Reviewerly/exHarmony huggingface dataset:
Modeling Paths for Explainable Knowledge Base Completion

A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones.A shortcoming of standard models is that they do not explain their predictions to make them verifiable easily to human inspection.In this paper, we propose the context path model (CPM) which generates explanations for new facts in KBC by providing sets of context paths as supporting evidence for these triples.For example, a new triple (Theresa May, nationality, Britain) may be explained by the path (Theresa May, born in, Eastbourne, contained in, Britain).The CPM is formulated as a wrapper that can be applied on top of various existing KBC models.We evaluate it for the well-established TransE model.We observe that its performance remains very close despite the added complexity, and that most of the paths proposed as explanations provide meaningful evidence to assess the correctness.
Mel-Frequency Cepstral Coefficients of Voice Source Waveforms for Classification of Phonation Types in Speech

Voice source characteristics in different phonation types vary due to the tension of laryngeal muscles along with the respiratory effort.This study investigates the use of mel-frequency cepstral coefficients (MFCCs) derived from voice source waveforms for classification of phonation types in speech.The cepstral coefficients are computed using two source waveforms:(1) glottal flow waveforms estimated by the quasi-closed phase (QCP) glottal inverse filtering method and (2) approximate voice source waveforms obtained using the zero frequency filtering (ZFF) method.QCP estimates voice source waveforms based on the source-filter decomposition while ZFF yields source waveforms without explicitly computing the sourcefilter decomposition.Experiments using MFCCs computed from the two source waveforms show improved accuracy in classification of phonation types compared to the existing voice source features and conventional MFCC features.Further, it is observed that the proposed features have complimentary information to the existing features.
On Intra-Class Variance for Deep Learning of Classifiers

Abstract A novel technique for deep learning of image classifiers is presented. The learned CNN models higher offer better separation of deep features (also known as embedded vectors) measured by Euclidean proximity and also no deterioration of the classification results by class membership probability. The latter feature can be used for enhancing image classifiers having the classes at the model’s exploiting stage different from from classes during the training stage. While the Shannon information of SoftMax probability for target class is extended for mini-batch by the intra-class variance, the trained network itself is extended by the Hadamard layer with the parameters representing the class centers. Contrary to the existing solutions, this extra neural layer enables interfacing of the training algorithm to the standard stochastic gradient optimizers, e.g. AdaM algorithm. Moreover, this approach makes the computed centroids immediately adapting to the updating embedded vectors and finally getting the comparable accuracy in less epochs.
A Novel Candidate Solution Generation Strategy for Fruit Fly Optimizer

Fruit fly optimization algorithm (FOA) is one of the swarm intelligence algorithms proposed for solving continuous optimization problems. In the basic FOA, the best solution is always taken into consideration by the other artificial fruit flies when solving the problem. This behavior of FOA causes getting trap into local minima because the whole population become very similar to each other and the best solution in the population during the search. Moreover, the basic FOA searches the positive side of solution space of the optimization problem. In order to overcome these issues, this study presents two novel versions of FOA, pFOA_v1 and pFOA_v2 for short, that take into account not only the best solutions but also the worst solutions during the search. Therefore, the proposed approaches aim to improve the FOA’s performance in solving continuous optimizations by removing these disadvantages. In order to investigate the performance of the novel proposed FOA versions, 21 well-known numeric benchmark functions are considered in the experiments. The obtained experimental results of pFOA versions have been compared with the basic FOA, SFOA which is an improved version of basic FOA, SPSO2011 which is one of the latest versions of particle swarm optimization, firefly algorithm called FA, tree seed algorithm TSA for short, cuckoo search algorithm briefly CS, and a new optimization algorithm JAYA. The experimental results and comparisons show that the proposed versions of FOA are better than the basic FOA and SFOA, and produce comparable and competitive results for the continuous optimization problems.
Particle Filter Vehicles Tracking by Fusing Multiple Features

Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of important traffic parameters, which is the basis of the traffic condition evaluation and the reasonable traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.
Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?

We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages.We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches.We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other neural approaches.
Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Aiming at the problems in the bearing-only tracking (BOT) of underwater weak target in the presence of interferers, such as discontinuity of tracking trajectory and target loss, a novel joint algorithm based on interference suppression (IS) and Kalman filter (ISKF) is proposed in this paper. In the proposed algorithm, in order to smooth the corrupted trajectory of weak target and improve the precision of tracking, a feedback structure which connects IS module and Kalman filter (KF) module is built. Furthermore, the feedback structure renders the measured results of bearing trajectory shareable mutually between IS module and KF module in real-time. In ISKF joint algorithm, IS module is employed as a preprocessing module to eliminate the power of interferers, and KF module is employed as tracker to obtain the bearing trajectory of weak target. In the initial step, the coarse target bearing and interference bearing are estimated using the conventional beamforming (CBF) method. In the updated step, the precise BOT trajectory of weak target is jointly updated using IS and KF. Moreover, when the target intersects with the interference, the ISKF joint algorithm can still track the weak target effectively with the aid of prediction model of KF. Finally, the sea trial results show that the proposed algorithm can achieve more accurate and robust real-time tracking than KF only, which benefits from the feedback between the IS module and the KF module.
Bitcoin Security with Post Quantum Cryptography

