Reviewerly/exHarmony huggingface dataset:
Recommendation to Group of Users Using the Relevance Concept

Group recommender systems (GRSs) have played an important role in numerous online applications by providing recommendation to the group of users where satisfaction of the entire group is a major concern. In traditional GRSs, the relevance of all the groups and the items is considered equal which does not produce accurate recommendations. In this paper, we propose a formalization of the GRS based on the relevance concept using profile merging scheme where collaborative filtering (CF) is applied on each group profile to generate effective recommendations to the group by considering the ratings of the items, the relevance of the groups and the relevance of the items. Further, our GRS framework provides relevant similarity measures, relevant prediction and recommendation quality measures. The experimental results on the benchmark MovieLens dataset demonstrate the efficacy of our proposed GRS framework.
Inferring the Source of Official Texts: Can SVM Beat ULMFiT?

Official Gazettes are a rich source of relevant information to the public. Their careful examination may lead to the detection of frauds and irregularities that may prevent mismanagement of public funds. This paper presents a dataset composed of documents from the Official Gazette of the Federal District, containing both samples with document source annotation and unlabeled ones. We train, evaluate and compare a transfer learning based model that uses ULMFiT with traditional bag-of-words models that use SVM and Naive Bayes as classifiers. We find the SVM to be competitive, its performance being marginally worse than the ULMFiT while having much faster train and inference time and being less computationally expensive. Finally, we conduct ablation analysis to assess the performance impact of the ULMFiT parts.
Autocalibration of Outlier Threshold with Autoencoder Mean Probability Score

Anomaly detection is a widely studied field in computer science with applications ranging from intrusion and fraud detection, medical diagnosis and quality assurance in manufacturing. The underlying premise is that an anomaly is an observation that doesn't conform to what is considered to be normal. A problem however is in defining the threshold that draws the line between what is normal and what is an anomaly which is largely dependent on a domain expert or from empirical testing that would yield the best result. Another problem is that the availability of data with regards to what is not normal is highly unavailable in real world scenarios making it difficult for traditional machine learning techniques to build a classification model. In this study, we propose a method that automatically determines the outlier threshold using a semi-supervised learning approach with autoencoders. To validate the performance of our proposed approach, we perform several experiments in comparison with traditional outlier detection approaches as well as an existing semi-supervised approach in one class classification, specifically OneClassSVM. The goal of this study is to eventually apply the method for autocalibration of anomaly detection of frames in video sequences. Initial results are also presented in a computer vision task.
Improving Data Explainability in Analysis of Designed Computer Simulation Experiments

Dimension reduction of data generated from a complex simulation model is an important aspect, for the purpose of better understanding the behaviour of data, and it is often needed in many fields of study, including computer simulation and modelling. Also, improving data explainability is highly desirable for studying dynamics of complex simulation models, dynamics of which depends on many parameters, and has become an important aspect in machine learning and artificial intelligence. In this work, we initiate an approach, combining principal component analysis, K-means clustering and ANOVA-F test, in order to analyze the data from a designed simulation experiment. We propose a new method for optimal selection of numbers of clusters for data clustering. The proposed method is illustrated by an analysis of agent-based computer simulation. Our study has demonstrated the usefulness of the proposed method in both explainable data analytic and analysis of complex systems.
One-Pass Sparsified Gaussian Mixtures

We present a one-pass sparsified Gaussian mixture model (SGMM). Given N data points in P dimensions X, the model fits K Gaussian distributions to X and (softly) classifies each point to these clusters. After paying an up-front cost of O(NP log P) to precondition the data, we subsample Q entries of each data point and discard the full P-dimensional data. SGMM operates in O(KNQ) time per iteration for diagonal or spherical covariances, independent of P, while estimating the model parameters in the full P-dimensional space, making it one-pass and hence suitable for streaming data. We derive the maximum likelihood estimators for the parameters in the sparsified regime, demonstrate clustering on synthetic and real data, and show that SGMM is faster than GMM while preserving accuracy.
A High-throughput Low-area Entropy Source Based on Asynchronous Feedback Unit

Entropy source is the key to the design of a true random number generator (TRNG). According to the problem of large hardware cost and no theoretical guidance for feedback coefficient selection of MFRO-based entropy source, an entropy source based on a pure digital asynchronous feedback unit (AFU) is proposed in this paper, which can be used as a general unit of entropy source based on ring oscillator. And also, we established a random Boolean network models to guide its feedback coefficient selection. The self-feedback is added to improve the chaotic characteristics of the AFU-based entropy source. The experiment result shows that, the hardware cost is reduced 53.125% and the entropy value is increased 11% compared with MFRO when the array size is 16x16. Finally, the proposed AFU-based entropy source is prototyped on the Xilinx xc7a200tfbg484 FPGA. The hardware cost is 30 LUTs and the throughput is greater than 300Mbps. The entropy source passed all NIST randomness tests without any post-processing.
Tear Off Your Disguise: Phishing Website Detection Using Visual and Network Identities

