Reviewerly/exHarmony huggingface dataset:
POEM: Position Order Enhanced Model for Session-based Recommendation Service

Session-based recommendation, which aims to predict the next action of an anonymous user base on the interaction information in a session, plays a crucial role in many online services. Recent works solve the problem with the latest deep learning techniques and have achieved good performance on some datasets. However, they have some shortcomings that affect their practical application value: a) the drift process of users' interests in the browsing is not well explored; b) the association between a user's current interests and general preferences in the session is not adequately considered. They mostly assume that the last interaction has a significant impact on the next interaction, which makes them work well only in limited scenarios and specific datasets. To address these limitations, we propose a session-based recommendation model called POEM, which explicitly considers the impact of interaction order relationships on recommendations by emphasizing position attributes in the session. Specifically, POEM models the macro and micro importance of each item in the session, the influence of user interaction order on the item-level collaboration, and the session-level collaboration reflected in the user interest drift process, respectively. Extensive experiments of the effectiveness, efficiency, and universality on three real-world datasets show that our method outperforms various state-of-the-art session-based recommendation methods consistently.
ADCR: An Adaptive TOOL to select ”Appropriate Developer for Code Review” based on Code Context

Code review is one of the crucial steps in the software development process. Despite having many experts, assigning the appropriate one is often challenging, time-consuming, and inefficient for industrial developers and researchers who demand instant solutions. An automated code review system can serve as a proficient and alternative opportunity for those necessities. This paper aims to identify appropriate reviewers for a selected task based on data analysis using Natural Language Processing (NLP) techniques. Appropriate Developer for Code Review (ADCR) is proposed taking into account a set of data that comprises reviewers' information-responsiveness, experience, and acquaintanceship-benefits of the proposed methods including unbiased review accountability and the early feed-back opportunity for the developers. Additionally, a tool is developed to process the automated review and speed up the development cycles.
Systemic view of human-data interaction

Human-Data Interaction (HDI) is a growing field concerned by placing humans at the center of data flows and providing mechanisms for people to interact explicitly with systems and data. Understanding HDI from a sociotechnical perspective, we argue that technical and human issues must be approached in an interconnected way throughout a data lifecycle. In this paper, grounding our discussions in a conceptual artifact named Extended Semiotic Framework, we discuss how different interested parties, different levels of signs, and different stages in data lifecycle can affect HDI. We apply this artifact into a local COVID-19 information website and use its results to inform the website redesign. Our discussion and results show the Extended Semiotic Framework as capable to promote a systemic view considering both human and technical issues, as well as to identify problems and challenges at different data stages.
Novel Framework for Modelling High Speed Interface Using Python for Architecture Evaluation

This paper presents a framework for modelling Serdes system, SymbaPy, based on Python scripting language along with efficient numerical computation libraries. This paper discusses the building blocks of Serdes to create a framework in Python for system models. The paper also discusses the features of Python language which makes it an adequate tool for modelling the Serdes. We also demonstrate application of this framework by creating a model for MIPI DPHY and CPHY which generates the eye-diagrams at different points in the communication chain.
Improving Human Players’ T-Spin Skills in Tetris with Procedural Problem Generation

Researchers in the field of computer games interest in creating not only strong game-playing programs, but also programs that can entertain or teach human players. One of the branches is procedural content generation, aiming to generate game contents such as maps, stories, and puzzles automatically. In this paper, automatically generated puzzles are used to assist human players in improving the playing skills for the game of Tetris, a famous and popular tile-matching game. More specifically, a powerful technique called T-spin is hard for beginners to learn. To assist beginners in mastering the technique, automatically generated two-step to T-spin problems are given for them to solve. Experiments show that the overall ability for beginners to complete T-spin during play is improved after trained by the given problems. The result demonstrates the possibility of using automatically generated problems to assist human players in improving their playing skills.
Graph Based Hybrid Approach for Long-Tail Item Recommendation in Collaborative Filtering

Recommender system plays a vital role in e-commerce business by providing personalized product recommendation. However, most of the existing recommender system are accuracy-centric and biases on recommending popular items. However, recommending relevant long tail items is another research challenge in recommendation community. In this article, we propose a graph based approach to enhance the long tail items in recommendation. The preliminary results are very encouraging on standard rating dataset. Proposed approach outperforms recently introduced recommender systems which focus on long tail item recommendation.
Flashlight CNN Image Denoising

