Reviewerly/exHarmony huggingface dataset:
Networked event-based collision avoidance of mobile objects with trajectory planning based on Bézier curves

This paper proposes an event-based method to ensure a collision-free movement of two mobile objects. The vehicles are locally controlled and connected by a communication network. In the hierarchical structure considered, a stand-on object can freely change its trajectory, whereas a give-way object has to plan its trajectory so as to avoid collisions with the stand-on object. The give-way object is provided with an event-based collision avoidance unit, which predicts the behaviour of the stand-on object, generates events and plans collision-free trajectories. A method for trajectory planning is introduced that is based on Bézier curves. The trajectory of the stand-on object is communicated to the give-way object, with which the trajectory of the give-way object can be adapted so as to avoid collisions. As the stand-on object can change its trajectory at any time under system limitations, its possible future positions are estimated by the give-way object to be able to react on the current movement of the stand-on object. If the spatial separation between the two objects deceeds a threshold, communication is invoked by the give-way object. In contrast to the existing literature this approach is performed online and with a low communication effort. The effectiveness of the method is demonstrated by a simulation study with two quadrotors. The main result is the guaranteed collision-free movement of the objects if the give-way object generates events with an appropriately chosen event threshold. The events are separated from each other by a determined minimum period of time.
The role of evaluations in reaching decisions using automated systems supporting forensic analysis

Automated systems allow forensic practitioners to perform analysis tasks that would otherwise be infeasible. However, unless the outputs of such systems are critically evaluated using a scientifically-based framework, there is a risk of undetected errors or bias resulting in wrong decisions. Furthermore, decisions based on automated system outputs that are not well understood or clearly explainable could violate fundamental human rights. These risks can apply to any automated system that supports forensic analysis, and are raised when machine learning is involved. This work presents a framework based on principles of scientific interpretation, and provides an evaluation hierarchy for automated systems, including machine learning approaches, to strengthen forensic conclusions. Specifically, three levels of evaluation are presented: performance, understandability and forensic evaluation. Approaches to clearly conveying the weight of forensic evaluations are discussed. Each level of evaluation is demonstrated in relation to actual automated systems. Finally, requirements for designing automated systems supporting forensic analysis are proposed, and future work is discussed.
Identification of Malicious Injection Attacks in Dense Rating and Co-Visitation Behaviors

Personalized recommender systems are pervasive in different domains, ranging from e-commerce services, financial transaction systems to social networks. The generated ratings and reviews by users toward products are not only favourable to make targeted improvements on the products for online businesses, but also beneficial for other users to get a more insightful review of the products. In reality, recommender systems can also be deliberately manipulated by malicious users due to their fundamental vulnerabilities and openness. However, improving the detection performance for defending malicious threats including profile injection attacks and co-visitation injection attacks is constrained by the challenging issues: (1) various types of malicious attacks in real-world data coexist; (2) it is difficult to balance the commonality and speciality of rating behaviors in terms of accurate detection; and (3) rating behaviors between attackers and anchor users caused by the consistency of attack intentions are extremely similar. In this article, we develop a unified detection approach named IMIA-HCRF, to progressively discriminate malicious injection behaviors for recommender systems. First, disturbed data are empirically eliminated by implementing both the construction of association graph and enhancement of dense behaviors, which can be adapted to different attacks. Then, the smooth boundary of dense rating (or co-visitation) behaviors is further segmented using higher order potentials, which is finally leveraged to determine the concerned injection behaviors. Extensive experiments on both synthetic data and real-world data demonstrate that the proposed IMIA-HCRF outperforms all baselines on various metrics. The detection performance of IMIA-HCRF can achieve an improvement of 7.8% for mixed profile injection attacks as well as 6% for mixed co-visitation injection attacks over the baselines in terms of FAR (false alarm rate) while keeping the highest DR (detection rate). Additional experiments on real-world data show that IMIA-HCRF brings an improvement with the advantage of 11.5% FAR in average compared with the baselines.
Collective Attack and Improvement on “Mediated Semi‐Quantum Key Distribution Using Single Photons”

Abstract Lin et al. proposed a mediated semi‐quantum key distribution protocol in 2019. This study shows that Lin et al.'s protocol has a security loophole that could mount to the collective attack to reveal some information about the secret key without being detected. To overcome this problem, an improved protocol is proposed and its security is proven.
Less Provisioning: A Hybrid Resource Scaling Engine for Long-Running Services With Tail Latency Guarantees

