Reviewerly/exHarmony huggingface dataset:
Competency Evaluation System of English Teaching Post based on K-Means Clustering Algorithm

With the rapid development of big data, user data information is increasingly perfect, data warehouse integration is more reasonable, and data quality is constantly improving, so the value of data is increasing. Based on parallel processing of K-means clustering algorithm, this paper extracts ability constraint information, integrates K-means clustering algorithm, clusters and integrates various index parameters of post competency. From the final experimental results, this method improves the execution efficiency and accuracy compared with other methods, and can be used in practice.
Feature-Based Interpretation of the Deep Neural Network

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.
Blockchain as a Sustainable Service-Enabler: A Case of Wind Turbine Traceability

Blockchain technology enables new paths for supply chain management by serving as a medium of transparent information sharing across organizational bounds. This paper highlights a developing supply chain case in which the goal is to improve the service and maintenance of wind turbines by introducing blockchain-enabled traceability. The increased transparency is intended to facilitate better opportunities for proactive maintenance of the turbines by giving commodity components a unique identification code (QR) and sharing information on said components throughout their lifecycle by appending it to a blockchain solution thereby creating an immutable, tamperproof history. The paper focuses on identifying and analyzing the benefits these technologies enable in terms of economic, social and environmental value (the triple bottom line). The case will first present the current situation in the supply chain service and proceed to evaluate the blockchain-enabled solution.
On the Diversity and Explainability of Recommender Systems

This paper introduces an enterprise app recommendation problem with a new "to-business'' use case, which aims to assist a sales team acting as the bridge connecting the applications and developers with the customers who apply these apps to solve their business problems. Our recommender system is an assistant to the sales team, helping recommend relevant apps to the customers for their businesses and increasing the likelihood of improving sales revenue. Besides recommendation accuracy, recommendation diversity and explainability are even more crucial since they provide more exposure opportunities for app developers and improve the transparency and trustworthiness of the recommender system. To allow the sales team to explore unpopular but relevant apps and understand why such apps are recommended, we propose a novel framework for improving aggregate recommendation diversity and generating recommendation explanations, which supports a wide variety of models for improving recommendation accuracy. The model in our framework is simple yet effective, which can be trained in an end-to-end manner and deployed as a recommendation service easily. Furthermore, our framework can also apply to other generic recommender systems for improving diversity and generating explanations. Experiments on public and private datasets demonstrate the effectiveness of our framework and solution.
Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection

In this study, we propose a new approach based on Grammar-based Genetic Programming (GBGP), token competition, multi-objective optimization, and ensemble learning for solving Financial Fraud Detection (FFD) problems. Token competition is a niching technique to maintain diversity among individuals. It can be used to adjust the objective values of each individual, and the individuals with similar objective values but different meanings are separated. Financial fraud is a serious problem that often produces destructive results in the world and it is exacerbating swiftly in many countries. It refers to many activities including credit card fraud, money laundering, insurance fraud, corporate fraud, etc. The major consequences of financial fraud are loss of billions of dollars each year, investor confidence, and corporate reputation. Therefore, a research area called FFD is obligatory, in order to prevent the destructive results caused by financial fraud. We comprehensively compare the proposed approach with Logistic Regression, Neural Networks, Support Vector Machine, Bayesian Networks, Decision Trees, AdaBoost, Bagging, and LogitBoost on four FFD datasets including two real-life datasets. The experimental results showed the effectiveness of the new approach. It outperforms existing data mining methods in different aspects.
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?

We design blackbox transfer-based targeted adversarial attacks for an environment where the attacker’s source model and the target blackbox model may have disjoint label spaces and training datasets. This scenario significantly differs from the "standard" blackbox setting, and warrants a unique approach to the attacking process. Our methodology begins with the construction of a class correspondence matrix between the whitebox and blackbox label sets. During the online phase of the attack, we then leverage representations of highly related proxy classes from the whitebox distribution to fool the blackbox model into predicting the desired target class. Our attacks are evaluated in three complex and challenging test environments where the source and target models have varying degrees of conceptual overlap amongst their unique categories. Ultimately, we find that it is indeed possible to construct targeted transfer-based adversarial attacks between models that have non-overlapping label spaces! We also analyze the sensitivity of attack success to properties of the clean data. Finally, we show that our transfer attacks serve as powerful adversarial priors when integrated with query-based methods, markedly boosting query efficiency and adversarial success.
An Empirical Study on Adaptive Inference for Pretrained Language Model

