Reviewerly/exHarmony huggingface dataset:
Trajectory Planning with Obstacle Avoidance of 3 DoF Robotic Arm for Test Tube Handling System

This paper presents a trajectory planning method with obstacle avoidance strategy of 3 DoF parallel link robotic arm to perform the task of autonomous test tube handling. For implementation in real-world environment such as in the process of material handling within healthcare setting, robotic arm frequently needs to deal with obstacles come up on its way of finishing its task. Hence to maintain a smooth actuation and a safer manipulation point-to-point from a start to a goal position, a method of trajectory planning with obstacle avoidance is required. In this paper, the method of cubic polynomials trajectory generation with distance calculation for obstacle avoidance has been performed. The result of our experiments shows that our proposed method is successfully demonstrated with a high degree of accuracy and precision in our research setup. The average position error of trajectory tracking is not more than 0.43 cm during the experiment with obstacle, and 0.35 cm during the experiment without obstacle.
Practical Lattice-Based Zero-Knowledge Proofs for Integer Relations

We present a novel lattice-based zero-knowledge proof system for showing that (arbitrary-sized) committed integers satisfy additive and multiplicative relationships. The proof sizes of our schemes are between two to three orders of magnitude smaller than in the lattice proof system of Libert et al. (CRYPTO 2018) for the same relations. Because the proof sizes of our protocols grow linearly in the integer length, our proofs will eventually be longer than those produced by quantum-safe succinct proof systems for general circuits (e.g. Ligero, Aurora, etc.). But for relations between reasonably-sized integers (e.g. $512$-bit), our proofs still result in the smallest zero-knowledge proof system based on a quantum-safe assumption. Of equal importance, the run-time of our proof system is at least an order of magnitude faster than any other quantum-safe scheme.
Wendy, the Good Little Fairness Widget

The advent of decentralized trading markets introduces a number of new challenges for consensus protocols. In addition to the 'usual' attacks -- a subset of the validators trying to prevent agreement -- there is now the possibility of financial fraud, which can abuse properties not normally considered critical in consensus protocols. We investigate the issues of attackers manipulating or exploiting the order in which transactions are scheduled in the blockchain. More concretely, we look into order fairness, i.e., ways we can assure that the order of transactions relative to each other is fair. We show that one of the more intuitive definitions of fairness is impossible to achieve. We then present Wendy, a group of low overhead protocols that can implement different concepts of fairness. Wendy acts as an additional widget for an existing blockchain, and is largely agnostic to the underlying blockchain and its security assumptions, as long as they provide a known and always active set of validators. Furthermore, it is possible to implement fairness for some subsets of the transactions, and thus run several independent fair markets (as well as some unfair ones) on the same chain.
How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study

Software design is an important concern in modern code review through which multiple developers actively discuss and improve each single code change. However, there is little understanding of the impact of such developers' reviews on continuously reducing design degradation over time. It is even less clear to what extent and how design degradation is reversed during the process of each single code change's review. In summary, existing studies have not assessed how the process of design degradation evolution is impacted along: (i) within each single review, and (ii) across multiple reviews. As a consequence, one cannot understand how certain code review practices consistently contribute to either reduce or further increase design degradation as the project evolves. We aim at addressing these gaps through a multi-project retrospective study. By investigating 14,971 code reviews from seven software projects, we report the first study that characterizes how the process of design degradation evolves within each review and across multiple reviews. Moreover, we analyze a comprehensive suite of metrics to enable us to observe the influence of certain code review practices on combating or even accelerating design degradation. Our results show that the majority of code reviews had little to no design degradation impact in the analyzed projects. Even worse, this observation also applies, to some extent, to reviews with an explicit concern on design. Surprisingly, the practices of long discussions and high proportion of review disagreement in code reviews were found to increase design degradation. Finally, we also discuss how the study findings shed light on how to improve the research and practice of modern code review.
Zero Knowledge Proofs for Decision Tree Predictions and Accuracy

