Reviewerly/exHarmony huggingface dataset:
A physical model inspired density peak clustering

Clustering is an important technology of data mining, which plays a vital role in bioscience, social network and network analysis. As a clustering algorithm based on density and distance, density peak clustering is extensively used to solve practical problems. The algorithm assumes that the clustering center has a larger local density and is farther away from the higher density points. However, the density peak clustering algorithm is highly sensitive to density and distance and cannot accurately identify clusters in a dataset having significant differences in cluster structure. In addition, the density peak clustering algorithm's allocation strategy can easily cause attached allocation errors in data point allocation. To solve these problems, this study proposes a potential-field-diffusion-based density peak clustering. As compared to existing clustering algorithms, the advantages of the potential-field-diffusion-based density peak clustering algorithm is three-fold: 1) The potential field concept is introduced in the proposed algorithm, and a density measure based on the potential field's diffusion is proposed. The cluster center can be accurately selected using this measure. 2) The potential-field-diffusion-based density peak clustering algorithm defines the judgment conditions of similar points and adopts different allocation strategies for dissimilar points to avoid attached errors in data point allocation. 3) This study conducted many experiments on synthetic and real-world datasets. Results demonstrate that the proposed potential-field-diffusion-based density peak clustering algorithm achieves excellent clustering effect and is suitable for complex datasets of different sizes, dimensions, and shapes. Besides, the proposed potential-field-diffusion-based density peak clustering algorithm shows particularly excellent performance on variable density and nonconvex datasets.
16‐1: <i>Invited Paper:</i> Hybrid Full‐Color Micro‐LED Display with Quantum Dots Color Conversion by Using Inkjet‐Printing and Photo‐Lithography Methods

In this work, a full‐color micro‐LED display is demonstrated with blue micro‐LED as backlight, photoluminescence quantum dots as color conversion layers. Both the inkjet‐printing and photolithography methods are used to fabricate the color conversion layers, in which the backlight and color conversion layers are bonded together by a UV glue.
Multi-Objective level generator generation with Marahel

This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language. We use NSGA-II, a multi-objective optimization algorithm, to search for generators for three different problems (Binary, Zelda, and Sokoban). We restrict the representation to a subset of Marahel language to push the evolution to find more efficient generators. The results show that the generated generators were able to achieve good performance on most of the fitness functions over these three problems. However, on Zelda and Sokoban they tend to depend on the initial state than modifying the map.
Virtual and augmented reality application in production engineering teaching-learning processes

Paper aims: Structure virtual and augmented reality applications in engineering teaching-learning processes, with emphasis on production engineering, highlighting application gaps.Originality: First studies that, to which subjects applied virtual and augmented reality in engineering teaching-learning processes, the gaps of application in production engineering, and a discussion about the impacts. Research method:The research method applied was the systematic literature review.Main findings: Structuring of virtual or augmented reality applications in engineering, discussion about the application in production engineering, opportunities for future research, how low application affects graduated professional education, consequently, organizational competitiveness. Implications for theory and practice:The applications of virtual and augmented reality bring developing student skills more actively and cognitively, making training more complete, increasing their skills, and supporting the competitiveness of organizations through professionals who can contribute more broadly and effectively.
An Efficient Approach for Barcode Encoding/Decoding Using Pattern Substitution

The storage of data with reduced space and faster retrieval has always been the main concern in every field. Barcodes ensure faster storage and retrieval of data in a secured form. This paper enlightens the importance of barcodes in every field of today’s world and a proposed new barcode generation method with a picture embedded overcoming the traditional methods of storing information. Maintaining details of patient, student, etc., have always been into confusion due to manual maintenance. It would be a revolutionary change in maintaining patients’ detail using barcodes in the form of a person’s picture avoiding the manual error.
Comparative Study of Classifiers on Human Activity Recognition by Different Feature Engineering Techniques

