Reviewerly/exHarmony huggingface dataset:
An Empirical Study of Thresholds for Code Measures

Background. Practical use of software code measures for faultiness estimation often requires setting thresholds on measures, to separate software modules that are likely to be faulty from modules that are likely to be non-faulty. Several threshold proposals exist in the literature. However, different proposals may recommend different threshold values for the same measure, so practitioners may be unsure which threshold value they should use.Objective. Our goal is to investigate whether it is possible to define a single threshold for a code measure, or at least a small range within which to select a threshold.Method. We carried out an empirical study based on two collections of datasets available in the SEACRAFT repository. For each dataset, we built all statistically significant univariate Binary Logistic Regression fault-proneness models, using a set of the most commonly used code measures as independent variables. We then derived thresholds for single software measures by setting an acceptability threshold to fault-proneness. We then checked whether the distribution of the thresholds obtained for the same measure is concentrated enough that it is possible to take a value of the code measure as a “universal” threshold for it. We repeated the same method with bivariate Binary Logistic Regression fault-proneness models.Results. The threshold distributions we obtained are quite dispersed, so it does not seem to be possible to define “universal” thresholds for the code measures we considered.Conclusions. According to the collected evidence, it appears hardly possible to effectively control software faultiness by recommending that the most commonly used code measures satisfy fixed thresholds.
Fortune at the Bottom of the Information Pyramid: Leveraging Data Logs to Derive Experience Insights, a Reflective Case Study

In this case study, we aim to discuss and reflect on the methodology adopted by the UX team to come up with user and usage analytics insights in the absence of the same by using data generated from system logs and creating custom queries to derive reports to measure user experience. Thus data led to information. This then helped UX designers to derive behaviour and usage insights which translated to design recommendation hypothesis which were then validated against business objectives through stakeholder interactions. Thus information created knowledge and helped create a business value as well as evangelise UX within a technology driven organisation. Through this exercise designers learnt how data could be leveraged as a medium to create qualitative insights. The business witnessed the RoI of design as the findings were backed by data. This study also paved a path for engagements that are in progress between designers and data scientists to collaborate using Splunk and other analytics tools to mine richer user insights and drive strategy through data driven design approach.
Classifying Semantic Relationships of Utility-Specific Terminologies via LSTM Networks along Shortest Dependency Paths

The inconsistency of vocabulary terms used by different utility organizations makes data integration from distinct sources a challenging task. A semantic resource that classifies utility terminologies into sets of synonyms, hyponyms, and meronyms enables the computers to interpret the lexical semantics and to avoid mismatches when integrating heterogeneous utility data. However, constructing such semantic resources requires significant amounts of effort and time. This paper presents a shortest dependency path (SDP)-long short-term memory (LSTM) approach to automatically classify the semantic relations (i.e., is-a, part-of, is-similar, and random relationships) of two utility terms in a definition sentence. SDP is the shortest dependency path between two domain terms in a sentence, which retains the most informative information to relation classification while eliminating irrelevant words in the sentence. SDP-LSTM leverages LSTM units to pick up heterogeneous feature information along the SDPs and conducts automatic feature learning for semantic classification. The proposed approach was tested on a corpus of definition texts collected from utility design manuals. The preliminary results show an overall accuracy of over 80% and thus, the newly created method can serve as a good starting point to construct a semantic resource for the utility domain.
Regularized Kelvinlet Inversion for Real-Time Image Deformation and Video Time Warping

We present a novel approach for image deformation and video time warping. Our technique involves the inversion of the nonlinear regularized Kelvinlet equations, leading to higher quality results and time/space efficiency compared to naive solutions. Inversion is performed by a per-pixel optimization process, being inherently parallel and achieving real-time performance in Full HD videos (over 300 fps). We demonstrate our method on a variety of images and videos, in addition to discussing some important technical and theoretical details.
Visual Surveillance using Background Model Image Generated by GAN