Adversaries create phishing websites that spoof the visual appearances of frequently used legitimate websites in order to trick victims into providing their private information, such as bank accounts and login credentials. Phishing detection is an ongoing combat between the defenders and the attackers, where various defense mechanisms have been proposed, such as blacklists, heuristics, data mining, etc. In this paper, we present a new perspective on the identification of phishing websites. The proposed solution, namely PhishFencing, consists of three main steps: (1) filtering: a list of trusted and non-hosting websites is used to eliminate pages from legitimate hosts; (2) matching: a sub-graph matching mechanism is developed to determine if an unknown webpage contains logo images of whitelisted legitimate websites–once a match is detected, the unknown webpage is considered a suspicious page; (3) identification: host features are utilized to identify whether a suspicious webpage is hosted on the same cluster of servers as the corresponding legitimate pages–if not, the suspicious page is tagged as phishing. Compared with existing approaches in the literature, PhishFencing introduces an autonomous mechanism to replace the manual process of collecting and refreshing groundtruth data. As a in-network solution, PhishFencing could also partially detect phishing pages hosted on HTTPS servers, without requiring any support from clients. Through intensive experiments, we show that PhishFencing is very effective in comparing with the literature.
A Conceptual Model of Requirement Engineering in Cloud Project Delivery for Thai Government Organizations

The shift to cloud computing has affected the future of software engineering in several ways i.e., multilateral software development, scalability, and new technology stack such as an open-source software, plus infrastructure as code such as container, serverless architecture and software defined network (SDN). In order to support the cloud project delivery for government organizations, requirements engineering (RE) is a crucial step in software engineering that determines whether a project will be successful or result in a failure. RE steps include requirement elicitation, requirement analysis, requirement specification and requirement validation. Data is collected by semi-structured interview from the providers and the users of cloud services in 11 Thai government organizations, and from cloud service provider for meeting enterprise requirements and user requirements. The results reveal nine key issues that affect cloud project delivery: (1) lack of trust with external cloud service provider by generation X and baby boomer (2) lack of transparency as regards the legal agreement about the cloud user's personal data protection responsibility by cloud service provider, (3) the issues of reliability, security and service level agreements (4) lack of knowledge and lack of understanding of cloud technology (5) required training and a learning by doing (6) the policy to use government cloud services instead of developing their own cloud, (7) older people at top-level tend to resist cloud technology, (8) people in general lack of knowledge and understanding of cloud technology (9) problems about pricing model, it's impact on 23 cloud requirements (functional and non-functional) as well as factors that involved in RE phases. Based on these results this paper presents a Conceptual Model of Requirement Engineering for Cloud Project Delivery in Thai Government Organizations.
Design of a Blockchain-Based Smart Contract Model for Child Labor Alleviation

The achievements of west and central Africa in producing more than 60% of the world's cocoa, and sub-Saharan Africa's achievement in producing 13% of the world's cotton, hide child labor. These significant levels of production often involve child exposure to issues such as a lack of education; pesticides; dangerous farming tools; work accidents; human trafficking; etc. Blockchain offers an immutable register that allows for digital transactions, smart contract creation, as well as end-to-end product traceability. The main aim of this article is to provide an intelligent contract framework that protects child labor in farming while further enlightening understandings of adoption-related challenges. This framework considers conditions that farmer associations need to satisfy and gives them a tool to improve children's welfare. A research model for the adoption of this tool has been proposed and validated through surveys in the cotton and cacao sectors.
Simulation of Intelligence Evolution in Object-Oriented Systems

In classical artificial intelligence and machine learning fields, the aim is to teach a certain program to find the most convenient and efficient way of solving a particular problem. However, these approaches are not suitable for simulating the evolution of human intelligence, since intelligence is a dynamically changing, volatile behavior, which greatly depends on the environment an agent is exposed to. In this paper, we present several models of what should be considered, when trying to simulate the evolution of intelligence of agents within a given environment. We explain several types of entropies, and introduce a dominant function model. By unifying these models, we explain how and why our ideas can be formally detailed and implemented using object-oriented technologies. The difference between our approach and that described in other papers also — approaching evolution from the point of view of entropies — is that our approach focuses on a general system, modern implementation solutions, and extended models for each component in the system.
Residual-based Fast Single Image Fog Removal

