Reviewerly/exHarmony huggingface dataset:
Corporate Social Responsibility and Competitive Advantage: Relationships and Mechanisms

Purpose: This study is intended to gain insight into the association of Corporate Social Responsibility (CSR) implementations and the achievement of competitive advantage focusing on the mediation role of company reputation (CR) in this relationship.Design/Methodology/Approach: Semi-structured interview technique was used to confirm the validity of the research model.In addition, the purposive sampling method has been used targeting senior decision makers in different well-known 5-star hotels and resorts in Jordan.A total of three hundred (300) questionnaires were distributed.While two hundred and forty (240) questionnaires were retrieved from respondents, two hundred and eleven (211) questionnaires were valid and usable.Findings: The results elucidated the mechanism of CSR and competitive advantage relationship.Although the results confirmed the positive (direct and indirect) effect of the social, the legal, and the ethical dimensions of (CSR) on competitive advantage, data analysis did not support any significant impact for the environmental dimension on competitive advantage (CA) through the company reputation (CR) as a mediator variable.Practical Implications: This study raises awareness of (CSR) implementations in serviceoriented developing economies through highlighting the mechanism and relationships of CSR and competitive advantage.It is hoped that, it will contribute to generate interest towards the concept of ``Environmental Corporate Social Responsibility`` (ECSR), as a strategic alternative at the aim of the achievement of competitive advantage.Originality/Value: The study goes beyond examining the causal relationship between (CSR) and competitive advantage.It focuses on achieving a deep understanding of this relationship by exploring the mechanisms of its work through illustrating the mediation role of reputation.
Hierarchical Text Classification Using CNNs with Local Approaches

In this paper, we discuss the application of convolutional neural networks (CNNs) for hierarchical text classification using local top-down approaches. We present experimental results implementing a local classification per node approach, a local classification per parent node approach, and a local classification per level approach. A 20Newsgroup hierarchical training dataset with more than 20 categories and three hierarchical levels was used to train the models. The experiments involved several variations of hyperparameters settings such as batch size, embedding size, and number of available examples from the training dataset, including two variation of CNN model text embedding such as static (stat) and random (rand). The results demonstrated that our proposed use of CNNs outperformed flat CNN baseline model and both the flat and hierarchical support vector machine (SVM) and logistic regression (LR) baseline models. In particular, hierarchical text classification with CNN-stat models using local per parent node and local per level approaches achieved compelling results and outperformed the former and latter state-of-the-art models. However, using CNN with local per node approach for hierarchical text classification underperformed and achieved worse results. Furthermore, we performed a detailed comparison between the proposed hierarchical local approaches with CNNs. The results indicated that the hierarchical local classification per level approach using the CNN model with static text embedding achieved the best results, surpassing the flat SVM and LR baseline models by 7 % and 13 %, surpassing the flat CNN baseline by 5 %, and surpassing the h-SVM and h-LR models by 5 % and 10 %, respectively.
Arbitrary clutter extended target probability hypothesis density filter

Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET-PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET-PHD filter is modelled as Poisson RFS, which is reasonable for only some scenarios in reality. An easily implemented ET-PHD filter for arbitrary clutter process, has not yet been studied. In this work, another form of the general PHD filter, suitable for arbitrary clutter and target measurement is derived using a probability generating functional (PGFL)-based RFS framework. The proposed filter is equivalent to the general PHD filter proposed by Clark and Mahler, but it is easily implemented in an arbitrary clutter process because its clutter process is denoted by the probability density function . Then, an arbitrary clutter ET-PHD (AC-ET-PHD) filter is simplified from the general PHD filter derived by us. To reduce the computational complexity of the proposed filter, a variant of the distance partitioning algorithm is put forward. Simulation results show that the AC-ET-PHD filter can be applied to the scenarios with the non-Poisson clutter process, which means that it will be useful for multiple extended targets tracking in a more complicated clutter process.
Improved Deep Forest Mode for Detection of Fraudulent Online Transaction

