Reviewerly/exHarmony huggingface dataset:
ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference

Neural language representation models such as BERT, pre-trained on large-scale unstructured corpora lack explicit grounding to real-world commonsense knowledge and are often unable to remember facts required for reasoning and inference. Natural Language Inference (NLI) is a challenging reasoning task that relies on common human understanding of language and real-world commonsense knowledge. We introduce a new model for NLI called External Knowledge Enhanced BERT (ExBERT), to enrich the contextual representation with real-world commonsense knowledge from external knowledge sources and enhance BERT’s language understanding and reasoning capabilities. ExBERT takes full advantage of contextual word representations obtained from BERT and employs them to retrieve relevant external knowledge from knowledge graphs and to encode the retrieved external knowledge. Our model adaptively incorporates the external knowledge context required for reasoning over the inputs. Extensive experiments on the challenging SciTail and SNLI benchmarks demonstrate the effectiveness of ExBERT: in comparison to the previous state-of-the-art, we obtain an accuracy of \(95.9\%\) on SciTail and \(91.5\%\) on SNLI.
Interpreting and explaining pagerank through argumentation semantics

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.
A stochastic approximation approach to simultaneous feature weighting and selection for nearest neighbour learners

Nearest neighbour (NN) learners are some of the most popular methods in supervised machine learning with feature selection and weighting being two fundamental tools for improving their performance. In this study, we introduce a novel methodology for simultaneous feature selection and weighting for NN learners based on simultaneous perturbation stochastic approximation (SPSA). This is a pseudo-gradient descent optimisation algorithm that approximates gradient information from noisy objective function measurements without a need for an explicit functional form of the objective function nor its derivatives. In particular, we show how the process of simultaneous feature selection and weighting can be optimised within a stochastic approximation framework with repeated cross-validation (CV) performance as the objective function, which we call SPSA-FWS. We provide extensive computational experiments for assessment of this approach and we compare performance of SPSA-FWS to other feature weighting methods. Our results indicate that SPSA-FWS outperforms existing feature weighting algorithms for the most part and it stands as a competitive new method for this task. Specifically, when compared against its unweighted counterpart and feature weighting alone with 5-repeated 5-fold CV accuracy being the performance metric, SPSA-FWS provides improvements of up to 177.88% with a mean improvement of 20.52% for classification tasks and a mean improvement of 0.19 in the R-Squared metric for regression tasks respectively. In addition to its superior performance, SPSA-FWS has two attractive features: (1) it can be used in conjunction with any performance metric and any variant of nearest neighbour learners, and (2) it can be hybridised with other feature weighting methods.
Deep Q-learning with Explainable and Transferable Domain Rules

Recent research on deep reinforcement learning (RL) has shown its capability in automating sequential decision-making and control tasks. However, RL agents require a large number of interactions with the environment and are lacking in transferability. These problems severely restrict the adoption of deep RL in real-world tasks. Taking the learning process of human beings as inspiration, a promising way to solve these problems is to integrate transferable domain knowledge into deep RL. In this work, we propose a method called Deep Q-learning with transferable Domain Rules (DQDR) that combines transferable domain knowledge to enhance the sample efficiency and transferability of RL algorithms. We extract domain knowledge from human beings and express it into a set of rules, then couple this knowledge with the deep Q-network (DQN). The experiments are conducted by comparing this DQDR with other proposed knowledge-based methods and applying this approach to a series of CartPole and FlappyBird tasks with different system dynamics. The empirical results show that our approach can accelerate the learning process and improve the transfer capability of RL algorithms.
The Online Exam System Research Based on the MVC Framework

In this paper, the MVC framework was first introduced and the common paper constructing algorithm was analyzed in detail. Then, the paper proposed the online exam system design idea and main functional module based on the MVC framework, as well as analyzed and designed the system. Specifically, the online exam system gets involved in test base management, examinee management, paper inspection management, random paper generation, exam management, paper inspection management, inquiry statistics, examination paper backup, score management, and system management. The system applies the MVC framework and also adopts the three-tier network structure application system based on B/S mode to enhance the system maintainability and practicability.
Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

