Reviewerly/exHarmony huggingface dataset:
Training Algorithm Based on the Levenberg–Marquardt Method for Training Decision Neural Network to Solve Multiple Objective Decision-Making Problems

The Decision Neural Network (DNN) which is a special type of neural network has been used for estimating the utility function in multi-objective decision-making (MODM) problems. This method utilizes indirect priority evaluation methods in training the neural network. Therefore, the learning capacity of the network has increased. Besides, due to the unique structure of these networks, a larger volume of training data is easily created and also the possibility of using imprecise comparisons between options is provided. Therefore, the circumstances for the decision makers (DMs) are simplified. Developing DNN training methods can provide a powerful tool for solving multi-objective decision-making problems. In this article, in order to improve the DNN training, for a more efficient estimate of the utility function for multi-objective problems, a training algorithm, based on the Levenberg-Marquardt method has been developed. The results of the numerical samples show the proposed approach’s efficiency in estimating the linear and especially non-linear and complex utility functions. Also, the results of using the improved DNN for recognizing the preferred solution in multi-objective decision-making problems show higher convergence speed and better response quality in this training approach.
Design of a Home Automation App to Assist Elderly and Limited Mobility People

This research focuses on a solution to assist elderly and limited-mobility people. It aims to improve the autonomy, and, consequently, the quality of life of this target audience by automating daily tasks conducted at home, such as turning on the lights and manipulating electronic devices. However, it is important to consider the costs and quality attributes (e.g., usability) related to the design of solutions to automate a specific environment, that may include hardware platforms and physical adaptations. In this context, the authors present in this chapter the software requirements discovery and elicitation of a home automation app considering the real needs of the elderly and limited-mobility people. Additionally, we conduct the requirements specification using the unified modeling language (UML) to improve completeness, along with graphical user interface (GUI) prototypes. Finally, we present a mobile app prototype using the Android and Arduino platforms to illustrate a use scenario of the solution.
Network and Data Transfer Security Management in Higher Educational Institutions

This chapter explored communications security through the use of an empirical survey to assess the extent of network and data transfer security management in Ghanaian higher educational institutions. Network security management controls consist of monitoring of networks, posture checking, network segmentation, and defense-in-depth. Data transfer security management includes encryption, media access control, and protection of data from public networks. Data were collected from information technology (IT) personnel. The ISO/IEC 21827 maturity model for assessing IT security posture was used to measure the controls. Overall, the result showed that the institutions were at the planned stage of communications security management. In particular, network monitoring, defense-in-depth, and the protection of data from public networks were the most applied controls. Conversely, posture checking was the least applied control. Higher educational institutions need to review their communications security plans and better manage network and data transfer security controls to mitigate data breaches.
OUP accepted manuscript

Abstract In the next few years, the number of devices connected to the web will increase dramatically. This has encouraged the development of complexes and intelligent service-based applications using new paradigms and technologies such as edge computing and cloud computing. These applications are mainly built by the interaction between heterogonous web services. Answering end-users’ queries demands that these services collect, integrate and communicate sensitive data, which makes the vulnerability of data a serious issue. Thereby, this issue is a critical problem in the automatic services composition while users are frequently unquieted about the security and control of their sensitive data. To address this problem, our main idea is that avoiding disclosure of sensitive information can be ensured by selecting services where their mutual trust is high while preserving a good quality of service (QoS) as possible. A new Heuristic Trust-aware Web Services Composition Approach (CWS_SMA) is proposed. CWS_SMA aims to: (i) compute an optimal trust and QoS-aware web services composition based on the mathematical coordinate system, (ii) improve the composition response time based on a set of cooperative intelligent agents. Eventually, the simulations results show the effectiveness of the proposed solution.
Towards Enhanced Social Well-being for the Disabled Using Humanoid Robot with Eye Tracker

With the progress of modern science and technology, more and more researchers begin to pay attention to how to use intelligent systems to help disabled people live better. However, it is not enough to only focus on the basic life needs of disabled people, and their well-being should not be ignored. In this work, an intelligent system is designed to help disabled people to play Chinese chess with others in real life without any physical movement, which can improve the social well-being of the disabled. The system consists of three parts: visual recognition, eye tracking, and robotic manipulation. In the test, the system was able to complete the task in 13 out of 15 rounds, indicating that the system can work well in most cases.
A Key-phrase Extraction Method Based on Multi-size Convolution Windows for Scientific Literatures

