Reviewerly/exHarmony huggingface dataset:
Exploring intrinsic information content models for addressing the issues of traditional semantic measures to evaluate verb similarity

Semantic similarity measures play an important role in many natural language processing and information retrieval activities. It is highly challenging to measure semantic similarity with higher accuracy. A notable branch of semantic similarity evaluation based on information content (IC) is popular in this aspect. Intrinsic information content (IIC) models are another wing of IC based evaluation. Both IC based and IIC based approaches majorly handled similarity evaluation of nouns. Research related to semantic similarity assessment of verb pairs are rarely discussed. To bridge this gap, this work examines various IC based, IIC based approaches on verb pairs. A detailed discussion of the existing measures and their drawbacks are mentioned in this work. Strategies based on information content, length and depth of the concepts are discussed and tested on benchmark datasets. Existing intrinsic information content models are enhanced by addressing various issues like (a) dealing concepts with no path in WordNet and (b) handling the synonym sets of verb concepts. Measures based on path length, intrinsic information content, combined strategies and non-linear strategies for verb pairs are thoroughly inspected. This paper also presents novel strategies to understand novel aspects that are not addressed before. The strategies are experimented by generating the synonym sets of required parts-of-speech which proved very effective in improving the correlation with human judgment. Results on benchmark datasets specify that the proposed approaches for verb similarity will be a guiding factor for understanding the natural language processing tasks.
Key-Guided Identity Document Classification Method by Graph Attention Network

Identify documents, such as personal identity cards, driving licenses, passports, and residence permits, are indispensable and necessary in human life. Various industries, like hotel accommodation, banking service, traffic service and car renting, need identify documents to extract and verify the personal information. Automatic identity document classification can help people to quickly acquire the valid information from the documents, saving the labor cost and improving the efficiency. However, due to the inconsistency and diversity of identify documents between different countries, automatic classification of these documents is still a challenging problem. We propose an identity document classification method in this paper. GAT (Graph Attention Network) and its edge-based variant are exploited to generate the graph embedding, which fuse visual features and textual features together. Moreover, the key information is learnt to guide to a better representation of the document feature. Extensive experiments on the identify document dataset have been conducted to prove the effectiveness of the proposed method.
Inhibitors to Interface Management Implementation: Perceptions of Construction Stakeholders

Interfaces are omnipresent facets of a project management and their presence in construction domain is copious. Owing to inter-disciplinary interactions at the cross points, it is of paramount importance to apprehend complications which emerge at those interfaces. However, at interface point, possibility of conflicts are obvious owing to interactions, transfer of responsibilities, and exchange of information or resources. At this cross over, in case of misalignment of requirements from either sides of interfaces, then there is a scope for yielding of interface problems which ultimately result in disputes. Nevertheless, optimum stakeholder understanding through unified process such as Interface Management possibly eradicates such interface issues. The current study explores inhibitors to Interface Management adoption and through analysis of variance (ANOVA), exploration of viewpoints of contractors, owners, consultants and academicians through survey of 99 respondents was performed. Additionally, the study identifies top 10 critical inhibitors from initial thirty inhibitors identified from content analysis of the literature. Subsequently, the mitigation measures have been mapped from 4 experts using Nvivo. Also, the responses gathered instituted impact and frequency measures with regards to capturing of inhibitors towards IM adoption. The findings indicate that out of 60 possible cases (both frequency and impact), 53 cases show agreement over all inhibitors to IM adoption from all four categories of stakeholders. One key differing opinion among stakeholders is “Novice at dealing with regional and government specific legal aspects”. Thus, this study provides a roadmap for strategic orientation of organization which intends to institutionalize Interface Management.
Probabilistic day ahead dynamic available transfer capability estimation in solar integrated deregulated power system

