Reviewerly/exHarmony huggingface dataset:
A Formation Reconfiguration Method for Multiple Unmanned Surface Vehicles Executing Target Interception Missions

This paper presents a formation reconfiguration method for multiple unmanned surface vehicles (MUSVs) executing target interception missions. In its essence, the objective can be described as follows: given a target at a given position and a set of USVs, maneuver the USVs from their initial positions towards the target so that they end forming a “protective barrier” in the vicinity of the target. In its simplest form, this barrier can be simply obtained through a colinear formation of the USVs, with the center at an appropriate distance from the target. This can be done in two distinct phases: maneuver planning and vehicle task assignment. In terms of maneuver planning, an adaptive null-space-based (ANSB) strategy is adopted. In order to address explicitly the maneuverability constraints of each vehicle (for example, intrinsic limitations on the rate of turn while progressing at a given linear speed), an improved adaptive null space method (IMANSB) is proposed for vehicle speed assignment. In terms of task assignment, by introducing the virtual attractive force and rigid body dynamics, a method of redefined Dubins path was proposed, which can more quickly match the optimal path that conforms to the heading angle of the departure and the heading angle of the destination. And, a method of formation reconfiguration for sea surface target interception of USV was presented, including spatial decoupling search and redefined Dubins path planning, in order to solve the problem of multi-objective optimization of the MUSV formation internal reconfiguration which is helpful to accelerate formation forming by considering the influence of cooperative time consistency and total distance. To verify the feasibility of proposed method, a simulation platform for the formation control of MUSVs was established, and a simulation experiment was conducted.
When APIs are intentionally bypassed

Application programming interfaces (APIs) have become ubiquitous in software development. However, external APIs are not guaranteed to contain every desirable feature, nor are they immune to software defects. Therefore, API users will sometimes be faced with situations where a current API does not satisfy all of their requirements, but migrating to another API is costly. In these cases, due to the lack of communication channels between API developers and users, API users may intentionally bypass an existing API after inquiring into workarounds for their API problems with online communities. This mechanism takes the API developer out of the conversation, potentially leaving API defects unreported and desirable API features undiscovered. In this paper we explore API workaround inquiries from API users on Stack Overflow. We uncover general reasons why API users inquire about API workarounds, and general solutions to API workaround requests. Furthermore, using workaround implementations in Stack Overflow answers, we develop three API workaround implementation patterns. We identify instances of these patterns in real-life open source projects and determine their value for API developers from their responses to feature requests based on the identified API workarounds.
HARP

Modern machine learning programs are often written in Python, with the main computations specified through calls to some highly optimized libraries (e.g., TensorFlow, PyTorch). How to maximize the computing efficiency of such programs is essential for many application domains, which has drawn lots of recent attention. This work points out a common limitation in existing efforts: they focus their views only on the static computation graphs specified by library APIs, but leave the influence from the hosting Python code largely unconsidered. The limitation often causes them to miss the big picture and hence many important optimization opportunities. This work proposes a new approach named HARP to address the problem. HARP enables holistic analysis that spans across computation graphs and their hosting Python code. HARP achieves it through a set of novel techniques: analytics-conscious speculative analysis to circumvent Python complexities, a unified representation augmented computation graphs to capture all dimensions of knowledge related with the holistic analysis, and conditioned feedback mechanism to allow risk-controlled aggressive analysis. Refactoring based on HARP gives 1.3--3X and 2.07X average speedups on a set of TensorFlow and PyTorch programs.
PDKSAP : Perfected Double-Key Stealth Address Protocol without Temporary Key Leakage in Blockchain

The stealth address protocol is to create a one-time temporary output address of a transaction, hide its real output address, destroy the association between the input address and the real output address to achieve privacy protection for user identities in the transaction. However, the widely used double-key stealth address protocol (DKSAP) requires the sender to transmit the temporary public key along with the transaction, which enables attackers to easily identify stealth and non-stealth transactions and can lead to the loss of some private information. We propose a double-key stealth address protocol without temporary key leakage -- PDKSAP by which senders and receivers maintain local transaction record databases to record the number of transactions with other users. Senders and receivers generate a temporary key pair for a transaction based on the number of transactions between them, which prevent leaking the transaction temporary key. Finally, we verify the protocol through experiments.
Explaining Text Matching on Neural Natural Language Inference

Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose lack of interpretability prohibits both straightforward integration and diagnosis for further improvement. We target the task of generating token-level explanations for NLI from a neural model. Many existing approaches for token-level explanation are either computationally costly or require additional annotations for training. In this article, we first introduce a novel method for training an explanation generator that does not require additional human labels. Instead, the explanation generator is trained with the objective of predicting how the model’s classification output will change when parts of the inputs are modified. Second, we propose to build an explanation generator in a multi-task learning setting along with the original NLI task so the explanation generator can utilize the model’s internal behavior. The experiment results suggest that the proposed explanation generator outperforms numerous strong baselines. In addition, our method does not require excessive additional computation at prediction time, which renders it an order of magnitude faster than the best-performing baseline.
Analysis of coordinated behavior structures with multi-agent deep reinforcement learning

Abstract Cooperation and coordination are major issues in studies on multi-agent systems because the entire performance of such systems is greatly affected by these activities. The issues are challenging however, because appropriate coordinated behaviors depend on not only environmental characteristics but also other agents’ strategies. On the other hand, advances in multi-agent deep reinforcement learning (MADRL) have recently attracted attention, because MADRL can considerably improve the entire performance of multi-agent systems in certain domains. The characteristics of learned coordination structures and agent’s resulting behaviors, however, have not been clarified sufficiently. Therefore, we focus here on MADRL in which agents have their own deep Q-networks (DQNs), and we analyze their coordinated behaviors and structures for the pickup and floor laying problem , which is an abstraction of our target application. In particular, we analyze the behaviors around scarce resources and long narrow passages in which conflicts such as collisions are likely to occur. We then indicated that different types of inputs to the networks exhibit similar performance but generate various coordination structures with associated behaviors, such as division of labor and a shared social norm, with no direct communication.
COMET: A conceptual coupling based metrics suite for software defect prediction

Identifying defective software components is an essential activity during software development which contributes to continuously improving the software quality. Since relatively numerous defects are due to violated software dependencies, coupling metrics could increase the performance of software defect prediction. Among various measures expressing the coupling between software components, the conceptual coupling metrics capture similarities based on the semantic information contained in the source code. We are introducing a new conceptual coupling based metric suite, named COMET, for software defect prediction. Experiments conducted on publicly available data sets, using both unsupervised and supervised learning models, emphasize that COMET metrics suite is superior to the software metrics widely used in the defect prediction literature.
Universal Design-Based Framework to Assess Usability and Inclusion of Buildings

Universal Design (UD) offers different sets of principles that can be used as reference in design practice to meet the needs of the vast majority of a population. However, there is a lack of an accountable approach to measure and analyze the built environment through UD performance.This study aims to develop an evaluation framework to assess UD in public buildings to determine, in addition to accessibility requirements, the usability and inclusion of projects for different users.Multicriteria Decision Analysis (MCDA) was adopted as research methodology to systematically and scientifically develop the framework, which was structured based on knowledge derived from: an in-depth literature review on UD evaluation and workshops with stakeholders and experts. The selection and comparison of a pool of criteria is described including the cognitive mapping technique for translating information gathered by workshops.A hierarchical framework was created, consisting of three main categories of UD (i.e. physical-spatial quality, sensorial-cognitive quality, and social quality), eight criteria (i.e. usability, functionality, safety/security, wayfinding, understanding, environmental factors, well-being, and social inclusion), and 21 indicators. The proposed framework can be considered as an innovative approach in the field of accessible design evaluation since it explores the relation among a multiplicity of aspects, including human performance and social factors, to evaluate the quality of UD buildings.
Quantum synth

In this paper we present the Quantum Synth project, an interface between Qiskit and Max for controlling sound synthesis parameters encoded on the basis states of a quantum computer. This sound synthesis is obtained from the potential measured outcomes of a quantum circuit. The effects of using fundamental quantum operations as found in the Bell circuit, for the generation of entangled states, and the Grover's search algorithm have been demonstrated. The interface has been designed to be used by music performers and composers in their creative process, and as a resource to both learn Quantum Computing and analyze the intrinsic noise of real quantum hardware.
A Super-Fast Deep Network for Moving Object Detection

