Reviewerly/exHarmony huggingface dataset:
CPKD: Concepts-Prober-Guided Knowledge Distillation for Fine-Grained CNN Explanation

Explainable artificial intelligence (XAI) for CNN has received increasing research attentions, especially in security-critical domains. Some researches exploit knowledge distillation based technique to transform the sophisticated deep black-box model to a simple yet explainable model. However, traditional distillation-based methods suffer from two typical dilemmas during the explanation: (1) they can hardly be self-explanatory; (2) they are unable to provide concept-based fine-grained explanation, which causes that the explanation result is not straightforward and human-understandable. In this work, we forgo these dilemmas by proposing Concepts-Prober-guided Knowledge Distillation (CPKD), a novel distillation framework for general and fine-grained CNN explanation. CPKD employs techniques of concepts prober and distillation to generate a self-explanatory Concept-related Decision Tree (CDT) with semantic concepts explicitly organized at the tree nodes, thus bringing a concept-based explanation for CNN at a fine-grained and high-fidelity level. The comparison experiments and qualitative analysis result show that CPKD improves 3% of student model's accuracy than that of state-of-the-art concept-based explanation and is more effective in terms of interpretability than that of distillation-based explanation methods.
Bipartite Graph Recommendation Algorithm Based on Negative Scores and Item Attributes

Recommender systems are used to alleviate the problem of the overload information. The mass diffusion has found application on bipartite graph to recommend information for users. Aiming at the problem that traditional bipartite graph recommendation algorithms ignore negative ratings and set coarsely initial resources, a bipartite graph recommendation algorithm based on negative scores and item attributes is studied. The proposed method sets the initial resources of the items differently according to the deviation degree between the score and the average. At the same time, the propagation of negative scores and positive scores on the bipartite graph is considered respectively, and then the aversion to the items is obtained by combining the negative scores and item attributes to adjust the positive resources, so as to make the final resource distribution more reasonable. The results in MovieLens dataset show that the improved algorithm not only ensures the diversity of recommendation, but also improves the accuracy compared with the traditional algorithm.
Digitizing the Warranty of Habitability

The warranty of habitability was touted fifty years ago as a gamechanger in rebalancing power between tenants and landlords. Under the warranty, a residential tenant’s duty to pay rent is conditioned on a landlord’s obligation to make repairs. Scholars who have studied the warranty of habitability have focused on its defensive use, primarily when a tenant is already in eviction proceedings. Consensus has emerged that the warranty as a defensive shield has failed to deliver meaningful benefits to tenants living in poor housing conditions. This Article explores whether an affirmative use of the warranty, coupled with a new technology and community organizing approach, can improve tenant outcomes. Specifically, the authors de-signed, built, and implemented a novel tool available for tenants to bring pro se actions for money damages in small claims courts for breaches of the warranty of habitability. The Warranty of Habitability Abatement of Rent Mathematical Calculator (“H.A.R.M. Calculator”) is an efficiency application that allows law students and attorney volunteers to assist tenants in preparing small claims court pleadings. Tenants then file their complaints, and, when successful, obtain judgments for money damages against their current or former landlords. This Article contributes to the poverty law, housing law, and legal technology literatures by focusing on the warranty of habitability in a new way. An affirmative, tenant-centered remedy has the possibility of shifting power dynamics between tenants and land-lords. Through initial data collected, the authors have developed working hypotheses that the tool will test through future research.
The Choir Conductor: Interpreter or Maestro?

Nowadays choir singing is divided into a wide typology of different groups, distinguished by being professional or amateur in nature, the type of voices depending on age and pitch (high, deep and mixed) or the type of associations or entities to which they belong. Some of these variables may have an impact on the aims and methodology of the rehearsal.
Enhanced Authentication Mechanism using Virtual Middlebox for IoT Devices

With the proliferation of IoT (Internet of Things) devices, security issues have become increasingly serious. A key security issue is that a large amount of IoT devices are authenticated via weak passwords for the convenience of management. Due to the large quantity of IoT devices, it is tough to use complex passwords or change passwords regularly. To this end, we propose an authentication mechanism employing virtual middle boxes to monitor the access information of physical devices and then send them to an authentication server for enhanced authentication combining OAuth with CoAP. The evaluation results show that our approach can effectively identify the device access including weak passwords and improve device authentication security without changing the original services of the IoT devices.
A wavelet-based set of constructive masses for the Minkowskian Feynman propagator

