Reviewerly/exHarmony huggingface dataset:
Encrypted polynomial control based on tailored two‐party computation

Summary Encrypted control enables confidential controller evaluations in cloud‐based or networked control systems. Roughly speaking, an encrypted controller is a modified control algorithms that is capable of computing encrypted control actions based on encrypted system states. Encrypted control has been realized using different tools from cryptography such as homomorphic encryption, secret sharing, and multiparty computation. However, the vast majority of existing encrypted controllers is linear or makes intensive use of encrypted linear operations. In this article, we present a novel and flexible method for the encrypted implementation of arbitrary polynomial controllers. Technically, our approach builds on tailored two‐party computation combined with secret sharing and additively homomorphic encryption.
Estimating the number of clusters via a corrected clustering instability

We improve instability-based methods for the selection of the number of clusters k in cluster analysis by developing a corrected clustering distance that corrects for the unwanted influence of the distribution of cluster sizes on cluster instability. We show that our corrected instability measure outperforms current instability-based measures across the whole sequence of possible k, overcoming limitations of current insability-based methods for large k. We also compare, for the first time, model-based and model-free approaches to determining cluster-instability and find their performance to be comparable. We make our method available in the R-package cstab.
Adaptive Feature Aggregation for Video Object Detection

Object detection, as a fundamental research topic of computer vision, is facing the challenges of video-related tasks. Objects in videos tend to be blurred, occluded, or out of focus more frequently. Existing works adopt feature aggregation and enhancement to design video-based object detectors. However, most of them do not consider the diversity of object movements and the quality of aggregated context features. Thus, they can not generate comparable results given blurred or crowded videos. In this paper, we propose an adaptive feature aggregation method for video object detection to deal with these problems. We introduce an adaptive quality-similarity weight, with a sparse and dense temporal aggregation policy, into our model. Compared with both image-based and video-based baselines on Im-ageNet and VIRAT datasets, our work consistently demonstrates better performance. Especially, our model improves the average precision of person detection in VIRAT from 85.93% to 87.21%. Several demonstration videos of this work are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
The Trade-Off of Virtual Reality Training for Dart Throwing: A Facilitation of Perceptual-Motor Learning With a Detriment to Performance

Advancements in virtual reality (VR) technology now allow for the creation of highly immersive virtual environments and for systems to be commercially available at an affordable price. Despite increased availability, this access does not ensure that VR is appropriate for training for all motor skills. Before the implementation of VR for training sport-related skills takes place, it must first be established whether VR utilization is appropriate. To this end, it is crucial to better understand the mechanisms that drive learning in these new environments which will allow for optimization of VR to best facilitate transfer of learned skills to the real world. In this study we sought to examine how a skill acquired in VR compares to one acquired in the real world (RW), utilizing training to complete a dart-throwing task in either a virtual or real environment. We adopted a perceptual-motor approach in this study, employing measures of task performance (i.e. accuracy), as well as of perception (i.e. visual symptoms and oculomotor behavior) and motor behaviors (i.e. throwing kinematics and coordination). Critically, the VR-trained group performed significantly worse in terms of throwing accuracy compared to both the RW-trained group and their own baseline performance. In terms of perception, the VR-trained group reported greater acute visual symptoms compared to the RW-trained group, though oculomotor behaviors were largely the same across groups. In terms of motor behaviors, the VR-trained group exhibited different dart-throwing kinematics during training, but in the follow-up test adapted their throwing pattern to one similar to the RW-trained group. In total, VR training impaired real-world task performance, suggesting that virtual environments may offer different learning constraints compared to the real world. These results thus emphasize the need to better understand how some elements of virtual learning environments detract from transfer of an acquired sport skill to the real world. Additional work is warranted to further understand how perceptual-motor behaviors are acquired differently in virtual spaces.
DoughNets: Visualising Networks Using Torus Wrapping