Visual surveillance requires robust foreground and background separation capabilities in various environments. Although various traditional algorithms based on background subtraction methods have been proposed, problems such as hard shadows, camouflage, and ghost effects remain. Recently, deep learning-based foreground detection methods have been proposed. Deep learning-based methods outperform traditional algorithms in various unmanned surveillance datasets. However, even deep learning-based methods show insufficient generalization ability in certain datasets. For data that have not been trained, a number of errors are detected. Even among deep learning-based methods, there are methods that show higher generalization ability by using a background image. In this paper, we propose a method of using GAN to generate background images.
Web Performance Optimization Framework

To achieve optimal performance, you need a performance optimization framework that defines the performance optimization methods and tools for end-to-end performance optimization. A web performance optimization (WPO) framework addresses various concerns of web performance optimizations and provides a blueprint for the web community to achieve performance goals. This chapter proposes a web reference architecture that identifies all the systems, layers, and components involved in end-to-end performance optimization. A web performance optimization framework looks at web performance holistically and elaborates on the performance optimization methods for each layer.
Analysis and Implementation of Digital Watermarking Using Spread Spectrum Method

In this research paper, a new approach for digital watermarking using the spread spectrum method is proposed that can address the enduring issues faced with copyrighting digital data. Discrete Wavelet Transform (DWT) technique is applied to overcome the problems concerning authentication and protection of digital media. The aim is to hide the watermark image in the host image without affecting its robustness. After going through the implementation, the invisibility of the watermark image is almost 100%, and the robustness of the host image is around 80%, which is good enough to ensure the protection of contents from different attacks. This watermarking method will ensure the authentication of digital media, and it can be used for limiting usage control, content labeling to protect the ownership, and making an assertion about the owner.
A Binary Relevance Adaptive Model-Selection for Ensemble Steganalysis

Steganalysis is an interesting classification problem in order to discriminate the images, including hidden messages from the clean ones. There are many methods, including deep CNN networks to extract fine features for this classification task. Nevertheless, a few researches have been conducted to improve the final classifier. Some state-of-the-art methods try to ensemble the networks by a voting strategy to achieve more stable performance. In this paper, a selection phase is proposed to filter improper networks before any voting. This filtering is done by a binary relevance multi-label classification approach. The Logistic Regression (LR) is chosen here as the last layer of network for classification. The large-margin Fisher's linear discriminant (FLD) classifier is assigned to each one of the networks. It learns to discriminate the training instances which associated network is suitable for or not. Xu-Net, one of the most famous state-of-the-art Steganalysis models, is chosen as the base networks. The proposed method with different approaches is applied on the BOSSbase dataset and is compared with traditional voting and also some state-of-the-art related ensemble techniques. The results show significant accuracy improvement of the proposed method in comparison with others.
An Approach for Exploring New Frontiers for Optimizing Query Volume for Farmer Call Centre—KCC Query Pattern

Kisan Call Centre (KCC) dataset, as found on data.gov.in, contains a set of attributes, including two of the most important ones—Query Text and Query Type. Since the primary objective of KCC lies in resolution of farmers’ queries, the most important part of their communication lies with the operator—in understanding the farmer’s query and recording it correctly.
Efficient Network Compression Through Smooth-Lasso Constraint

The powerful capabilities of deep convolutional neural networks make them useful in various fields. However, most edge devices are difficult to afford the huge amount of parameters and high computational cost. Therefore, it is highly imperative to compress these huge models to make them lightweight to enable real-time inference on edge devices. Channel pruning is a mainstream method of network compression. Generally, the Lasso constraint is imposed on the scaling factor in the batch normalization layer to make them tend to zero for selecting unimportant channels and then prune them. However, Lasso is a non-smooth function that is not derivable at zero, we experimentally find that when the value of the loss function is small, it is difficult to decline continuously. Aiming at the above problems, this paper proposes a pruning strategy based on the derivable function Smooth-Lasso, using Smooth-Lasso as a regularization constraint to perform sparse training and then prune the network. Experiments on benchmark datasets and convolutional networks show that our method can not only make the loss function converge quickly, but also save more storage space and computational cost than the baseline method while maintaining the same level of accuracy as the original network.
Metalinguistic Focus in P-HYPE Semantics