Adaptive inference has been proven to improve bidirectional encoder representations from transformers (BERT)'s inference speed with minimal loss of accuracy. However, current work only focuses on the BERT model and lacks exploration of other pretrained language models (PLMs). Therefore, this article conducts an empirical study on the application of adaptive inference mechanism in various PLMs, including generative pretraining (GPT), GCNN, ALBERT, and TinyBERT. This mechanism is verified on both English and Chinese benchmarks, and experimental results demonstrated that it is able to speed up by a wide range from 1 to 10 times if given different speed thresholds. In addition, its application on ALBERT shows that adaptive inference can work with parameter sharing, achieving model compression and acceleration simultaneously, while the application on TinyBERT proves that it can further accelerate the distilled small model. As for the problem that too many labels make adaptive inference invalid, this article also proposes a solution, namely label reduction. Finally, this article open-sources an easy-to-use toolkit called FastPLM to help developers adopt pretrained models with adaptive inference capabilities in their applications.
Improvement of People Counting by Pairing Head and Face Detections from Still Images

Video or image-based people counting in real-time has multiple applications in intelligent transportation, density estimation or class management, and so on. This problem is usually carried out by detecting people using conventional detectors. However, this approach can be failed when people stay in various postures or are occluded by each other. In this paper, we notice that even a main part of human body is occluded, their face and head are still observable. We then propose a method that counts people based on face and head detection and pairing. Instead of deploying only face or head detector, we apply both detectors as in many cases the human does not turn his/her face to camera then head detector takes advantage. Otherwise, face detector produces reliable results. The fact of combining both head and face detection results will lead to duplicated responses for one person. We then propose a simple yet effective alignment technique to pair a face with a head of a person. Subsequently, the remaining heads and faces which are not paired with any other faces or heads will be added to our people counter to increase the true positive rate. We evaluate our proposed method on four datasets (Hollywood, Casablanca, Wider Face, and our own dataset). The experimental results show an improvement of average precision and recall comparing to the original head or face detectors.
An Ethereum Blockchain Based Electronic Health Record System for Inter-Hospital Secure Data Sharing

Medical Records (MR) consisting of Personal Health Record (PHR), Electronic Health Record (EHR), dan Electronic Medical Record (EMR). Electronic Health Record (EHR) is a medical record that can be accessed and have certain limitations. The challenge in implementing MR is how to be able to collect, store, and analyze patient data in a comprehensive and integrated manner without having to violate patient privacy. The implementation of MR is how to transmit data between hospitals. To solve the problems and issues faced in implementing MR, there is a technology that can overcome this, one of which is using blockchain technology. Blockchain is a distributed ledger that managed by many participants in an availability manner. Ethereum is a blockchain platform that anyone can use to create and deploy decentralized applications that run on the blockchain network. The data stored on the Ethereum blockchain will always be there and can be seen as long as the blockchain network is still online. With ethereum, the process of sharing data will be easy to do using a computer belonging to the hospital as a blockchain node. The result of implementing blockchain into medical record is now the data can be shared securely between hospitals.
From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey

Humans are naturally endowed with the ability to write in a particular style. They can, for instance, re-phrase a formal letter in an informal way, convey a literal message with the use of figures of speech or edit a novel by mimicking the style of some well-known authors. Automating this form of creativity constitutes the goal of style transfer. As a natural language generation task, style transfer aims at rewriting existing texts, and specifically, it creates paraphrases that exhibit some desired stylistic attributes. From a practical perspective, it envisions beneficial applications, like chatbots that modulate their communicative style to appear empathetic, or systems that automatically simplify technical articles for a non-expert audience. Several style-aware paraphrasing methods have attempted to tackle style transfer. A handful of surveys give a methodological overview of the field, but they do not support researchers to focus on specific styles. With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task. We organize them in a hierarchy, highlighting the challenges for the definition of each of them, and pointing out gaps in the current research landscape. The hierarchy comprises two main groups. One encompasses styles that people modulate arbitrarily, along the lines of registers and genres. The other group corresponds to unintentionally expressed styles, due to an author's personal characteristics. Hence, our review shows how these groups relate to one another, and where specific styles, including some that have not yet been explored, belong in the hierarchy. Moreover, we summarize the methods employed for different stylistic families, hinting researchers towards those that would be the most fitting for future research.
BeFit—A Real-Time Workout Analyzer