As the rapid development of online transactions, transaction frauds have also emerged seriously. The fraud strategies are characterized by specialization, industrialization, concealment and scenes. Anti-fraud technologies face many challenges under the trend of new situations. In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection model. Based on the BaggingBalance method we propose, we establish a global sample imbalance processing mechanism to deal with the problem of sample imbalance. In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability. Via the three-month real online transactions data of a China's bank, the experimental results show that, evaluating by the metric of precision and recall rate, the proposed model has a beyond 10 % improvement compared to the random forest model, and a beyond 5 % improvement compared to the original deep forest model.
Indonesian-german bicultural literacy comprehension: The students’ inference perspective

To comprehend a text, one needs to understand the relationship of various elements in it.The skill that helps connect the non-textual components to the textual ones is making inferences.This research aims to reveal different types of inferences made by the students with German-language proficiency of level B1.A qualitative research approach was applied to obtain more objective, comprehensive, valid, reliable, and in-depth data.The data were analyzed with a narrative research design to describe the students' responses based on the rubric assessment.The results showed that inference within the framework of cognitive dimensions is a form of students' knowledge.They unite pieces of information, then predict, conclude, and synthesize a bicultural text's meaning.The inference types found were the superordinate goal, instantiation of noun category, causal antecedent, the emotion of the reader, referential, thematic, and character emotional reaction.The results of research on inference have a contribution to literacy comprehension and teaching.The various inferences produced by students prioritize the broadness of vocabulary in predicting reading comprehension.
Distribution of The Boolean Functions of n Variables According to Their Algebraic Degrees

Knowledge about the enumeration and distribution of Boolean functions according to their algebraic degrees is important for the theory as well as for its applications. As of now, this knowledge is not complete: for example, it is well-known that half of all Boolean functions have a maximal algebraic degree. In the present paper, a formula for the number of all Boolean functions of n variables and algebraic degree = k is derived. A direct consequence from it is the assertion (formulated already by Claude Carlet) that when n to infty, almost a half of all Boolean functions of n variables have an algebraic degree = n-1. The results obtained by this formula were used in creating the sequence A319511 in the OEIS. The discrete probability a random Boolean function to have a certain algebraic degree is defined and the corresponding distribution is computed, for 3
Weighted k-Nearest Neighbour for Image Spam Classification

E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.
House building tips (HBT) corpus dataset as a resource to discover Malay architectural ingenuity and identity

House Building Tips is the title of a classic text containing historical information on early house construction in Malay communities. These tips were written by a scholar with knowledge of house construction through observation of the surrounding environment. In Malaysia, written sources or records of house construction are scarce and underexposed. As such, this research was conducted to guarantee the written legacy of the construction of Malay houses. The purpose of this paper is to introduce a statistical data source of house building tips that is laden with Malay ingenuity and identity. The wordlists generated from this study can become a source of reference for the field of Malay architecture. Accordingly, this study utilises the quantitative method by applying the Linguistic Corpus Statistical Approach; these data utilise specific corpus development procedures, beginning with text collection, scanning and cleaning processes, text annotation, and data storing in plain text. Next, the data analysis procedure utilises a corpus software, LancsBox, to generate specialised wordlists. The bubble graphs are developed based on these wordlists through the Tableau software, and illustrate the most used lexical items with the raw and relative frequency values. This facilitates searches for, and the reading of, architectural words and architectural word references. These data represent written sources that need to be preserved and become points of reference concerning Malay architectural ingenuity and identity.
Chaos-based second-order bas for multimodal function optimization

