Reviewerly/exHarmony huggingface dataset:
Robust and precise identification of the hygro-expansion of single fibers: a full-field fiber topography correlation approach

Abstract Over the past decades, natural fibers have become an important constituent in multiple engineering- and biomaterials. Their high specific strength, biodegradability, low-cost production, recycle-ability, vast availability and easy processing make them interesting for many applications. However, fiber swelling due to moisture uptake poses a key challenge, as it significantly affects the geometric stability and mechanical properties. To characterize the hygro-mechanical behavior of fibers in detail, a novel micromechanical characterization method is proposed which allows continuous full-field fiber surface displacement measurements during wetting and drying. A single fiber is tested under an optical height microscope inside a climate chamber wherein the relative humidity is changed to capture the fiber swelling behavior. These fiber topographies are, subsequently, analyzed with an advanced Global Digital Height Correlation methodology dedicated to extract the full three-dimensional fiber surface displacement field. The proposed method is validated on four different fibers: flat viscose, trilobal viscose, 3D-printed hydrogel and eucalyptus, each having different challenges regarding their geometrical and hygroscopic properties. It is demonstrated that the proposed method is highly robust in capturing the full-field fiber kinematics. A precision analysis shows that, for eucalyptus, at 90% relative humidity, an absolute surface strain precision in the longitudinal and transverse directions of, respectively, 1.2 × 10 -4 and 7 × 10 -4 is achieved, which is significantly better than existing techniques in the literature. The maximum absolute precision in both directions for the other three tested fibers is even better, demonstrating that this method is versatile for precise measurements of the hygro-expansion of a wide range of fibers. Graphic abstract
A modified digital signature algorithm to improve the biomedical image integrity in cloud environment

Biomedical industries produce an enormous amount of medical data day by day which requires a huge storage space and processing power. Cloud computing provides enough space to store this huge amount of data. Cloud computing technology offers the required scalability with computing power through cloud service providers. In this scenario, medical data sharing in the form of images securely is very important in bio-medical industries. The cloud facilitates a better platform for sharing crucial data between the physicians and patients. Hence, data integrity and authenticity are a challenging issue today in cloud while sharing these medical images through the Internet. For overcoming these issues, we propose a modified digital signature scheme that ensures data integrity and authenticity of the biomedical image. Here, the third-party auditor checks the shared medical image whether it is damaged or modified by applying the proposed digital signature scheme. Moreover, an existing Adler32 hash function is used for creating a digital signature with 8 bits and the proposed digital signature scheme provides 16 bits of output signature which is better than the existing digital signatures. Finally, the proposed scheme proved its efficiency by conducting various experiments.
Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices

Deep neural networks (DNNs) continue to demonstrate superior generalization performance in an increasing range of applications, including speech recognition and image understanding. Recent innovations in compression algorithms, design of efficient architectures and hardware accelerators have prompted a rapid growth in deploying DNNs on mobile and IoT devices to redefine user experiences. Relying on the superior inference quality of DNNs, various voice-enabled devices have started to pervade our everyday lives and are increasingly used for, e.g., opening and closing doors, starting or stopping washing machines, ordering products online, and authenticating monetary transactions. As the popularity of these voice-enabled services increases, so does their risk of being attacked. Recently, DNNs have been shown to be extremely brittle under adversarial attacks and people with malicious intentions can potentially exploit this vulnerability to compromise DNN-based voice-enabled systems. Although some existing work already highlights the vulnerability of audio models, very little is known of the behaviour of compressed on-device audio models under adversarial attacks. This paper bridges this gap by investigating thoroughly the vulnerabilities of compressed audio DNNs and makes a stride towards making compressed models robust. In particular, we propose a stochastic compression technique that generates compressed models with greater robustness to adversarial attacks. We present an extensive set of evaluations on adversarial vulnerability and robustness of DNNs in two diverse audio recognition tasks, while considering two popular attack algorithms: FGSM and PGD. We found that error rates of conventionally trained audio DNNs under attack can be as high as 100%. Under both white- and black-box attacks, our proposed approach is found to decrease the error rate of DNNs under attack by a large margin.
Aggregation Support for Modern Graph Analytics in TigerGraph

