Reviewerly/exHarmony huggingface dataset:
Regularizing Multilayer Perceptron for Generalization Using KL-Divergence

Generalization implies the ability to recognize an input pattern not used during the training of a neural network. Effective regularization strategies are one of the ways to make the generalization capability of the neural network better. In this article, the objective is to improve the generalization of multilayer perceptron (MLP) by regularization with the help of KL-Divergence (KLD) based weight adaption technique. For the purpose, we modify the conventional weight adaption criterion of MLP by incorporating KLD as an additional regularization term with the mean squared error function. We have tested the proposed regularizing approach on ten benchmark classification data sets. Our experimental results show that the KLD based regularized MLP provides better classification accuracy compared to the conventional MLP. We also observe that the efficiency of the proposed method is highly dependent on the coefficient (β) of the regularization term. The proposed method shows the best result for β = 0.02 for eight out of ten classification data sets.
Application of machine learning and cloud computing in social media behavior analysis

It is of great theoretical significance and practical value to analyze the characteristics of users and behaviors in social networks, to study the personalized recommendation algorithms of users, to explore the inherent laws of event development, and to predict the movement of information or opinions. This paper analyzes the Weibo behavior through machine learning and cloud computing technology. Moreover, this paper studies and analyzes traditional network algorithms, and proposes a microblog recommendation algorithm based on statistical features. At the same time, the research content of this paper focuses on microblog contents, user characteristics, user preferences, and influence levels. The algorithm has simple structure and strong computing performance and performs feature data mining through cloud computing big data method, which is suitable for online mining microblog behavior. In addition, the performance of the algorithm was analyzed by design comparison experiments. The research indicates that the research algorithm proposed in this paper has certain advantages, which can be applied to network behavior analysis mining, and can provide theoretical reference for subsequent related research.
Validation study of three-dimensional scanning of footwear impressions

Three-dimensional impressions are typically photographed and cast for further comparison when reference items become available to investigators. Three-dimensional scanning has been proposed as a more time-efficient and objective, less destructive method for footwear impression analysis. This project sought to acquire repeat scans of outsole footwear impressions and corresponding shoes at high resolutions to investigate the precision and accuracy of 3D scanning on footwear and impressions. Impressions were created using three footwear types representative of footwear encountered in casework. Scans of impressions were created using the Artec Spider structured-light scanner. Calculations of the statistical variability between scans were carried out using the CloudCompare software package. The distance between corresponding points within the impression tested for precision (n = 6) averaged 0.45 mm, σ 0.29 mm. When comparing the impression to the reference shoe, the distance between corresponding points averaged 2.41 mm, σ 1.98 mm. The maximal differences within the scans were at the toe of the shoe and may be due to flexion, as well as damage to the soil around the edges of the impression. This research has validated the hardware and software used to acquire data from a 3D impression and from the reference item that produced the trace.
A Comic Head Images Generation Algorithm Based on Improved Deep Convolutional Generative Adversarial Networks

In this paper, a generative adversarial network is applied to the generation of comic head images. Aiming at solving the problems of poor image quality, single generated samples, and slow model convergence in the generation process, a caricature head image generation algorithm based on improved deep convolutional generative adversarial network (DCGAN) is proposed. By adjusting the network structure, the ability to generate models and discriminate models is better balanced. The quality of the generated samples is enhanced, meanwhile, the convergence speed of the entire network is also improved. The experimental results show that the algorithm has a good effect on the generation of comic head images.
Supporting Teacher Assessment in Chinese Language Learning Using Textual and Tonal Features

Assessment in the context of foreign language learning can be difficult and time-consuming for instructors. Distinctive from other domains, language learning often requires teachers to assess each student’s ability to speak the language, making this process even more time-consuming in large classrooms which are particularly common in post-secondary settings; considering that language instructors often assess students through assignments requiring recorded audio, a lack of tools to support such teachers makes providing individual feedback even more challenging. In this work, we seek to explore the development of tools to automatically assess audio responses within a college-level Chinese language-learning course. We build a model designed to grade student audio assignments with the purpose of incorporating such a model into tools focused on helping both teachers and students in real classrooms. Building upon our prior work which explored features extracted from audio, the goal of this work is to explore additional features derived from tone and speech recognition models to help assess students on two outcomes commonly observed in language learning classes: fluency and accuracy of speech. In addition to the exploration of features, this work explores the application of Siamese deep learning models for this assessment task. We find that models utilizing tonal features exhibit higher predictive performance of student fluency while text-based features derived from speech recognition models exhibit higher predictive performance of student accuracy of speech.
DARPA's Role in Machine Learning

