Reviewerly/exHarmony huggingface dataset:
HPSG Grammar Supporting Arabic Preference Nouns and Its TDL Specification

An Arabic preference noun (i.e. comparative and superlative) is among the nominal forms derived from a verb that we have treated. Our work is pure morphological processing to generate automatically the derivatives of nouns and verbs from canonical forms. This treatment is an important step in Natural Language Processing (NLP). It represents a pretreatment for syntactic analyses and contributes in the construction of extensional lexicon. In this paper, we present a part of our work, the Head Driven Phrase Structure Grammar (HPSG) representation of preference nouns for Arabic language. The choice of this formalism is justified. In fact, HPSG is a unification grammar based on a set of principles, essentially inheritance. This principle allows treating several phenomena with a minimum number of rules.. For this reason, we start by classifying different patterns of preference nouns and representing them with HPSG. After that, each pattern was specified with Type Description Language (TDL). Then, it was validated on Linguistic Knowledge Building (LKB) system. The obtained results are encouraging which proves the effectiveness of our system.
A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion

This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.
Complex Systems: On Design and Architecture of Adaptable Dashboards

Over the years dashboards have become an essential part of managers’ toolkit. The recent developments in the field of IT allowed companies to build complex monitoring and metric-driven solutions for their business needs. The increasing amount of complexity in these dashboards resulted in the increased cost of maintenance and further development. In addition, large corporations have experienced concerns with designing dashboards that are suitable for multiple roles within the organization, i.e. showing the appropriate metrics to people at different positions. By having a self-adjusting, adaptable dashboard, businesses would not only increase the productivity of their workers but could benefit from a fully-fledged Adaptable System (AS) that requires little to no maintenance while performing better than a manually-built and maintained dashboard. Nevertheless, such a system would have a broader set of additional requirements that will be discussed later. This paper presents the design and the architecture of types of adaptable dashboards that address the above-mentioned concerns.
Implementation of CMOS Logic Circuits with Perfect Fault Detection Using Preservative Reversible Gates

In reversible circuits, a fault as a change in logic value at a circuit node always alters an output logic value, so observability of faults at the output is 100%. In other words, reversible circuits are latent-fault-free. Our motivation is to incorporate this unique feature of reversible circuits to design CMOS circuits having perfect or 100% Concurrent Error Detection (CED). For this purpose we propose a new, fault preservative, and reversible gate library called Even Target - Mixed Polarity Multiple Control Toffoli (ET-MPMCT). By using ET-MPCT, we ensure that the parity, even or odd, is preserved at all levels including the output level unless there is a faulty node. Our design strategy has two steps for a reversible function: 1) implement the reversible functions with the ET-MPMCT library; and 2) apply reversible-to-CMOS gate conversion. In case an irreversible function needs to be synthesized then its reversible form is used followed by the two design steps. As a result, we have come up with a CMOS circuit having 100% CED. The performance of our approach is compared with other CED schemes in the literature in terms of area, detection rate, and power consumption. Simulations are done with Cadence Genus tool using TSMC 0.18 μm technology. Clearly, results are in favor of our proposed technique.
An Optimal Algorithm to Find Champions of Tournament Graphs

A tournament graph $$T = \left( V, E \right) $$ is an oriented complete graph, which can be used to model a round-robin tournament between n players. In this short paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. Our goal is to solve the problem by minimizing the number of arc lookups, i.e., the number of matches played. We prove that finding a champion requires $$\varOmega (\ell n)$$ comparisons, where $$\ell $$ is the number of matches lost by the champion, and we present a deterministic algorithm matching this lower bound without knowing $$\ell $$ . Solving this problem has important implications on several Information Retrieval applications including Web search, conversational AI, machine translation, question answering, recommender systems, etc.
Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction

Dialogue act (DA) represents the meaning of an utterance at the illocutionary force level (Austin 1962) such as questions, requests, and greetings. Since DAs take charge of the most fundamental part of communication, we believe that the elucidation of DA learning mechanism is important for cognitive science and artificial intelligence, especially for cognitive robotics. The purpose of this study is to verify that scaffolding takes place when a human teaches a robot, and to let a robot learn to estimate DAs and to make a response based on them step by step utilizing scaffolding provided by a human. To realize that, it is necessary for the robot to detect changes in utterance and rewards given by the partner and continue learning accordingly. Experimental results demonstrated that participants who continued interaction for a sufficiently long time often gave scaffolding for the robot. Although the number of experiments is still insufficient to obtain a definite conclusion, we observed that 1) the robot quickly learned to respond to DAs in most cases if the participants only spoke utterances that match the situation, 2) in the case of participants who built scaffolding differently from what we assumed, learning did not proceed quickly, and 3) the robot could learn to estimate DAs almost exactly if the participants kept interaction for a sufficiently long time even if the scaffolding was unexpected.
Secure Identity-Based Proxy Signature With Computational Diffie-Hellman for Cloud Data Management

