Reviewerly/exHarmony huggingface dataset:
Fault Text Classification Based on Convolutional Neural Network

The fault text records various fault information of the power system operation, and it is an important data source for analyzing the power system operation. The text management of power faults is becoming more and more intelligent, and the task of classification of fault texts has gradually changed from manual operation to automatic classification of the system. In order to realize automatic classification and improve the classification efficiency and accuracy of power fault texts, in view of the characteristics of power fault short texts, this paper proposes a Convolutional Neural Networks (CNN) short text based on a mixture of word vectors and character vectors. Classification model, which inputs the processed data set information to this classification model to classify short texts of power failures. The experimental results show that the accuracy rate of the proposed model on the power fault classification dataset can reach 88.35%. Compared with other classification models, the feature extraction ability is stronger and the classification effect is better.
3rd Body As Starting Point Workshop: Exploring Themes for Inbodied Interaction Research and Design

Inbodied Interaction focuses on understanding how the state of the body's internal processes affects human performance, with a goal of aligning interactive technology to support all aspects of human performance - cognitive, social, physical. In this third Body As Starting Point workshop, we aim to bring together insights from the past two workshops and the 2019 Inbodied Interaction Summer School to present and refine emerging themes and co-define case studies to further the state of Inbodied Interaction research and design practices. This one-day hands-on workshop invites newcomers and experts to help shape the future of Inbodied Interaction through the foundations we have co-created with previous participants. The key takeaways of the workshop are to review existing themes, formulate case studies, research questions, and form research teams to chart a path forward for HCI designs that take the body as a starting point.
Identifying users based on their eye tracker calibration data

The purpose of the paper is to test the possibility of identifying people based on the input they provide to an eye tracker during the calibration process.
Toward Exploratory Design with Stakeholders for Understanding Exergame Design

Prior work has explored improving the efficacy of exergames through participatory design with children. Children are not necessarily able to make informed decisions about their fitness, so their perspectives form only half the picture. Adults who are invested in the problem of children's fitness (e.g., PE teachers) are a valuable missing perspective. As a first step to understanding what we can learn from these stakeholders to aid the design of exergames, we conducted one in-depth interview with a PE teacher and several focus groups with children. Our findings showed that, although both children and the PE teacher like similar game elements, children viewed the elements through the lens of fun while the PE teacher viewed the elements through the lens of effectiveness. Our preliminary findings establish the importance of including such stakeholders in the formative design of exergames.
Candidate iO from Homomorphic Encryption Schemes

Abstract We propose a new approach to construct general-purpose indistinguishability obfuscation (iO). Our construction is obtained via a new intermediate primitive that we call split fully homomorphic encryption (split FHE), which we show to be sufficient for constructing iO. Specifically, split FHE is FHE where decryption takes the following two-step syntactic form: (i) a secret decryption step that uses the secret key and produces a hint which is (asymptotically) shorter than the length of the encrypted message, and (ii) a public decryption step that only requires the ciphertext and the previously generated hint (and not the entire secret key) and recovers the encrypted message. In terms of security, the hints for a set of ciphertexts should not allow one to violate semantic security for any other ciphertexts. Next, we show a generic candidate construction of split FHE based on three building blocks: (i) A standard FHE scheme with linear decrypt-and-multiply (which can be instantiated with essentially all LWE-based constructions), (ii) a linearly homomorphic encryption scheme with short decryption hints (such as the Damgård-Jurik encryption scheme, based on the DCR problem), and (iii) a cryptographic hash function (which can be based on a variety of standard assumptions). Our approach is heuristic in the sense that our construction is not provably secure and makes implicit assumptions about the interplay between these underlying primitives. We show evidence that this construction is secure by providing an argument in an appropriately defined oracle model. We view our construction as a big departure from the state-of-the-art constructions, and it is in fact quite simple.
Description-aware Fashion Image Inpainting with Convolutional Neural Networks in Coarse-to-Fine Manner

