the intended meaning of the command given by the user. After interpreting the intended meaning, text to speech conversion is used to give appropriate output in the form of speech. This device might provide a platform to visually impair to do their day to day tasks more easily like listening to music, checking weather conditions, checking current time or even doing a simple mathematical calculation. Many experiments and results were accomplished and documented.
Continuous software engineering and its support by usage and decision knowledge: An interview study with practitioners

Abstract Continuous software engineering (CSE) emerged as a process that is increasingly applied by practitioners. However, different perceptions impede its adoption in industry. Furthermore, opportunities through utilizing usage and decision knowledge remain unexploited. We conducted a semi‐structured interview study with 24 practitioners from 17 companies to study how practitioners apply CSE during software evolution and how usage and decision knowledge can support CSE. Regarding the application of CSE, we identified five perspectives on CSE with tool‐ and methodology‐driven definitions most prevalent. Automated tests, involved users, and shared rulesets are perceived as most relevant for CSE. Practitioners report more positive than negative experiences; however, more than half of their responses were neutral. Practitioners' future plans for CSE focus on enhancement, expansion, and on‐demand adaption. Regarding the integration of usage and decision knowledge into CSE, practitioners perceive accountability and traceability as major benefits, while raising concerns about its feasibility and user groups. As short‐term extensions, practitioners expect improvements regarding automation and role aspects, while long‐term additions to integration and experimentation capabilities are demanded. We conclude that CSE remains partially difficult to capture for practitioners, while their attitude toward integrating usage and decision knowledge into CSE is positive.
Formation an informative description of recognizable objects

The formation of a characteristic space in classification problems can be divided into two stages: the choice of the initial description of objects and the formation of an informative description of objects on the basis of a reduction in the dimension of the space of the original description.
Outstanding

Room-scale virtual reality games allow players to experience an unmatched level of presence. A major reason is the natural navigation provided by physical walking. However, the tracking space is still limited, and viable alternatives or extensions are required to reach further virtual destinations. Our current work focuses on traveling over (very) large distances--an area where approaches such as teleportation are too exhausting and WIM teleportations potentially reduce presence. Our idea is to equip players with the ability to switch from first-person to a third-person god-mode perspective on demand. From above, players can command their avatar similar to a real-time strategy game and initiate travels over large distance. In our first exploratory evaluation, we learned that the proposed dynamic switching is intuitive, increases spatial orientation, and allows players to maintain a high degree of presence throughout the game. Based on the outcomes of a participatory design workshop, we also propose a set of extensions to our technique that should be considered in the future.
Animations in Cross-Platform Mobile Applications: An Evaluation of Tools, Metrics and Performance

Along with the proliferation of high-end and performant mobile devices, we find that the inclusion of visually animated user interfaces are commonplace, but that research on their performance is scarce. Thus, for this study, eight mobile apps have been developed for scrutiny and assessment to report on the device hardware impact and penalties caused by transitions and animations, with an emphasis on apps generated using cross-platform development frameworks. The tasks we employ for animation performance measuring, are those of (i) a complex animation consisting of multiple elements, (ii) the opening sequence of a side menu navigation pattern, and (iii) a transition animation during in-app page navigation. We employ multiple performance profiling tools, and scrutinize metrics including frames per second (FPS), CPU usage, device memory usage and GPU memory usage, all to uncover the impact caused by executing transitions and animations. We uncover important differences in device hardware utilization during animations across the different cross-platform technologies employed. Additionally, Android and iOS are found to differ greatly in terms of memory consumption, CPU usage and rendered FPS, a discrepancy that is true for both the native and cross-platform apps. The findings we report are indeed factors contributing to the complexity of app development.
Tell Me More

In this study, we apply a "conversational search" to the design of the news service of smart speakers so that users can actively get richer information while listening to the news. We designed a research prototype called "Anchor," where a smart speaker news assistant provides users with news about specific keywords and responds to users' questions. We recruited 21 participants and conducted a user study where they consumed the news with Anchor, followed by post hoc interviews. The results of the qualitative analysis revealed the following. (1) People preferred interactive news to news briefings. (2) People found it useful to get answers on their questions by talking with the assistant. (3) Although users were allowed to ask questions anytime, they often hesitated, as they did not want to miss the whole flow of the news. (4) However, they had difficulty recalling the questions they had not asked. Based on these findings, we discuss the implications for news design in a voice-only user interface.
A Server-Side JavaScript Security Architecture for Secure Integration of Third-Party Libraries

The popularity of the JavaScript programming language for server-side programming has increased tremendously over the past decade. The Node.js framework is a popular JavaScript server-side framework with an efficient runtime for cloud-based event-driven architectures. One of its strengths is the presence of thousands of third-party libraries which allow developers to quickly build and deploy applications. These very libraries are a source of security threats as a vulnerability in one library can (and in some cases did) compromise an entire server. In order to support the secure integration of libraries, we developed N ODE S ENTRY , the first security architecture for server-side JavaScript. Our policy enforcement infrastructure supports an easy deployment of web hardening techniques and access control policies on interactions between libraries and their environment, including any dependent library. We discuss the design and implementation of N ODE S ENTRY and present its performance and security evaluation.
Brightness correction and shadow removal for video change detection with UAVs

Unmanned aerial vehicles (UAVs) on patrol that recognize scene changes are a powerful instrument in a lot of surveillance and reconnaissance applications, e.g., protection of convoys from improvised explosive devices (IEDs) by early detecting deployment traces, intruder or manipulation detection for real estates and military camps, or danger recognition and assessment in disaster monitoring. For that purpose, a visual-optical camera records video streams in which an automatic video change detection computes relevant or suspicious changes between two patrols. The main challenge is to identify true scene changes, i.e. to filter out changes caused by camera brightness variations, illumination changes due to cloudiness or time of day, or varying shadows cast by clouds or scene objects. Different partly overlapping or orthogonal solutions for all that, like chromatic adaptation, local mean adjustment, retinex, intensity equalization, and CIE Lab shadow removal, are evaluated and advantages/disadvantages are compared. Finally, the optimal method selection and combination is determined in order to maximize the change detection performance. Depicted results document the reached progress.