Reviewerly/exHarmony huggingface dataset:
Scalable Distributed Hashing for Approximate Nearest Neighbor Search

Hashing has been widely applied to the large-scale approximate nearest neighbor search problem owing to its high efficiency and low storage requirement. Most investigations concentrate on learning hashing methods in a centralized setting. However, in existing big data systems, data is often stored across different nodes. In some situations, data is even collected in a distributed manner. A straightforward way to solve this problem is to aggregate all the data into the fusion center to obtain the search result (aggregating method). However, this strategy is not feasible because of the prohibitive communication cost. Although a few distributed hashing methods have been proposed to reduce this cost, they only focus on designing a distributed algorithm for a specific global optimization objective without considering scalability. Moreover, existing distributed hashing methods aim at finding a distributed solution to hashing, meanwhile avoiding accuracy loss, rather than improving accuracy. To address these challenges, we propose a Scalable Distributed Hashing (SDisH) model in which most existing hashing methods can be extended to process distributed data with no changes. Furthermore, to improve accuracy, we utilize the search radius as a global variable across different nodes to achieve a global optimum search result for every iteration. In addition, a voting algorithm is presented based on the results produced by multiple iterations to further reduce search errors. Theoretical analyses of communication, computation, and accuracy demonstrate the superiority of the proposed model. Numerical simulations on three large-scale and two relatively small benchmark datasets also show that the SDisH model achieves up to 44.75% and 10.23% accuracy gains compared to the aggregating method and state-of-the-art distributed hashing methods, respectively.
A Discrete Adapted Dragonfly Algorithm For Solving The Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a combinatorial optimization problem which has a plethora of real-world applications in various domains. Since large scale TSP problems are difficult to be solved by deterministic algorithms in a reasonable amount of time, heuristic and meta-heuristic algorithms such as swarm intelligence algorithms are usually used for solving TSP. These algorithms provide near-optimal solutions in a feasible amount of time. The Dragonfly Algorithm (DA) is a recent swarm intelligence algorithm and it has shown to have a higher performance as compared to other swarm intelligence algorithms in various applications. Since the original DA algorithm is proposed for solving continuous optimization problems, it cannot be used for solving TSP and although a binary version of the algorithm, called BDA, is also proposed, it is not suitable for solving TSP. Hence, in this paper, a variant of the DA algorithm is proposed. DA is adapted for solving TSP by adapting its equations and making use of the method of swap sequences to update the position of the artificial dragonflies in the search space. The algorithm is employed to solve a TSP problem which consists of four cities and the results show that the proposed algorithm is able to provide the optimal TSP path.
Evolved Extended Kalman Filter for first-order dynamical systems with unknown measurements noise covariance

The present work focuses on an open problem in the design of Extended Kalman filters: the lack of knowledge of the measurement noise covariance. A novel extension of the analytic behaviors framework, which integrates a theoretical formulation and evolutionary computing, has been introduced as a design methodology for the construction of this unknown parameter. The proposed methodology is developed and applied for the design of Evolved Extended Kalman Filters for nonlinear first-order dynamical systems. The proposed methodology applies an offline evolutionary synthesis of analytic nonlinear functions, to be used as measurement noise covariance, aiming to minimize the Kalman criterion. The virtues of the methodology are exemplified through a complex, highly nonlinear, first-order dynamical system, for which 2649 optimized replacements of the measurement noise covariance are found. Under different scenarios, the performance of the Evolved Extended Kalman Filter with unknown measurement noise covariance is compared with that of the conventional Extended Kalman Filter where the measurement noise covariance is known. The robustness of the Evolved Extended Kalman Filter is demonstrated through numerical evaluation.
Efficient Channel Attention Based Encoder–Decoder Approach for Image Captioning in Hindi

Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language modeling, respectively. In the existing literature, most of the works have been carried out for image captioning in the English language. This article presents a novel method for image captioning in the Hindi language using encoder–decoder based deep learning architecture with efficient channel attention. The key contribution of this work is the deployment of an efficient channel attention mechanism with bahdanau attention and a gated recurrent unit for developing an image captioning model in the Hindi language. Color images usually consist of three channels, namely red, green, and blue. The channel attention mechanism focuses on an image’s important channel while performing the convolution, which is basically to assign higher importance to specific channels over others. The channel attention mechanism has been shown to have great potential for improving the efficiency of deep convolution neural networks (CNNs). The proposed encoder–decoder architecture utilizes the recently introduced ECA-NET CNN to integrate the channel attention mechanism. Hindi is the fourth most spoken language globally, widely spoken in India and South Asia; it is India’s official language. By translating the well-known MSCOCO dataset from English to Hindi, a dataset for image captioning in Hindi is manually created. The efficiency of the proposed method is compared with other baselines in terms of Bilingual Evaluation Understudy (BLEU) scores, and the results obtained illustrate that the method proposed outperforms other baselines. The proposed method has attained improvements of 0.59%, 2.51%, 4.38%, and 3.30% in terms of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores, respectively, with respect to the state-of-the-art. Qualities of the generated captions are further assessed manually in terms of adequacy and fluency to illustrate the proposed method’s efficacy.
Aspect-Based Emotion Analysis on Speech for Predicting Performance in Collaborative Learning

This full research paper focuses on a natural language processing (NLP) driven approach to extract emotions from the speech in collaborative learning environments and analyze how they correlate with the learner's performance. Social competency is one of the base competencies that have been the target of many educational researchers in engineering and computing education during the past several years. Studies show that the low level of individual's performance is not just due to lack of intellectual or cognitive competencies but also lack of social skills impact performance in both educational and industrial domain. For this reason, Engineering Education is encapsulating social skills into the curriculum to prepare students for the fourth industrial revolution (i.e., Industry 4.0). Towards this goal in earlier work [1], we proposed a model to identify the correlation between the sentiments extracted from students' speech in teams and their performance. The results of polarity sentiment analysis showed a strong positive correlation between students' positive feelings in teams and their individual performance in the course. This study takes a further step and conducts multi-class emotion analysis on students' speech in teams. The process consists of two steps:1) extracting different classes of sentiment such as joy, anger, anxiety, etc., and identifying their correlation with students' performance using collaborative speech in an introductory programming course (CS1), 2) Aspect-Based Emotion Analysis (ABEA). The approach we adopt is the supervised machine learning method and rule-based models on speech datasets. After pre-processing the text, we identify multi classes of sentiments. Aspect extraction is accomplished through the Part of Speech (POS) tagging, and patterns are extracted from the identified aspects. Finally, we use the combination of emotion classes and aspect patterns as feature vectors to train the K-Nearest Neighbor (KNN) algorithm to predict students' performance.
Believe It or Not

Most theories of information security policy (ISP), except a few focused on the insider-centric view, are grounded in the control-centric perspective, and most ISP compliance models stem from Western countries. Regulatory focus theory (RFT) proposes two modes of motivational regulation, promotion and prevention focused that are supposed to motivate employee compliance in a trade-off. Culture is crucial to the study of ISP that puts control over human connections. Chinese guanxi, a specific dimension of Chinese culture, is better understood underlying the trust-distrust frame. To bridge the theoretical gap between the control-centric and the insider-centric perspectives, we develop an ISP behavioral model by taking an integrated approach from RFT and the trust-distrust frame. We employed scenario-based events about information security misconduct in the workplace to examine employees’ compliance intention and non-violation choice of ISP upon counterfactual thinking. Our empirical results improve the theoretical and practical implications of security practices.
Performance Analysis of Fine-tune Transferred Deep Learning

Nowadays, artificial intelligence (AI) and deep learning becomes the most important research issues. The history of AI technology started from machine learning, convolution neural networks (CNN), recurrent neural network (RNN), long short-term memory (LSTM), then developed to the latest technology of generative adversarial network (GAN). Deep learning is improving quickly. Data augmentation and transfer learning are currently advanced functions in the field of deep learning, which accelerates the whole training of the neural networks and improves model accuracy. This study implements these two functions and analyzes the performance in a deep learning model. Due to our performance study, researchers can make the most efficient AI model. To find a better deep learning model, we evaluate five deep learning strategies. In this way, we compare different learning effects and observe model accuracy. According to analysis charts and data tables, this study shows that transferred deep learning and fine-tune strategy not only increase the accuracy but also give insignificant overfitting. We also prove that fine-tune transfer learning significantly saves more time and costs in future tests and experiments.
The impact of analogical effects and social factors on the spelling of partially homophonous verb forms in informal social media writing