This chapter explains a secure smart cloud framework based on identity-based proxy signature (IDBPS) scheme on Computational Diffie-Hellman (CD-H) assumption and AckIBE for data management. The objective of this chapter is to construct a secure hierarchical structure of homogeneous and heterogeneous cloud centers. This structure gives various types of computing services in the support of data analysis and information management. In this, the authors also introduce a security-related solution based on acknowledgment identity-based encryption (AckIBE), an IDBPS on computational Diffie-Hellman assumption, and identity-based proxy re-encryption to face critical security issues of the proposed framework.
Research on Gyro Fault Diagnosis Method Based on Wavelet Packet Decomposition and Multi-class Least Squares Support Vector Machine

In order to diagnose the faulty gyro, a method of fault diagnosis for the genus gyro based on wavelet packet decomposition and least squares support vector machine is proposed. Four kinds of gyro fault signal models are established by studying the characteristics of the output signals in the gyro fault state. The wavelet packet decomposition is used to perform time–frequency domain decomposition of gyro signals in various states, and the wavelet packet energy entropy value in each frequency band is calculated as a feature vector for characterizing the gyro fault state. The “one-to-many” multi-class least-squares support vector machine is used to identify the feature vectors in one state, and the parameters of LSSVM are optimized by using firefly algorithm and tenfold cross-validation method, respectively. The results show that the two methods are The diagnostic accuracy rate of the gyro fault data is more than 90%, and the calculation speed is fast, and the fault diagnosis of the gyro is successfully realized.
A Natural Immersive Closed-Loop Interaction Method for Human–Robot “Rock–Paper–Scissors” Game

The existing Kinect somatosensory games and other human–computer interaction are lack of natural interaction experience. We made research on a natural immersive closed-loop interaction method for human–robot “rock–paper–scissors” game. Kinect was used to make somatosensory dynamic recognition. Mechanical arm was used to make gestures to interact with human and to show the result of the game. System through the development of Modbus master and Modbus slave based on Arduino to control the mechanical arm, and used the multithread concurrent to achieve a better real-time experience. The PC was programmed to determine the outcome and record the result. The natural immersive closed-loop interaction method has the advantages of natural interaction, fast response, and strong expansibility.
A Prior Setting that Improves LDA in both Document Representation and Topic Extraction

Latent Dirichlet Allocation (LDA), as the most popular topic model, models documents as mixtures of topics and topics as mixtures of words. Topic mixture well represents documents while words mixture extracts meaningful topics from a corpus. The nature of LDA makes it a powerful tool in documents organizing and corpus summarizing. One limitation of LDA is that its performance depends heavily on the priors. Researchers show priors matters in LDA and propose methods to learn the priors for better modeling, regardless of using symmetric priors. However, LDA modeling ability does not necessarily consent with the performance of LDA in documents representation and topic extraction. In this paper, we propose a novel prior setting for LDA. The setting improves LDA in both documents representation and topic extraction performance. We experiment to compare our setting with symmetric priors and previously proposed priors that enhances modeling ability. Experiments on the topic quality show that LDA with our prior setting extracts better topics than LDA with other kinds of prior settings. We compare LDA document representation ability through tasks such as document clustering and document classification. These experiments demonstrate LDA with our proposed priors represents document better. Moreover, our analyses also reveal that better modeling does not necessarily lead to better performance in documents representation and topic extraction.
Ambiguous, informal, and unsound: metaprogramming for naturalness

Program code needs to be understood by both machines and programmers. While the goal of executing programs requires the unambiguity of a formal language, programmers use natural language within these formal constraints to explain implemented concepts to each other. This so called naturalness – the property of programs to resemble human communication – motivated many statistical and machine learning (ML) approaches with the goal to improve software engineering activities.
A Method of Bug Report Quality Detection Based on Vector Space Model