Available transfer capability (ATC) between the interfaces needs to be planned well in advance for a secure power transaction in a deregulated power system. Independent system operator (ISO) is responsible for determining ATC at least 24 hours before and made available for the decision makers. This work proposes an effective method to find probabilistic day ahead dynamic ATC (PDA-DATC) in a solar integrated deregulated power system. Solar photo voltaic (PV) output is stochastic which brings uncertainties in PDA-DATC calculation. Hence, it is important to predict solar PV output accurately. In this work, the solar PV output is predicted using neural network. The time-series historical data such as hourly global direct radiation, global diffusion radiation, air temperature, and sun height are taken as the input variable. The input feature selection methods such as mutual information and random forest (RF) techniques are applied to choose the input features. The impact of solar PV on PDA-DATC is analyzed. Also, the penetration of solar PV on a single location and scattered locations are studied and the results are compared for different loading conditions such as high peak, moderate, and low peak periods. The dynamic voltage stability limit namely the Hopf bifurcation limit is used as a limiting criterion for DATC computation. A new swarm intelligent-based optimization algorithm namely dragon fly algorithm (DFA) is used as an optimization algorithm for PDA-DATC calculation. The DATC results are compared with the conventional sequential quadratic program, and the test results proved the superiority of the proposed method. The proposed method is validated on the New England 39 bus system.
Partially Recurrent Network With Highway Connections

It is difficult to train deep recurrent neural networks (RNNs) to learn the complex dependencies in sequential data, because of gradient problems and learning conflicts. To address these learning problems, this article proposes a partially recurrent network (PR-Net), which consists of the partially recurrent (PR) layers with highway connections. The proposed PR layer explicitly arranges the neurons into a memory module and an output module, which are implemented by highway networks. The memory module places memory on the information highway, to maintain long short-term memory (LSTM). The output module places external input on the information highway, to approximate the complex transition function from one step to the next. Because of the highway connections in the output module, the PR layer can pass gradient information across layers easily, to allow the training of the PR-Net with multiple stacked PR layers. Furthermore, the explicit separation of the memory module and output module can reduce the learning burden of RNNs that which neurons are responsible for memory and which are for output. With a comparable number of parameters, sublayers in the recurrent layer, and stacked recurrent layers, the proposed PR-Net outperformed the recurrent highway network and LSTM on three sequence learning benchmarks, significantly.
Association between mass collaboration and knowledge management: a case ofJordan companies

Purpose The key purpose of this research paper was to identify the association between mass collaboration and knowledge management in the context of Jordanian companies. Apart from that, this study also aims to examine the moderating effect of trust and leadership on the association between mass collaboration and knowledge management. Design/methodology/approach In this study, the researcher has followed theprimary quantitative method. For data collection, the researcher has conducted a survey questionnaire, whereas the sample was based on 323 participants from the manufacturing sector of Jordan specifically for data analysis; the technique of structural equation modeling was implemented. Findings All the independent variables, including organizational structure, adoptedtechnologies in mass collaboration and collaborative learning techniques, have a significantimpact on knowledge management and leadership. Moreover, leadership was also found to be significantly moderating the association between adopted technologies in mass collaboration and knowledge management. Similarly, trust also significantly moderates the association of organizational structure and adopted technologies in mass collaboration significantly with knowledge management. Research limitations/implications All study respondents were from Jordan, which might limit the generalizability of the findings. The researchers also invited for more researchers in the incorporation of the time sequence in the proposed causal relations and in the organization level through which mass collaboration and knowledge management. Originality/value This study promises to make a valuable contribution to the existing literature, as there was a lack of evidence in the previous studies regarding the impact of mass collaboration on knowledge management within the context of Jordan.
Extraction of Space Containing Noise and Forecast of Complex Data by Multiway Tree Bi-Level GA

In order to avoid overfitting when dealing with complex data with a pattern recognition model, it is necessary to remove in advance the extraordinary values that deviate significantly from the true pattern of the population. In this study, the sample space was divided into a highly versatile space and a low versatility space by using the globally optimal decision tree. Then, the space with a low evaluation value was defined as the space with relatively large noise, and the pattern recognition model was created except for the data that belongs to the space with relatively large noise.
Automatic Assessment of Benton Visual Retention Test Results: A Pilot Study

Visual memory plays an important role in learning to read, write, spell, or draw. The dysfunction of visual memory can be detected by applying tests, such as Benton Visual Retention Test (Benton Test, BVRT), which is based on displaying ten patterns for ten seconds per each pattern and asking the subject to reproduce the displayed patterns. The purpose of our study was to automate the Benton Test by developing a desktop application. We automated the assessment of hand-drawn geometrical shapes (triangles, circles, rectangles) by applying machine learning and image processing techniques, namely, the ResNet50 network trained for recognition of triangles, circles, rectangles and other shapes, filling in the shape, and determining the type of triangle. The proposed algorithm proved its reliability on subjects with limited ability to draw shapes and was the part of a desktop application which may find its use in screening visual perception and visual memory.
Privacy Preservation Of Mobile Users Using L-Diversity