The key-phrase extraction is important for the downstream tasks in natural language process, and has attracted a lot of attention. Compared with other documents, scientific literatures contain many long phrases. Most existing methods perform poor on these literatures. To address this problem, a key-phrase extraction method based on multi-size convolution windows (KE-MCW) is proposed for scientific literatures in this paper. More specifically, in order to represent more contextual information, a convolutional neural network(CNN) with multi-size filters is introduced to map the documents into distributed feature vectors, then each vector can represent different size phrases. Next, in order to determine whether each word is a part of a keyphrase, a deep recurrent neural network is used to mark the role of each word. Finally, the attention mechanism is used to further judge the importance of each phrase. Experimental results show that our proposed method performs better than some competitive methods for technology literatures.
ReCUS: Reconvolution and Upsampling Network for Object Detection

Most of the mainstream object detection models such as RetinaNet, SSD, YOLO, and Faster RCNN hardly achieve a good balance between detection accuracy and speed. A major reason is rich deep feature semantic information of images is not fully exploited. To solve this problem, a novel deep convolutional network structure termed as reconvolution and upsampling network (ReCUS) is proposed. In the ReCUS, a modified path aggregation network(mPAN) is added after the backbone, which is beneficial to strengthen the foreground salient feature information and weaken background information. Moreover, two new spatial pyramid pooling (SPP) modules are embedded before output heads for multi-scale fusion of local and global features. The experiments show that the effectiveness of our proposed ReCUS. Furthermore, the better detectability of the ReCUS network is demonstrated for both small scale objects and large scale objects.
CBFL: Improving Software Fault Localization by Analyzing Statement Complexity

Software fault localization, which is an important software quality assurance technology, provides the location of the faults in software to improve the efficiency of debugging and repairing. In previous research, software fault localization techniques, such as spectrum-based, mutation-based, and program slicing, have been widely used and achieved good results. However, many statements could have same suspicious values by using these techniques, which will consume large amount of manual effort to confirm and affect the accuracy of fault localization. For example, using Ochiai or DStar to locate 395 faulty versions of 6 projects in Defects4J, nearly 70 % of the faulty versions have more than one suspicious statement are ranked as top tied 1. To address the above problem, this paper proposes a complexity-based fault localization (CBFL) technique to further improve the accuracy of fault localization. Firstly, a set of metrics for measuring the complexity of statements is proposed, and the metrics of each statement in projects are extracted to construct a classification model. Then, the classification model is used to predict the faulty probability of the statements which are ranked as top tied 1 by SBFL, MBFL or other techniques, and these statements are reranked according to the estimated faulty probability to improve the accuracy of fault localization. This paper implements a fault localization tool CDStar based on the CBFL, and conducts experiments on the Defects4J dataset. Comparing with the DStar, the results show that CBFL outperforms DStar in terms of Einspect @1 and EXAM.
Graph Based Extractive News Articles Summarization Approach leveraging Static Word Embeddings

With enormous and voluminous data being generated on a regular basis at an exponential speed, there is a demanding need for concise and relevant information to be available for the masses. Traditionally, lengthy textual contents are manually summarized by Linguists or Domain Experts, which are highly time consuming and unfairly biased. There is a dire need for Automatic Text Summarization approaches to be introduced in this broad spectrum. Extractive Summarization is one such approach where the salient information or excerpts are identified from a source and extracted to generate a concise summary. TextRank is an unsupervised extractive summarization technique incorporating graph-based ranking of extracted texts and finding the most relevant excerpts to generate a concise summary. In this paper, the prospects of a domain agnostic algorithm like TextRank for various domains of News Article Summarization are explored, exploring its efficiency in domain specific tasks and conveniently drawing various insights. NLP based pre-processing approaches and Static Word Embeddings were leveraged with semantic cosine similarity for the efficient ranking of textual data and performance evaluation on various domains of BBC News Articles Summarization datasets through ROUGE metrics. A commendable ROUGE score is achieved.
Influence of Big Data Information Processing Technology on English Reading Anxiety

