Reviewerly/exHarmony huggingface dataset:
A Pseudo-Distance Algorithm for Collision Detection of Manipulators Using Convex-Plane-Polygons-based Representation

Robots have become indispensable parts for the industrial automated workshop. The distance calculation between the industrial robot and obstacles is a fundamental problem for the robotic collision-free motion control. But existing methods for this problem have the disadvantage that the accuracy and execution efficiency cannot be guaranteed at the same time. In this paper, a pseudo-distance algorithm is presented based on the convex-plane-polygons-based representation to solve this problem. In this algorithm, convex plane polygons (CPPs) and cylinders are utilized to represent obstacles and the manipulator, respectively. The spatial relationship between each CPP and each cylinder is discussed through defining condition parameters, and is divided into six conditions including three special conditions when the contact happens, and the other three conditions when there exists a certain distance between the CPP and the cylinder. The distance is modelled under different relations based on the theory of mathematics and geometry. The pseudo distance is determined through selecting the smallest value from distances between CPPs and cylinders. The numerical experiment is performed based on the proposed and two previous algorithms to estimate the shortest distance between an industrial robot and a groove-shape obstacle. And an application example is performed to show the significance of this work on the robotic motion control. Results indicate that the proposed algorithm is more efficient than previous algorithms under a certain precision requirement, and can be effectively applied in the robotic motion control.
Three-dimensional reconstruction of CT image features based on multi-threaded deep learning calculation

Traditional technology uses serial processing method in CT image feature extraction. It is prone to loss of image data, which causes problems such as ring distortion of the reconstructed image and long reconstruction time. Therefore, a three-dimensional (3D) reconstruction algorithm for CT image features based on multi-threaded deep learning calculation is designed. Feature image textures are segmented using parameters such as gray, mean, and variance. Fusion calculates co-occurrence logarithm of the gray level with segmented sub-block and its pixels. Co-occurrence probability of the sub-block gray levels is obtained, followed by getting optimal feature volume data, which is stored in 3D texture and 1D texture for interpolation calculation.The optimal feature volume data is stored in 3D and 1D texture for interpolation calculation. Rotation matrix is stored in the global storage space of CUDA, which is used for multi-threaded calculations. After completing multi-threaded batch CT image hardware configuration and algorithm flow settings, the thread's index and bounding box is calculated. 3D reconstruction of CT image features is achieved by model accumulation. The experimental simulation proves that local detail information loss is small after reconstruction of the proposed method. Its reconstruction time is short and has good applicability.
Cylinders extraction in non-oriented point clouds as a clustering problem

Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed "in-the-wild", when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters.
Insights Into Efficient k-Nearest Neighbor Classification With Convolutional Neural Codes

The increasing consideration of Convolutional Neural Networks (CNN) has not prevented the use of the k-Nearest Neighbor (kNN) method.In fact, a hybrid CNN-kNN approach is an interesting option in which the network specializes in feature extraction through its activations (Neural Codes), while the kNN has the advantage of performing a retrieval by means of similarity.However, this hybrid approach also has the disadvantages of the kNN search, and especially its high computational cost which is, in principle, undesirable for large-scale data.In this paper, we present the first comprehensive study of efficient kNN search algorithms using this hybrid CNN-kNN approach.This has been done by considering up to 16 different algorithms, each of which is evaluated with a different parametrization, in 7 datasets of heterogeneous composition.Our results show that no single algorithm is capable of covering all aspects, but rather that each family of algorithms is better suited to specific aspects of the problem.This signifies that Fast Similarity Search algorithms maintain their performance, but do not reduce the cost as much as the Data Reduction family does.In turn, the Approximated Similarity Search family is postulated as a good option when attempting to balance accuracy and efficiency.The experiments also suggest that considering statistical transformation algorithms such as Linear Discriminant Analysis might be useful in certain cases.
Improving Key Mismatch Attack on NewHope with Fewer Queries