Due to the increased availability of locality-based cyber services in mobile applications seclusion protection for users is critical and stands one of the major difficulties of study. The aim of the paper is to propose algorithm to maintain the privacy position of mobile device users on location based cyber services in a region of concern algorithm based on division Privacy Position of Mobile Users on Cyber Services (PPCS). In contrast to existing draw breath methods, our PPCS methodology is dumb places and the semantically information of these places is taken into account. The PPCS algorithm allows the locations created to exclude or decrease exposure to the actual location of a user. We show that PPCS is impervious to conniving and inferential abuse. We also examine the effectiveness of our proposed approach and demonstrate the usefulness of comprehensive simulations.
Learning Hierarchical Visual-Semantic Representation with Phrase Alignment

Effective visual-semantic representation is critical to the image-text matching task. Various methods are proposed to develop image representation with more semantic concepts and a lot of progress has been achieved. However, the internal hierarchical structure in both image and text, which could effectively enhance the semantic representation, is rarely explored in the image-text matching task. In this work, we propose a Hierarchical Visual-Semantic Network (HVSN) with fine-grained semantic alignment to exploit the hierarchical structure. Specifically, we first model the spatial or semantic relationship between objects and aggregate them into visual semantic concepts by the Local Relational Attention (LRA) module. Then we employ Gated Recurrent Unit (GRU) to learn relationships between visual semantic concepts and generate the global image representation. For the text part, we develop phrase features from related words, then generate text representation by learning relationships between these phrases. Besides, the model is trained with joint optimization of image-text retrieval and phrase alignment task to capture the fine-grained interplay between vision and language. Our approach achieves state-of-the-art performance on Flickr30K and MS-COCO datasets. On Flickr30K, our approach outperforms the current state-of-the-art method by 3.9% relatively in text retrieval with image query and 1.3% relatively for image retrieval with text query (based on [email protected]). On MS-COCO, our HVSN improves image retrieval by 2.3% relatively and text retrieval by 1.2% relatively. Both quantitative and visual ablation studies are provided to verify the effectiveness of the proposed modules.
Neural Network Algorithms for Fraud Detection: A Comparison of the Complementary Techniques in the Last Five Years

Purpose: The purpose of this research is to analyse the complementary updates and techniques in the optimization of the results of neural network algorithms (NNA) in order to detect financial fraud, providing a comparison of the trend, addressed field and efficiency of the models developed in current research.Design/Methodology/Approach: The author performed a qualitative study where a compilation and selection of literature was carried out, in terms of defining the conceptual analysis, database and search strategy, consequently selecting 32 documents. Subsequently, the comparative analysis was carried out, in turn being able to determine the most used and efficient complementary technique in the last five years.Findings: The results of the comparative analysis depicted that in 2019 there was a greater impact of research based on NNA with 11 studies. 27 complementary updates and techniques were identified related to NNA, where deep neural network algorithms (DNN), convolutional neural network (CNN) and SMOTE neural network. Finally, the evaluation of effectiveness in the collected techniques achieved an average accuracy ranging between 79% and 98.74% with an overall accuracy value of 91.32%.Originality/Value: Being a technique which is applied and compared in diverse studies, ANNs uses a wide range of mechanisms concerning training and classification of data. According to the findings of this research, the complementary techniques contribute to the progress and optimization of algorithms regarding financial fraud detection, having a high degree of effectiveness concerning on-line and credit card fraud.
Searching for similar binary code based on the influence of basic block vertex

Binary code similarity detection (BCSD) has many security applications and has attracted a lot of focus. But existing solutions for BCSD problems mainly focus on comparing the similarity between two binary functions and what need to be compared is not always a pair of equal-size binary programs. To solve the problem of searching similar binary code for a small size program in a large size program, in this manuscript, we propose a searching method based on the influence of basic block vertex. Firstly, we formalize the similar code searching problem as a subgraph matching problem and extract the attributed control flow graphs (ACFGs) of the binary programs. Then, we propose a novel metrics to measure the influence of each basic block vertex in query ACFGs and choose the vertex with the largest influence value as the central node. Next, we take the matching nodes of the central node in target ACFG as seed nodes and extending each candidate sub-area from the seed nodes. At last, these candidate sub-areas are further filtered and similar subgraphs are verified in these sub-areas. Experimental results indicate that the proposed method can find similar binary code efficiently.
Low-Complexity Kötter’s Interpolation for List Decoding of Elliptic Codes