We continue our pursuit of Minkowskian quantum field theory—specifically, a nonperturbative analysis of the characteristic functional generating the time-ordered Wightman distributions for the quartic scalar field interaction. As in a previous paper, our starting point is the Minkowskian Feynman propagator for the free scalar field theory analyzed by a system of expansion functions in space-time. Here, we choose a significantly different system of expansion functions. We start with an L2-orthonormal wavelet basis {Ψw:w∈W}, where each Ψw has the properties of a Daubechies wavelet in the temporal coordinate and the properties of a Meyer wavelet in the spatial coordinates. We consider the matrix elements Gww′(m) of the mass-m Feynman propagator based on the expansion functions vw=Lw−1Ψw, where Lw denotes the length scale of w∈W. For every finite Λ⊂W, we let GΛ(m) denote the Λ × Λ matrix of such elements. We define a constructive mass to be any value of m such that GΛ(m) is nonsingular for each finite Λ⊂W. We prove that the set of such masses is at least co-countable (although we expect every mass value to be constructive). For an arbitrary constructive mass m and an arbitrary finite Λ⊂W, we rigorously construct the interacting characteristic functional as an integral in amplitudes, where the Λ-cutoff on the quartic interaction is supplemented by a cutoff on the free-field action defined by GΛ(m)−1.
An image encryption algorithm based on multiple chaotic systems and DNA encoding

Aiming at the problems of image encryption algorithm in terms of security and encryption effect, an image encryption algorithm based on multiple chaotic maps and DNA encoding is proposed. First, using Logistic map to randomly scramble the rows and columns of the plaintext image, and convert the result into a binary matrix; second, according to the DNA coding rules, convert the binary matrix into a DNA matrix, and use 2D-LSMM map and Logistic-Sine system to perform in DNA encoding operation, XOR operation is performed on the obtained encoding matrix to obtain an encrypted DNA matrix; finally, according to the DNA decoding rules, the encrypted DNA matrix is converted into a digital image to obtain a ciphertext image. Experimental simulation and performance analysis show that the algorithm has better encryption and decryption effects, larger key space, and higher security and robustness.
Do We Need Change Detection for Dynamic Optimization Problems?: A Survey

Solving dynamic optimization problems is more challenging than static ones. When a change in the objective landscape occurs, the search process may not be powerful enough to track new optima. For population based algorithms this is referred to as diversity loss problem. Furthermore, the memory of old optima becomes outdated and if not correctly dealt with, the evolution of the search process may be misguided. Recently, a new interesting trend in dealing with optimization in dynamic environments has emerged toward developing new algorithms that are able to effectively handle changes without using any change detection scheme, and hence no extra computational cost is needed. There exist several works in the literature that attempt to maintain diversity without change detection. However, not that much work has been devoted to studies that investigate the possibility to overcome the outdated memory problem without expensive change detection. This study presents a comprehensive survey of the various change detection based methods. As part of this survey, we include a classification of the change detection schemes and we identify the main features of each method.
Cooking STEAM: A Case Study on Establishing a STEAM Learning Community using a Performative Framework and Cooking

The work reported in this paper is informed by PerFECt, a performative framework supporting collaborative learning and creativity that addresses issues related to the effective use of digital technologies to establish and sustain learning communities. Its core principles are reinterpreted to implement unplugged collaborative learning activities to creatively explore sciences, technology and mathematics with special emphasis on the core principles of algorithmic design by presenting recipes in the form of flow diagrams. By presenting the execution of algorithms within a dramabased (theatrical) framework many creative explorations are offered within the so called STEAM education. The adoption of playful activities that put the children’s bodies in motion is well aligned with pedagogical approaches that emphasize embodied knowledge and performance and open up opportunities for deeper personalized learning experiences that promote inclusion of children from different cultural backgrounds.
Density Distillation for Fast Nonparametric Density Estimation