Modern resource management frameworks guarantee low tail latency for long-running services using the resource over-provisioning method, resulting in serious waste of resources and increasing the service costs greatly. To reduce the over-provisioning cost, we present HRSE, a hybrid resource scaling engine that enables much more efficient resource provisioning for both periodic and non-periodic workloads of long-running services while guaranteeing the tail latency Service Level Objective (SLO). HRSE employs a convolution-based time series analysis to identify periodic patterns in workloads. If periodic patterns are discovered, HRSE estimates the just-right amount of resources based on the periodic features through a <i>top-</i> <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> based collaborative filtering approach. Otherwise, it leverages wavelet-clustering to capture the short-term patterns in non-periodic workloads and predict the resource demands for the near future. To further enforce the tail latency SLO, HRSE uses an online reprovisioning mechanism that dynamically adjusts the resources to mitigate the performance uncertainty due to workload burstinesses. We fully implement HRSE on top of Docker and conduct extensive experiments using traces from production systems. Testbed experiments show that HRSE is able to increase the average resource utilization to 43 and 45 percent for periodic and non-periodic workloads respectively while guaranteeing the same tail latency objective.
Classification of Text Documents

Nowadays, the text information is available in electronic form which is easily accessible to people, due to whom it is increasing day by day, but the challenging issue is to organize the information. The existing algorithm such as naïve Bayes classifier uses the maximum posterior estimation for building a classifier, but it is dependent on huge number of training samples for more accuracy and the other algorithm known as genetic algorithm that begins with an initial population which is constructed from randomly generated rules, but the accuracy of this algorithm depends on set of training examples. In this paper, we are using brute-force approach to classify the different categories of documents.
Towards extending real-time EMG-based gesture recognition system

In biomedical applications, the Electromyographic (EMG) signal is used to record the electrical activity of the muscles during their contraction. EMG signal classification stands at the core of real time systems that aim to discriminate between user's movements without relying on other environmental conditions (as it is the case with gesture classification based on video). The classification of EMG signals is usually used to translate the sign language or to design computer interfaces based on gesture recognition. In this paper, we propose a continuation of our previous work, on a real-time automatic hand gesture recognition using fully connected neural networks and temporal features extracted from the EMG signals. This approach leads to a recognition of an increased number of gestures, starting from a system trained for discriminating between a small number of classes of recognized gestures. More precisely, using an innovative transfer learning method, we preserved the performance of the system (99.31% overall accuracy compared to previous 99.78%), while doubling the number of recognized gestures.
SkillCheck: An Incentive-based Certification System using Blockchains

Skill verification is a central problem in workforce hiring. Companies and academia often face the difficulty of ascertaining the skills of an applicant since the certifications of the skills claimed by a candidate are generally not immediately verifiable and costly to test. Blockchains have been proposed in the literature for skill verification and tamper-proof information storage in a decentralized manner. However, most of these approaches deal with storing the certificates issued by traditional universities on the blockchain. Among the few techniques that consider the certification procedure itself, questions like (a) scalability with limited staff, (b) uniformity of grades over multiple evaluators, or (c) honest effort extraction from the evaluators are usually not addressed. We propose a blockchain-based platform named SkillCheck, which considers the questions above, and ensure several desirable properties. The platform incentivizes effort in grading via payments with tokens which it generates from the payments of the users of the platform, e.g., the recruiters and test takers. We provide a detailed description of the design of the platform along with the provable properties of the algorithm.
The CASAD Matrix Method: Introduction of a Technique for the Documentation, Analysis, and Optimization of Context-Aware Systems

The availability of precise sensors and the extensive usage of smart devices enable the development of systems that can sense their environment and recognize specific contexts. In recent years, so-called context-aware systems gained increasing popularity as they can provide situational assistance and automate nondeterministic processes. Despite their advantages, these systems still play a minor role in industrial applications due to reasons such as the increased efforts required for the engineering and development process and the absence of methods to handle the inherent complexity. This paper introduces the CASAD matrix method that facilitates the development of context-aware systems and reduces their complexity to a manageable level. The matrix is based on the design structure matrix method of Steward [1] and allows the structured documentation, analysis, and optimization of context-related systems properties. Further, different approaches for analyzing the matrix are shown and their outcomes discussed. Finally, the practical application is demonstrated in an industrial use case. Thereby, the CASAD matrix method is used to document, analyze, and optimize the contextually assisted activities, the contextual services, and the contexts of a context-aware assistance system for the shop floor of an injection molding company.
LeapGestureDB: A Public Leap Motion Database Applied for Dynamic Hand Gesture Recognition in Surgical Procedures