Inpainting a particular missing region in an image is a challenging vision task, and promising improvements on this task have been achieved with the help of the recent developments in vision-related deep learning studies. Although it may have a direct impact on the decisions of AI-based fashion analysis systems, a limited number of studies for image inpainting have been done in fashion domain, so far. In this study, we propose a multi-modal generative deep learning approach for filling the missing parts in fashion images by constraining visual features with textual features extracted from image descriptions. Our model is composed of four main blocks which can be introduced as textual feature extractor, coarse image generator guided by textual features, fine image generator enhancing the coarse output, and lastly global and local discriminators improving refined outputs. Several experiments conducted on FashionGen dataset with different combination of neural network components show that our multi-modal approach is able to generate visually plausible patches to fill the missing parts in the images.
Application of Word2vec in Phoneme Recognition

In this paper, we present how to hybridize a Word2vec model and an attention-based end-to-end speech recognition model. We build a phoneme recognition system based on Listen, Attend and Spell model. And the phoneme recognition model uses a word2vec model to initialize the embedding matrix for the improvement of the performance, which can increase the distance among the phoneme embedding vectors. At the same time, in order to solve the problem of overfitting in the 61 phoneme recognition model on TIMIT dataset, we propose a new training method. A 61-39 phoneme mapping comparison table is used to inverse map the phonemes of the dataset to generate more 61 phoneme training data. At the end of training, replace the dataset with a standard dataset for corrective training. Our model can achieve the best result under the TIMIT dataset which is 16.5% PER (Phoneme Error Rate).
Potential-Based Differential Evolution Algorithm With Joint Adaptation of Parameters and Strategies

In the differential evolution (DE) algorithm, numerous studies have independently performed strategy adaptation and parameter adaptation. However, the strategy and parameters are interrelated in their impact on algorithm performance. It is well known that different problems and evolutionary stages require different appropriate parameters and strategies, but the fact that the same is true for different individuals is ignored. Few studies have focused on the difference in fitness values between two successive generations, which contains substantial evolution information. This study proposes a potential-based DE algorithm with joint adaptation of parameters and strategies (JAPSPDE). In JAPSPDE, a new population classification scheme, a new classification evolution mechanism, and a new joint adaptation mechanism are proposed to circumvent the three abovementioned issues. In the population classification scheme, individuals are divided into potential and unpotential individuals according to the improvement in fitness values between two generations. A classification evolution mechanism is applied by evolving potential individuals and unpotential individuals in two ways. In addition, a three-dimensional probability array is constructed to achieve joint adaptation of parameters and strategies. Finally, after properly combining the above algorithmic components, JAPSPDE can find the most appropriate combination of control parameters and mutation strategies for specific problems, stages, and individuals. The performance of JAPSPDE is evaluated in comparison with five well-known DE algorithms on BBOB2012 and CEC2014 and with six up-to-date evolution algorithms on CEC2014. The comparison results demonstrate the competitive performance of JAPSPDE.
Rankings Correlation Study : Brand Search Volume vs. Brand Sales Volume

Hadoop MapReduce is a commonly used data processing engine for big data analysis. An iterative MapReduce parallel computing workflow was designed to analyze the logs of search engines in fast-moving consumer goods (FMCG) e-commerce websites. Based on the search results of the brand facet in each search, the workflow mines the potential correlation between each keyword search and brand awareness. In the workflow, the contribution value of each search to the search popularity of each brand in the brand facet search result is calculated to obtain a list of contribution value. Then, the contribution value list is integrated to calculate the search volume rank-order of each brand. If higher rank-order of brand search volume tend to have higher rank-order of the brand sales volume, this fact can help companies design their sales strategies. Therefore, it is important to test the relationship between the rank-order of the two types of volumes. The Spearman's correlation coefficient measures the rank-order relationship between brand search volume and brand sales volume to show the correlation is significant at the 0.01 level (two-tailed). There is a strong and positive rank-order correlation between brand search volume and brand sales volume, which was statistically significant ($\rho$=.770, p=.009).
Mind the (persuasion) gap

Decision support systems (DSS) help users to make more informed and more effective decisions. In recent years, many intelligent DSS (IDSS) in business contexts involve machine learning (ML) methods, which make them inherently hard to explain and comprehend logically. Incomprehensible predictions, however, might violate the users' expectations. While explanations can help with this, prior research also shows that providing explanations in all situations may negatively impact trust and adherence, especially for users experienced in the decision task at hand. We used a human-centered design approach with domain experts to design a DSS for funds management in the construction industry and identified a strong need for control, personal involvement, and adequate data. To create an adequate level of trust and reliance, we contrasted the system's predictions with the values derived from an analytic hierarchical process (AHP), which makes the relative importance of our users' decision-making criteria explicit. We developed a prototype and evaluated its acceptance with 7 construction industry experts. By identifying situations in which the ML prediction and the domain expert potentially disagree, the DSS can identify a persuasion gap and use explanations more selectively. Our evaluation showed promising results and we plan to generalize our approach to a wider range of explainable artificial intelligence (XAI) problems, e.g., to provide explanations with arguments tailored to the user.
Modulation recognition algorithm using innovative CNN and cyclic-spectrum graph