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning models that are claimed to achieve high accuracy remains challenging, and the correctness and consistency of machine learning predictions in real products lack any security guarantees. In this paper, we initiate the study of zero knowledge machine learning and propose protocols for zero knowledge decision tree predictions and accuracy tests. The protocols allow the owner of a decision tree model to convince others that the model computes a prediction on a data sample, or achieves a certain accuracy on a public dataset, without leaking any information about the model itself. We develop approaches to efficiently turn decision tree predictions and accuracy into statements of zero knowledge proofs. We implement our protocols and demonstrate their efficiency in practice. For a decision tree model with 23 levels and 1,029 nodes, it only takes 250 seconds to generate a zero knowledge proof proving that the model achieves high accuracy on a dataset of 5,000 samples and 54 attributes, and the proof size is around 287 kilobytes.
Mobile Robot Path Planning Based on Improved DDPG Reinforcement Learning Algorithm

Mobile robotics has a wide range of applications and path planning is key to its realization. Mobile robots need to explore the environment autonomously to find their destinations. The Deep Deterministic Policy Gradient (DDPG) algorithm, a classical algorithm in deep reinforcement learning, has a large advantage in continuous control problems. However, the DDPG algorithm suffers from the problems of low training efficiency and slow convergence caused by the high proportion of illegal policies due to the lack of policy action filtering. In this paper, we propose a mobile robot path planning method based on an improved DDPG reinforcement learning algorithm, which uses a small amount of a priori knowledge to accelerate the training of deep reinforcement learning, reduce the number of trial and error, and adopt an adaptive exploration method based on the ε-greedy algorithm. Dynamically adjust the exploration factor to rationally allocate the probability of exploration and exploitation. The adaptive exploration method can improve the exploration efficiency, reduce the exploration duration and speed up the convergence of the algorithm. Simulation experiments are conducted in a grid environment, and the results show that the proposed algorithm can successfully find the optimal path. Moreover, the comparison experiments between Q-learning, SARSA and the proposed algorithm demonstrate that the proposed algorithm has better path planning performance, spends the least computation time and converges the fastest.
Online Multi-modal Person Search in Videos

The task of searching certain people in videos has seen increasing potential in real-world applications, such as video organization and editing. Most existing approaches are devised to work in an offline manner, where identities can only be inferred after an entire video is examined. This working manner precludes such methods from being applied to online services or those applications that require real-time responses. In this paper, we propose an online person search framework, which can recognize people in a video on the fly. This framework maintains a multi-modal memory bank at its heart as the basis for person recognition, and updates it dynamically with a policy obtained by reinforcement learning. Our experiments on a large movie dataset show that the proposed method is effective, not only achieving remarkable improvements over online schemes but also outperforming offline methods.
Wavelet-Based Dual Recursive Network for Image Super-Resolution

Although remarkable progress has been made on single-image super-resolution (SISR), deep learning methods cannot be easily applied to real-world applications due to the requirement of its heavy computation, especially for mobile devices. Focusing on the fewer parameters and faster inference SISR approach, we propose an efficient and time-saving wavelet transform-based network architecture, where the image super-resolution (SR) processing is carried out in the wavelet domain. Different from the existing methods that directly infer high-resolution (HR) image with the input low-resolution (LR) image, our approach first decomposes the LR image into a series of wavelet coefficients (WCs) and the network learns to predict the corresponding series of HR WCs and then reconstructs the HR image. Particularly, in order to further enhance the relationship between WCs and image deep characteristics, we propose two novel modules [wavelet feature mapping block (WFMB) and wavelet coefficients reconstruction block (WCRB)] and a dual recursive framework for joint learning strategy, thus forming a WCs prediction model to realize the efficient and accurate reconstruction of HR WCs. Experimental results show that the proposed method can outperform state-of-the-art methods with more than a 2× reduction in model parameters and computational complexity.
Student Perceptions of Semester-Long In-Class Virtual Reality: Effectively Using “Google Earth VR” in a Higher Education Classroom