We investigate visualisations of networks on a 2-dimensional torus topology, like an opened-up and flattened doughnut. That is, the network is drawn on a rectangular area while "wrapping" specific links around the border. Previous work on torus drawings of networks has been mostly theoretical, limited to certain classes of networks, and not evaluated by human readability studies. We offer a simple interactive layout approach applicable to general graphs. We use this to find layouts affording better aesthetics in terms of conventional measures like more equal edge length and fewer crossings. In two controlled user studies we find that torus layout with either additional context or interactive panning provided significant performance improvement (in terms of error and time) over torus layout without either of these improvements, to the point that it is comparable to standard non-torus layout.
TS2FA: Trilateration System Two Factor Authentication

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor is something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In the existing 2FA system, a user is required to act in order to implement the second layer of authentication which is not very user-friendly. Smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cellphone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In the proposed research, a new factor is used - something that is in the user's environment with minimal user involvement. Data from these broadcast messages is utilized to implement a 2FA scheme by determining whether two devices are proximate or not to ensure that they belong to the same user.
Probabilistic Databases for All

In probabilistic databases the data is uncertain and is modeled by a probability distribution. The central problem in probabilistic databases is query evaluation, which requires performing not only traditional data processing such as joins, projections, unions, but also probabilistic inference in order to compute the probability of each item in the answer. At their core, probabilistic databases are a proposal to integrate logic with probability theory. This paper accompanies a talk given as part of the Gems of PODS series, and describes several results in probabilistic databases, explaining their significance in the broader context of model counting, probabilistic inference, and Statistical Relational Models.
Pac-Euglena: A Living Cellular Pac-Man Meets Virtual Ghosts

The advancement of biotechnology enabled the development of "biotic video games", where human players manipulate real biological samples for fun and educational human-biology interactions. However, new design principles are needed to both leverage and mitigate biological properties (e.g., variability and stochasticity), and create unique play experiences that transcend traditional video games. This paper describes the implementation of Pac-Euglena, a biotic Pac-Man analog, where players guide live microscopic Euglena cells with light stimuli through a physical microfluidic maze. Through use of multi-modal stimuli, a mixed biology-digital-human reality is achieved, enabling cell interactions with virtual ghosts and collectibles. Through an iterative design process, we illustrate challenges and strategies for designing games with living organisms. A user study (n=18, conducted at a university event) showed that Pac-Euglena was fun, stimulated curiosity, and taught users about Euglena. We conclude with five general guidelines for the design and development of biotic games and HBI interfaces.
Inferring Bug Signatures to Detect Real Bugs

Static tools like Findbugs allow their users to manually define bug patterns, so they can detect more types of bugs, but due to the complexity and variety of programs, it is difficult to manually enumerate all bug patterns, especially for those related to API usages or project-specific rules. Therefore, existing bug-detection tools (e.g., Findbugs) based on manual bug patterns are insufficient in detecting many bugs. Meanwhile, with the rapid development of software, many past bug fixes accumulate in software version histories. These bug fixes contain valuable samples of illegal coding practices. The gap between existing bug samples and well-defined bug patterns motivates our research. In the literature, researchers have explored techniques on learning bug signatures from existing bugs, and a bug signature is defined as a set of program elements explaining the cause/effect of the bug. However, due to various limitations, existing approaches cannot analyze past bug fixes in large scale, and to the best of our knowledge, no previously unknown bugs were ever reported by their work. The major challenge to automatically analyze past bug fixes is that, bug-inducing inputs are typically not recorded, and many bug fixes are partial programs that have compilation errors. As a result, for most bugs in the version history, it is infeasible to reproduce them for dynamic analysis or to feed buggy/fixed code directly into static analysis tools which mostly depend on compilable complete programs. In this paper, we propose an approach, called <small>DePa</small>, that extracts bug signatures based on accurate partial-code analysis of bug fixes. With its support, we conduct the first large scale evaluation on 6,048 past bug fixes collected from four popular Apache projects. In particular, we use <small>DePa</small> to infer bug signatures from these fixes, and to check the latest versions of the four projects with the inferred bug signatures. Our results show that <small>DePa</small> detected 27 unique previously unknown bugs in total, including at least one bug from each project. These bugs are not detected by their developers nor other researchers. Among them, three of our reported bugs are already confirmed and repaired by their developers. Furthermore, our results show that the state-of-the-art tools detected only two of our found bugs, and our filtering techniques improve our precision from 25.5 to 51.5 percent.
Awareness, Navigation, and Use of Feed Control Settings Online