As a vehicle for recording and tracking defects, bug report provides basis for solving software quality problems. However, moving fake or duplication bug report in multi-person and parallel software testing project is a labor-intensive job. Therefore, this paper proposes a method based on vector space model for automatic dealing with this problem. We built a matching library according to the test requirements and confirmed bug reports and used vector space model to calculate the similarity between the bug report and the matching library. Then the correctness of the bug report is detected based on this similarity.
Text Summarization using Partial Textual Entailment based Graphs

Information explosion has boosted research communities to propose many text summarization methods using different approaches. Text connectedness is a potential textual attribute to identify significant sentences from the source document to form a meaningful summary. A recent work that employs a graph based method where nodes represent sentences and where Textual Entailment (TE) is used to measure connectedness relationship between sentences. TE is a recent and good indicator of text inference in NLP applications. The minimum vertex cover method is used to generate cover of vertices that correlates to summaries of the document. The method has yielded good summaries. However, we have observed the quality of summarization is limited by the accuracy of TE determination. This paper attempts to overcome this limitation by employing Partial Textual Entailment (PTE) in the same. Therefore, this paper proposed a modified method which has shown competitive results on standard dataset.
Exploring the Attention Mechanism in Deep Models: A Case Study on Sentiment Analysis

Interpreting what a deep learning model has learned is a challenging task. In this paper, we present a deep learning architecture relying upon an attention mechanism. The main focus is put on the exploratory evaluation of attention-based deep learning models on lexicons of affective words, and examination whether the word valence is the most significant information or not. Obtained evaluation results lead to a conclusion that word valences do play a significant role in sentiment analysis, but possibly models rely upon other dimensions perhaps not distinguishable by humans.
Multi-View Learning of Network Embedding

In recent years, network representation learning on complex information networks attracts more and more attention. Scholars usually use matrix factorization or deep learning methods to learn network representation automatically. However, existing methods only preserve single feature of networks. How to effectively integrate multiple features of network is a challenge. To tackle this challenge, we propose an unsupervised learning algorithm named Multi-View Learning of Network Embedding. The algorithm preserves multiple features that including vertex attribute, network global and local topology structure. Features are treated as network views. We use a variant of convolutional neural networks to learn features from these views. The algorithm maximizes the correlation between different views by canonical correlation analysis, and learns the embedding that preserve multiple features of networks. Comprehensive experiments are conducted on five real networks. We demonstrate that our method can better preserve multiple features and outperform baseline algorithms in community detection, network reconstruction and visualization.
Combination of XOR Binary Algorithm and Steganography Using Least Significant Bit (LSB) Method for Data Security

Along with the development of technology and information today, data security is a problem that needs to be done to maintain the confidentiality of data or information both personally and within an organization. Cryptography is one of the sciences or arts that learns about how to secure data or information from irresponsible parties who want to destroy important data in the form of text and image files. This research, data security technique uses two algorithms, namely steganography to enter text in image media using LSB (Least Significant Bit) method and Binary XOR used to convert messages to binary with XOR keywords and generate pixel values of messages from 8 bits of images with LSB (Least Significant Bit). After encrypted the image will be sent to the user, and to view the original data, the user must decrypt the data with the message key and retrieve the LSB (Least Significant Bit) value, from the encrypted image.
Image Moments and Moment Invariants

Moments are widely used to describe mathematical functions due to their numerical simplicity and numerous physical interpretations. The concept has been used for many years in diverse areas such as mechanics and statistics and has been successfully applied to pattern recognition, registration, object matching, data compression, and image understanding. In statistics, moments have been used to describe the shape characteristics of probability density or mass functions. The idea that moments could be used to characterize 2D image functions is attributed to Hu [7], who defined the 2D geometric moment and developed 2D geometric moment invariants for pattern recognition. Since then, many types of moments and their invariants have been proposed. The introduction of orthogonal moments by Teague [15], which was based on the theory of orthogonal polynomials, revolutionized visual pattern recognition and led to the development of efficient algorithms for shape analysis. The success of orthogonal moments, which are obtained from the projection of the 2D image function onto the higher order orthogonal polynomials, is due to their ability to capture higher order nonlinear structures of image functions. Perhaps, the greatest advantage of geometric and orthogonal moments is that they may be used to derive moment invariants. These invariants are computed by subjecting the original moments to certain affine transformations that possess invariance to scaling, shifting, and rotation of a pattern. This robustness to geometric distortions makes moment invariants appealing as features for pattern recognition. Starting from the definition of geometric moments, this chapter proceeds with generalized definition of orthogonal moments, details of some popular orthogonal moments, and formulation of their geometric invariants.
Security Requirements Analysis – A Vision for an Automated Toolchain