The research in the dynamic hand gestures recognition field has been promoted thanks to the recent success of the emerging Leap Motion controller (LMC) sensor, especially in the human-computer interaction field. During surgery, surgeons need to make more effective use of computers through a touchless human-computer interaction. In this paper, we present a set of gestures that are about commands to manipulate medical images. The public availability of hand gesture datasets for dynamic hand gestures, in particular with the LMC as an acquisition device, is currently quite poor. Therefore, we collect a more challenging dataset, called “LeapGestureDB”. The dataset is presented as text files, which contains 6,600 samples of 11 different dynamic hand gestures performed by over 120 participants. It is hosted online for the public interest, and especially to enhance research results. We suggest an approach of three-dimensional dynamic hand gesture recognition. We extract spatial, frequential and spatio-frequential features from the three-dimensional positions of the fingertips and the palm center delivered from the input sensor. These descriptors are fed into a support vector machine classifier to determine the performed gestures. We first evaluate our work on an existing dataset, namely “RIT dataset”, and then we use our collected dataset. We achieved a better accuracy rate of 93% comparing to results obtained from the “RIT dataset”.
The Influence of Virtual Reality Learning System on the Learning Attitudes of Design History

Virtual reality technology has huge potential, it can improve traditional teaching models, inject fresh ideas into traditional teaching, and enhance students’ understanding of content and learning motivation. Design history courses are facing a situation where students’ interest in learning is not high. In order to solve this problem, this study integrates virtual reality technology into the teaching process of design history courses, and explores the impact of virtual reality learning systems on student’s learning attitudes. In this study, 70 students from the design department of a university in Southeast China were selected for experimental design. The students in the experimental group studied the virtual reality learning system, while the students in the control group studied in the traditional way. Experimental results show that compared with traditional teaching, virtual reality learning system significantly improves students’ learning attitudes.
A Philological Perspective on Meta-scientific Knowledge Graphs

This paper discusses knowledge graphs and networks on the scientific process from a philological viewpoint. Relevant themes are: the smallest entities of scientific discourse; the treatment of documents or artefacts as texts and commentaries in discourse; and links between context, (co)text, and data points. As an illustration of the micro-level approach, version control of linguistic examples is discussed as a possible field of application in this discipline. This underlines the claim for data points to be treated like unique entities, which possess metadata of the datum and any text generating knowledge from it.
Implementation of a Predictive Model for Fraud Detection in Motor Insurance using Gradient Boosting Method and Validation with Actuarial Models

Machine Learning provides greater ability to identify in-depth patterns in the data that are normally invisible or difficult to identify using other methods. One of the major application of it is seen in insurance claims fraud detection, which is a classification problem. In this work, Gradient Boosting Method (GBM) was used to create a predictive model which was applied to motor insurance claims data. The dataset was highly imbalanced; this problem was addressed using Synthetic Minority Oversampling Technique (SMOTE). The results achieved were remarkable with F1 score around 98% and the accuracy 99%. This was cross-validated by industry experts using extreme value theory (EVT), an actuarial model. The predictive model presented in this paper can be customized, tested and extended to other lines of business.
A Blockchain-based Decentralized Data Sharing Infrastructure for Off-grid Networking

Off-grid networks are recently emerging as a solution to connect the unconnected or provide alternative services to networks of possibly untrusted participants. The systems currently used, however, exhibit limitations due to their centralized nature and thus prove inadequate to secure trust. Blockchain technology can be the tool that will enable trust and transparency in such networks. In this paper, we introduce a platform for secure and privacy-respecting decentralized data sharing among untrusted participants in off-grid networks. The proposed architecture realizes this goal via the integration of existing blockchain frameworks (Hyperledger Fabric, Indy, Aries) with an off-grid network device and a distributed file system. We evaluate the proposed platform through experiments and show results for its throughput and latency, which indicate its adequate performance for supporting off-grid decentralized applications.
Pattern classification with Evolving Long-term Cognitive Networks

This paper presents an interpretable neural system—termed Evolving Long-term Cognitive Network—for pattern classification. The proposed model was inspired by Fuzzy Cognitive Maps, which are interpretable recurrent neural networks for modeling and simulation. The network architecture is comprised of two neural blocks: a recurrent input layer and an output layer. The input layer is a Long-term Cognitive Network that gets unfolded in the same way as other recurrent neural networks, thus producing a sort of abstract hidden layers. In our model, we can attach meaningful linguistic labels to each neuron since the input neurons correspond to features in a given classification problem and the output neurons correspond to class labels. Moreover, we propose a variant of the backpropagation learning algorithm to compute the required parameters. This algorithm includes two new regularization components that are aimed at obtaining more interpretable knowledge representations. The numerical simulations using 58 datasets show that our model achieves higher prediction rates when compared with traditional white boxes while remaining competitive with the black boxes. Finally, we elaborate on the interpretability of our neural system using a proof of concept.
Label Embedding for Multi-label Classification Via Dependence Maximization