Control settings are abundant and have significant effects on user experiences. One example of an impactful but understudied area is feed settings. In this study, we investigated awareness, navigation, and use of feed settings. We began by creating a taxonomy of feed settings on social media and search sites. Via an online survey, we measured awareness of Facebook feed settings. An in-person interview study then investigated how people navigated to and chose to set feed settings on their own feeds. We discovered that many participants did not believe ad personalization feed settings existed. Furthermore, we discovered a misalignment in the expectation and the function of settings, especially of ad personalization settings for many participants. Despite all participants struggling to find at least one setting, participants overall wanted to use settings: 94% altered at least one setting they encountered. From these results, we discuss implications and suggest design guidelines for settings.
A new Foe in biometrics: A narrative review of side-channel attacks

When in 1965 the British MI5 placed a microphone in the Egyptian embassy to listen to the clicks emitted by the internal rotors in their mechanical encrypting machine, a new foe to IT systems was born: side-channel attacks (SCAs). This attacking methodology takes advantage of physical measurements such as sound, time or power consumption, leaked from security systems, in order to disclose their secrets. Since that distant first successful attempt by James Bond’s colleagues, SCAs have become more and more sophisticated, targeting a wide range of security systems. Among them, over the last two decades, different works have analysed the vulnerabilities of biometric systems to these attacks. In spite of this few pioneering studies, compared to other technologies, the analysis of SCAs in biometrics is still in its infancy. The current article is an attempt to draw the attention of the community towards this potential threat by: placing SCAs within the overall context of biometric vulnerabilities, reviewing the state of the art, presenting possible countermeasures against them, and providing a quick look into the future regarding the challenges to be faced by biometrics to effectively confront this new adversary.
Verge-it: Gaze Interaction for a Binocular Head-Worn Display using Modulated Disparity Vergence Eye Movement

The Midas touch problem is a well-known problem in eye gaze interaction techniques. We present Verge-it as a Midas touch free input technique using modulated disparity vergence eye movement for a binocular head-worn display. We conducted a feasibility study under two different visual background conditions, namely dynamic background (TV) and static background (Wall) conditions. This study revealed a low false positive rate (TV: 0%, Wall: 2.10%) for Verge-it and acceptable performance.
I/O Efficient Approximate Nearest Neighbour Search based on Learned Functions

Approximate nearest neighbour search (ANNS) in high dimensional space is a fundamental problem in many applications, such as multimedia database, computer vision and information retrieval. Among many solutions, data-sensitive hashing-based methods are effective to this problem, yet few of them are designed for external storage scenarios and hence do not optimized for I/O efficiency during the query processing. In this paper, we introduce a novel data-sensitive indexing and query processing framework for ANNS with an emphasis on optimizing the I/O efficiency, especially, the sequential I/Os. The proposed index consists of several lists of point IDs, ordered by values that are obtained by learned hashing (i.e., mapping) functions on each corresponding data point. The functions are learned from the data and approximately preserve the order in the high-dimensional space. We consider two instantiations of the functions (linear and non-linear), both learned from the data with novel objective functions. We also develop an I/O efficient ANNS framework based on the index. Comprehensive experiments on six benchmark datasets show that our proposed methods with learned index structure perform much better than the state-of-the-art external memory-based ANNS methods in terms of I/O efficiency and accuracy.
Artifacts for Detecting Timestamp Manipulation in NTFS on Windows and Their Reliability