Abstract The present study examines unintentional spelling errors on past participles produced by Flemish teenagers in private online writing. Previous psycholinguistic research on verb spelling errors in Dutch mainly focused on identical homophones ( Bosman 2005 ; Frisson &amp; Sandra 2002 ; Sandra et al. 1999 ). The present study, however, deals with past participles that are only partially homophonous with other forms in the inflectional paradigm and investigates whether the spelling of these verbs is affected by whole-word frequency, paradigmatic and bigram support for the correct spelling and the token frequency of the past participles’ morphological family. The error rates reflect the effect of both paradigmatic and bigram support. Moreover, the unique database makes it possible to analyze the impact of three social factors ( Gender, Educational track and Age ). Our results reveal an effect on the error rates of all social variables. Finally, these social factors do not interact with paradigmatic and bigram support.
Digital Twin Based Remote Resource Sharing in Internet of Vehicles using Consortium Blockchain

With the evolving Internet of Vehicles (IoVs), the onboard resources of vehicles in computing and communication are experiencing fast growth. The sharing of road information and computing results among vehicles in proximity can effectively improve the utility of IoVs. However, remote inter-vehicular resource sharing, e.g., information and computing resource sharing, remains an under-explored issue. Motivated by this, we propose a novel digital twin based fair trading platform built upon consortium blockchain to enable city-wide vehicular resource sharing. Specifically, we first develop a digital twin based vehicular platform to enable vehicular resource sharing in the cloud. To track and secure the resource sharing among digital twins, the consortium blockchain is deployed, which is enforced by the designed smart contracts with an efficient Proof-of-Stake (PoS) consensus algorithm. In addition, an innovative incentive mechanism is devised to motivate the city-wide resource sharing for vehicles, which can maximize the profits of task publishers. Using extensive evaluations, we show the effectiveness of the proposed system.
QLogicE: Quantum Logic Empowered Embedding for Knowledge Graph Completion

Knowledge graph completion (KGC) is an important technique for implicitly identifying missing entities or relations in knowledge graphs (KGs) that are employed in various real-world applications such as question answering, information retrieval, and making recommendations. Knowledge graph embedding (KGE) is a typical KGC approach that embeds entity and relation vectors into a low-dimensional vector space for this purpose. In this paper, we present a novel KGE approach called QLogicE. It integrates translation and quantum embedding to capture features of elements within a fact, in the form of word embedding and the logical relationship among facts over KG, in the form of quantum logic. Extensive experimental results on challenging benchmark datasets confirm that the proposed approach QLogicE achieves impressive and (sometimes) surprising performance on 8 embedding dimensions, whereas state-of-the-art KGE approaches typically achieve their best performance at approximately 200 embedding dimensions. In addition, the proposed model achieves 94.84% [email protected] on the challenging dataset FB15k237, which is almost twice as good as the best performance reported in this metric.
Introduction to conformal predictors

This paper aims to provide a compact but accessible introduction to Conformal Predictors (CP), a Machine Learning method with the distinguishing property of producing predictions that exhibit a chosen error rate. This property, referred to as validity, is backed by not only asymptotic, but also finite-sample probabilistic guarantees. CPs differ from the conventional approach to prediction in that they introduce hedging in the form of set-valued predictions. The CP validity guarantees do not require assumptions such as priors, but are of broad applicability as they rely solely on exchangeability. The CP framework is universal in the sense that it operates on top of virtually any Machine Learning method. In addition to the formal definition, this introduction discusses CP variants that can be computed efficiently (Inductive or “split” CP) or that are suitable for imbalanced data sets (class-conditional CP). Finally, a short survey of the field provides references for relevant research and highlights the variety of domains in which CPs have found valuable application.
Skeleton parsing for complex question answering over knowledge bases