The Boltzmann machine (BM) and restricted Boltzmann machine (RBM) models are representative stochastic neural networks, in which neuron states are determined by stochastic activation functions. They are widely used in many applications. However, when analog circuits are used to realize a neural network, noise are not avoidable. The noise could come from external environments, such as power supplies and thermal noise, and they will affect the neurons’ stochastic behaviour in the BM and RBM models. Hence it is important to theoretically study how the noise affect the operation of the BM and RBM models. To best of our knowledge, there are little works related to the analysis on noisy BMs and noisy RBMs. This paper considers that there are additive noise in the inputs of the neurons, and theoretically studies the behaviors of the two models under this imperfect condition. It is found that the effect of additive noise is similar to increasing the temperature factors of the two models. Since the input noise may make the networks to have wrong stochastic behaviour, there is Kullback Leibler (KL) divergence loss in noisy BMs and noisy RBMs. Based on the Gaussian-distributed noise assumption, a noise compensation method is proposed to suppress the effect of additive noise. Experiments show that the proposed noise compensation method can greatly suppress the KL divergence loss. In addition, from the experimental results, our method is also effective for handling non-Gaussian noise.
A Fuzzy-Based Requirement Selection Method for Considering Value Dependencies in Software Release Planning

Requirement selection is an essential component of software release planning, which finds, for a given budget, an optimal subset of the requirements with the highest value. However, due to the dependencies among software requirements, selecting or ignoring a requirement may impact the values of others. But such Value Dependencies are imprecise and hard to capture; they have been ignored by the existing requirement selection methods, increasing the risk of value loss in software projects. To address this, we have proposed a fuzzy-based optimization method with two main components: (i) a fuzzy-based technique for modeling value dependencies and capturing their imprecision, and (ii) an Integer Linear Programming (ILP) model that takes into account value dependencies in software requirement selection. The scalability and effectiveness of the method in mitigating value loss are demonstrated through simulations.
Implementation of SHA 256 using MATLAB and on FPGA by the Application of Block Chain Concepts

In recent years block chain technology have gained popularity because of its secure and decentralized architecture. Block chain is a fault-tolerant distributed ledger without administrators. Block chain is originally derived from crypto currency, but it is possible to be applied to other industries. Security is enhanced by the methods of hashing, immutable ledger property, consensus protocol, proof of work (POW) mechanism etc. The design put forward facilitates the conversion of a randomly long input message to a message digest of fixed length and is implemented on FPGA. The use of FPGA provides the added advantage of fast parallel processing and computing, adaptability and real time application. The objective is to optimize the design and efficiently implement the conversion of a randomly long message to a fixed length output using the concepts of block chain.
Evaluating obsidian calibration sets with portable X-Ray fluorescence (ED-XRF) instruments

Available calibrations for obsidian transform X-ray fluorescence (XRF) spectral data into analytical units such as parts per million or weight percent through complex algorithms built around known geologic rock references (USGS and NIST) or independent matrix matched sets (Bruker from MURR and PYRO from Yale) characterized by other analytical methods, complex physics-based models of atomic energy dispersion, or both. The current portable XRF (pXRF) industry uses two main part per million predictive methods: Lucas-Tooth empirical calibration (regression-based) and Fundamental Parameters with standards. These procedures are often exclusive to a given pXRF manufacturer and claims of superiority are often cited. In addition to these associations, obsidian calibration sets are also available to either create your own regression calibration or fine-tune a Fundamental Parameters algorithm using a post-processing slope correction, thus potentially complicating pXRF accuracy through unique user customizations beyond claims by the manufacturer. This paper reviews calibration methods and examines the accuracy of three in-use pXRF instruments with respect to calibration methods and obsidian calibration sets thereby independently evaluating claims of accuracy by manufacturers. Research of this kind serves to inform new users of how to evaluate and disclose instrument and calibration accuracy before the analysis of archaeological materials.
Reservoir Computing for Scalable Hardware with <scp>Block‐Based</scp> Neural Network

Abstract This paper presents a reservoir computing technique using Asynchronous Block‐Based Neural Networks (ABBNN). Echo State Networks and Liquid State Machines introduced a new paradigm in artificial neural networks (ANN), known as Reservoir Computing (RC). A recurrent neural network (RNN) in Reservoir Computing is generated randomly, and only a readout is trained. The reservoir computing greatly facilitated the practical RNN application and outperformed classical RNN in many tasks. ABBNN is an extended version of the classical block‐based neural network model. ABBNN, an evolvable neural network model, provides a model‐free estimation of nonlinear dynamical systems. To propose a hardware‐aware model of reservoir computing and high‐speed training, we introduce Block‐Based Reservoir Computing (BBRC). BBRC provides a flexible architecture. The architecture based on the basic blocks provides a regularity that supports scalable hardware. In contrast, BBRC also supports randomness based on the internal configuration. Both are significant features in achieving an optimal hardware reservoir computer. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Computationally Efficient RGB-T UAV Detection and Tracking System