Deep learning methods have been actively applied to intelligent video surveillance for moving object detection in recent years and demonstrated impressive results. However, these models render superior accuracy at the cost of high computational complexity. In this work, we devised a new deep network structure that significantly improves inference speed, yet requires 10 times smaller model size and achieves 10 times reduction in floatingpoint operations as compared to existing deep learning models with tolerable accuracy loss.
Dropout Probability Estimation in Convolutional Neural Networks by the Enhanced Bat Algorithm

In recent years, deep learning has reached exceptional accomplishment in diverse applications, such as visual and speech recognition, natural language processing. The convolutional neural network represents a particular type of neural network commonly used for the task of digital image classification. A common issue in deep neural network models is the high variance problem, or also called over-fitting. Over-fitting occurs when the model fits well with the training data and fails to generalize on new data. To prevent over-fitting, several regularization methods can be used; one such powerful method is the dropout regularization. To find the optimal value of the dropout rate is a very time-consuming process; hence, we propose a model to find the optimal value by utilizing a metaheuristic algorithm instead of a manual search. In this paper, we propose a hybridized bat algorithm to find the optimal dropout probability rate in a convolutional neural network and compare the results to similar techniques. The experimental results show that the proposed hybrid method overperforms other metaheuristic techniques.
Proposed Model to Intelligent Recommendation System based on Markov Chains and Grouping of Genres

A recommendation system (RS) provides users with a list of recommended tags or resources that they may like. Still, most systems are vulnerable with certain limitations and gaps related to the recommendation environment. Several RSs do not take into account the changes of the user preference over time to ensure his satisfaction. Thus, the role of these systems is limited to define the general orientations of each user according to their observed preferences without shading light on the importance of the user preferences in short time. In this regard, we propose a new approach of recommendation for the entertainment industry that permits to guide the user towards suggestions that are more relevant according to their previous interactions. That means, we offer our users suggestions to decrease the time and frustration of discovering engaging content. Thereby, we have created our own solution, a Recommendation System based on Markov Chains and Grouping of Genres (RSMCG). It is a simple method that enables to construct an intelligent system which explores the Markov chains to predict the following actions taking into consideration the most recent actions of the user. Also, we adapt a machine learning algorithm DBSCAN clustering in order to exactly identify the interest of each user and provide adaptable answers. This approach has been studied and evaluated on the basis of a movie collection of the most popular video streaming service “Netflix”. The results of the experiments show the efficiency of our hypothesis in comparison to a sample of the user’s real needs.
Fuzzy Graph Neural Network for Few-Shot Learning

Recent works have shown that graph neural net-works (GNNs) can substantially improve the performance of few-shot learning benefitting from their natural ability to learn inter-class uniqueness and intra-class commonality. However, previous GNN methods have not achieved satisfactory performance due to the absence of a strong relational inductive bias which determines how entities interact and are isolated. In this paper, inspired by the fuzzy theory, we propose a novel meta-learning method called Fuzzy GNN (FGNN), which obtains superior relational inductive biases in each episode, for few-shot learning. Specifically, we employ an edge-focused GNN to perform the edge prediction by iteratively updating the edge-labels. According to the output of edge prediction, we design a fuzzy membership function to achieve more exact relationship representations for node classification. The parameters of the FGNN are learned by episodic training with mixed loss including node-label and edge-label. Extensive experimental evaluation clearly demonstrates the effectiveness of FGNN. The results show that our method achieves state-of-the-art performance and a significant improvement over other GNN methods on two few-shot learning benchmarks.
A Joint Neural Model for User Behavior Prediction on Social Networking Platforms

Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users’ two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many neural-based models have been designed to predict a single users’ behavior, i.e., social link behavior or consumption behavior. Compared to the classical shallow models, these neural-based models show better performance to drive a user’s behavior by modeling the complex patterns. However, there are few works exploiting whether it is possible to design a neural-based model to jointly predict users’ two kinds of behaviors to further enhance the prediction performance. In fact, social scientists have already shown that users’ two kinds of behaviors are not isolated; people trend to the consumption recommendation of friends on social platforms and would like to make new friends with like-minded users. While some previous works jointly model users’ two kinds of behaviors with shallow models, we argue that the correlation between users’ two kinds of behaviors are complex, which could not be well-designed with shallow linear models. To this end, in this article, we propose a neural joint behavior prediction model named Neural Joint Behavior Prediction Model (NJBP) to mutually enhance the prediction performance of these two tasks on social networking platforms. Specifically, there are two key characteristics of our proposed model: First, to model the correlation of users’ two kinds of behaviors, we design a fusion layer in the neural network to model the positive correlation of users’ two kinds of behaviors. Second, as the observed links in the social network are often very sparse, we design a new link-based loss function that could preserve the social network topology. After that, we design a joint optimization function to allow the two behaviors modeling tasks to be trained to mutually enhance each other. Finally, extensive experimental results on two real-world datasets show that our proposed method is on average 7.14% better than the best baseline on social link behavior while 6.21% on consumption behavior prediction. Compared with the pair-wise loss function on two datasets, our proposed link-based loss function improves at least 4.69% on the social link behavior prediction and 4.72% on the consumption behavior prediction.
Deepmpc: A Mixture Abr Approach Via Deep Learning And Mpc