Machine learning methods provide a way for artificial intelligence systems to learn from experience. This article describes four threads of machine learning research supported and guided by the Defense Advanced Research Projects Agency — probabilistic modeling for speech recognition, probabilistic relational models, the integration of multiple machine learning approaches into a task‐specific system, and neural network technology. These threads illustrate the Defense Advanced Research Projects Agency way of creating timely advances in a field.
Social Networks as Communication Channels: A Logical Approach

Agents use channels to communicate publicly. An agent announcing a statement on a communication channel poses non trivial questions about the nature of such a statement as well as about the attitude of the agent herself. Does the agent know whether the statement is true? Is this agent announcing that statement or its contrary in any other channel? Extensions to Dynamic Epistemic Logics have been proposed in the recent past that give account to public announcements. One major limit of these logics is that announcements are always considered truthful. It is however clear that, in real life, incompetent agents may announce false things, while deceitful agents may even announce things they do not believe in. In this paper, we shall provide a logical framework, called Multiple Channel Logic, able to relate true statements, agent beliefs, and announcements on communication channels. We discuss syntax and semantics of this logic and show the behaviour of the proposed deduction system. Lastly, we shall present a classification of agents based on the above introduced behaviour analysis.
A Multi-Lingual and Text-Speech Dialog Support System for e-health

Abstract This paper reports a web-based remote collaborative medical diagnosis system. This system provides a dialogue platform using text and speech, by Chinese and Uyghur, containing information such as disease names and symptoms for different medical classifications. The interactive text and speech between doctors or patients can be translated bilingually between Chinese and Uyghur automatically. At last, diagnosis report will be generated for the doctor and the patient. Finally, the performance of the e-health service system is evaluated.
Afterword: Data in Transit

Abstract The naïve fantasy that data have an immediate relation to the phenomena of the world, that they are “objective” in some strong, ontological, sense of that term, that they are the facts of the world directly speaking to us, should be finally laid to rest by the papers collected in this volume. In this afterword, I propose that these papers, investigating data journeys in fields from particle physics to urban planning, show that even the primary, original, state of data is not free from researchers’ value- and theory-laden selection and organization.
An Overview on Digital Forensics Tools used in Crime Investigation for Forgery Detection

Image Forensics has lot of importance in digital forensics in crime investigation process. As a coin has two different sides, many anti-Forensic tools are available to help criminals for hiding traces of forgery which also have been evolved with advancement in modern technology. In this paper the major focus is on overview of available forensics tools and frequent image processing techniques involved in it to investigate crime related digital traces.
Use of technological tool for the consolidation of mathematical pre-knowledge in higher education

Abstract Currently, information and communication Technologies (ICT) have permeated the different educational, political, economic and sociocultural contexts, contributing to the inclusion and development of society by optimizing time, efforts and resources. On the other hand, (ICT) are considered powerful facilitators of the teaching-learning process at all training levels because they visibly improve the way of acquiring knowledge, becoming essential tools for educational transformation and innovation. Therefore, the purpose of this present study is to analyze the results of the first implementation of the PREIN - UTS virtual course developed in the web Khan-Academy tool. The sample was formed by new students who entered the first semester of the technological level in the academic period 2018-II of a higher education institution located in Bucaramanga Santander (N=801). The methodological approach was quantitative of exploratory type, with the use of descriptive statistics and SPSS software for data analysis. Among the research results it was evidenced that the PREIN course is an innovative and easily accessible strategy, that contributes to the assurance of mathematical skills of the upcoming students that will enter proximally.
A Prize, a Prediction, and a Drama

We like to think that we are involved in dramatic and exciting work. We certainly talk a good game. Changing the world! Reinventing society! Disrupting! Yet, anyone who has spent any time in computer science knows that it is a game of inches, not miles. Much of our work is fine and detailed. We do it hunched over a keyboard, with our faces illuminated by the light of a screen, and hope that the result will gain us 2% or 5% or maybe even 10%. This month’s article from the body of knowledge both confirms our conventional model and gives us at least a suggestion of drama along with a reminder of how our contributions fit into the wider world.
The keyword to success: Comparative analysis of Computer Science research across representative universities in Latin America