In view of the defects that the existing modulation recognition algorithm of communication signals has low recognition rate and poor adaptability. A scheme for automatic modulation recognition using innovative convolutional neural network (CNN) and cyclic-spectrum graph was proposed. In our proposed scheme, cyclic spectrum theory is utilized to establish a concise features representation of a unified graph which can accurately express the statistical features of modulated signals. Then, an innovative CNN model using small convolution kernels is designed to enhance features extraction, so as to deeply analyze the features of modulated signals in graph domain. When the signal- to- noise ratio (SNR) is -2dB, the average recognition rate of proposed algorithm can reach 90.48% with limited computing resources. The final test results indicate the graph features are highly discriminant, and CNN has good performance in extracting high- dimensional features. The algorithms can be effectively applied in communication network to strengthen the adaptability of communication switching equipment.
DNN and CNN Approach for Human Activity Recognition

One of the common causes of low back pain is postural stress. When sitting or walking, poor posture may result in spinal dysfunction. Increased pressure on the spine can cause tension and spasms in the lumbar muscles and cause low back pain. Monitoring of daily activities becomes more important, especially to help sick and elderly people. Recognition of unstructured daily activities is a more difficult and important task. In this study, we use Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) to study spinal movement and postural stress through two sensors connected to the pelvis and spine of a healthy subject. Body kinematics data consist of four categories: standing, sitting, walking and other activities. We compared the accuracy of DNN and CNN methods for the identification and labeling of daily activities. We observed the results of deep learning methods with different hyperparameter values and obtained the optimum values.
TRACK

Due to the growth of Big Data processing technologies and cloud computing services, it is common to have multiple tenants share the same computing resources, which may cause performance anomalies. There is an urgent need for an effective performance anomaly detection method that can be used within the production environment to avoid any late detection of unexpected system failures. To address this challenge, we introduce, TRACK, a new black-box training workload configuration optimization with a neural network driven methodology to identify anomalous performance in an in-memory Big Data Spark streaming platform. The proposed methodology revolves around using Bayesian optimization to find the optimal training dataset size and configuration parameters to train the model efficiently. TRACK is validated on a real Apache Spark streaming system and the results show that the TRACK achieves the highest performance (95% for F-score) and reduces the training time by 80% to efficiently train the proposed anomaly detection model in the in-memory streaming platform.
Practical Anonymous Subscription with Revocation Based on Broadcast Encryption

In this paper we consider the problem where a client wishes to subscribe to some product or service provided by a server, but maintain their anonymity. At the same time, the server must be able to authenticate the client as a genuine user and be able to discontinue (or revoke) the client's access if the subscription fees are not paid. Current solutions for this problem are typically constructed using some combination of blind signature or zero-knowledge proof techniques, which do not directly support client revocation (that is, revoking a user before expiry of their secret value). In this paper, we present a solution for this problem on the basis of the broadcast encryption scheme, suggested by Boneh et al., by which the server can broadcast a secret to a group of legitimate clients. Our solution allows the registered client to log into the server anonymously and also supports client revocation by the server. Our solution can be used in many applications, such as location-based queries. We formally define a model for our anonymous subscription protocol and prove the security of our solution under this model. In addition, we present experimental results from an implementation of our protocol. These experimental results demonstrate that our protocol is practical.
An evolutionary approach for optimal multi-objective resource allocation in distributed computing systems