We describe how GSQL, TigerGraph's graph query language, supports the specification of aggregation in graph analytics. GSQL makes several unique design decisions with respect to both the expressive power and the evaluation complexity of the specified aggregation. We detail our design showing how our ideas transcend GSQL and are eminently portable to the upcoming graph query language standards as well as the existing pattern-based declarative query languages.
Mining top-rank-k frequent weighted itemsets using WN-list structures and an early pruning strategy

Frequent weighted itemsets (FWIs) are a variation of frequent itemsets (FIs) that take into account the different importance or weights for each item. Many algorithms have been introduced for mining FWIs recently. However, the traditional algorithms for mining FWIs produce a large number of FWIs which causes difficulties when applied with intelligent systems. Therefore, this study first introduces the problem of mining top-rank-k FWIs from weighted databases that combines the mining and ranking phases into one without finding all FWIs to increase their usability in practical applications. As the second contribution, three baseline algorithms for mining top-rank-k FWIs, namely TFWIT, TFWID and TFWIN that use state-of-the-art data structures, namely tidset, diffset and WN-list structures, are developed. Next, this study proposes the threshold raising strategy and the early pruning strategy supported by a new theorem to effectively mine top-rank-k FWIs. An improved version of TFWIN named TFWIN+ employs these strategies to improve the performance of mining top-rank-k FWIs and is more efficient when compared to the original version. Finally, the empirical evaluations in terms of processing time and memory usage among these algorithms were conducted to show the effectiveness of TFWIN+. The experimental results show that TFWIN+ outperforms TFWIT, TFWID and TFWIN for mining top-rank-k FWIs.
Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies

Physical disability has affected many people's lives across the world. One of these disabilities that strongly affected some large category of people is visual lose. Blind people often face difficulties in moving around freely such as: in crossing the street, in reading, driving or socializing. They often rely on using certain aid devices to reach certain places or perform any other daily activities such as walking sticks. There are ongoing scientific researches in the area of rectifying blindness, but it has to go long way to achieve the solution. Also, there are research unleashes the ideas of assisting the blind people deficiency but lacks in technological aspects of implementation. This research project aims at helping blind people of all categories to achieve their day to day tasks easier through the use of a smart device. By using artificial intelligent and image processing, this smart device is able to detect faces, colors and deferent objects. The detection process is manifested by notifying the visually impaired person through either a sound alert or vibration. Additionally, this study presents a palpable survey that entails visually impaired people from the local community. Subsequently, the project uses both Open CV and Python for programming and implementation. The exertion of this project prototype investigates the algorithms which are used for detecting the objects. Also, it demonstrates how this smart device could detects certain physical object and how it could send a warning signal when faced by any obstacles. Overall, this research will be a positive addition in the world of health care sector by supporting blind people with the use of smart technology.
UniBud: A Virtual Academic Adviser

Academic advising is a challenging and time-consuming task. It requires well-trained dedicated professionals who give one-on-one consultations with students. While this approach is somewhat successful. In practice, advisers spend much time repeatedly answering questions that can be efficiently learned by an intelligent system. There have been several attempts to automate academic advising by means of expert systems and chatbots. However, functionality has been prioritized over usability. In response, we contribute, UniBud, a virtual academic adviser which is designed with usability principles in mind. UniBud uses DialogFlow, a natural language understanding platform, to build voice-based interactions with students. The interactions allow students to inquire about course information, enrollment, and other general enquiries. UniBud is not meant to replace traditional academic advising. Instead, it will support a limited set of academic enquiries that can relieve human academic advisers to assist students with more involving enquiries. We plan to test the usability of the system and collect feedback from potential users.
StructSim: Querying Structural Node Similarity at Billion Scale

Structural node similarity is widely used in analyzing complex networks. As one of the structural node similarity metrics, role similarity has the good merit of indicating automorphism (isomorphism). Existing algorithms to compute role similarity (e.g., RoleSim and NED) suffer from severe performance bottlenecks, and thus cannot handle large real-world graphs. In this paper, we propose a new framework StructSim to compute nodes' role similarity. Under this framework, we prove that StructSim is guaranteed to be an admissible role similarity metric based on the maximum matching. While maximum matching is too costly to scale, we then devise the BinCount matching to speed up the computation. BinCount-based StructSim admits a precomputed index to query one single pair in O(k log D) time, where k is a small user-defined parameter and D is the maximum node degree. Extensive empirical studies show that StructSim is significantly faster than existing works for computing structural node similarities on the real-world graphs, with comparable effectiveness.
Elementary doctrines as coalgebras