Scientific publications are often used to rank higher education institutions, countries, research centers, using parameters such as number of citations and relevance. At the same time, there is a lack of comparative information among the research conducted in Latin America. In this study, we aim to compare the Computer Science research in which Latin American scientists have participated, by analyzing the co-occurrence networks of keywords chosen for the corresponding scientific publications. To this aim, we collect information, from the Scopus website, about the scientific publications whose authors belong to one of the two best universities in eleven Latin American countries filtering scientific publications only in the area of Computer Science. Next, we build graphs per university, calculate some structural measures. Additionally, we define a metric named University Keyword Diversity (UKD) as a variation of the statistical equation of entropy for the keywords appearing in the university research articles. We correlate such metrics with the number of citations and the university ranking finding correlations as high as 0.8. Our results show that the university with the highest UKD in Latin America is Universidad Nacional Autónoma de México (UNAM) and that its corresponding graph is one of the richest in number of nodes and relations.
Far from Success – Far from Feedback Acceptance? The Influence of Game Performance on Young Students’ Willingness to Accept Critical Constructive Feedback During Play

In a learning situation, feedback is of great importance in order to help a student to correct a possible misconception. However, previous research shows that many students tend to avoid feedback regarding failures, including critical constructive feedback (CCF) that is intended to support and guide them. This is especially true for lower-achieving students, who might perceive feedback as an ego-threat, and therefore protect themselves by neglecting it. However, it has been shown that such neglect can be suppressed by using teachable agents (TA’s). Another, but less studied factor that influences feedback acceptance is the degree or extent of failure when trying to solve a task. The present study explores if and how momentary performance levels influence middle school students’ willingness to accept CCF when playing an educational game in history – with or without a TA. On the basis of teacher assessments of the students’ general skills, data logs and analyses of sequential patterns, we concluded that the willingness to accept CCF differs between students, but also between conditions and situations. One major finding is that a TA supports the students to more readily embrace CCF, even if the effect is larger for lower-achieving students. Another finding is that indications of being far from succeeding, such as low success rates or repeated trials and revisions, have a negative impact on feedback acceptance, even if a TA mitigates some of this influence. The implications of these results are discussed in relation to meta-cognitive aspects of learning and to educational software design.
The Emergence, Advancement and Future of Textual Answer Triggering

The recent emergence of Answer Triggering has been annexed to the task of Answer Sentence Selection. Fundamentally, Answer Triggering challenges Question Answering systems to be capable of handling Answer Retrieval with two clauses: detecting whether a candidate answer set contains an answer or not; and if yes, retrieve that answer to the user. Till now, this task has proved itself comparatively harder, with most Answer Triggering models achieving $$F_1$$ scores below 0.44. Meanwhile, due to little literature on the task, the potentials of this task in unlocking vital contributions to Natural Language Processing is hindered. Insights of this “new” NLP task assuredly needs to be known to researchers for immense contributions to kickstart again. This survey presents extensive details and analyses of a myriad of methods used by benchmark models on the task of Answer Triggering. Additionally, it discusses the datasets that emerged concurrently with the task. It again identifies the challenges, discusses solutions and provides recommendations for the advancement of such current systems and future ones.
Hand over face segmentation using MPSPNet

Accurate hand segmentation is vital in many applications where the hands play a central role. Examples include sign language recognition, action recognition, and gesture recognition. A relatively unexplored obstacle to correct hand segmentation is the case of hands overlapping with the face. Inspired by the hand over face segmentation problem, we have developed the novel Multi-level Pyramid Scene Parsing Network (MPSPNet) for semantic segmentation. We evaluate MPSPNet on two standard object segmentation datasets (NYUDv2, PASCAL VOC) and two recently published and challenging datasets focusing on scenarios in which the hands overlap the face, VLM-HandOverFace and HOF. Additionally, we compare our method against several state-of-the-art hand segmentation methods such as RefineNet and PSPNet. We empirically show that the proposed method achieves a 6% improvement in mIOU compared with RefineNet on the VLM-HandOverFace dataset and a 15% improvement in mIOU compared with PSPNet on the HOF dataset.
IM<sub>NRFixer</sub>: A hybrid approach to alleviate class‐imbalance problem for predicting the fixability of Non‐Reproducible bugs