Virtual reality (VR) usage is increasing in higher education, yet VR retains significant financial, technological, and time costs. Given these challenges, understanding student perceptions of the legitimacy of educational VR is important. In-class VR was introduced in two semesters with four total face-to-face Geography courses at Arkansas Tech University. Throughout each semester, individual students periodically used the HTC Vive's Google Earth VR app to virtually visit places relevant to that day's lecture. The VR video was mirrored to a classroom screen for all students to see. These VR sessions formed parts of many class meetings, in contrast to recent studies that examine educational VR as short-term experiments or out-of-classroom trials. Students were surveyed about their perceptions before and after the semester, and overall student views of classroom VR were positive. Perceptions were similar between students who used VR in class and students who did not. These findings are important as educational institutions continue to invest more time and resources into VR.
easyQuake: Putting Machine Learning to Work for Your Regional Seismic Network or Local Earthquake Study

Abstract We developed a Python package—easyQuake—that consists of a flexible set of tools for detecting and locating earthquakes from International Federation of Digital Seismograph Networks-collected or field-collected seismograms. The package leverages a machine-learning driven phase picker, coupled with an associator, to produce a Quake Markup Language (QuakeML) style catalog complete with magnitudes and P-wave polarity determinations. We describe how nightly computations on day-long seismograms identify lower-magnitude candidate events that were otherwise missed due to cultural noise and how those events are incorporated into the Oklahoma Geological Survey statewide network upon analyst manual review. We discuss applications for the package, including earthquake detection for regional networks and microseismicity studies in arbitrary user-defined regions. Because the fundamentals of the package are scale invariant, it has wide application to seismological earthquake analysis from regional to local arrays and has great potential for identifying early aftershocks that are otherwise missed. The package is fast and reliable; the computations are relatively efficient across a range of hardware, and we have encountered very few (∼1%) false positive event detections for the Oklahoma case study. The utility and novelty of the package is the turnkey earthquake analysis with QuakeML file output, which can be dropped directly into existing real-time earthquake analysis systems. We have designed the functions to be quite modular so that a user could replace the provided picker or associator with one of their choosing. The Python package is open source and development continues.
CNN spatiotemporal features and fusion for surveillance video forgery detection

Surveillance cameras are widely used to provide protection and security; also their videos are used as strong evidences in the courts. Through the availability of video editing tools, it has become easy to distort these evidences. Sometimes, to hide the traces of forgery, some post-processing operations are performed after editing. Hence, the authenticity and integrity of surveillance videos have become urgent to scientifically validate. In this paper, we propose inter-frame forgeries (frame deletion, frame insertion, and frame duplication) detection system using 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatically feature extraction; Gaussian RBF multi-class support vector machine (RBF-MSVM) is used for classification process. The experimental results show that the efficiency of the proposed system for detecting all inter-frame forgeries, even when the forged videos have undergone additional post-processing operations such as Gaussian noise, Gaussian blurring, brightness modifications and compression.
Rapid fair sampling of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> spin Hamiltonian with a laser simulator

This paper presents a new simulator based on dissipatively coupled lasers for rapid and efficient fair sampling of XY spin Hamiltonian with complex ground state manifolds. The authors use the multiple longitudinal modes of each laser to form an ensemble of identical but independent simulators to provide statistical fair sampling.
Approximation algorithms for quantum many-body problems

We discuss classical algorithms for approximating the largest eigenvalue of quantum spin and fermionic Hamiltonians based on semidefinite programming relaxation methods. First, we consider traceless $2$-local Hamiltonians $H$ describing a system of $n$ qubits. We give an efficient algorithm that outputs a separable state whose energy is at least $\lambda_{\max}/O(\log n)$, where $\lambda_{\max}$ is the maximum eigenvalue of $H$. We also give a simplified proof of a theorem due to Lieb that establishes the existence of a separable state with energy at least $\lambda_{\max}/9$. Secondly, we consider a system of $n$ fermionic modes and traceless Hamiltonians composed of quadratic and quartic fermionic operators. We give an efficient algorithm that outputs a fermionic Gaussian state whose energy is at least $\lambda_{\max}/O(n\log n)$. Finally, we show that Gaussian states can vastly outperform Slater determinant states commonly used in the Hartree-Fock method. We give a simple family of Hamiltonians for which Gaussian states and Slater determinants approximate $\lambda_{\max}$ within a fraction $1-O(n^{-1})$ and $O(n^{-1})$ respectively.
Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.
Hear Sign Language: A Real-time End-to-End Sign Language Recognition System