Aiming at solving a drawback of the second-order beetle antenna search (SOBAS), a variant of the beetle antenna search (BAS), that it is difficult to find the global optimal solution and the low convergence accuracy when applied to the multimodal optimization functions with high dimension or large variable region, a chaotic-based second-order BAS algorithm (CSOBAS) is proposed by introducing chaos theory into the SOBAS. The algorithm mainly has three innovations: 1) chaos initialization: choosing the one with the smallest fitness function value from twenty beetles with different positions for iterative search; 2) using chaotic map to tune the randomization parameter in the detection rule; 3) imposing a chaotic perturbation on the current beetle to hope to help the search to jump out the local optimal solution. Eight different chaotic maps are used to demonstrate their impact on the simulation results. With six typical multimodal functions, performance comparisons between the CSOBAS and the SOBAS are conducted, validating the effectiveness of the CSOBAS and its superiority compared to the SOBAS. What?s more, the CSOBAS with an appropriate chaotic map can achieve a very good convergence quality compared to other swarm intelligence optimization algorithms while maintaining an individual.
Mind the Gap: Discrepancy Between Customer Expectation and Perception on Commercial Chatbots Usage

The advent of artificial intelligence gives rise to chatbots. Chatbots are widely adopted by marketers as query-answering tool to build, manage, and grow customer relationship by responding to customers’ questions in a human-being manner automatically. While the far-reaching potential of chatbots for commercial purposes has been widley recognized by marketers, this study attempts to advance marketers’ understanding on chatbots from the perspective of users – customers. Understanding customer expectation and perception on chatbots usage may help marketers to ensure customer experience consistently meets or exceeds their expectation. The purpose of this study is to find out if there is discrepancy between customer expectation and perception of chatbots performance, what causes the discrepancy, and how to minimize the discrepancy. Specifically, this study investigates the reasons why customers use chatbots, their usage experience, and their attitude towards chatbots in terms of perceived usefulness, perceived ease of use, and perceived helpfulness. Respondents expressed that chatbots are useful and easy to use, yet the responses provided by chatbots are not very accurate and humorous. The findings revealed that respondents expect marketers to improve the chatbots performance in areas of human-like communication and the entertaining features. This study provide marketers with implications on how to design and improve commercial chatbots to close or minimize the gap between customer expectation and experience.
Performance Assessment of Local Government Organizations on Flood Disaster Prevention and Preparedness in Gunung Mas Regency

Performance assessment requires the demands of accountability, implementation of the control functions and development needs of local government organizations in the handling and prevention of floods in the region.The method used in this research is qualitative method.Data collection techniques are carried out using participatory observations, interviews and documentation studies.Data analysis is done with descriptive techniques through three flow of activities that are one unity, namely: data reduction, data presentation and conclusion drawing.The results showed the performance assessment of the Gunung Mas District Disaster Management Agency on the prevention and preparedness of flood disasters from the aspect of understanding the auth and aspects of work accuracy tends to be many activities in the field of disaster prevention and preparedness carried out and aspects of innovation, work speed and cooperation are still minimal socialization of disaster-aware culture through simulation in the field and the lack of innovation in the field of disaster prevention and preparedness.
Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently utilizes unified memory for reducing the latency of data transfer during network model training. In addition, we propose the layer-wise processor selection method to consider the latency generated by the difference in the processor performing the forward propagation step and the backpropagation step in the same layer. The experiments were performed on an ODROID-XU4 with the ResNet-18 model, and the experimental results indicate that the proposed method reduces the latency by at most 28.4% compared to the central processing unit (CPU) and at most 21.8% compared to the graphics processing unit (GPU). Through experiments using various batch sizes to measure the average power consumption, we confirmed that device heterogeneity is alleviated by performing on-device learning using the proposed method.
Online Feature Screening for Data Streams With Concept Drift

Screening feature selection methods are often used as a preprocessing step for reducing the number of variables before training step. Traditional screening methods only focus on dealing with complete high dimensional datasets. Modern datasets not only have higher dimension and larger sample size, but also have properties such as streaming input, sparsity and concept drift. Therefore a considerable number of online feature selection methods were introduced to handle these kind of problems in recent years. Online screening methods are one of the categories of online feature selection methods. The methods that we proposed in this research are capable of handling all three situations mentioned above. Our research study focuses on classification datasets. Our experiments show proposed methods can generate the same feature importance as their offline version with faster speed and less storage consumption. Furthermore, the results show that online screening methods with integrated model adaptation have a higher true feature detection rate than without model adaptation on data streams with the concept drift property. Among the two large real datasets that potentially have the concept drift property, online screening methods with model adaptation show advantages in either saving computing time and space, reducing model complexity, or improving prediction accuracy.
Assessment of Groundwater Quality and Health Risk of Heavy Metals: A study from the Tarkwa Mining Area, Ghana