Abstract Software maintenance is an important phase in the software development life cycle. Software projects maintain bug repositories to gather, organize, and keep track of bug reports. These bug reports are resolved by numerous software developers. Whenever the reported bug does not get resolved by the assigned developer, he marks the resolution of bug report as Non‐Reproducible (NR). When NR bugs are reconsidered, few of them get resolved, and their resolution changes from NR to fix (NRF). The main aim of this paper is to predict these fixable NRF bug reports. A major challenge in predicting NRF bugs from NR bugs is that only a small portion of NR bugs get fixed, i.e., class‐imbalance problem. For example, NRF bugs account for only 8.64%, 4.73 %, 4.56%, and 1.06% in NetBeans, Eclipse, Open Office, and Mozilla Firefox projects respectively. In this paper, we work on improving the classification performance on these imbalanced datasets. We propose IM NRFixer , a novel and hybrid NRF prediction tool. IM NRFixer uses three different techniques to combat class‐imbalance problem: undersampling, oversampling, and ensemble models. We evaluate the performance of IM NRFixer models on four large and open‐source projects of Bugzilla repository. Our results show that IM NRFixer outperforms conventional machine learning techniques. IM NRFixer achieves performance up to 71.7%, 93.1%, 91.7%, and 96.5% while predicting the minority class (NRF) for NetBeans, Eclipse, Open Office, and Mozilla Firefox projects, respectively.
Enabling the efficiency of Blockchain Technology in Tele-Healthcare with Enhanced EMR

A blockchain is a growing list of records, called blocks, that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp and a transaction data. A blockchain consists of a structure of data that represents a financial ledger entry, or a record of transactions. Each transaction is digitally signed to ensure its authenticity, therefore no one tampers with it. So the ledger itself along with an existing transactions within it are assumed to be with great integrity. The basic advantages of blockchain technology includes the features of decentralization, Peer to peer network, immutability, security and transparency. If once the data has been re-entered into the record, it will not get deleted instead, it gets updated. Every block in the blockchain has a permanent timestamp that indicates authentication and verification. Here, we promote the facility of having a smart contract between the patient and a healthcare sector. It highly concentrates in the field of diabetes as it is a rapidly growing disease in today's world. It requires regular checkups to keep the disease in control. This paper consolidates the features of blockchain technology to produce an enhanced EMR in the field of diabetes.
JADE/JaCaMo+2COMM: Programming Agent Interactions

We present 2COMM, a middleware in which social relationships, created during agent interactions, are represented as social commitments. These relationships are reified as resources in the agents' environment, and can be directly manipulated by the agents themselves via standard operations. We show that this perspective induces an agent programming schema that is independent of the actual agent platform. The uniformity of the approach is exemplified in two well-known agent platforms: JADE and JaCaMo.
Research on the Matrix Mode and Related Information System of Government WeChat Public Account Operation

Government WeChat matrix, which has played an important role in the openness of government affairs, public opinion supervision and public services, is one of the main forms of e-government in contemporary China. We took prefecture-level city WeChat public account as the core research object, analyzed the matrix operation mode of government affairs WeChat public account, and proposed a star matrix model. In the meantime, we also designed the corresponding information management system, using first normal form and third normal form mixed database design mode, using dynamic generated static web page as the front-end user interface, to ensure the stability and security of the system, as far as possible to reduce the waiting time for system access, improve the efficiency of implementation. This matrix structure construction and information system implementation method proposed in this paper has certain reference value for the research of related fields.
Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor

Reading text and unified text detection and recognition from natural images are the most challenging applications in computer vision and document analysis. Previously proposed end-to-end scene text reading methods do not consider the frequency of input images at feature extraction, which slows down the system, requires more memory, and recognizes text inaccurately. In this paper, we proposed an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading. The OctConv can extract features by factorizing the input image based on their frequency. It is a direct replacement of convolutions, orthogonal and complementary, for reducing redundancies and helps to boost the reading text through low memory requirements at a faster speed. In the text reading process, features are first extracted from the input image using Feature Pyramid Network (FPN) with OctConv Residual Network with depth 50 (ResNet50). Then, a Region Proposal Network (RPN) is applied to predict the location of the text area by using extracted features. Finally, a time-restricted attention encoder-decoder module is applied after the Region of Interest (RoI) pooling is performed. A bilingual real and synthetic scene text dataset is prepared for training and testing the proposed model. Additionally, well-known datasets including ICDAR2013, ICDAR2015, and Total Text are used for fine-tuning and evaluating its performance with previously proposed state-of-the-art methods. The proposed model shows promising results on both regular and irregular or curved text detection and reading tasks.