Timestamps have proven to be an expedient source of evidence for examiners in the reconstruction of computer crimes. Consequently, active adversaries and malware have implemented timestomping techniques (i.e., mechanisms to alter timestamps) to hide their traces. Previous research on detecting timestamp manipulation primarily focused on two artifacts: the $MFT as well as the records in the $LogFile. In this paper, we present a new use of four existing windows artifacts – the $USNjrnl, link files, prefetch files, and Windows event logs – that can provide valuable information during investigations and diversify the artifacts available to examiners. These artifacts contain either information about executed programs or additional timestamps which, when inconsistencies occur, can be used to prove timestamp forgery. Furthermore, we examine the reliability of artifacts being used to detect timestamp manipulation, i.e., testing their ability to retain information against users actively trying to alter or delete them. Based on our findings we conclude that none of the artifacts analyzed can withstand active exploitation.
Discovering Public Wi-Fi Vulnerabilities Using Raspberry pi and Kali Linux

Free wireless Internet is getting so common that we're starting to expect it pretty much everywhere we go from coffee shops to airports, which is great in a lot of ways but it also means there are more opportunities for hackers to fool People because anyone can sit down somewhere set up a mobile hotspot or wireless network of their own and give it a believable name like an airport name, and if someone use their network they can watch and record all the network traffic including any usernames password an credit card details. This report introduces testing public Wi-Fi security using Rasbperry pi and Kali Linux, by performing attacks including DNS Spoofing, Wi-Fi password Cracking, Man in the Middle and Evil Twin, it also discusses Public Wi-Fi vulnerabilities and how to prevent or avoid such attacks.
Phasking on Paper: Accessing a Continuum of PHysically Assisted SKetchING

When sketching, we must choose between paper (expressive ease, ruler and eraser) and computational assistance (parametric support, a digital record). PHysically Assisted SKetching provides both, with a pen that displays force constraints with which the sketcher interacts as they draw on paper. Phasking provides passive, "bound" constraints (like a ruler); or actively "brings" the sketcher along a commanded path (e.g., a curve), which they can violate for creative variation. The sketcher modulates constraint strength (control sharing) by bearing down on the pen-tip. Phasking requires untethered, graded force-feedback, achieved by modifying a ballpoint drive that generates force through rolling surface contact. To understand phasking's viability, we implemented its interaction concepts, related them to sketching tasks and measured device performance. We assessed the experience of 10 sketchers, who could understand, use and delight in phasking, and who valued its control-sharing and digital twinning for productivity, creative control and learning to draw.
GridAlert: Using a Sensor-Based Technology to Monitor Power Blackouts in Kenyan Homes

Power blackouts (outages) are a common occurrence in Kenyan households. They affect people's livelihoods, and damage their property (household electrical items). We explore the role of GridAlert-a sensor-based technology we designed-in monitoring power blackouts. We worked with local technicians to design GridAlert's housing and integrate GridAlert with Kenya's electricity infrastructure. Then, we used interview, observation, diary, and data logging methods to understand 18 households' experiences using the system. Our findings provide insights for using sensor-based technology to monitor power usage and blackouts in Kenyan households. We also present participants' thoughts about GridAlert's housing, and about how it influenced their actions when using the system. We use these findings to discuss design insights for power monitoring systems, and to offer new perspectives on the role of technology in monitoring blackouts in Kenyan households.
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem

In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with $$1\,000$$ nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.
Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach

Knowledge about the software used in scientific investigations is necessary for different reasons, including provenance of the results, measuring software impact to attribute developers, and bibliometric software citation analysis in general. Additionally, providing information about whether and how the software and the source code are available allows an assessment about the state and role of open source software in science in general. While such analyses can be done manually, large scale analyses require the application of automated methods of information extraction and linking. In this paper, we present SoftwareKG—a knowledge graph that contains information about software mentions from more than 51,000 scientific articles from the social sciences. A silver standard corpus, created by a distant and weak supervision approach, and a gold standard corpus, created by manual annotation, were used to train an LSTM based neural network to identify software mentions in scientific articles. The model achieves a recognition rate of .82 F-score in exact matches. As a result, we identified more than 133,000 software mentions. For entity disambiguation, we used the public domain knowledge base DBpedia. Furthermore, we linked the entities of the knowledge graph to other knowledge bases such as the Microsoft Academic Knowledge Graph, the Software Ontology, and Wikidata. Finally, we illustrate, how SoftwareKG can be used to assess the role of software in the social sciences.