Single image haze removal is a challenging problem to address, and various constraints/priors have been previously considered to obtain acceptable dehazing solutions. In this paper, we propose a trainable end-to-end system for single image dehazing called ReDehazeNet based on the residual and dilation convolutional neural networks. The first part of the networks incorporated into the system is used for recovering a coarse clear image, which is predicted by adopting a context aggregation sub-network that can capture the global structure information. The second part of the network adopts a novel hierarchical convolutional neural network to further refine the details of the clean image by integrating the local context information. Experiments on benchmark images show that ReDehazeNet outperforms several existing state-of-the-art methods while being highly efficient and easy to use.
Topical classification of domain names based on subword embeddings

A good domain name can help a company rapidly increase their brand awareness, attract more visitors, and therefore obtain more customers. Due to the exponential increase in the number of domain names, registrants are often frustrated because their preferred domain names are already taken. In order to enhance registrants’ satisfaction and efficiency, as well as to increase the revenue of registrars (e.g. GoDaddy, Yahoo, Squarespace), it is important to suggest alternative domain names that are available. The first step is to detect registrants’ needs by classifying the attempted domain name to one of the categories. This study is the first that defines the problem of domain name classification, which classifies a registrant’s preferred domain name into pre-defined categories. The paper proposes deep neural networks with subword embeddings that are built in multiple strategies. We build embeddings for character n-grams of a domain name by learning from training data, learning from external corpus, or learning from external corpus and adjusting based on training data. The experiments show that the proposed methods significantly outperform the baselines.
An Access Control Mechanism for Pre-trained ML Models in Mobile Apps

Utilizing machine learning inference in mobile platforms have been made realistic since recent smartphones may have high-performance processors and hardware accelerators of signal and image processing. However, it could bring issues. One of the most significant issues is that any machine learning inferences can be unlimitedly applied to personal data which once the application got permitted to. Furthermore, it could be quite hard to understand the behavior of a `pre-trained' machine learning model and the behavior of an installed application can be transparently modified afterword by replacing a used model. This research aims to implement access control against such functionalities of machine learning inference for mobile platforms. In this paper, we describe how to verify used pre-trained models and how to implement permission control at Android Neural Networks API for Android as a target platform. And we also show an evaluation result with a prototype implementation.
Design and implementation of an android application for face detection and age estimation

Abstract Age is an important attribute of identity and social interaction as well for medical processing. Face is consists several elements to predict the human age such as facial wrinkles and youthfulness. Age estimation from the face by intelligent human-machine interfacing is required to capture picture that may contains different parts as well the human face. In order to estimate age, we need to detect only the face information. This research proposes an android application to take photo for anyone using android camera or upload photo from gallery, then it will determine the age for everybody in the photo. The application can guess the physical age for the faces depends on internet servers. The application tested on many persons and the results was correct under some conditions such as (the light of picture, the normal state for face, camera accuracy), the other results was correct 90- 95% under other conditions. This application easy to use, and simply you can use it to detect the age of your friends. This proposed application can be used in security, parks and swimming pools entrances, and medical clinics
Word Sense Disambiguation using Bidirectional LSTM

Word Sense Disambiguation is considered one of the challenging problems in natural language processing(NLP). LSTM-based Word Sense Disambiguation techniques have been shown effective through experiments. Models have been proposed before that employed LSTM to achieve state-of-the-art results. This paper presents an implementation and analysis of a Bidirectional LSTM model using openly available datasets (Semcor, MASC, SensEval-2 and SensEval-3) and knowledge base (WordNet). Our experiments showed that a similar state of the art results could be obtained with much less data or without external resources like knowledge graphs and parts of speech tagging.
Increasing the Effects of Auxiliary Function by Multiple Extrema in Global Optimization

A new filled function is proposed in this paper for finding a better minimizer of smooth unconstrained global optimization problems. First, a new definition of the filled function for unconstrained problems with a closed bounded domain is given under reasonable assumptions. The proposed filled function is continuously differentiable and contains two parameters. Then a new algorithm is proposed depending on the theoretical and numerical properties of the proposed filled function. The application of the algorithm on different test problems gave positive acceptable results.
A No-Reference Quality Metric for Parameter Tuning of Edge-Aware Filters – An Anti-Image Forensic Method