Lawvere's hyperdoctrines mark the beginning of applications of category theory to logic. In particular, existential elementary doctrines proved essential to give models of non-classical logics. The clear connection between (typed) logical theories and certain Pos-valued functors is exemplified by the embedding of the category of elementary doctrines into that of primary doctrines, which has a right adjoint given by a completion which freely adds quotients for equivalence relations. We extend that result to show that, in fact, the embedding is 2-functorial and 2-comonadic. As a byproduct we apply the result to produce an algebraic way to extend a first order theory to one which eliminates imaginaries, discuss how it relates to Shelah's original, and show how it works in a wider variety of situations.
Fostering HCI Research in, by, and for Latin America

Over the last 20 years, the Latin American Human-Computer Interaction (HCI) community has been working to shed light on how the diverse populations in the region are adopting, using, and making sense of computational technologies. Latin America's tense socio-political context, plurality of languages, collectivist culture, and historical relationship with the Global North make it a unique and rich space for HCI research. Considering the growing number of studies about Latin American communities and the emergent efforts to contribute to the HCI literature, we propose to host a SIG meeting at the 2020 ACM CHI conference. Our goal is to consolidate these efforts to better promote HCI research in, by, and for Latin America, by (1) bringing together researchers, practitioners, and students who are interested in engaging with Latin America through their research and practice, (2) envisioning a shared research agenda, and (3) identifying strategies for making its contributions more visible and impactful in the international community.
Performance and Experience of Throwing in Virtual Reality

Throwing is a fundamental movement in many sports and games. Given this, accurate throwing in VR applications today is surprisingly difficult. In this paper we explore the nature of the difficulties of throwing in VR in more detail. We present the results of a user study comparing throwing in VR and in the physical world. In a short pre-study with 3 participants we determine an optimal number of throwing repetitions for the main study by exploring the learning curve and subjective fatigue of throwing in VR. In the main study, with 12 participants, we find that throwing precision and accuracy in VR are lower particularly in the distance and height dimensions. It also requires more effort and exhibits different kinematic patterns.
Officers Never Type: Examining the Persistence of Paper in e-Governance

The Global South has seen a proliferation of e-governance initiatives aimed at digitizing governmental service delivery. However, paper continues to remain the primary medium of bureaucracy. During ethnographic fieldwork at the CM Helpline, a state-wide e-governance initiative in central India, we observed that even tech-savvy bureaucrats who fully supported both the initiative and its paper-to-electronic transition ensured that paper continues to persist in abundance. Drawing upon scholarship from HCI, anthropology, and science & technology studies, we theorize this contradiction to uncover the circulations of power between people, paper, and electronic systems. We suggest that designers should recognize that new systems often disempower existing actors. The process of transition should integrate new systems into the existing ecosystem and plan for the graceful retirement of older technologies. In addition to machine errors, systems should be resilient to human errors. Finally, new systems should attend to sociocultural and historical specificities.
texSketch: Active Diagramming through Pen-and-Ink Annotations

Learning from text is a constructive activity in which sentence-level information is combined by the reader to build coherent mental models. With increasingly complex texts, forming a mental model becomes challenging due to a lack of background knowledge, and limits in working memory and attention. To address this, we are taught knowledge externalization strategies such as active reading and diagramming. Unfortunately, paper-and-pencil approaches may not always be appropriate, and software solutions create friction through difficult input modalities, limited workflow support, and barriers between reading and diagramming. For all but the simplest text, building coherent diagrams can be tedious and difficult. We propose Active Diagramming, an approach extending familiar active reading strategies to the task of diagram construction. Our prototype, texSketch, combines pen-and-ink interactions with natural language processing to reduce the cost of producing diagrams while maintaining the cognitive effort necessary for comprehension. Our user study finds that readers can effectively create diagrams without disrupting reading.
Design and Evaluation of a VR Training Simulation for Pump Maintenance