The paper presents a comparative discussion of classification approaches for human activity recognition tasks based on the feature sets through extensive feature selection techniques. The original dataset on Human Activity Recognition from Continuous Ambient Sensor is collected from UCI machine learning repository. Amongst the 12 activities mentioned in the dataset, five specific activities (Watching TV, Reading, Talking over Phone, Cooking and Eating) have been selected for the purpose of this research. The scraped dataset is analyzed through four feature selection methods for extracting important features upon statistical significance of features and node impurity. From the actual dataset with 37 attributes, the feature selection methodologies give four distinct features sets. Later on, Principal Component Analysis is applied on the five feature sets including the original scraped dataset to reduce feature space and five principal components are selected to cover more than 90% data variance of the feature sets. Based on the 37 features present in the actual dataset and obtained sets of important features, performance of five classifier models (K Nearest Neighbors, Decision Tree, Random Forest, Gaussian Naïve Bayes and MLP classifier using Back propagation) are evaluated. The selection of feature set based on different approaches of feature importance generates difference in outputs for each feature set on each classifier. The result shows that Multi-layer Perceptron using Back propagation algorithm achieves better accuracy on human activity recognition on the five feature sets. The research findings highlight the necessity of data preprocessing and significant feature selection for getting better accuracy score for noisy time-series data of HAR activity.
User Sequential Behavior Classification for Click-Through Rate Prediction

The user's behavior sequence can well reflect the user's interest and intention, but there is less research on the use of user behavior sequence in the field of CTR prediction. Therefore, introducing the idea of sequence recommendation into CTR prediction is an exciting idea. Aimless browsing by users is a common phenomenon in many recommended scenarios (e-commerce, music, video streaming), which has not been paid attention to in previous research. To this end, this paper introduces the concept of user browsing status, and divides it into Discover and Intent, which respectively represent the user's unintentional status and intentional status. A new framework named User Status Recognition Framework (USRF) is proposed to solve this problem. USRF can perform both CTR prediction and next-item recommendation. The framework captures the weights of two user status from the current user historical behavior sequence, models the two different status separately to mine user interests, and combines the captured user status to make more accurate recommendations. In addition, in order to solve the problem that less attention has been paid to the complex connection between candidate items and interacted items in the previous sequence recommendation research, this paper uses the attention mechanism to model the relationship between candidate items and interacted items, implements a simple model for CTR prediction based on USRF. Experiments on three different scene datasets show good results on both the AUC and F1-score, proving the advantages of the framework.
Focal Loss Improves the Model Performance on Multi-Label Image Classifications with Imbalanced Data

Dealing with imbalanced data has always been a great challenge in statistical learning. In classification problems, instances for different classes are unequal in an imbalanced dataset. Therefore, traditional machine learning classification algorithms are usually sensitive to this imbalance both in the training and inference processes. In this paper, by addressing the class imbalance on the basis of Focal Loss, we introduce an approach to improve the performance of convolutional neural networks (CNNs) on the multi-label image classification with an extremely imbalanced dataset. As focal loss puts more focus on hard and misclassified examples when comparing with classic cross-entropy loss, our results demonstrate that such loss function indeed achieves a significant improvement of CNN models (ResNet-50, ResNet-101, and SE-ResNeXt-101) regarding the mean F2 scores on the test set.
Code2Text: Dual Attention Syntax Annotation Networks for Structure-Aware Code Translation

Translating source code into natural language text helps people understand the computer program better and faster. Previous code translation methods mainly exploit human specified syntax rules. Since handcrafted syntax rules are expensive to obtain and not always available, a PL-independent automatic code translation method is much more desired. However, existing sequence translation methods generally regard source text as a plain sequence, which is not competent to capture the rich hierarchical characteristics inherently reside in the code. In this work, we exploit the abstract syntax tree (AST) that summarizes the hierarchical information of a code snippet to build a structure-aware code translation method. We propose a syntax annotation network called Code2Text to incorporate both source code and its AST into the translation. Our Code2Text features the dual encoders for the sequential input (code) and the structural input (AST) respectively. We also propose a novel dual-attention mechanism to guide the decoding process by accurately aligning the output words with both the tokens in the source code and the nodes in the AST. Experiments on a public collection of Python code demonstrate that Code2Text achieves better performance compared to several state-of-the-art methods, and the generation of Code2Text is accurate and human-readable.
Forward Model Learning for Motion Control Tasks

In this work, we study the capabilities and limitations of forward model learning agents and their applications to motion-control tasks. Forward model learning agents learn to approximate the environment dynamics to apply planning algorithms for action-selection. While previous work has shown that forward model learning agents can efficiently learn to play simple video games, we extend their applicability to domains with continuous state and action spaces. Our experiments show that such agents are quickly able to learn an approximate model of their environment, which suffices to solve several simple motion-control tasks. Comparisons with deep reinforcement learning further highlight the sample efficiency of forward model learning agents.
Maximal-value condition of coherence measures holds for mixed states if and only if it does for pure states