NewHope is a lattice cryptoscheme based on the Ring Learning With Errors (Ring-LWE) problem, and it has received much attention among the candidates of the NIST post-quantum cryptography standardization project. Recently, key mismatch attacks on NewHope have been proposed, where the adversary tries to recover the server's secret key by observing the mismatch of the shared key from chosen queries. At CT-RSA 2019, Bauer et al. first proposed a key mismatch attack on NewHope, and then at ESORICS 2019, Qin et al. proposed an improved version with success probability of 96.9% using about 880,000 queries. In this paper, we further improve their key mismatch attacks on NewHope. First, we reduce the number of queries by adapting the terminating condition to the response from the server using an early abort technique. Next, the success rate of recovering the secret key polynomial is raised by setting a deterministic condition for judging its coefficients. We also improve the method of generating queries. Furthermore, the search range of the secret key in Qin et al.'s attack is extended without increasing the number of queries. As a result, about 73% of queries can be reduced compared with Qin et al.'s method under the success rate of 97%. Moreover, we analyze the trade-off between the number of queries and the success rate. In particular, we show that a lower success rate of 20.9% is available by further reduced queries of 135,000, simultaneously.
MoniPoly—An Expressive q-SDH-Based Anonymous Attribute-Based Credential System

Modern attribute-based anonymous credential (ABC) systems benefit from special encodings that yield expressive and highly efficient show proofs on logical statements. The technique was first proposed by Camenisch and Groß, who constructed an SRSA-based ABC system with prime-encoded attributes that offers efficient AND, OR and NOT proofs. While other ABC frameworks have adopted constructions in the same vein, the Camenisch-Groß ABC has been the most expressive and asymptotically most efficient proof system to date, even if it was constrained by the requirement of a trusted message-space setup and an inherent restriction to finite-set attributes encoded as primes. In this paper, combining a new set commitment scheme and an SDH-based signature scheme, we present a provably secure ABC system that supports show proofs for complex statements. This construction is not only more expressive than existing approaches, but it is also highly efficient under unrestricted attribute space due to its ECC protocols only requiring a constant number of bilinear pairings by the verifier; none by the prover. Furthermore, we introduce strong security models for impersonation and unlinkability under adaptive active and concurrent attacks to allow for the expressiveness of our ABC as well as for a systematic comparison to existing schemes. Given this foundation, we are the first to comprehensively formally prove the security of an ABC with expressive show proofs. Specifically, building upon the q-(co-)SDH assumption, we prove the security against impersonation with a tight reduction. Besides the set commitment scheme, which may be of independent interest, our security models can serve as a foundation for the design of future ABC systems.
A novel randomized machine learning approach: Reservoir computing extreme learning machine

In this study, a novel approach that is based on reservoir computing, which is a successful method in modeling sequential datasets, and extreme learning machines, which has a high generalization capacity, was proposed to model a non-sequential dataset or system. The proposed approach does not require any optimization stage; each weight (except weights in the output layer), biases, the number of neurons in the reservoir, activation functions and the parameters of activation functions were determined arbitrarily and the weights in the output layer were calculated based on these arbitrarily assigned parameters. The proposed approach was evaluated and validated with 60 different benchmark datasets. Obtained results were compared with literature findings and results obtained by each of the extreme learning machine (ELM), randomized artificial neural network, random vector functional link, stochastic ELM, and pruned stochastic ELM methods. Achieved results are successful enough to be employed in classification and regression.
NSEEN: Neural Semantic Embedding for Entity Normalization

Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.
Practicing Without a License: Design Research as Psychotherapy

This paper considers the potential for participants to experience psychotherapeutic effects through their involvement in design research. Drawing on literature in human-computer interaction, psychotherapy, and feminist sociology, I argue that vulnerable participants may experience qualitative interviews therapeutically when they engage in reflexive activity about sensitive topics with researchers who employ psychotherapeutic techniques that encourage disclosure and reflection. I discuss ethical concerns and suggest the need for trauma-informed research practices, updated consent procedures, and revised pedagogy that better support researchers and participants engaged in emotionally charged encounters.
Foundations for Designing Public Interactive Displays that Provide Value to Users