This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additive white Gaussian noise (AWGN). FlashLight CNN demonstrates state of the art performance when compared quantitatively and visually with the current state of the art image denoising methods.
Predicting the BI-RADS Lexicon for Mammographie Masses Using Hybrid Neural Models

The mammographic image analysis carried out by radiologists describes findings to infer the type of breast pathology to lead patients to adequate treatments. For such an analysis, the experts use a lexicon in the Breast Imaging-Reporting and Data System (BI-RADS), through which the shape and margin of the tumor are described, as well as the breast parenchymal density to determine the risk of developing breast cancer. In this sense, the development of technological tools to assist radiologists in the mammographic description is justified. In this article, two hybrid classification techniques based on neural models are proposed to predict the BI-RADS lexicon attributes for masses. These hybrid models comprise a pre-trained convolutional neural network (AlexNet) for feature extraction and neural models for pattern classification, namely, multilayer perceptron (MLP) and dendrite morphological neuron (DMN). The obtained classification results suggest that the hybrid neural model schemes obtain an accuracy of 91% for predicting the shape and density attributes and an 85% accuracy for predicting margin attributes. The Wilcoxon test indicates no significant differences between both hybrid models. Therefore, the proposed models can be useful for predicting attributes included in the BI-RADS lexicon for masses.
Fooling A Deep-Learning Based Gait Behavioral Biometric System

We leverage deep learning algorithms on various user behavioral information gathered from end-user devices to classify a subject of interest. In spite of the ability of these techniques to counter spoofing threats, they are vulnerable to adversarial learning attacks, where an attacker adds adversarial noise to the input samples to fool the classifier into false acceptance. Recently, a handful of mature techniques like Fast Gradient Sign Method (FGSM) have been proposed to aid white-box attacks, where an attacker has a complete knowledge of the machine learning model. On the contrary, we exploit a black-box attack to a behavioral biometric system based on gait patterns, by using FGSM and training a shadow model that mimics the target system. The attacker has limited knowledge on the target model and no knowledge of the real user being authenticated, but induces a false acceptance in authentication. Our goal is to understand the feasibility of a black-box attack and to what extent FGSM on shadow models would contribute to its success. Our results manifest that the performance of FGSM highly depends on the quality of the shadow model, which is in turn impacted by key factors including the number of queries allowed by the target system in order to train the shadow model. Our experimentation results have revealed strong relationships between the shadow model and FGSM performance, as well as the effect of the number of FGSM iterations used to create an attack instance. These insights also shed light on deep-learning algorithms' model shareability that can be exploited to launch a successful attack.
Leveraging Profanity for Insincere Content Detection - A Neural Network Approach

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features as profanity is correlated with honesty according to psychology research. We tested our model on the questions datasets from CQA platform to detect the insincere content. Our integrated neural network model enabled us to achieve a high performance of F1-score, 94.01%, compared to the standard machine learning algorithms.
A Markov Model to Detect Sensor Failure in IoT Environments

The Internet of things is a rapidly expanding paradigm which is fundamentally altering the way in which we interact with technology. A range of new services are enabled by this technological revolution, one of which is the task of activity recognition from performing classification on sensed information, which is an area of research related to assisted living. The ability to reliably sense the environment is a crucial aspect of this application area, which has, to date, been prone to error and poor data quality. Therefore, it is essential that we are able to identify potentially anomalous data before it can have a serious effect in the application domain and evoke dangerous consequences. This work presents a novel Markov-based technique for detecting anomalous sensor events in constrained Internet of Things environments. Results from the experiment found a recall score of 97.9% and an F-measure of 92.0%, which represents a promising step forward in this research direction.
A High Capacity Reversible Watermarking Algorithm Based on Block-Level Prediction Error Histogram Shifting