Through technological advancements more and more companies consider virtual reality (VR) for training of their workforce, in particular for situations that occur rarely, are dangerous, expensive, or very difficult to recreate in the real world. This creates the need for understanding the potentials and limitations of VR training and establish best practices. In pursuit of this, we have developed a VR Training simulation for a use case at Grundfos, in which apprentices learn a sequential maintenance task. We evaluated this simulation in a user study with 36 participants, comparing it to two traditional forms of training (Pairwise Training and Video Training). This case study describes the developed virtual training scenario and discusses design considerations for such VR simulations. The results of our evaluation support that, while VR Training is effective in teaching the procedure for a maintenance task, traditional approaches with hands-on experience still lead to a significantly better outcome.
Free-Space Optical Quantum Communications in Turbulent Channels With Receiver Diversity

An optical quantum communication system with receiver diversity is proposed for free-space communication using the optical combining technique and a generalized Kennedy receiver. The quantum channel model of the proposed system is established using the P -representation in the presence of both turbulence and thermal noise. A conditional dynamics based Kennedy receiver with threshold detection is proposed to mitigate the influence of both turbulence and thermal noise. The error probabilities for different types of generalized Kennedy receiver, i.e., the Kennedy receiver, the Type-II receiver, and the conditional dynamics based Kennedy receiver, are analytically studied and the lower bounds for these error probabilities are obtained. Numerical results show that both the Kennedy receiver and the Type-II receiver fail in the presence of either turbulence or thermal noise. The performance of the proposed conditional dynamics based Kennedy receiver with threshold detection can surpass the standard quantum limit given by the homodyne receiver in either weak turbulence or small thermal noise.
A Tale of Two Trees: One Writes, and Other Reads

Abstract The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T 3 , a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access machine (ORAM) to protect SPV clients’ requests from potentially malicious nodes. The key novelty of T 3 lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output (UTXO) Set. Our experiment shows that T 3 is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
Challenges and Opportunities of Leveraging Intelligent Conversational Assistant to Improve the Well-being of Older Adults

Recent advancements and economic feasibility have led to the widespread adoption of conversational digital assistants for everyday work. While research has focused on the use of these conversational assistants such as Siri, Google Assistant or Alexa, by young adults and families, very little work focuses on the acceptance and adaptability amongst the older adults. This SIG aims to discuss the use and benefits of these conversational digital assistants for the well being of older adults. The goals for this SIG are to: (i) explore the acceptance/adoption of voice-based conversational agents for older adults. (ii) explore anthropomorphism in the design of conversational digital assistants. (iii) understand triggers (scenarios of use) that can initiate the process of reminiscence thus leading to meaningful conversation. (iv) explore conversational User Experience. (v) explore the co-existence of non-conversational use cases.
A Survey on SQL Injection Attacks, Detection and Prevention

Since the uses of Web in daily life is increasing in past 20 years and becoming trend now, almost every Web application has its own database to store important data. An attacker can get or even modify the data from database through SQL injection vulnerability, so it is one of the most serious problems in Web application security. In this paper, we present a detailed review on various types of SQL injection attacks and detection techniques based on machine learning. we also propose future expectations and possible development of countermeasures against SQL injection vulnerability at the end of the article.
Exploring the Design Space of User-System Communication for Smart-home Routine Assistants

AI-enabled smart-home agents that automate household routines are increasingly viable, but the design space of how and what such systems should communicate with their users remains underexplored. Through a user-enactment study, we identified various interpretations of and feelings toward such a system's confidence in its automated acts. That confidence and their own mental models influenced what and how the participants wanted the system to communicate, as well as how they would assess, diagnose, and subsequently improve it. Automated acts resulted from false predictions were not generally considered improper, provided that they were perceived as reasonable or potentially useful. The participants' improvement strategies were of four general types, all of which will be discussed. Factors affecting their preferred levels of involvement in automated acts and their interest in system confidence were also identified. We conclude by making practical design recommendations for the user-system communication design spaces of smart-home routine assistants.
Under Surveillance: Technology Practices of those Monitored by the State

This paper documents the experiences of those living under state surveillance. We interviewed our participants about how they lived under threat, and how it changed their technology practices. Our participants spanned three groups - journalists who reported from countries where their activities were illegal; activists who took part in civil disobedience, and individuals who worked in illegal activities that would have likely led to prosecution. In our analysis we cover four themes: first, 'the imagined surveillant'. Second, the danger and dependencies of technology use, third, their coping strategies, and lastly how belonging to a group can protect but also expose. In our discussion we cover how we can design for dissidents, and how to deal with the difficult questions this raises. We conclude by advocating for research that takes into account a critical view of the state in HCI and more broadly for an anti-surveillance stance in the design of technologies.