In a future quantum world with a large quantum computer, the security of the digital signatures used for Bitcoin transactions will be broken by Shor’s algorithm. Bitcoin has to switch to post-quantum cryptography. In this paper, we show that the post quantum signatures based on LWE and ring LWE are the most promising to use in the presence of large quantum computers running Shor’s algorithm.
Gendered Pronoun Resolution using BERT and an Extractive Question Answering Formulation

The resolution of ambiguous pronouns is a longstanding challenge in Natural Language Understanding.Recent studies have suggested gender bias among state-of-the-art coreference resolution systems.As an example, Google AI Language team recently released a genderbalanced dataset and showed that performance of these coreference resolvers is significantly limited on the dataset.In this paper, we propose an extractive question answering (QA) formulation of pronoun resolution task that overcomes this limitation and shows much lower gender bias (0.99) on their dataset.This system uses fine-tuned representations from the pre-trained BERT model and outperforms the existing baseline by a significant margin (22.2% absolute improvement in F1 score) without using any hand-engineered features.This QA framework is equally performant even without the knowledge of the candidate antecedents of the pronoun.An ensemble of QA and BERT-based multiple choice and sequence classification models further improves the F1 (23.3% absolute improvement upon the baseline).This ensemble model was submitted to the shared task for the 1st ACL workshop on Gender Bias for Natural Language Processing.It ranked 9th on the final official leaderboard.
Controller encryption for discrete event systems

The use of networked controllers is rising and with it the requirements for safe and secure communication. In this paper, a controller encryption scheme for discrete event systems is presented that secures the communication and the information inside the controller. By adapting a somewhat homomorphic encryption scheme to encrypt the controller parameters and variables, it is much more difficult for attackers to gather information about the controller and plant. The homomorphic encryption scheme allows the evaluation of encrypted data without additional decryption and encryption steps. Thus the controller can be evaluated completely in the encrypted domain. We show how to change a controller based on a signal interpreted Petri net (SIPN) into an encrypted SIPN controller and illustrate the approach by a simulation example.
Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution

Gender bias has been found in existing coreference resolvers.In order to eliminate gender bias, a gender-balanced dataset Gendered Ambiguous Pronouns (GAP) has been released and the best baseline model achieves only 66.9% F1.Bidirectional Encoder Representations from Transformers (BERT) has broken several NLP task records and can be used on GAP dataset.However, fine-tune BERT on a specific task is computationally expensive.In this paper, we propose an end-toend resolver by combining pre-trained BERT with Relational Graph Convolutional Network (R-GCN).R-GCN is used for digesting structural syntactic information and learning better task-specific embeddings.Empirical results demonstrate that, under explicit syntactic supervision and without the need to fine tune BERT, R-GCN's embeddings outperform the original BERT embeddings on the coreference task.Our work significantly improves the snippet-context baseline F1 score on GAP dataset from 66.9% to 80.3%.We participated in the Gender Bias for Natural Language Processing 2019 shared task, and our codes are available online.
Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.
A Large-Gap Clone Detection Approach Using Sequence Alignment via Dynamic Parameter Optimization

Large-gap clones, a kind of clones that reuses code with many edits, are very common in software development practice and widespread in software systems. The detection of such clones is very crucial. However, due to a large number of edits, most of the current work fails to detect such clones effectively. This paper aims to find an effective approach for accurate detection of large-gap clones. We transform the code clone detection problem into a biological sequence alignment question and propose a novel approach that combines code fingerprint with sequence alignment. The sequence alignment is Smith-Waterman algorithm based, but shows significant improvements using dynamic parameter acquisition strategy. Furthermore, we design new rational criteria for clone identification. The proposed approach is automatically evaluated extensively by more than 10 million lines of code for general clones detection. We further conduct an empirical study on five large-scale Java projects to manually measure the approach for large-gap clones detection. The experimental results show that the proposed approach can effectively detect large-gap clones and exhibit good performance, and at the same time remains the competitiveness with existing advanced detection tools in detecting general clone detection.
A Clutter Suppression Method Based on SOM-SMOTE Random Forest

Clutter suppression is always a significant but difficult problem in search radars. In complex clutter environment, conventional method may generate a large number of clutter residual plots. Therefore, we propose a novel clutter suppression method based on SOM-SMOTE Random Forest (SOM-SMOTE-RF) from the perspective of classifying targets and clutters. Firstly, we design signal-level features like amplitude, phase, spatial correlation, and range profile of radar echoes in order to construct a high-dimensional feature vector. Secondly, SOM-SMOTE is proposed by combining Self Organizing Map (SOM) and the Synthetic Minority Over-sampling Technique (SMOTE) to solve the imbalanced dataset problem in training Random Forest. Real data shows that SOM-SMOTE-RF suppresses 82% clutters with only 5% targets loss.
Question Answering in Knowledge Bases