This letter proposes the low-complexity Kötter’s interpolation algorithm for list decoding of elliptic codes. Given a multiplicity matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {M}$ </tex-math></inline-formula> , the decoding output list size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> is first determined and the corresponding interpolation module <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathbf {M}, l}$ </tex-math></inline-formula> is defined. The minimum element in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathbf {M}, l}$ </tex-math></inline-formula> is the desired interpolation polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}(x, y, z)$ </tex-math></inline-formula> . By arranging the interpolation constraints in a specific order, a number of Hasse derivative evaluations can be saved during the polynomials update. Furthermore, the weighted degree constraint of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}(x, y, z)$ </tex-math></inline-formula> is derived and utilized to eliminate unnecessary polynomials. These two methods are applied to hard and soft algebraic list decoding, yielding a significantly low interpolation complexity. Finally, complexity reduction of the proposed interpolation technique is analyzed and validated by the simulation results.
Intelligent Control of Manipulator Based on Deep Reinforcement Learning

Traditional manipulator control requires the establishment of complex models and mathematical equations, and requires manual adjustment of parameters, which cannot adapt to tasks and environmental changes. With the rapid development of artificial intelligence, deep reinforcement learning algorithms show strong online adaptability and self-learning capabilities for complex systems, and have gradually become one of the important research hotspots in the field of intelligent control and artificial intelligence in recent years. Therefore, this paper combines the deep reinforcement learning algorithm with the manipulator control system, and uses the deep deterministic policy gradient (DDPG) algorithm to achieve the end position control of the manipulator. On this basis, this paper presents a scheme to improve the reward function. Finally, the DDPG algorithm is used for interactive training with the manipulator model. The experimental results show that the deep reinforcement learning algorithm can realize the control of the manipulator, and the improved reward function scheme can greatly improve the stability of training and the task completion rate.
Simplifying regular expressions further

We describe a cumulative series of transformations to simplify regular expressions, and investigate their effectiveness and cost. Transformations depending on increasingly powerful comparisons of expressions give results clearly superior to commonly used algebraic simplifications. Early in the series, efficient transformations enabled by language-invariant attributes are surprisingly effective. Later in the series, transformations depending on comparisons of expressed languages are made feasible by bounding the size of subexpressions to which they are applied. We set out the principles of our transformations, address some key implementation issues, and evaluate the results of systematic test measurements.
Towards New Model for Handling Inconsistency Issues in DL-Lite Knowledge Bases

The lightweight description logic (DL-lite) represents one of the most important logic specially dedicated to applications that handle large volumes of data. Managing inconsistency issues, in order to effectively query inconsistent DL-Lite knowledge bases, is a topical issue. Since assertions (ABoxes) come from a variety of sources with varying degrees of reliability, there is confusion in hierarchical knowledge bases. As a consequence, the inclusion of new axioms is a main factor that causes inconsistency in this type of knowledge base. Often, it is too expensive to manually verify and validate all assertions. In this article, we study the problem of inconsistencies in the DL-Lite family and we propose a new algorithm to resolve the inconsistencies in prioritized knowledge bases. We carried out an experimental study to analyze and compare the results obtained by our proposed algorithm, in the framework of this work, and the main algorithms studied in the literature. The results obtained show that our algorithm is more productive than the others, compared to standard performance measures, namely precision, recall and F-measure.
Exploration of block-wise dynamic sparseness

Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, we present a new method for \emph{dynamic sparseness}, whereby part of the computations are omitted dynamically, based on the input. For efficiency, we combined the idea of dynamic sparseness with block-wise matrix-vector multiplications. In contrast to static sparseness, which permanently zeroes out selected positions in weight matrices, our method preserves the full network capabilities by potentially accessing any trained weights. Yet, matrix vector multiplications are accelerated by omitting a pre-defined fraction of weight blocks from the matrix, based on the input. Experimental results on the task of language modeling, using recurrent and quasi-recurrent models, show that the proposed method can outperform a magnitude-based static sparseness baseline. In addition, our method achieves similar language modeling perplexities as the dense baseline, at half the computational cost at inference time.