Nonparametric density estimation has been extensively used in various application scenarios and theoretical models. However, the modeling of these powerful methods is inseparable from the sample data and comes at the cost of repeated and intensive kernel calculations, which makes their efficiency greatly affected by the sample scale, data dimension, and evaluation scale. Inspired by the knowledge distillation method, a student-teacher paradigm model named density convolutional neural network (DCNN) is proposed in this article. The method extracts the density knowledge of the samples based on the density convolution rule and transfers it to a compact and small deep neural network, in order to separate the sample data from the modeling and avoid the cumbersome kernel calculations. Experimental results show the superiority of the proposed method to various nonparametric estimation methods in terms of accuracy, stability, processing efficiency, and low-storage advantage. Especially, for the estimation speed, a univariate density estimation on 1.0E + 08 evaluation points using GPU only takes 1.57 s, and a 10-D multivariate density estimation on 1.0E + 08 evaluation points only takes 10.50 s, which makes our method very suitable for real-time and large-scale repetitive density estimation tasks.
Recognition of American Sign Language using Deep Learning

Deaf and mute communities have always faced a communication barrier, but advances in the field of Deep Learning are reducing this barrier. As a form of communication, sign language is one of the most ancient and natural, but since few people speak it and interpreters are extremely rare, this paper proposes to use neural networks to handle fingerspelling based on American Sign Language. A comparative study for Sign Language Recognition (SLR) is presented by implementing a variety of Deep Learning models. The paper proposes a CNN architecture that outperforms (by around 4&#x0025;) various pretrained models for SLR.
Identifying Biased Reviews

Online reviews play a significant role in our community contributing to the prediction of the marketing situation, making industries modifying their advertising policies. Many consumers choose online reviews for making the decision to buy a specific product. In recent years, product sellers provide some lucrative offers to write biased reviews which are usually very positive that increases the rating of the products significantly. So it is very important to detect biased reviews for online shopping to help the consumers in their decision making to buy proper products. In this work, a new method has been developed for detecting those biased reviews generated on some products at Amazon. At first online reviews of Amazon product like- Fire Tablet, Alkaline Batteries, etc. are collected. Then sentiment analysis is introduced for calculating the sentiment score of the text reviews with the help of natural language processing. Naïve-Bayes-Analyzer model and TextBlob library are used to calculate the sentiment scores. Finally, statistical measurements are used to detect biased reviews.
Compiler IR-Based Program Encoding Method for Software Defect Prediction

With the continuous expansion of software applications, people's requirements for software quality are increasing.Software defect prediction is an important technology to improve software quality.It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy.However, the current encoding methods are mainly based on the traditional manual features or the AST of source code.Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST, which affects the expression of semantic features.To overcome the above deficiencies, we combined with the Convolutional Neural Networks (CNN) and proposed a novel compiler Intermediate Representation (IR) based program encoding method for software defect prediction (CIR-CNN).Specifically, our program encoding method is based on the compiler IR, which can eliminate a large amount of noise information in the syntax structure of the source code and facilitate the acquisition of more accurate semantic information.Secondly, with the help of data flow analysis, a Data Dependency Graph (DDG) is constructed on the compiler IR, which helps to capture the deeper semantic information of the program.Finally, we use the widely used CNN model to build a software defect prediction model, which can increase the adaptive ability of the method.To evaluate the performance of the CIR-CNN, we use seven projects from PROMISE datasets to set up comparative experiments.The experiments results show that, in WPDP, with our CIR-CNN method, the prediction accuracy was improved by 12% for the AST-encoded CNN-based model and by 20.9% for the traditional features-based LR model, respectively.And in CPDP, the AST-encoded DBNbased model was improved by 9.1% and the traditional features-based TCA+ model by 19.2%, respectively.
Application of Computer Virtual Reality Technology and Artificial Intelligence in Remote Network Course

With its unique characteristics of full immersion and interactivity, virtual reality technology provides powerful support for the creation of teaching situations and greatly changes the way students learn. The article uses design and development research methods to build a deep learning field model based on virtual reality technology. The author designs an immersive English learning system based on Kinect somatosensory equipment, introduces somatosensory recognition and speech recognition technology into English teaching, and supports virtual scenes with machine dialogue, pronunciation correction, real-time translation, reading training, English games and other functions. It has the advantages of humanized design, easy operation, customizable and strong scalability, and provides comprehensive help for English learners.
Efficient Data Augmentation Techniques for Improved Classification in Limited Data Set of Oral Squamous Cell Carcinoma