In this work, we propose a long-term UAV detection and tracking system from RGB-Thermal (RGB-T) sequences. The system consists of a high resolution daylight visible camera and a thermal camera mounted on a UAV (airborne), for the detection of flying intruders. The framework is composed of the detection and tracking modules. The primary detection module based on the YOLOv4 method is optimized for small UAV detection and works both on the RGB and Thermal domains. To alleviate the issue of temporarily losing the intruder, we employ a discriminative correlation filter based object tracker, which is initialized with the output of the detection module and tracks the target at a higher speed. The dimensionality reduction is applied to the features for tracking to improve the performance. Meanwhile, we utilize the infrared signal as a spatial regularization term of the tracker to suppress the boundary effects that stem from circular convolution, leading to a more robust appearance model and tracking performance. The tracker is efficiently optimized via the Alternating Direction Method of Multiplier (ADMM). We evaluate our method on multiple visual and thermal tracking benchmarks, as well as field tests with a prototype platform. The experimental results demonstrate that our system can achieve accurate, robust and continuous detection and tracking of UAVs under complex circumstances.
3D Human Reconstruction in the Wild with Collaborative Aerial Cameras

Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine and entertainment. The core challenges in developing a motion-capture system that operates in outdoors environments are: (1) 3D inference requires multiple simultaneous viewpoints of the target, (2) occlusion caused by obstacles is frequent when tracking moving targets, and (3) the camera and vehicle state estimation is noisy. We present a real-time aerial system for multi-camera control that can reconstruct human motions in natural environments without the use of special-purpose markers. We develop a multi-robot coordination scheme that maintains the optimal flight formation for target reconstruction quality amongst obstacles. We provide studies evaluating system performance in simulation, and validate real-world performance using two drones while a target performs activities such as jogging and playing soccer. Supplementary video: https://youtu.be/jxt91vx0cns
On the distribution of winners’ scores in a round-robin tournament

In a classical chess round-robin tournament, each of $n$ players wins, draws, or loses a game against each of the other $n-1$ players. A win rewards a player with 1 points, a draw with 1/2 point, and a loss with 0 points. We are interested in the distribution of the scores associated with ranks of $n$ players after ${{n \choose 2}}$ games, that is, the distribution of the maximal score, second maximum, and so on. The exact distribution for a general $n$ seems impossible to obtain; we obtain a limit distribution.
The Evaluation Framework of User Experience in Educational Virtual Reality Games: The Case of ilab-x Platform

User experience in educational virtual reality games has become an important research topic. However the virtual experimental items of the ilab-x platform in China is only evaluated from the popularity index, ranking interaction index, ranking praise index, ranking attractiveness index, and lack of a more comprehensive and in-depth user experience evaluation. Based on the literature review, this paper sorts out the basic framework of user experience, combined with Normans evaluation framework, and proposes an evaluation framework of user experience for virtual reality educational games, which is divided into three dimensions: sensory layer, behavior layer and emotional layer, as well as 5 first-level indicators and 20 secondary indicators. A total of 30 university students which from Zhejiang University of Technology participated in this study and their attitudes while using a virtual experimental item in the ilab-x platform were recorded by a questionnaire and interview. The results showed that the overall satisfaction of the subjects was lower in the sensory dimension and the scores in the behavioral and emotional dimensions were higher than those in the sensory dimension.
The Improved Security System in Smart Wheelchairs for Detecting Stair Descent using Image Analysis

A smart wheelchair requires a security system for its users to feel safe and comfortable. The process of observing road conditions is one of the solutions to maintaining user safety, which one of these hurdles can be a sudden transition of the situation in surface road height level for example, such as a descending staircase. Integration system for safety in smart wheelchairs consists of three main parts, namely input (Camera), output (Driver motor Left and Right), and main processing (Mini PC). The proposed research will be carried out stair decent detection using a Gray Level Co-occurrence matrix (GLCM) algorithm method as an extraction feature algorithm. The usage of GLCM methods can be applied to images that have textures. While if we look at the descent of the stairs also has a different texture when compared to the usual floor. Support Vector Machine (SVM) is used for the classification of stairs descent and floor. SVM algorithms have advantageous in it is effortless and strong consistency of implementation in classification. In this research propose combination methods between the texture features using GLCM and classification method using SVM to obtain effective detection stairs descent and floor.The proposed method by setting the GLCM parameter with a value of d = 1 and θ = 135o, and SVM classification using the Radial Basis Function Kernel (RBF Kernel) has an accuracy of 87 for detecting the stair descent with relatively fast computation time equal to 0.007 second.
Design of the Smart Objectives System in the Management of a Research Project