Sign language recognition (SLR) bridges the communication gap between the hearing-impaired and the ordinary people. However, existing SLR systems either cannot provide continuous recognition or suffer from low recognition accuracy due to the difficulty of sign segmentation and the insufficiency of capturing both finger and arm motions. The latest system, SignSpeaker, has a significant limit in recognizing two-handed signs with only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i> smartwatch. To address these problems, this paper designs a novel real-time end-to-end SLR system, called DeepSLR, to translate sign language into voices to help people “hear” sign language. Specifically, two armbands embedded with an IMU sensor and multi-channel sEMG sensors are attached on the forearms to capture both coarse-grained arm movements and fine-grained finger motions. We propose an attention-based encoder-decoder model with a multi-channel convolutional neural network (CNN) to realize accurate, scalable, and end-to-end continuous SLR without sign segmentation. We have implemented DeepSLR on a smartphone and evaluated its effectiveness through extensive evaluations. The average word error rate of continuous sentence recognition is 10.8 percent, and it takes less than 1.1s for detecting signals and recognizing a sentence with 4 sign words, validating the recognition efficiency and real-time ability of DeepSLR in real-world scenarios.
A secure distributed ledger for transactive energy: The Electron Volt Exchange (EVE) blockchain

The adoption of blockchain for Transactive Energy has gained significant momentum as it allows mutually non-trusting agents to trade energy services in a trustless energy market. Research to date has assumed that the built-in Byzantine Fault Tolerance in recording transactions in a ledger is sufficient to ensure integrity. Such work must be extended to address security gaps including random bilateral transactions that do not guarantee reliable and efficient market operation, and market participants having incentives to cheat when reporting actual production/consumption figures. Work herein introduces the Electron Volt Exchange framework with the following characteristics: (1) a distributed protocol for pricing and scheduling prosumers' production/consumption while keeping constraints and bids private, and (2) a distributed algorithm to prevent theft that verifies prosumers' compliance to scheduled transactions using information from grid sensors (such as smart meters) and mitigates the impact of false data injection attacks. Flexibility and robustness of the approach are demonstrated through simulation and implementation using Hyperledger Fabric.
Learning to Update for Object Tracking With Recurrent Meta-Learner

Model update lies at the heart of object tracking. Generally, model update is formulated as an online learning problem where a target model is learned over the online training set. Our key innovation is to \emph{formulate the model update problem in the meta-learning framework and learn the online learning algorithm itself using large numbers of offline videos}, i.e., \emph{learning to update}. The learned updater takes as input the online training set and outputs an updated target model. As a first attempt, we design the learned updater based on recurrent neural networks (RNNs) and demonstrate its application in a template-based tracker and a correlation filter-based tracker. Our learned updater consistently improves the base trackers and runs faster than realtime on GPU while requiring small memory footprint during testing. Experiments on standard benchmarks demonstrate that our learned updater outperforms commonly used update baselines including the efficient exponential moving average (EMA)-based update and the well-designed stochastic gradient descent (SGD)-based update. Equipped with our learned updater, the template-based tracker achieves state-of-the-art performance among realtime trackers on GPU.
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing

Knowledge graphs (KGs) can vary greatly from one domain to another.Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage of domain-specific parallel graphtext data; at the same time, adapting a model trained on a different domain is often impossible due to little or no overlap in entities and relations.This situation calls for an approach that (1) does not need large amounts of annotated data and thus (2) does not need to rely on domain adaptation techniques to work well in different domains.To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.We evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome.Our system outperforms strong baselines for both text↔graph conversion tasks without any manual adaptation from one dataset to the other.In additional experiments, we investigate the impact of using different unsupervised objectives. 1