Edge-Aware Filters (EAF) are less frequently detected by forensic tools compared to the median filter. However, EAFs also blur the edges, if their operational parameters are not tuned properly. Objective image quality metrics which reflect the quality of the smoothed images are necessary for tuning the operational parameters. A novel formulation of a no-reference composite metric, termed as Denoising Performance Metric (DPM) is introduced in this paper. DPM exhibited a correlation of 0.98 ± 0.02 and 0.89 ± 0.09 with the Mean Opinion Score (MOS) and Peak Signal to Noise Ratio (PSNR) between the test images and the noise-free benchmark image, respectively. The correlation observed for type-2 Vector Mean Squared Error (VMSE) with MOS and PSNR are 0.97 ± 0.02 and 0.79 ± 0.25, respectively. The proposed metric is observed to be superior to type-2 Vector Mean Squared Error (VMSE) in terms of its correlation with the subjective fidelity ratings. It can be used for tuning operational parameters of EAF to enhance their ability to tackle forensic tools.
Online Phishing and Solutions

This article introduces and defines online phishing, which is an online crime of deceiving users into accessing fraudulent emails, web links, and websites to steal their sensitive private information. Online phishing is the top starting point for cyberattacks and the leading cause for identity theft that brings financial and other losses to individual and organizational victims. Phishing is primarily a social engineering attack that exploits human vulnerabilities due to lack of user awareness and protection. This article classifies phishing into two general groups based on the primary method and technique involved: social engineering and technical subterfuge. The article discusses how various specific types of phishing work and proposes a comprehensive set of solutions.
The influence of WhatsApp on improvements for fish farmers: A lesson from Semarang City, Indonesia

This study compares the use of WhatsApp by fish farmers, between users and non-users and the improvement of these fish farmers’ financial conditions and aquaculture knowledge. Questionnaires were distributed to 60 fish farmers: 30 who already used WhatsApp and 30 who did not. Result shows that fish farmers who were WhatsApp users showed a higher score in both financial conditions and aquaculture knowledge than non-WhatsApp users. This indicates that fish farmers who use WhatsApp have higher knowledge and have better financial conditions than fish farmers who are not WhatsApp user.
A Class of Binary Cyclic Codes and Their Weight Distributions

Let m, k be positive integers with m=2k and k≥3. Let C(u, ν) is a class of cyclic codes of length 2m-1 whose parity-check polynomial is mu(x)mν(x), where mu(x) and mν(x) are the minimal polynomials of α-u and α-ν over GF(2). For the case $(u,\nu)=(1,\frac{1}{3}(2^m-1))$, the weight distributions of binary cyclic codes C(u, ν) was determined in 2017. This paper determines the weight distributions of the binary cyclic codes C(u, ν) for the case of (u, ν)=(3, 2k-1+1). The application of these cyclic codes in secret sharing is also considered.
A Comparison Between Statistical and Symbolic Learning Approaches for Generative Policy Models

Generative Policy Models (GPMs) have been proposed as a method for future autonomous decision making in a distributed, collaborative environment. To learn a GPM, previous policy examples that contain policy features and the corresponding policy decisions are used. Recently, GPMs have been constructed using both symbolic and statistical learning algorithms. In either case, the goal of the learning process is to create a model across a wide range of contexts from which specific policies may be generated in a given context. Empirically, we expect each learning approach to provide certain advantages over the other. This paper assesses the relative performance of each learning approach in order to examine these advantages and disadvantages. Several carefully prepared data sets are used to train a variety of models across different learning algorithms, where models for each learning algorithm are trained with varying amounts of labelled examples. The performance of each model is evaluated across a variety of metrics which indicates the strength of each learning algorithm for the different scenarios presented and the amount of training data provided. Finally, future research directions are outlined to fully realise GPMs in a distributed, collaborative environment.
Residual Attention Encoding Neural Network for Terrain Texture Classification

Terrain texture classification plays an important role in computer vision applications, such as robot navigation, autonomous driving, etc. Traditional methods based on hand-crafted features often have sub-optimal performances due to the inefficiency in modeling the complex terrain variations. In this paper, we propose a residual attention encoding network (RAENet) for terrain texture classification. Specifically, RAENet incorporates a stack of residual attention blocks (RABs) and an encoding block (EB). By generating attention feature maps jointly with residual learning, RAB is different from the usually used which only combine feature from the current layer with the former one layer. RAB combines all the preceding layers to the current layer and is not only minimize the information loss in the convolution process, but also enhance the weights of the features that are conducive to distinguish between different classes. Then EB further adopts orderless encoder to keep the invariance to spatial layout in order to extract feature details before classification. The effectiveness of RAENet is evaluated on two terrain texture datasets. Experimental results show that RAENet achieves state-of-the-art performance.