P-HYPE is a hyperintensional situation semantics in which hyperintensionality is modelled as a ‘side effect’, as this term has been understood in natural language semantics [, ], and in functional programming []. In [], we combine []’s perspective-sensitive semantic theory with a hyperintensional situation semantics, HYPE [], using monads from category theory in order to ‘upgrade’ an ordinary intensional semantics to a possible hyperintensional counterpart. In [] we expand our semantic theory to capture cases of intra-sentential anaphora. We here supplement the framework with the pointed power set monad so that we can account for certain cases of hyperintensionality involving metalinguistic focus [] which seem to resist treatment in P-HYPE as it stands. This gives a mini case study in how to combine monads together in order to integrate different side effects in one language, one of the advantages of monads as a tool in compositional semantics.
Analysis and Visualization of Farmer Call Center Data

In the last chapter, the focus was on cleaning and pre-processing of the Kisan Call Center (KCC) query data. Just to reiterate that Kisan Call Center is the Indian name for farmer call center. The agenda of this chapter is to perform exploratory data analysis (EDA) or descriptive data analysis. The objective of exploratory analysis is to form meaningful conclusions from the open source KCC query dataset so as to help the farmers and the government. To begin with, let us define—what is data analysis and how it is being used? Data analysis is a process of inspecting, cleansing, transforming data and preparing a model with the goal of discovering useful information, notifying conclusions and supporting decision-making. While there are various different forms of data analysis, in this chapter the concentration is on descriptive statistics, which is also known as exploratory data analysis and confirmative data analysis. This begs the question as to what is EDA? Putting it in simple words, exploratory data analysis is an approach to understand the data distribution, to discover any specific pattern or trend, to summarize the main characteristics and to explain the results using visual methods. EDA is used to test hypotheses and gather proof to prove the same. Usually, the data analysis is done, when data is too huge to form conclusions humanely, hence programming tools are used to analyse it. However, that is not enough; there is a need for visualization tool to comprehend the conclusions in a better way.
Knowledge distillation and data augmentation for NLP light pre-trained models

Abstract Model lightweight aims to solve the problems of various large models for slow training and high resource requirement. Knowledge distillation can be a good solution to these problems. We built a lightweight model that meet the competition requirements and have prominent NLP capabilities. RoBERTa-tiny-clue was used as our backbone model. We tested the effect of soft labels and hard labels on knowledge distillation, made knowledge distillation, fine-tuned this model to get a lighter model with better performance, and then applied it downstream NLP tasks. We also adopted a series of data augmentation methods to improve the performance of the model on downstream tasks, customized different optimization solutions for four tasks. Based on open-source pre-trained model RoBERTa-tiny-clue and public available datasets, we achieved 15 times smaller and 10 times faster than BERT-base, and 95% of BERT-base performance on downstram NLP tasks. Using suitable data augmentation methods for the trained lightweight model, the performance of the model on various downstream tasks reaches or exceeds BERT-base.
Anatomy and Geometry Constrained One-Stage Framework for 3D Human Pose Estimation

Although significant progress has been achieved in monocular 3D human pose estimation, the correlation between body parts and cross-view geometry consistency have not been well studied. In this work, to fully explore the priors on body structure and view-relationship for 3D human pose estimation, we propose an anatomy and geometry constrained one-stage framework. First of all, we define a kinematic structure model in deep learning framework which represents the joint positions in a tree-structure model. Then we propose bone-length and bone-symmetry losses based on the anatomy prior, to encode the body structure information. To further explore the cross-view geometry information, we introduce a novel training mechanism for multi-view consistency constraints, which effectively reduces unnatural and implausible estimation results. The proposed approach achieves state-of-the-art results on both Human3.6M and MPI-INF-3DHP data sets.
Differential Privacy for Distributed Consensus with Partial Observations

In a network, distributed privacy-preserving algorithms protect sensitive information by adding noise to the message before it is transmitted to other nodes in the network. Most existing works on differentially private distributed algorithm assumed that the adversary has access to all messages transmitted through the network. However, this is not a realistic assumption when dealing with real-world applications with large networks such as power networks. Very often the adversary can only breach a small part of the network and has limited observation of the messages. In this paper, we study how partial observations affect the privacy guarantee of a distributed consensus algorithm. For the distributed consensus problem, we show that observing different (set of) messages in the network could lead to very different privacy guarantee of the distributed algorithm.
Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm And Support Vector Machine Based Smote Technique