Maintaining one’s physical fitness is of utmost importance. Exercising regularly is very important as it helps improve the quality of life. However, incorrect posture during exercises may lead to severe long-term injuries such as back pain, Tendinitis or even hamstring strains. Hence, this application BeFit is proposed that analyzes the posture of the user performing a particular workout by comparing their workout to the reference image or video provided by the system. The system will analyze the angles between the limbs of the body and compare it to the reference video or image using the Cosine rule. After synchronizing user and reference image or video the system gives a green skeleton if the user posture is correct and a red skeleton if the user posture is incorrect. This model has been achieved using the PoseNet library on Tensorflow. The maximum score that the PoseNet model achieves ranges from 0.92874 to 0.98325 for all the key points. With the help of this model, fitness enthusiasts can perform a particular workout accurately at the comfort of their home without getting injured and with proper guidance.
Generalized Weight Agnostic Neural Networks for Configurable and Continual Autonomous Systems

Implementing pervasive intelligence faces the challenges of efficient, continual, and configurable learning on embedded devices. We address these challenges with two novel extensions to Weight Agnostic Neural Networks (WANNs) namely – (i) Multi-weight extension and (ii) Multi-objective extension. In the multi-weight extension, we extend the idea of single shared weight WANNs to multiple weights to enable efficient continual learning. Our results across four different tasks implemented on Raspberry Pi demonstrate that the multi-weight WANNs achieve higher reward as compared to single shared weight WANNs and can be fine-tuned on device in orders of magnitude smaller time. In the multi-objective extension of WANNs, we extend the idea of a single reward function to multiple competing rewards and demonstrate sensitive and monotonic trade-off between multiple competing rewards to enable efficient configurable learning. We also demonstrate robustness of WANNs for changes in the weight parameters and changes in the environmental conditions as compared to the Proximal Policy Optimization(PPO) algorithm. These significant results open the possibility of truly autonomous agents using WANNs on low compute and power budget devices.
SentiSeries: A Trilogy of Customer Reviews, Sentiment Analysis and Time Series

The customer rating is a great source of user feedback knowledge. Despite the large number of reviews on online retailing, one knows very little on how consumers feel about the goods and services provided by retail companies. The focus of this research is on analyzing sentiment in reviews aligned with the time series analysis that unveils quite interesting and decisive insights. The trends in user behavior, count frequency and sentiments are used to decipher polarity shift. Ensemble of different methods is used for time series analysis, sentiment analysis, and subject modeling. Time series segmentation helps in differentiating critical time points that contribute to major shifts in sentiment patterns. The study of timesteps leading up to and after these crucial moments using a subject modeling technique help in proper analysis and prediction of user behavior in a ceaseless manner. The research deepens the knowledge of online retailing consumer behavior and yields valuable strategic lessons for optimizing online retailing service deliverables. Finally, an in-depth investigation establishes first in the domain metrics such as sentiment velocity and acceleration for sentiment monitoring. The findings presented here are to date the best in the literature.Asesh, Aishwarya
Information Encryption and Decryption Analysis, Vulnerabilities and Reliability Implementing the RSA Algorithm in Python

The processing and transmission of information has increased its effectiveness in recent decades. From mathematical models the security and integrity of the data are guaranteed. In spite of that, interceptions in the signal, attacks and information theft can happen in the transmission process. This paper presents a RSA algorithm analysis, using 4, 8 and 10 bits prime numbers with short messages. The encryption and decryption process implemented in python allowed the computational resources use. Processing time and data security are evaluated with a typical computational infrastructure required for its operation; in order to identify vulner-abilities and their reliability level when ideal conditions are available to perform a cryptanalysis.
A Machine Learning approach to study soft-tissue facial characteristics as indicators of woman attractiveness