Multi-label classification has aroused extensive attention in various fields. With the emergence of high-dimensional label space, academia has devoted to performing label embedding in recent years. Whereas current embedding approaches do not take feature space correlation sufficiently into consideration or require an encoding function while learning embedded space. Besides, few of them can be spread to track the missing labels. In this paper, we propose a Label Embedding method via Dependence Maximization (LEDM), which obtains the latent space on which the label and feature information can be embedded simultaneously. To end this, the low-rank factorization model on the label matrix is applied to exploit label correlations instead of the encoding process. The dependence between feature space and label space is increased by the Hilbert–Schmidt independence criterion to facilitate the predictability. The proposed LEDM can be easily extended the missing labels in learning embedded space at the same time. Comprehensive experimental results on data sets validate the effectiveness of our approach over the state-of-art methods on both complete-label and missing-label cases.
Do eye movements enhance visual memory retrieval?

When remembering an object at a given location, participants tend to return their gaze to that location even after the object has disappeared, known as Looking-at-Nothing (LAN). However, it is unclear whether LAN is associated with better memory performance. Previous studies reporting beneficial effects of LAN have often not systematically manipulated or assessed eye movements. We asked 20 participants to remember the location and identity of eight objects arranged in a circle, shown for 5 s. Participants were prompted to judge whether a location statement (e.g., “Star Right”) was correct or incorrect, or referred to a previously unseen object. During memory retrieval, participants either fixated in the screen center or were free to move their eyes. Results reveal no difference in memory accuracy and response time between free-viewing and fixation while a LAN effect was found for saccades during free viewing, but not for microsaccades during fixation. Memory performance was better in those free-viewing trials in which participants made a saccade to the critical location, and scaled with saccade accuracy. These results indicate that saccade kinematics might be related to both memory performance and memory retrieval processes, but the strength of their link would differ between individuals and task demands.
Liveness and Consistency of Bitcoin and Prism Blockchains: The Non-lockstep Synchronous Case

Bitcoin is a peer-to-peer payment system proposed by Nakamoto in 2008. Since then, a number of protocols based on the Nakamoto consensus have been proposed to improve the blockchain throughput while maintaining a similar level of security. In addition to bitcoin, this work also studies the Prism protocol proposed by Bagaria, Kannan, Tse, Fanti, and Viswanath in 2018. The liveness and consistency properties of the bitcoin and the Prism backbone protocols have been established by assuming either explicitly or implicitly that the blockchains have finite lifespan. While the lifespan can be arbitrarily large, it is unsatisfying for the security guarantee to be dependent on this parameter. In addition, most analyses also assume lockstep synchrony, where by the end of each round all honest miners have complete information about all blocks published until then. This paper presents a streamlined and strengthened analysis of the liveness and consistency of bitcoin and Prism protocols without the finite lifespan assumption. Also, we use the non-lockstep synchronous model which assumes the block propagation delays to be heterogeneous, arbitrary, and upper bounded by some constant. A probabilistic guarantee is also provided for a transaction to become permanent in the final ledger of all honest miners. In lieu of order optimal results, these properties take the form of explicit bounds, which provide improved design references for public transaction ledger protocols.
HpQC: A New Efficient Quantum Computing Simulator

With the continuous popularization of quantum computing, high-efficiency quantum computing simulators have attracted researchers’ attention because the running time and memory overhead of quantum computing is increased exponentially, which means that it is challenging to be simulated on a traditional computer. The current mainstream work solves this problem by using multi-node clusters, and we find that its single-node performance has not been effectively exerted. This paper proposes HpQC (High-performance Quantum Computing), a simulator that can efficiently parallel quantum computing on a single-node multi-core processor. First, HpQC used AVX2 and FMA instruction sets to maximize the advantages of SIMD (Single Instruction Multiple Data) vectorizations; second, it reduced the CPU calculation cycle by using faster and more efficient bit operations; and finally, we designed innovation data structure to utilize spatial locality of cache effectively. Besides, this article selects the state-of-the-art quantum computing simulator, QuEST (the Quantum exact simulation toolkit), as the benchmark for performance evaluation. For the quantum fourier transform, experimental results show that HpQC can achieve an average acceleration of 2.20x (GNU compiler) and 1.91x (Intel compiler), respectively, compared to QuEST. As for the random quantum circuit program, HpQC can achieve an average speedup of 1.74x (GNU compiler) and 1.51x (Intel compiler), respectively, compared to QuEST.