Answering complex questions involving multiple relations over knowledge bases is a challenging task. Many previous works rely on dependency parsing. However, errors in dependency parsing would influence their performance, in particular for long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This lightweight formalism and its BERT-based parsing algorithm help to improve the downstream dependency parsing. To show the effectiveness of skeleton, we develop two question answering approaches: skeleton-based semantic parsing (called SSP) and skeleton-based information retrieval (called SIR). In SSP, skeleton helps to improve structured query generation. In SIR, skeleton helps to improve path ranking. Experimental results show that, thanks to skeletons, our approaches achieve state-of-the-art results on three datasets: LC-QuAD 1.0, GraphQuestions, and ComplexWebQuestions 1.1.
Virtual-Reality graph visualization based on Fruchterman-Reingold using <i>Unity and SteamVR</i>

The paper describes a method for the immersive, dynamic visualization of undirected, weighted graphs. Using the Fruchterman-Reingold method, force-directed graphs are drawn in a Virtual-Reality system. The user can walk through the data, as well as move vertices using controllers, while the network display rearranges in realtime according to Newtonian physics. In addition to the physics behind the employed method, the paper explains the most pertinent computational mechanisms for its implementation, using Unity, SteamVR, and a Virtual-Reality system such as HTC Vive (the source package is made available for download). It was found that the method allows for intuitive exploration of graphs with on the order of [Formula: see text] vertices, and that dynamic extrusion of vertices and realtime readjustment of the network structure allows for developing an intuitive understanding of the relationship of a vertex to the remainder of the network. Based on this observation, possible future developments are suggested.
SolSaviour: A Defending Framework for Deployed Defective Smart Contracts

A smart contract cannot be modified once deployed. Bugs in deployed smart contracts may cause devastating consequences. For example, the infamous reentrancy bug in the DAO contract allows attackers to arbitrarily withdraw ethers, which caused millions of dollars loss. Currently, the main countermeasure against contract bugs is to thoroughly detect and verify contracts before deployment, which, however, cannot defend against unknown bugs. These detection methods also suffer from possible false negative results.
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning

Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent's behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent's long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how vision-based RL agents make sequential decisions. In addition, we further demonstrate that our method is able to provide valuable causal interpretations for vision-based RL agents from the temporal perspective.
Trajectory Tracking Using Linear State Feedback Controller for a Mecanum Wheel Omnidirectional

This paper presents a method to design a linear state feedback controller for the mecanum wheel omnidirectional to track a NURBS curve trajectory. Firstly, the robot's kinematic model and kinematic error were established to design a linear state feedback controller. Then, determine the parameters of the controller according to the varying angular velocity for the desired trajectory. An omnidirectional robot platform has been experimental designed and manufactured for experimental verification of the proposed controller. The simulation and experimental results show that the kinematic controller devel-oped by this study has high-performance trajectory tracking with minor tracking errors.
The New Dexterity Omnirotor Platform: Design, Modeling, and Control of a Modular, Versatile, All-Terrain Vehicle

Micro Aerial Vehicles (MAV) with Vertical Takeoff and Landing (VTOL) capabilities, such as quadrotors, have offered significant value to many research fields and markets. However, only recently, MAV began to be explored as systems capable of interacting with the environment, performing manipulation tasks, and participating in more versatility-demanding operations. Pursuing the goal of turning flying machines into more versatile instruments, many researchers have resorted to using tilting rotor mechanisms to create new aerial vehicle concepts. Nevertheless, most such new concepts are bulky and lack the required versatility, and are restricted to particular applications. In this work, we address these issues by proposing a novel coaxial, versatile, modular tilt-rotor UAV concept. The Omnirotor platform can apply its full thrust in any direction, regardless of the frame’s orientation where it is mounted. The platform does not have any limitations regarding rotation’s range. It can change its thrust direction continuously without needing to unwind back to a specific configuration. With the addition of control surfaces between the coaxial rotors, the Omnirotor is turned into a functional VTOL MAV with hovering capabilities that can be used as a ground vehicle, a UAV, and an all-terrain vehicle.
Aspect-based Sentiment Analysis using Dependency Parsing

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.