Resource allocation in a distributed computing system is the process of allocating the workload across multiple computing resources to optimize the required performance criteria. In this article, a resource allocation problem that arises in a distributed system consisting of multiple heterogeneous servers is addressed. The problem is modeled as a multi-objective problem with two conflicting objectives: (a) to minimize the users’ expected response time and (b) to reduce the utilization imbalance between servers. To satisfy these objectives simultaneously, first, both the objectives are considered in an integrated manner, and an optimization problem is formulated. Second, the optimization problem is cast into a game-theoretic setting and modeled as a non-cooperative game, called a non-cooperative resource allocation game. Finally, to solve the game, a differential evolution-based co-evolutionary framework (DECEF) is proposed. To evaluate the performance of DECEF, a rigorous simulation study is carried out. Furthermore, to assess the relative performance of DECEF, it is compared against two existing approaches, from various aspects, including system utilization, system heterogeneity, and system size. The experimental results show that DECEF provides better system-wide performance while optimizing both the objectives.
Auto-Hierarchical Data Algorithm: Focus on Increasing Users’ Motivation and Duration In Virtual Reality

Virtual reality (VR) exergames have become part of increasingly popular serious games because of both the fast development of VR technology and diversified dissemination of exergames. In this paper, we designed a built-in algorithm which based on the fatigue parameter to timely adjust the exergame's difficulty, especially in body-building/fitness exercise. The calculation method of fatigue index is obtained by using analytic hierarchy process (AHP) with six influencing factors: (1) average Heart Rate (avgHR); (2) Borg CR 6-20; (3) Calorie(s); (4) Accuracy Rate (Acc%); (5) Intensity; (6) Exercise time. Result from the experiments with 8 participants shows that the fitness exergame sorted by the auto-hierarchical algorithm we designed will be more effective than the traditional process by partly increasing both users' motivation and duration of playing exergame in the current phase.
An Attentive Pruning Method for Edge Computing

Nowadays, the power of an edge computing hardware is usually the bottleneck of AI application. Most of the embedding devices in factory have no GPU, some of them even using Intel i3 or ARM chips. Model compression is very convenient and effective way to handle this problem. But it still has two major limitations, the arbitrariness in pruning ratio setup and inevitable accuracy drop. In this paper, we propose an adaptive network channel pruning method without a priori knowledge of pruning ratio. This method is based on the attentive weights from modified SE block, establishes a detectable and trainable mask learning module from the original to-be-prune network. Moreover we make innovative modifications on SE block, to enhance the sparsity of attentive weights. Extensive experiments afterwards indicate that our method can not only accelerate model inference process or equivalently decrease model footprint, but also get better performance in test set. Even a tiny network like Yolo cifar-10 with 15 layers can be pruned about 50% FLOPs without accuracy decrease using proposed method.
Fanaticism Category Generation Using Tree-Based Machine Learning Method

Abstract This research has produced categorization of political fanaticism on social media. The results of this study divide political fanaticism into 4 categories: positive, neutral, negative and very negative. The background of the research is that currently social media cannot recognize the elements of fanaticism and does not care about the spread of negative fanaticism. One of the best solutions to prevent the spread of negative fanaticism on social media is to develop a computer system which is able to recognize, categorize fanaticism and reject the entry of negative fanaticism. This research extracts features of sentiment, emotions, and expressions of hatred in opinions on social media. The results of the feature extraction are then processed into knowledge using tree-based machine learning methods, namely Random Forest and ID3. The validation result shows that the knowledge generated by the Random Forest process outperforms other techniques with an accuracy of 91.82%. Knowledge from the Random Forest can be used to categorize fanatical texts based on sentiment, emotion and hatred in further research
Optimizing Knowledge Graphs through Voting-based User Feedback

Knowledge graphs have been used in a wide range of applications to support search, recommendation, and question answering (Q&A). For example, in Q&A systems, given a new question, we may use a knowledge graph to automatically identify the most suitable answers based on similarity evaluation. However, such systems may suffer from two major limitations. First, the knowledge graph constructed based on source data may contain errors. Second, the knowledge graph may become out of date and cannot quickly adapt to new knowledge. To address these issues, in this paper, we propose an interactive framework that refines and optimizes knowledge graphs through user votes. We develop an efficient similarity evaluation notion, called extended inverse P-distance, based on which the graph optimization problem can be formulated as a signomial geometric programming problem. We then propose a basic single-vote solution and a more advanced multi-vote solution for graph optimization. We also propose a split-and-merge optimization strategy to scale up the multi-vote solution. Extensive experiments based on real-life and synthetic graphs demonstrate the effectiveness and efficiency of our proposed framework.