Question answering over knowledge bases aims to take full advantage of the information in knowledge bases with the ultimate purpose of returning answers to questions. To access the substantial knowledge within the KB, many model architectures are hindered by the bottleneck of accurately predicting relations that connect subject entities in questions to object entities in the knowledge base. To break the bottleneck, this article presents a novel model architecture, APVA, which includes a verification mechanism to check the correctness of predicted relations. Specifically, APVA takes advantage of KB-based information to improve relation prediction but verifies the correctness of the predicted relation by means of simple negative sampling in a logistic regression framework. The APVA architecture offers a natural way to integrate an iterative training procedure, which we call turbo training. Accordingly, we introduce APVA-TURBO to perform question answering over knowledge bases. We demonstrate extensive experiments to show that APVA-TURBO outperforms existing approaches on question answering.
Quantum process tomography with unknown single-preparation input states: Concepts and application to the qubit pair with internal exchange coupling

Quantum process tomography (QPT) methods aim at identifying, i.e., estimating, a given quantum process. QPT is a major quantum information processing tool, since it especially allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. However, usual QPT procedures are complicated, since they set several constraints on the quantum states used as inputs of the process to be characterized. In this paper, we extend QPT so as to avoid two such constraints. On the one hand, usual QPT methods require one to know, hence to very precisely control (i.e., prepare), the specific quantum states used as inputs of the considered quantum process, which is cumbersome. We therefore propose a blind, or unsupervised, extension of QPT (i.e., BQPT), which means that this approach uses input quantum states whose values are unknown and arbitrary, except that they are requested to meet some general known properties (and this approach exploits the output states of the considered quantum process). On the other hand, usual QPT methods require one to be able to prepare many copies of the same (known) input state, which is constraining. In contrast, we propose ``single-preparation BQPT methods'' (SBQPT) , i.e., methods which can operate with only one instance of each considered input state. These two concepts are here illustrated with practical (S)BQPT methods which are numerically validated, in the case when (i) random pure states are used as inputs and their required properties are especially related to the statistical independence of the random variables that define them and (ii) the considered quantum process is based on cylindrical-symmetry Heisenberg spin coupling. As a benchmark, we moreover introduce nonblind QPT methods dedicated to the considered Heisenberg process, we analyze their theoretical behavior (this requires the tools developed in this paper for random input states), and we numerically test their sensitivity to systematic and nonsystematic errors, which are most likely to occur in practice. This shows that, even for very low preparation errors (especially systematic ones), these nonblind QPT methods yield much lower performance than our SBQPT methods. Our blind and single-preparation QPT concepts may be extended, e.g., to a much wider class of processes and to SBQPT methods based on other quantum state properties, as outlined in this paper.
Finding Shortest Path for Road Network Using Dijkstra’s Algorithm

Roads play a Major role to the people live in various states, cities, town and villages, from each and every day they travel to work, to schools, to business meetings, and to transport their goods. Even in this modern era whole world used roads, remain one of the most useful mediums used most frequently for transportation and travel. The manipulation of shortest paths between various locations appears to be a major problem in the road networks. The large range of applications and product was introduced to solve or overcome the difficulties by developing different shortest path algorithms. Even now the problem still exists to find the shortest path for road networks. Shortest Path problems are inevitable in road network applications such as city emergency handling and drive guiding system. Basic concepts of network analysis in connection with traffic issues are explored. The traffic condition among a city changes from time to time and there are usually huge amounts of requests occur, it needs to find the solution quickly. The above problems can be rectified through shortest paths by using the Dijkstra’s Algorithm. The main objective is the low cost of the implementation. The shortest path problem is to find a path between two vertices (nodes) on a given graph, such that the sum of the weights on its constituent edges is minimized. This problem has been intensively investigated over years, due to its extensive applications in graph theory, artificial intelligence, computer network and the design of transportation systems. The classic Dijkstra’s algorithm was designed to solve the single source shortest path problem for a static graph. It works starting from the source node and calculating the shortest path on the whole network. Noting that an upper bound of the distance between two nodes can be evaluated in advance on the given transportation network.
StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evidence for image (photographic) steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retain the original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.
Improving Students Listening Skill by Using Audio Visual Media

Teaching Listening skills to the students who learn English as a foreign language is a challenge for the teacher . This classroom action research (CAR) designed to improve the listening ability of students by using audio-visual media. The subject of this research are students of English, University of Bengkulu Diploma Program who take the course Listening For Lecture . This study was conducted in two cycles of progress namely; planning, observation, action and reflection. Moreover, collaborative learning methods are also applied to stimulate student activeness in the classroom. The results showed that the use of audio visual media is able to improve the results of listening course. In addition, the students also Become more motivated and enthusiastic during the lecture which can be seen in the group discussion stage that implement colaborative learning. Generally it can be concluded that audio visual media can improve the focus and concentration of students in listening courses. In addition to the use of audio visual media, the selection of the topic material also influence the results of students listening comprehension.