Application of an efficient management system is a prerequisite for a smooth and efficient running of any activity. Whilst the world is rapidly evolving, the SMART objectives (Specific, Measurable, Achievable, Relevant, Time) become essential for the management of research projects, human resources and personal development. This paper addresses the issue of management in science and discusses the implications of this approach at the micro-level, or the individual activity of the researchers. The following two situations are examined: management of the research process, carried out by the researcher, and management of a team that conducts the research. This study elucidates the strategy development of organization and monitoring of both the scientific and statistical results. The SMART management can take the strain off the project manager by enabling of permanent monitoring and gathering information for managerial reviews and external audits. It is proposed to design the science management system aligned to the financer’s policies and recommendations (government, foundations, business, etc.). Each research project has three key aspects: results, knowledge and impact, which are presented for the case of a scientific community within the Republic of Moldova. It should be noted that especially in the former Soviet countries, where the scientific communities are small and underfunded, the role of competition in research is exalted, and therefore, cooperation, strengthening and clustering of the research community becomes more important. Since the research management at the policy level has been pedaled over the competition, many fields and scientific schools, unfortunately, have been lost. The case study for the national research project RedoxPro implementation is given.
Content-based Image Retrieval using Tesseract OCR Engine and Levenshtein Algorithm

Image Retrieval Systems (IRSs) are applications that allow one to retrieve images saved at any location on a network.Most IRSs make use of reverse lookup to find images stored on the network based on image properties such as size, filename, title, color, texture, shape, and description.This paper provides a technique for obtaining full image document given that the user has some portions of the document under search.To demonstrate the reliability of the proposed technique, we designed a system to implement the algorithm.A combination of Optical Character Recognition (OCR) engine and an improved textmatching algorithm was used in the system implementation.The Tesseract OCR engine and Levenshtein Algorithm was integrated to perform the image search.The extracted text is compared to the text stored in the database.For example, a query result is returned when a significant ratio of 0.15 and above is obtained.The results showed a 100% successful retrieval of the appropriate file base on the match even when partial query images were submitted.
Generation of chaotic random binary sequences for cryptographic applications

Abstract In stream cipher systems, random key sequence generation is the primary task. The concept of chaotic systems can be considered in generating a random key sequence due to its inherent randomness property. Though the chaotic real sequences are random, the randomness will be lost after converting them to binary sequences. In this context, the authors propose a technique which uses a non‐linear feedback system to enhance the randomness of the binary sequences. In this work, three binary chaotic sequences using a logistic map are generated with three different initial conditions and each real value of the chaotic sequence is converted to ‘k’ bit binary value using a thresholding function. Some of the resulting sequences are not random. Hence, in this article a non‐linear structure is used to make the resulting sequence random. Here, a non‐linear function similar to the structure of the Simplified Data Encryption Standard is used to generate a sequence using two non‐random chaotic binary sequences. The resulting sequence is XORed with another non‐random chaotic binary sequence to get a random sequence. Sequences obtained from this nonlinear system are subjected to randomness tests and the results indicate that the sequences pass all the randomness tests. Hence, the sequences qualify as random key sequences, which can be used in a stream cipher based system to encrypt the data samples like audio, image, video, and so forth.
Towards soccer pass feasibility maps: the role of players’ orientation

- Once player tracking has been established as one of the main data sources in soccer, many challenges have emerged for data scientists, who attempt to recognize patterns from 2D trajectories in order to build tools that might help coaches to improve the performance of their teams. For instance, pass models predict where the ball should go next during pass events. However, existing models are mainly fed with players' location and prior data, hence omitting critical pieces of information such as players' body orientation. This paper presents a computational model to obtain pass feasibility maps, where player orientation is exploited and analysed. As a matter of fact, orientation proves to be crucial when modelling field-of-view and correct positioning of players, since it limits the potential receiving area of all candidates. Different proposals are given to evaluate the proposed pass feasibility map, reaching 0.46 and 0.79 in Top1 and Top3 accuracy, respectively, with a + 0.2 boost obtained after merging positional data with orientation.