As the organizations move towards more fully embracing agile software development and DevOps for mission-critical systems, the need to continuously and rapidly assess cyber security requirements will become increasingly important. Current security assessment tools currently focus on performing Cyber Vulnerability Analysis (CVA) on designs and software artifacts rather than requirements, missing critical steps to vet requirements and support design decisions early in the development lifecycle or as requirements change. This paper outlines the main concepts underlying a toolchain that would enable software developers to analyze cyber security requirements based on existing natural language requirement descriptions, without burdening the developer with learning new languages, models, or ontologies. The main contribution of this paper is a discussion of the concept of operations for automated security requirement analysis together with a discussion of the main functional components required to implement an automated analysis toolchain.
ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation

3D Ultrasound (3DUS) has been widely used in clinical diagnosis. However, volume segmentation of 3DUS is very challenging due to relatively poor image quality and usually small datasets. We propose an efficient and robust method (ARS-Net) for single organ segmentation in 3DUS. Our contributions are twofold. (i) We propose a 2.5D framework based on a 2D segmentation network with 2.5D input which can provide more contextual and spatial information. The proposed framework also overcomes the limitation of 3D networks, such as small datasets, lack of pre-trained models and high memory cost. (ii) To further enhance the performance in low signal-to-noise ratio (SNR) regions, we incorporate a new mechanism of adaptively rectified supervision (ARS) into the proposed 2.5D framework at training stage. Specifically, both pixel-wise reweighted dice loss and image-wise shape regularization loss are applied to improve the sensitivity and the specificity of segmentation. The experiment results on two representative and challenging datasets of 3DUS show that the proposed ARS-Net outperforms state-of-the-art methods with higher accuracy but lower complexity. The proposed novel network is robust to small datasets and can provide an accurate and fast volume segmentation tool for 3DUS.
The Problem of Algorithmic Corporate Misconduct

Technology will soon force broad changes in how we conceive of corporate liability. The law’s doctrines for evaluating corporate misconduct date from a time when human beings ran corporations. As corporate operations become increasingly automated, algorithms will come to replace employees as the leading cause of corporate harm. Rooted in an antiquated paradigm, the law presently identifies corporate misconduct with employee misconduct. If it continues to do so, the inevitable march of technological progress will increasingly immunize corporations from most civil and criminal liability.
JamesBot - an intelligent agent playing StarCraft II

The most popular method for optimizing a certain strategy based on a reward is Reinforcement Learning (RL). Lately, a big challenge for this technique are computer games such as StarCraft II which is a real-time strategy game, created by Blizzard. The main idea of this game is to fight between agents and control objects on the battlefield in order to defeat the enemy. This work concerns creating an autonomous bot using reinforced learning, in particular, the Q-Learning algorithm for playing StarCraft. JamesBot consists of three parts. State Manager processes relevant information from the environment. Decision Manager consists of a table implementation of the Q-Learning algorithm, which assigns actions to states, and the epsilon-greedy strategy, which determines the behavior of the bot. In turn, Action Manager is responsible for executing commands. Testing bots involves fighting the default (simple) agent built into the game. Although JamesBot played better than the default (random) agent, it failed to gain the ability to defeat the opponent. The obtained results, however, are quite promising in terms of the possibilities of further development.
Toward Projection Learning between Sensor Data and Semantic Word Vector for Zero-shot Learning

In this paper, we compare 4 learning projection models between sensor domain and text domain for Zero-shot learning (ZSL). In traditional activity recognition with sensor data, the task of collecting training dataset is too tough and costly to apply for social. Our challenge is making the task efficient. The Zero-shot learning's purpose is to recognize the unknown activity which is activity class out of training dataset. In our previous research, we propose the Zero-shot learning method using the word vectors made from Wikipedia corpus for recognizing the human living activities like breakfast, watching TV, etc. We found that this method success to recognize unknown activities and need to improve the projection function for performance. In this paper, we construct 4 learning models for projection and evaluate them with accelerometer sensor data annotated simple activities. As a result, we realize that (1) the learning method with twice projection is useful for performance. (2) It is difficult to identify the two unknown activities whose distance from known activities is closer than that between other combination of two unknown activities and the known ones.