The canons of beauty have undergone substantial changes over the years. Social, cultural and environmental factors influence the perception of beauty. For the face these considerations become even more important, being the key of all social interactions. Several studies have been carried out in this field, to try to give objectivity to these aspects. In this work, starting from a dataset consisting of linear and angular photogrammetric measurements of the faces of 65 women from two different groups, Machine Learning algorithms were trained and tested for the automatic classification of the two groups of individuals. Results were then compared and the predictive power of the adopted classifiers was discussed in terms of sensitivity and specificity.
Picture Tales: An Approach for Story Generation Using a Series of Images

Visual storytelling is a challenging problem in artificial intelligence that requires machines to understand a sequence of images to produce a narrative story. It takes image captioning a step forward and is a more complex task as it deals with coherence, continuity, and creativity in the generated text to form a complete story. In this paper, we put forth an approach based on the encoder-decoder architecture to generate a story for a sequence of images. We take into consideration the images and their respective descriptions to create coherent stories. We evaluate the performance of our model on the VIST dataset [1] and achieve highly comparable results with respect to the existing systems.
Virtualized Resource Image Storage System Based on Data Deduplication Techniques

With the rapid development of virtualization technology, OpenStack and Kubernetes as virtualization service have gradually become the core content of cloud computing. Virtualization technology is transforming to the direction of containerization. Many enterprise users are gradually increasing their support for container images while using virtual machine images. The number and scale of virtualized resource images have increased significantly. For example, there are currently more than 7 million container images stored in the Docker hub alone. In addition, the increase in private registries has also increased the performance and capacity requirements of the image storage system. Because there are a large number of redundant data and redundant files in the image file, using its high redundancy to reduce duplicate data can greatly reduce the ever-increasing registry storage requirements. Based on the characteristics of virtual machine images and container images, this paper proposes and designs a high-efficiency storage system for virtualized resource images given the large volume of virtualized resource images and high storage occupation. Through this system, the utilization rate of image storage can be improved and reduced the cost of store images.
Research on MDS and Semi-supervised Clustering Algorithm

With the rapid development of computer technology, the dimensions of data have exploded, and many data analyses have become very difficult. To solve the above problems, the use of dimension reduction method of the multidimensional scaling transformation (MDS) for data dimension reduction, get rid of some redundant information, save the storage space. In view of the traditional k-means clustering method, by introducing a pair constraint supervision information to guide the clustering process, formed a semi-supervised k means clustering approach. In addition, on the basis of the Cop-Kmeans algorithm based on Breadth-first search (BFS), I design an improvement based on data segmentation technology in terms of the selection of the initial clustering center. In this paper, MDS and the improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{BFS}+\text{Cop}$</tex> -Kmeans algorithm are fused to form the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{MDS}+\text{BCK}$</tex> algorithm, and on the UCI data set an experimental verification was performed. The first results of the test indicate that the performance of the present algorithm shows further improvements compared to the traditional k-mean algorithm, the Cop-Kmean means, and the unimproved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{BFS}\ +\ \text{Cop}$</tex> - Kmean algorithm.
The sociology of style: Writing and influence within literary families

Since Michael Bull's seminal work on the Walkman, media scholars have observed how sound and technology enact certain interactional frameworks in urban contexts. We know little about the materiality of listening in non-urban space. This article contributes to these conversations by examining how adventurers (climbers, bushwalkers) adopt or resist mobile music devices when they are in the bush or mountains. I show how device use is moralised in this context, wrapped up in broader conversations about what it means to be a bushwalker or climber, particularly with respect to its impacts on connection to place and the embodied experience of being there. The analysis shows three modes of mobile music device use or non-use: 1) resisting devices to fully connect with the immediate environment; 2) attending to both the sounds of place with selective engagement with music as an additional layer of experience; and 3) device use as a strategy to disconnect mind from body at times of difficulty or boredom.