Public interactive displays (PID) are a promising technology for providing information and collecting feedback in public spaces. Research on PIDs has shown that, like all public displays, their efficacy is reduced by display blindness. Rather than increase the visual attention-grabbing nature of PIDs, we propose that additional understanding is required around how and when these displays are able to offer value to users. We tackle this through a systematic analysis of PID studies published in the literature, which led to 9 aspects of value across 4 factors: people, location, community, and time. We discuss the identified aspects and their utility for the design of PIDs through a review of our own deployments carried out by 4 different labs across 5 countries. We conclude with a set of recommendations for identifying and optimising the intended value of future PIDs.
Worker-Centered Design: Expanding HCI Methods for Supporting Labor

HCI has long considered sites of workplace collaboration. From airline cockpits to distributed groupware systems, scholars emphasize the importance of supporting a multitude of tasks and creating technologies that integrate into collaborative work settings. More recent scholarship highlights a growing need to consider the concerns of workers within and beyond established workplace settings or roles of employment, from steelworkers whose jobs have been eliminated with post-industrial shifts in the economy to contractors performing the content moderation that shapes our social media experiences. This one-day workshop seeks to bring together a growing community of HCI scholars concerned with the labor upon which the future of work we envision relies. We will discuss existing methods for studying work that we find both productive and problematic, with the aim of understanding how we might better bridge current gaps in research, policy, and practice. Such conversations will focus on the challenges associated with taking a worker-oriented approach and outline concrete methods and strategies for conducting research on labor in changing industrial, political, and environmental contexts.
Insider Threat Risk Prediction based on Bayesian Network

Insider threat protection has received increasing attention in the last ten years due to the serious consequences of malicious insider threats. Moreover, data leaks and the sale of mass data have become much simpler to achieve, e.g., the dark web can allow malicious insiders to divulge confidential data whilst hiding their identities. In this paper, we propose a novel approach to predict the risk of malicious insider threats prior to a breach taking place. Firstly, we propose a new framework for insider threat risk prediction, drawing on technical, organisational and human factor perspectives. Secondly, we employ a Bayesian network to model and implement the proposed framework. Furthermore, this Bayesian network-based prediction model is evaluated in a range of challenging environments. The risk level predictions for each authorised users within the organisation are examined so that any insider threat risk can be identified. The proposed insider threat prediction model achieved better results when compared to the empirical judgments of security experts
Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress Management

Stress is caused by a variety of events in our daily lives. By anticipating stressful situations, we can prepare and better cope with stressors when they actually occur. However, many past-centric personal informatics (PI) tools focus on capturing events that already happened and analyzing the data. In this work, we examine how anticipation — a future-centric self-tracking practice — could be used to manage daily stress levels. To address this, we built MindForecaster, a calendar- mediated stress anticipation application that allows users to expect stressful events in advance, generates activities to mitigate stress, and evaluates actual stress levels compared to previously estimated stress levels. In a 30-day deployment with 47 users, the users who explicitly planned and executed coping interventions reported reduced stress more than those who only expected stressful events. We suggest design implications for stress management by incorporating the properties of anticipation into current PI models.
A Framework of Metrics for Differential Privacy from Local Sensitivity

Abstract The meaning of differential privacy (DP) is tightly bound with the notion of distance on databases, typically defined as the number of changed rows. Considering the semantics of data, this metric may be not the most suitable one, particularly when a distance comes out as larger than the data owner desired (which would undermine privacy). In this paper, we give a mechanism to specify continuous metrics that depend on the locations and amounts of changes in a much more nuanced manner. Our metrics turn the set of databases into a Banach space . In order to construct DP information release mechanisms based on our metrics, we introduce derivative sensitivity , an analogue to local sensitivity for continuous functions. We use this notion in an analysis that determines the amount of noise to be added to the result of a database query in order to obtain a certain level of differential privacy, and demonstrate that derivative sensitivity allows us to employ powerful mechanisms from calculus to perform the analysis for a variety of queries. We have implemented the analyzer and evaluated its efficiency and precision.
Design Space Exploration Based Methodology for Residue Number System Digital Filters Implementation