The leading adaptive bitrate (ABR) algorithm leverages model predictive control (MPC) method to determine next chunks' video bitrate, while it heavily relies on the accuracy of throughput prediction, which thereby fails to perform well in all considered network scenarios. In this paper, we propose DeepMPC, which enhances MPC via two deep learning-based modules, i.e., DL-based Throughput Predictor (DTP), which can precisely predict future bandwidth, and Discounted Factor Optimizer (DFO), which estimates the prediction error. Using trace-driven experiments, we illustrate that DeepMPC outperforms existing ABR schemes in all considered network conditions, with the improvements on average QoE of 5.91% - 56.1%. Moreover, we implement DeepMPC in real-world network environments and extensive experimental results demonstrate the superiority of DeepMPC against existing state-of-the-art approaches.
SHACL Satisfiability and Containment

The Shapes Constraint Language (SHACL) is a recent W3C recommendation language for validating RDF data. Specifically, SHACL documents are collections of constraints that enforce particular shapes on an RDF graph. Previous work on the topic has provided theoretical and practical results for the validation problem, but did not consider the standard decision problems of satisfiability and containment, which are crucial for verifying the feasibility of the constraints and important for design and optimization purposes. In this paper, we undertake a thorough study of the different features of SHACL by providing a translation to a new first-order language, called , that precisely captures the semantics of SHACL w.r.t. satisfiability and containment. We study the interaction of SHACL features in this logic and provide the detailed map of decidability and complexity results of the aforementioned decision problems for different SHACL sublanguages. Notably, we prove that both problems are undecidable for the full language, but we present decidable combinations of interesting features.
History of blockchain-Blockchain 1.0: Currency

Bitcoin is a cryptocurrency, which is not backed by any country's central bank or government. It can be traded for goods or services with vendors who accept bitcoins as payment. These bitcoins are the blocks of secure data. This data is transferred from one person to another and verifying the transaction, i.e., spending the money that requires high computing power to safely verify the individual transactions. The P2P network monitors and verifies the transfer of bitcoins between users. It can be used to book hotels, shopping, financial transactions, buy video games, etc. In this chapter, we describe the evolution of bitcoin cryptocurrency to evolution of blockchain and their usage in real-world entities.
AIMMX

Despite all of the power that machine learning and artificial intelligence (AI) models bring to applications, much of AI development is currently a fairly ad hoc process. Software engineering and AI development share many of the same languages and tools, but AI development as an engineering practice is still in early stages. Mining software repositories of AI models enables insight into the current state of AI development. However, much of the relevant metadata around models are not easily extractable directly from repositories and require deduction or domain knowledge. This paper presents a library called AIMMX that enables simplified AI Model Metadata eXtraction from software repositories. The extractors have five modules for extracting AI model-specific metadata: model name, associated datasets, references, AI frameworks used, and model domain. We evaluated AIMMX against 7,998 open-source models from three sources: model zoos, arXiv AI papers, and state-of-the-art AI papers. Our platform extracted metadata with 87% precision and 83% recall. As preliminary examples of how AI model metadata extraction enables studies and tools to advance engineering support for AI development, this paper presents an exploratory analysis for data and method reproducibility over the models in the evaluation dataset and a catalog tool for discovering and managing models. Our analysis suggests that while data reproducibility may be relatively poor with 42% of models in our sample citing their datasets, method reproducibility is more common at 72% of models in our sample, particularly state-of-the-art models. Our collected models are searchable in a catalog that uses existing metadata to enable advanced discovery features for efficiently finding models.