Reading anxiety is the fear and tension indirectly connected with the reading object. In the field of second language acquisition, the research mainly focuses on the reading anxiety of the uniqueness of the situation. Based on the specific practice of English reading teaching, this article discusses the current English reading anxiety problems, analyzes the main causes of the problems, and puts forward relevant countermeasures and suggestions to solve the English reading anxiety problems with the help of the platform combined with big data technology.
Convolutional Locality-Sensitive Dictionary Learning for Facial Expressions Detection

Facial Expression (FE) detection is a popular research area, particularly in the field of Image Classification, Pattern Recognition and Computer Vision. Sparse Representation (SR) and Dictionary Learning (DL) have significantly enhanced the classification performance of image recognition and also resolved the problem of the nonlinear distribution of face images and its implementation with DL. However, the locality structure of face image data containing more discriminative information, which is very critical for classification has not been fully explored by state-of-the-art existing SR-based approaches. Furthermore, similar coding results between test samples and neighboring training data, contained in the feature space are not being fully realized from the image features with similar image categorizations, to effectively capture the embedded discriminative information. In an attempt to resolve the forgoing issues, we propose a novel DL method, Convolutional locality-sensitive Dictionary Learning (CLSDL) for Facial Expression detection.
GenGPT: A Systematic Way to Generate Synthetic Goal-Plan Trees

Deciding “what to do next” is a key problem for BDI agents with multiple goals, which is termed the intention progression problem (IPP). A number of approaches to solving the IPP have been proposed in the literature, however, their evaluations are all taken in different forms. The lack of standard benchmarks and testbeds for evaluating the IPP makes it difficult for researchers to contribute to this topic. To foster research around the IPP and BDI agents, this paper proposes a way to generate test cases in the form of goal-plan trees which can be used to represent the agent’s intentions in various agent languages and platforms.
The histological diagnosis of breast cancer by employing scale invariant ResNet 18 with spatial supervised technique

Background: Breast cancer is one of the most prevalent cause of morbidity and mortality in women all over the world. Histopathological diagnosis is a vital component in the management of breast cancer. The application of artificial intelligence is yielding promising results for the better patientcare.
Report on the SIGDial 2021 special session on summarization of dialogues and multi-party meetings (SummDial)

The SummDial special session on summarization of dialogues and multi-party meetings was held virtually within the SIGDial 2021 conference on July 29, 2021. SummDial @ SIGDial 2021 aimed to bring together the speech, dialogue, and summarization communities to foster cross-pollination of ideas and fuel the discussions/collaborations to attempt this crucial and timely problem. When the pandemic has restricted most of our in-person interactions, the current scenario has forced people to go virtual, resulting in an information overload from frequent dialogues and meetings in the virtual environment. Summarization could help reduce the cognitive burden on the participants; however, multi-party speech summarization comes with its own set of challenges. The SummDial special session aimed to leverage the community intelligence to find effective solutions while also brainstorming the future of AI interventions in meetings and dialogues. We report the findings of the special session in this article. We organized the SummDial special session under the aegis of the EU-funded H2020 European Live Translator (ELITR) project. 1 Date : 29 July, 2021. Website : https://elitr.github.io/automatic-minuting/summdial.html.
Federated Data Integration for Heterogeneous Partitions Based on Differential Privacy

Federated learning has recently become a research hotspot in distributed learning, and its purpose is to jointly train machine learning models on the premise of protecting privacy. However, there are some problems with federated learning. Each machine learning algorithm must be modified in order to complete the training. The data partitioning is either horizontal or vertical, which is not flexible enough. In addition, there are many rounds of communication during the training process, so the training efficiency is low. In order to address these problems, we propose a generic federated integration method for multiple data sources. The method can integrate data in arbitrary partitions, protect the privacy based on differential privacy, and reduce communication cost based on singular value decomposition. After the data are modelled in this method, they can be transferred to the center for purpose of federated learning. Our method includes four algorithms. We give a theoretical proof on the method’s satisfying of differential privacy. Finally, experiments are conducted to demonstrate the performance of the method in prediction accuracy and data compression.
A Route to Intelligence: Oversimplify and Self-monitor

I want to try to do something rather more speculative than the rest of you have done. I have been thinking recently about how one might explain some features of human reflective consciousness that seem to me to be very much in need of an explanation.
Key Factors Determining the Expected Benefit of Customers When Using Artificial Intelligence