Abstract As the online Ojek services, people often talk about them by giving their opinions and opinions through various media, one of which is Google Play opinion given by the public to the services of online Ojek also diverse. Users provide review reviews or comments about the application, of course users will choose an app that has a good review. But monitoring the reviews of the general public is not easy, because the amount is very much to be processed so that researchers want to know the extent of the user review analysis of Gojek and Grab applications based on the review of user comments using the classification technique is using the NB algorithm and SVM based technique Smote. The results of the test with the highest accuracy result 81.09% and AUC value = 0.922 is the application Gojek while for application test results grab accuracy value of 73.20% and AUC value = 0.848. To that end, the implementation of the Support Vector Machine based Smote technique in this study has higher accuracy so that it can be used to provide solution to the sentiment analysis problems in the review user comments online Ojek application
Identifying Usability Risks for Mobile Application

Risk is an important factor that determines the success of mobile application development. Identifying risks will help developers minimize failures. Likewise, the determination of risks that affect usability can support taking preventive action before failure occurs. Previous researches have shown that many risks have been identified in mobile application development. However, it is rare to determine usability risks in mobile applications. This study aims to identify the risks that affect the usability of mobile application users. This study uses a literature study with several stages, namely: identifying a combination of usability attributes, identifying risk factors (potential risks), and determining the potential usability risk of each usability attribute at each stage of information system application development. This study resulted in 8 usability attributes, 107 risk factors (potential risks), and usability potential risks for each usability attribute at each stage of mobile application development. The results of this study can also determine the usability attributes that affect each phase of mobile application development.
Economic Worth-Aware Word Embeddings

Knowing the perceived economic value of words is often desirable for applications such as product naming and pricing. However, there is a lack of understanding on the underlying economic worths of words, even though we have seen some breakthrough on learning the semantics of words. In this work, we bridge this gap by proposing a joint-task neural network model, Word Worth Model (WWM), to learn word embedding that captures the underlying economic worths. Through the design of WWM, we incorporate contextual factors, e.g., product's brand name and restaurant's city, that may affect the aggregated monetary value of a textual item. Via a comprehensive evaluation, we show that, compared with other baselines, WWM accurately predicts missing words when given target words. We also show that the learned embeddings of both words and contextual factors reflect well the underlying economic worths through various visualization analyses.
Research on Computer Test Paper Generation Algorithm Based on Gene Expression Programming

Abstract This article analyzes the common classifications of computer test paper algorithms. This article includes random method, backtracking method, genetic algorithm, random location search method, ant colony algorithm, gene expression algorithm, etc. The author obtains the expected calculated values by studying the technologies of setting control parameters, creating initial populations, calculating individual fitness, optimizing preservation strategies, performing genetic operations, and generating new populations. The author’s purpose is to improve the application effect of the computer test paper algorithm and the practicability of the information in the question bank.
A Study of Jacobi-Fourier Moments via Image Reconstruction

In this paper, we have discussed the computational aspects regarding to Jacobi-Fourier moments. A k × k numerical scheme has been applied to improve the computing accuracy of Jacobi-Fourier moments. To verify our proposed method, image reconstructions of the higher orders of Jacobi-Fourier moments have been carried out. The experimental results of reconstructing a testing image sized at 512 × 512 are highly satisfying. We have also conducted a study on image reconstructions from uneven order pairs of Jacobi-Fourier moments, {n, m}, and concluded that the order n and repetition m preserve the circular and radial pattern information of image, respectively.
Fusion Model Classification Algorithm for Imbalanced Data

Imbalance data which the samples containing more discriminative information near the decision boundary may be ignored and derive a suboptimal model. In practice, ensemble model can achieve more precise and robust classification result. Therefore, this paper presents a fusion model classification algorithm concluding classification and regression tree, support vector machines, gradient boosting machine and random forests. Meanwhile integrated learning method of bagging and voting strategy of KNN were adapted to minimize the variance of the proposed method. The evaluation measures including accuracy, p-value, and recall rate were applied with air quality data in Shijiazhuang City, capital of China’s Hebei Province. The experiment results showed a better performance under proposed method than under single base model towards imbalanced data.
Visual Analytics Indicators for Mobility and Transportation

Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.