This study seeks to evaluate the hydrogeochemical characteristics of water in the Tarkwa mining area using the Piper and Chadha plots and to carry out a health risk assessment using the US Environmental Protection Agency (USEPA) health risk assessment model. A total of 39 groundwater sample points were used for this study. Results from the Piper and Chadha diagrams show that the dominant water types in the study area are Ca-HCO3 and Mixed Ca-Mg-Cl water types which indicates that groundwater in the area can be classified as fresh water. The hazard quotient (HQ) value for heavy metals estimated, suggested an acceptable level of noncarcinogenic inimical health risk. In relation to the HQ value, the Hazard Index (HQ) calculated was less than 1 suggesting that inhabitants will not be exposed to a potential health risk for the injection of heavy metals. Carcinogenic risk estimated for As (1.80×10-4) was higher than the acceptable risk. The carcinogenic risk estimated therefore indicated that, drinking of groundwater over a long period will increase the probability of cancer. It can be concluded that currently the groundwater in the Tarkwa area is safe for domestic purposes.&#x0D; &#x0D; Keywords: Hydrochemical Characteristics, Human Risk Assessment, Tarkwa Mining Area
Leveraging Aircraft Transponder Signals for Measuring Aircraft Fleet Mix at Non-Towered Airports

The general aviation sector has contributed significantly to the overall growth of the aviation industry. Fleet mix and operations information is important for the analysis of both the safety and the environmental and economic impact of general aviation activity. Factual data on the impact of environment and safety is particularly important, given the scarcity of airport improvement funds and the strong stakeholder engagement that often occurs when airport investments are evaluated. The research presented herein evaluates Mode S short and extended squitter data collected from three general aviation airports over a one-month period. This article demonstrates that by utilizing the unique ICAO code present in Mode S records in the United States, ICAO identification can be used as a primary key for retrieving information from publicly available databases, permitting the determination of aircraft type and engine models. An aircraft operation type with the corresponding fleet mix information can thus be estimated at non-towered airports. The techniques reported here are also useful in monitoring the increase in aircraft compliant with the FAA’s January 1, 2020 mandate for ADS-B out to operate in large portions of the national airspace system. For the three Indiana airports studied, the proportion of ADS-B transponder messages received ranged from approximately 29% at the Terre Haute Regional Airport (KHUF) to 79% at the Purdue University Airport (KLAF).
Motor Insurance Claim Status Prediction using Machine Learning Techniques

The insurance claim is a basic problem in insurance companies.Insurance insurers always have a challenge to the growing of insurance claim loss.Because there is the occurrence of claim fraud and the volume of claim data increases in the insurance companies.As a result, it is difficult to classify the insured claim status during the claim review process.Therefore, the aims of the study was to build a machine learning model that classifies and make motor insurance claim status prediction in machine learning approach.To achieve this study Missing value ratio, Z-Score, encoding techniques and entropy were used as data set preparation techniques.The final preprocessed data sets split using K-Fold cross validation techniques into training and testing sets.Finally the prediction model was built using Random Forest (RF) and Multi Class -Support Vector Machine (SVM).The performance of the models, RF and Multi -Class SVM classifiers were evaluated using Accuracy, Precision, Recall, and F-measure.The prediction accuracy of the model is capable of predicting the motor insurance claim status with 98.36% and 98.17% by RF and SVM classifiers respectively.As a result, RF classifier is slightly better than Multi-Class Support vector machines.Developing and implementing hybrid model to benefit from the advantages of different algorithms having graphical user interface to apply the solution to real world problem of the insurance company is a pressing future work.