The paper proposes a high capacity reversible watermarking algorithm based on block-level prediction error histogram shifting. This algorithm, in contrast to the known prediction error histogram shifting based ones, provides a higher embedding capacity, which is achieved by employing similarity among adjacent blocks rather than simply pixels inside of a block. The imperceptibility is ensured by the premise that once the block selected is small enough, the center pixels of adjacent blocks have high probability of similarity or equality. At last, the effectiveness and performance are evaluated by experiments and the result shows that our proposed algorithm has higher capacity under the same imperceptibility compared with the classic prediction error histogram shifting based reversible watermarking algorithm as well as the state-of-the-art.
Retrieval Based Chatbot on Tarumanagara University With Multilayer Perceptron

Abstract Natural Language Processing or NLP is a branch of Artificial Intelligence that focuses on processing language. One of the implementation of NLP is in the field of customer service in the form of chatbots. Tarumanagara University itself already has a customer service called Admission Tarumanagara. Admission can be contacted by telephone or sending an e-mail message. Making Chatbot will add ways to get information about Tarumanagara University. Chatbot is a computer program based on artificial intelligence that can simulate conversations between people. The system created is a Retrieval Based Chatbot system that can recognize user questions and answer user questions directly. Making a chatbot system will use a model with the Multilayer Perceptron method. The process for the system will go through two stages, namely the preprocessing stage and the introduction stage. The preprocessing stage consists of case lowering, tokenizing, and stemming. After that the model is made and the introduction phase of the trained model is carried out. Chatbot that has been made can be accessed using the LINE application via LINE ID @764vvcfm.
Real-time Efficient Facial Landmark Detection Algorithms

Lightweight models, high accuracy and real-time performance are essential for facial landmark detection algorithms. Considering these three aspects, this paper proposes a real-time and efficient face landmark algorithm. First, mobilenetV2 is used as the backbone network. Next, the traditional convolution operation is replaced with deeply separable convolution, and the shallow and deep feature maps are merged to enhance the context connection. Then multi-scale fusion output is used in the output layer to enhance the detection efficiency of small-sized faces. Finally, the Euler angle weights are introduced into the loss function, and the 14 key points in the average face model are compared with the predicted key points. During the training process, this paper proposes rotated the 300W and AFLW datasets in multi-angle to occlude the dataset and enhance the generalization ability of the model. The experimental results show that the proposed algorithm in this paper can achieve real-time and efficient facial landmark detection.
Applying Big Data and Machine Learning Approach to Identify Noised Data

Everyday, amount of data is growing up very fast and users are faced to work with different kinds of data such as photo, video, audio. Sometimes, condition of some piece of data is not appropriate to work with this data. it may happen if data has been corrupted by some factors that are not related to user. Since amount of data has become enough large it is better to use some approaches that allow user to handle data for reasonable time. So, applying big data and machine learning was reviewed in this article. As big data approach usually uses cluster computation therefore time for computing depends on specification of each node and number of nodes. Also, it is possible to use the same machine learning model within all nodes that leads to reduce time of identifying noised data. Combining both approaches shows pretty good results, that are acceptable to set down as reasonable for user.
Is There 'ONE way' of Learning? A Data-driven Approach

Intelligent Tutoring Systems (ITS) are required to intervene in a learning activity while it is unfolding, to support the learner. To do so, they often rely on performance of a learner, as an approximation for engagement in the learning process. However, in learning tasks that are exploratory by design, such as constructivist learning activities, performance in the task can be misleading and may not always hint at an engagement that is conducive to learning. Using the data from a robot mediated collaborative learning task in an out-of-lab setting, tested with around 70 children, we show that data-driven clustering approaches, applied on behavioral features including interaction with the activity, speech, emotional and gaze patterns, not only are capable of discriminating between high and low learners, but can do so better than classical approaches that rely on performance alone. First experiments reveal the existence of at least two distinct multi-modal behavioral patterns that are indicative of high learning in constructivist, collaborative activities.
Design of Efficient Reverse Converters for Residue Number System