While various coherence measures based on the framework for quantifying coherence [T. Baumgratz, M. Cramer, and M. B. Plenio, Phys. Rev. Lett. 113, 140401 (2014)] have been proposed, it is often asked whether a coherence measure fulfills the maximal value condition that only maximally coherent states should achieve the maximal value of a coherence measure [Y. Peng, Y. Jiang, and H. Fan, Phys. Rev. A 93, 032326 (2016)]. Although it may be easy to rule out the measures that violate the condition by giving a counterexample, it is usually complicated to confirm the validity of the condition for a general coherence measure due to the deal with all mixed states. In this paper, we prove that the maximal value condition of coherence measures holds for mixed states if and only if it holds for a special subset of pure states. Our finding can greatly reduce the examination of whether a coherence measure fulfills the maximal value condition since one only needs to consider a subset of pure states, avoiding the work of considering mixed states.
Multi-agent Path Finding with Kinematic Constraints via Conflict Based Search

The paper considers a problem of planning a set of collision-free trajectories for a group of mobile agents operating in the shared environment, i.e. multi-agent path-finding (MAPF). A modification of the Continuous Conflict Based Search (CCBS) algorithm is proposed that takes kinematic constraints into account. The resultant planner explicitly supports rotation actions as well as agents of different sizes and moving speeds. Thus, it is more suitable for a range of practical applications involving real robots subject to kinematic constraints. An extensive empirical evaluation is conducted in which the suggested algorithm is compared to the state-of-the-art MAPF planners. The results of this evaluation provide a clear evidence that the proposed method is as efficient as predecessor that is limited to translation-only action model.
Training Neural Network Over Encrypted Data

We are answering the question whenever systems with convolutional neural network classifier trained over plain and encrypted data keep the ordering according to accuracy. Our motivation is need for designing convolutional neural network classifiers when data in their plain form are not accessible because of private company policy or sensitive data gathered by police. We propose to use a combination of fully connected autoencoder together with a convolutional neural network classifier. The autoencoder transforms the data info form that allows the convolutional classifier to be trained. We present three experiments that show the ordering of systems over plain and encrypted data. The results show that the systems indeed keep the ordering, and thus a NN designer can select appropriate architecture over encrypted data and later let data owner train or fine-tune the system/CNN classifier on the plain data.
A Novel Approach to Classify Breast Cancer Tumors Using Deep Learning Approach and Resulting Most Accurate Magnification Factor

In the recent research, breast cancer has come out to be the biggest reason behind death among females. Detection of breast cancer in its earlier stages is really the need of time. Detection of cancerous tumor is really a long and time taking process which is very costly and requires a lot of workforce as well. Therefore, providing a computer assisted approach for the simpler classification areas could be a solution. This could simplify the complex examine process of pathologists. Hence, the most appropriate magnification factor based CNN framework is proposed which leads to lesser expenses and lesser workforce. The proposed framework considers the different magnification factor images to obtain the classification accuracy. The proposed CNN-based framework is sectioned into three parts: preprocessing, feature extraction based on CNN, and CNN-based classification. The preprocessing phase consists of four processes, i.e., reshaping, formatting, image labeling, and train–test splitting. These preprocessings are applied after training and testing procedure of CNN model. In experimental analysis, publicly available breast cancer dataset from histopathology is used. The dataset consists of seven thousand nine hundred and nine images obtained from 82 different patients. These images are distributed among four different magnification factors: 40X, 100X, 200X, and 400X. These magnification factors are utilized for training of CNN model and for obtaining the accuracy. In result analysis, four different experimentations are conducted to show the effectualness of the presented CNN-based framework. Also, the results exhibit that the CNN-based framework used in this work achieves the highest average accuracy of 97.63% among the other architectures and existing approaches.
A New Context-Aware Method Based on Hybrid Ranking for Community-Oriented Lexical Simplification

As a subtask of lexical substitution, lexical simplification aims to provide simplified words with the same semantic meaning in context. Focusing on the task, this paper proposes a new method based on a hybrid ranking strategy. The method consists of three parts including 1) substitution generation leveraging semantic dictionaries, 2) substitution selection utilizing part-of-speech tagging and word stemming, and 3) substitution ranking based on a hybrid approach. Through the evaluation on standard datasets, our method outperforms state-of-the-art baselines including Word2vec and Four-step method, indicating its effectiveness in lexical simplification.
On the Variations and Evolutions of API Usage Patterns