In the past decades, the Residue Number System (RNS) has been adopted in DSP as an alternative to the traditional two's complement number system (TCS) because of the savings in area and power dissipation.In this work, we first perform a comprehensive Design Space Exploration (DSE) to analyze the impact of state-of-the-art design tools and libraries on the implementation of the basic operations (i.e., addition and multiplication) used in DSP.From this DSE, we extract the characteristics of the stand-alone RNS and TCS operators in the different design corners, independently of the specific context of the application.Then, we propose a design methodology, based on the DSE, to fully automate the design of digital filters, hiding the detail of the RNS to the designer, and providing optimal power efficient implementations.Our methodology can enable the efficiency in computation (speed and power) in DSP and in emerging applications, such as Machine Learning and Internet-of-Things.
Order-Preserving Key Compression for In-Memory Search Trees

We present the High-speed Order-Preserving Encoder (HOPE) for in-memory search trees. HOPE is a fast dictionary-based compressor that encodes arbitrary keys while preserving their order. HOPE's approach is to identify common key patterns at a fine granularity and exploit the entropy to achieve high compression rates with a small dictionary. we first develop a theoretical model to reason about order-preserving dictionary designs. We then select six representative compression schemes using this model and implement them in HOPE. These schemes make different trade-offs between compression rate and encoding speed. We evaluate HOPE on five data structures used in databases: SuRF, ART, HOT, B+tree, and Prefix B+tree. Our experiments show that using HOPE allows the search trees to achieve lower query latency (up to 40% lower) and better memory efficiency (up to 30% smaller) simultaneously for most string key workloads.
Continuous outlier mining of streaming data in flink

In this work, we focus on distance-based outliers in a metric space, where the status of an entity as to whether it is an outlier is based on the number of other entities in its neighborhood. In recent years, several solutions have tackled the problem of distance-based outliers in data streams, where outliers must be mined continuously as new elements become available. An interesting research problem is to combine the streaming environment with massively parallel systems to provide scalable stream-based algorithms. However, none of the previously proposed techniques refer to a massively parallel setting. Our proposal fills this gap and investigates the challenges in transferring state-of-the-art techniques to Apache Flink, a modern platform for intensive streaming analytics. We thoroughly present the technical challenges encountered and the alternatives that may be applied, of which a micro-clustering-based one is the most efficient. We show speed-ups of up to 2.27 times over advanced non-parallel solutions, by using just an ordinary four-core machine and a real-world dataset. When moving to a three-machine cluster, due to less contention, we manage to achieve both better scalability in terms of the window slide size and the data dimensionality, and even higher speed-ups, e.g., by a factor of more than 11X. Overall, our results demonstrate that outlier mining can be achieved in an efficient and scalable manner. The resulting techniques have been made publicly available as open-source software.
Relationship Between Visual Complexity and Aesthetics of Webpages

Substantial HCI research investigated the relationship between webpage complexity and aesthetics, but without a definitive conclusion. Some research showed an inverse linear correlation, some other showed an inverted u-shaped curve, while the rest showed no relationship at all. Such a lack of clarity complicates hypothesis formulation and result interpretation for future research, and lowers the reliability and generalizability of potential advice for Web design practice. We re-collected complexity and aesthetics ratings for five datasets previously used in webpage aesthetics and complexity research. The results were mixed, but suggested an inverse linear relationship with a weaker u-shaped sub-component. A subsequent visual inspection of revealed several confounding factors that may have led to the mixed results, including some webpages looking broken or archaic. The second data collection showed that accounting for these factors generally eliminates the u-shaped tendency of the complexity-aesthetics relationship, at least, for a relatively homogeneous sample of English-speaking participants.