Deep Learning (DL) techniques as a subfield of data science are getting overwhelming attention mainly because of their ability to understand the underlying pattern of data in making classifications.These techniques require a considerable amount of data to efficiently train the DL models.Generally, when the data size is larger, the DL models perform better.However, it is not possible to have a considerable amount of data in different domains such as healthcare.In healthcare, it is impossible to have a substantial amount of data to solve medical problems using Artificial Intelligence, mainly due to ethical issues and the privacy of patients.To solve this problem of small dataset, different techniques of data augmentation are used that can increase the size of the training set.However, these techniques only change the shape of the image and hence the classification model does not increase accuracy.Generative Adversarial Networks (GANs) are very powerful techniques to augment training data as new samples are created.This technique helps the classification models to increase their accuracy.In this paper, we have investigated augmentation techniques in healthcare image classification.The objective of this research paper is to develop a novel augmentation technique that can increase the size of the training set, to enable deep learning techniques to achieve higher accuracy.We have compared the performance of the image classifiers using the standard augmentation technique and GANs.Our results demonstrate that GANs increase the training data, and eventually, the classifier achieves an accuracy of 90% compared to standard data augmentation techniques, which achieve an accuracy of up to 70%.Other advanced CNN models are also tested and have demonstrated that more deep architectures can achieve more than 98% accuracy for making classification on Oral Squamous Cell Carcinoma.
Big Data Financial Algorithm Technology Based on Machine Learning Technology

With the development and wide application of machine learning technology, the use of machine learning technology for economic algorithm technology research has become a new type of financial technology field. Today's financial big data has penetrated into all walks of life and has become an important factor of production. The extraction and application of massive amounts of data by humans heralds the arrival of a new wave of productivity growth and consumer surplus. Big data originally refers to a large number of data sets generated through batch processing or web search index analysis. This paper uses machine learning technology to explore and research big data financial algorithms, analyze risk control measures, report on the improvement and perfection of traditional finance, and analyze and study the future development of big data finance. The main research content of this paper is the analysis of big data financial algorithm technology by machine learning algorithms. Machine learning technology is one of the main methods to solve big data mining problems. Machine learning technology is a process of self-improvement using the system itself, so that computer programs can automatically improve performance through accumulated experience. This paper analyzes the relevant theories and characteristics of machine learning algorithms, and integrates them into the research of big data economic algorithm technology. The final result of the research shows that when the data volume is 1G, the training time of SVM is 8 minutes, while the training time of Bayesian is 12 minutes, and the data volume is relatively small. The SVM algorithm still has obvious advantages in training time.
Network Traffic Classification Based On A Deep Learning Approach Using NetFlow Data

Abstract Network traffic classification is of fundamental importance to a wide range of network activities, such as security monitoring, accounting, quality of service and forecasting for long-term provisioning purposes. This task has been increasingly implemented using machine learning methods due to the inability of conventional approaches to accommodate the increasing use of encryption. However, the application of machine learning methods to network traffic classification based on sampled NetFlow data is poorly developed despite the fact that NetFlow is a widely extended monitoring solution routinely employed by network operators. This study addresses this issue by proposing a network traffic classification module using NetFlow data in conjunction with a deep neural network. The performance of the proposed classification module is demonstrated by its application to two real-world datasets, and an average classification accuracy of 95% is obtained for $\sim $1.4 million test cases. Moreover, the performance of the proposed classifier is demonstrated to be superior to three other state-of-the-art classifiers. Accordingly, the proposed module represents a promising alternative for network traffic classification.
Automatic phase identification of earthquake based on the UBDN deep network

Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net’s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage.
TAGSYNTA: Tagalog Speech Synthesizer using Part of Speech in Prosody Analyzation

In this paper, we developed a system called, TagSynta: Tagalog Text to Speech Synthesizer using Part of Speech in Prosody Analyzation, which is a Tagalog Text-to-Speech Synthesizer with proper intonation of the word given a Tagalog sentence. It used Part of Speech as its identifier to reduce the reuse of audio processor for faster output which aims to solve the problem of accuracy in an automatic Text-to-Speech conversion of a Tagalog language. The system obtained 92.50% in converting a text into speech and 96.63% in giving a correct prosody of the words. The respondents of the research agreed that the system's output was above average in terms of the naturalness of the voice produced by the system.