Software developers can reduce the implementation cost by calling already provided functions through accessing library Application Programming Interface (API). APIs are often used in combination but how to combine them are not well-documented. Existing researches focused on how to extract API usage patterns or how to detect API misuse from existing software. This kind of research might be affected by dataset to analyze, so to improve mining results and to understand how the difference of API usage patterns affect the software health are important tasks. We conducted an analysis on variations of API usage pattern among software projects and their version history with Android SDK APIs and Android applications. Based on our analysis results, we made some suggestions for further API analysis. For example, there are many project-specific API usage patterns and long-life uncommon API usage patterns so that they might affect the mining result or checking software health status.
Augmented Reality in Web: Results and Challenges

The paper presents basic concepts of augmented reality applications and challenges in building them in the web. We describe the technical and algorithmic stack required to develop, implement and deploy the augmented reality application. Theoretical concepts behind marker detection and tracking are discussed. Two different pipelines are implemented: server-based with algorithms execution in the cloud and completely front-end solution that runs on a user device. We show advantages and disadvantages of each approach and analyze experimental results as well.
P‐217: <i>Late‐News‐Poster:</i> Convolutional Neural Network‐based Multi‐touch Detection Technique on Learning from Class‐imbalanced Dataset

The touchscreen has a lack of accuracy in discriminating between multi‐touch and single‐touch signals when touch signals are too close to each other. This causes the machine's incorrect expectation of the intended touch coordinates. We propose a novel convolutional neural network (CNN)‐based multi‐touch detection to increase the accuracy for detecting the number of touching points. Additionally, the modified loss function is proposed to train the proposed neural network for the class‐imbalanced dataset. The novel loss function is optimized by considering the number of data according to the class, and then, the detection accuracy is increased compared with the conventional approach. Finally, we compute the coordinates of the touch signal by using the result of CNN. Experimental results show that the detection accuracy of the proposed network with our novel loss function was 93.67%.
Quantum Algebraic Machine Learning

Quantum information processing is a rapidly growing industry with promising results in the near future. Its implementtation has serious impact on almost any areas of our lives: cyber-security, communication, AI, etc. Any computations can be considered as quantum ones and in this context classical computations are a subset of quantum computations. One of the most promising applications of quantum technologies is the AI, in particular, machine learning. The intriguing question in quantum machine learning is whether there are problems in machine learning and AI that fit quantum computers naturally, but are challenging on classical hardware. In this paper we research discrete optimization problem and ensemble learning in the context of pattern recognition problems. In particular, we are considering a person identification problem based on an image of his face using the ensemble learning method and approaches to the optimization problems in the context of a training of machine learning models.
Efficient Parallel Cycle Search in Large Graphs

Cycle is a fundamental structure in graphs. Motivated by the wide applications of cycle search, namely computing the cycles related to a vertex in the graph, we investigate efficient parallel algorithm to address the cycle search problem in large graphs. We first propose a two-phase paradigm tailored for the parallel cycle search problem. Based on the paradigm, we further devise a workload estimation method to improve the efficiency and scalability of the algorithm by balancing the work assigned to different threads. We experimentally evaluate our algorithms on real datasets and the results demonstrate the effectiveness of our approach.
Towards Software Value Co-Creation with AI

We present a vision called "value co-creation of software by artificial intelligence (AI) and developers." In this vision, AI and developers work in collaboration as equal partners to co-create business and societal values through software system development and operations. Towards this vision, we discuss AI automation for development focusing on machine learning by introducing examples, including our own. Finally, we envision the future of value co-creation by AI and developers.
Research on Preprocessing Methods for Time Series Classification Using Machine Learning Models in the Domain of Radial-Axial Ring Rolling

Machine learning models trained to predict certain outcomes bear great potential in a variety of applications. This research takes a step to elevate the hot forming technology of radial-axial ring rolling towards a fully digitalized and even more efficient forming technology. For successful machine learning the preprocessing step is essential. This paper presents current research regarding the most promising preprocessing approaches of time series data for the specific use case of classifying form errors of the radial-axial ring rolling process. By predicting form errors (in-situ), scrap and rework rates can be lowered due to an alert by the model for form errors in advance of a potential error, thus contributing to a more efficient industry. The data used exists in form of time series from log-data of an industrial used, single ring rolling machine. Concluding, the proposed preprocessing approaches are evaluated by comparing different model performances, trained on actual production data.