The main factors that affect the performance of residue number system (RNS) are speed and hardware complexity of the residue-to-binary converter. In this paper, two efficient reverse converters are proposed for the moduli sets [Formula: see text] and [Formula: see text] based on two-level approach. In the first level, a reverse converter is implemented based on new Chinese remainder theorem-I for the moduli set [Formula: see text]. The fourth modulus is joined in the second level for each converter and only simple binary adders are used in this level. The architecture of novel circuits mainly consists of simple adders thus leading to implementing efficient converters. To have a fair comparison, both unit gate model and simulation are used. The proposed converters and the recently represented converters have been implemented on Xilinx ISE 13.1 field-programmable gate array (FPGA) simulator to derive area and delay that are measured for the various ranges up to 256 bits. The experimental results show that the proposed converters have lower area–time complexity comparing to the state-of-the-art converter for similar moduli sets.
Modification fitness function of particle swarm optimization to improve the cluster centroid

Abstract Clustering is a classification technique. The clustering is brought to create models. The model is applied with the new input data to identify the class of that data. The goal of clustering is finding the best cluster centroid. The process of searching the best cluster centroid can use Particle Swarm Optimization. Many researches use it and obtain good results. But, searching the best cluster centroid by PSO suffers the problem from calculating the fitness function, because the fitness function is calculated by distances from the cluster centroid to all data inputs. These fitness functions may lead to the bad cluster centroid. So, this paper proposed the fitness function is calculated by the results from classification. The results from classification can lead to the good cluster centroid and create a good model to apply with the new input data. The proposed technique is tested on seven datasets from the UCI Machine Learning Repository and gives more satisfied search results in comparison with PSOs for the data clustering problems.
Elderly Monitoring and Action Recognition System Using Stereo Depth Camera

The proposed system used stereo type depth camera by examining the human action recognition and also sleep monitoring in the elderly care center. Different regions of interest (ROI) are extracted using the U-Disparity and V-Disparity maps. The main information used for recognition is 3D human centroid height relative to the floor and percentage of movement from frame differencing for sleep monitoring. The results from the experiments of the proposed method show that this system can detect the person location, sitting or lying and also sleep behaviors effectively.
Research on the less Stress Acquisition Method for the activity Information of actual Residents using the International Standard ECHONET Lite

There are many methods to obtain the activity information of actual residents, and image processing is one of main methods. However, this is less likely to spread of services using the activity information of actual residents, due to privacy issues such as mental stress caused by being monitored by cameras. In this research, we propose the acquisition method for the activity information of actual residents utilizing network home appliances and smart meter that using the international standard ECHONET Lite (ISO/IEO14543-4-3). We report on the experimental results of the propose method and the evaluation results of the subject.
Enhancing Cross-domain Recommendation through Preference Structure Information Sharing

Cross-domain recommendation can alleviate data sparsity problems by leveraging data from multiple domains. Hence it is becoming an emerging research topic. Existing approaches based on latent factor models usually attempt to utilize cross-domain information in the form of inner product or Euclidean distance. This kind of form only enables the model to incorporate non-structure similarity relations between entities. However, the studies in the literature of consumer behavior have disclosed that consumers make decisions following a structural characteristic. Inspired by this, we propose a new cross-domain recommendation model, named PSRec, which learns and shares users' inherent preference structure information during their decision making in relation to their product choices in different domains. In PSRec, the latent factors of items of different domains are mapped to a common space so that the factorized user latent vectors have similar preference structures. Moreover, users' preference structure information is transferred from an auxiliary domain to a target domain as a constraint to a non-negative matrix factorization algorithm. Extensive experiments on two real-world datasets demonstrate the effectiveness and competitive performance of PSRec compared to several state-of-the-art solutions.
A Kernel Perspective for the Decision Boundary of Deep Neural Networks

Deep learning has achieved great success in many fields, but they still lack theoretical understandings. Although some recent theoretical and experimental results have investigated the representation power of deep learning, little effort has been devoted to analyzing the generalization ability of deep learning. In this paper, we analyze deep neural networks from a kernel perspective and use kernel methods to investigate the effect of the implicit regularization introduced by gradient descent on the generalization ability. Firstly, we argue that the multi-layer nonlinear feature transformation in deep neural networks is equivalent to a kernel feature mapping and analyze our point from the perspective of the unique mathematical advantages of kernel methods and the method of constructing multi-layer kernel machines, respectively. Secondly, using the representer theorem, we analyze the decision boundary of deep neural networks and prove that the last hidden layers of deep neural networks converge to nonlinear SVMs. Systematical experiments demonstrate that the decision boundaries of neural networks converge to those of nonlinear SVMs.