The study aims to identify the relationship of Artificial Intelligence (AI) with the field of e-commerce; The relationship of AI with the e-commerce sector is about broad technologies such as data analysis and machine learning to make better and smarter actions to improve the experience of today’s digital shoppers. Given the importance of the mechanisms of artificial intelligence (AI) and the Internet of Things (IoT) in improving the e-commerce process, it was necessary to prepare this article to learn about the images of the use of AI techniques and how to use them to provide an easier and smarter shopping experience through the online Store.
Software Quality Prediction Using Machine Learning

With the emergence of Machine Learning, many companies are increasingly embracing this revolutionary approach, both in terms of growth and maintenance, to reduce software costs. This research aimed at building two models which is Software Defect Prediction Model (SDPM) which will be used to predict defects in software and Software Maintainability Prediction Model (SMPM) which will be used for Software Maintainability. Different classifiers, namely Random Forest, Decision Tree, Naïve Bayes and Artificial Neural Networks have been considered and then evaluated using different metrics such as Accuracy, Precision, Recall and Area Under the Curve (AUC). The two models have successfully been evaluated and Decision Tree has been chosen as compared to other classifiers which tends to perform much better. Finally a framework based on a set of guidelines that can be used to improve software quality has been devised.
Optimal Initialization Conditions Discovery to Improve Clustering Based Image Segmentation

A B S T R A C TIn this paper, we propose a solution to the most important factor that deteriorates the performance of the clustering algorithms. We propose an efficient approach for discovering optimal initialization conditions for clustering-driven high-quality image segmentation (EAIS). The proposed approach solves the key clustering issue by uniquely utilizing image histograms to determine the optimal initialization conditions for pixel clusters (segmentation). The proposed approach comprises five modules, deep image reconstruction, intra-histogram peaks determination, inter-histogram peaks association, mutual consensus-oriented cluster seeds merging, and morphological reconstruction-driven spatial post-processing. Diverse experimental results on the BSDS500 benchmark validate that our proposed approach outperforms state-of-the-art (SOTA) methods regarding segmentation quality and computational efficiency.
Genetic Predictors of Destructive and Constructive Forms of Digital Behavior of Young Adults

In the context of globalization there is an increasing tendencies of manifestation of extremism, negativism, aggression, asocial and dissocial behavior on the Internet. However, the question of the biological determination of destructive and constructive forms and transformations of users’ behavior remains open. The study was aimed to search for genetic predictors of digital behavior among young adults. The study involved 115 people (51 males, 64 females) aged 18 to 22 years. In order to study the digital behavior, we used “Strategies of digital behavior” questioner (Abakumova I.V., Ermakov P.N., Romek V.G., Kolenova A.S., Grishina A.V., Zvezdina G.P.). Molecular genetic analysis was performed through the DNA isolation from buccal epithelial cells. Statistical methods included comparative analysis, very simple structure method (VSS), principal component analysis, and ANOVA. It is shown that the majority of the sample have the most pronounced active “constructive” form of digital behavior (45%), active “destructive” and passive “constructive” are expressed almost equally (27% and 28%). The active-destructive form of digital behavior is reliably more often observed in carriers of the combination of BDNF G/G, COMT G/A, and DRD2 T/T polymorphisms. For the constructive forms of digital behavior, no biological determination or association with the studied genes was established. The results about the association of COMT, BDNF, and DRD2 genes with the behavioral strategies of Internet users, on the one hand, are consistent with the general knowledge about their role, on the other hand, our data expand the existing ideas about the biological determination of digital behavior.
Robust Group Anomaly Detection for Quasi-Periodic Network Time Series

Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.
Hierarchical Feature Selection via Large Margin Nearest Neighbor

Being in the era of big data, the complexity of data is getting higher and higher and the well organized data has been the important driving force for machine learning and data mining tasks, which means that raw data need to be processed to satisfy the tasks from different fields. Different classes in hierarchical structure gives rise to the hierarchical classification problem, which varies from the flat classification such as binary or multi-class classification problems in general, and now is naturally ubiquitous due to the rapid growth of data in the real world. Feature selection for hierarchical classification is confronted with the problem that traditional linear regression method cannot classify data well, especially in the context of classes with close relationships. In this paper, we introduce a novel feature selection method based on large margin nearest neighbor strategy (HFSLM), and apply it to the hierarchical classification problems. The key idea of this method is to project the data into a low-dimensional feature space and separate samples from different classes. Experimental results on several hierarchical class datasets show that our method is better than the representative HiFSRR method.