Reviewerly/exHarmony huggingface dataset:
Building an Ambidextrous Software Security Initiative

Improving software security in software development teams is an enduring challenge for software companies. In this chapter, the authors present one strategy for addressing this pursuit of improvement. The approach is ambidextrous in the sense that it focuses on approaching software security activities both from a top-down and a bottom-up perspective, combining elements usually found separately in software security initiatives. The approach combines (1) top-down formal regulatory mechanisms deterring breaches of protocol and enacting penalties where they occur and (2) bottom-up capacity building and persuasive encouragement of adherence to guidance by professional self-determination, implementation, and improvement support (e.g., training, stimulating, interventions). The ambidextrous governance framework illustrates distinct, yet complementary, global and local roles: (1) ensuring the adoption and implementation of software security practices, (2) enabling and (3) empowering software development teams to adapt and add to overall mandates, and (4) embedding cultures of improvement.
Application analysis of computer web data mining technology in E-commerce

With the development of the Internet, the proportion of e-commerce in people's life is becoming larger and larger. Online shopping has become a major way of shopping and helped people improve their life efficiency. Web data mining technology is a very important technology in e-commerce. The web can deeply mine and analyze the access methods, interest preferences, group characteristics and so on of e-commerce users. These information can help e-commerce push different user groups, and greatly improve the overall promotion effect and benefit of e-commerce. Starting from the web data mining technology, this paper expounds the application process of Web data mining technology used in e-commerce in detail, so as to provide a certain reference for the development of e-commerce.
Intelligent Technology and Automation in Hospitality

Based on the growing evolution of intelligent technology and automation in the provision of services, this chapter analyzes its incorporation into the 4- and 5-star national hotels and its consequent impact on decision and consumer choice. In this sense, the authors proceeded to characterize the technologies with greater appetence concerning the reality of the hotel industry. Furthermore, they investigated the appreciation made by the guests, researched trends, advantages, inconveniences, changes in behavior and procedures, given the COVID-19 pandemic.
The Science of Problem Solving

This chapter introduces the concept of a program as a solution to a problem. It presents the programming environment as an expression-evaluating mechanism and a program as a collection of expressions that include number, strings, and function applications.
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification

Data augmentation has shown its effectiveness in resolving the data-hungry problem and improving model's generalization ability. However, the quality of augmented data can be varied, especially compared with the raw/original data. To boost deep learning models' performance given augmented data/samples in text classification tasks, we propose a novel framework, which leverages both meta learning and contrastive learning techniques as parts of our design for reweighting the augmented samples and refining their feature representations based on their quality. As part of the framework, we propose novel weight-dependent enqueue and dequeue algorithms to utilize augmented samples' weight/quality information effectively. Through experiments, we show that our framework can reasonably cooperate with existing deep learning models (e.g., RoBERTa-base and Text-CNN) and augmentation techniques (e.g., Wordnet and Easydata) for specific supervised learning tasks. Experiment results show that our framework achieves an average of 1.6%, up to 4.3% absolute improvement on Text-CNN encoders and an average of 1.4%, up to 4.4% absolute improvement on RoBERTa-base encoders on seven GLUE benchmark datasets compared with the best baseline. We present an indepth analysis of our framework design, revealing the non-trivial contributions of our network components. Our code is publicly available for better reproducibility. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising

Online advertising and recommender systems often pose a multi-task problem, which tries to predict not only users’ click-through rate (CTR) but also the post-click conversion rate (CVR). Meanwhile, multi-functional information systems commonly provide multiple service scenarios for users, such as news feed, search engine and product suggestions. Users may leave similar interest information across various service scenarios. Thus the prediction/ranking model should be conducted in a multi-scene manner. This paper develops a unified r a nking m o del for this multi-task and multi-scene problem. Compared to previous works, our model explores independent/non-shared embeddings for each task and scene, which reduces the coupling between tasks and scenes. New tasks or scenes could be added easily. Besides, a simplified n e twork i s c h osen b e yond t h e embedding layer, which largely improves the ranking efficiency f o r online services. Extensive offline a n d o n line e x periments demonstrated the superiority of the proposed unified r a nking model.
Current Trends in Learning from Data Streams

This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection, learning from rare cases, and hyper-parameter tuning for streaming data.
Path and Information Content-Based Structural Word Sense Disambiguation

Word sense ambiguity is a fundamental characteristic of any natural language. Many words in every natural language have multiple meanings, termed as ambiguous words; ambiguous words impose a big challenge for natural language processing (NLP) applications. Word sense disambiguation (WSD) is the computational approach used to resolve these words sense ambiguities. Path-based and information content-based (IC-based) measures are the popular approaches in the similarity framework. Path-based approaches make use of geometrical distance and information content approaches make use of common information shared and measures similarity between the given concepts. We have studied, implemented, and evaluated various methods under these approaches in WSD over a dataset of 1000 English sentences, randomly picked from Times of India news headlines, this dataset covers 50 ambiguous words of English. The experimental results are presented, discussed and the experimentation has shown that the information content-based Lin similarity WSD approach outperforms all other approaches and yields an average precision of 0.77.
Online communication in the educational environment of Russia and Germany: functional and dysfunctional effects of digitalization

Nowadays, the ability of the higher education system to adapt to external conditions is facing the challenge of online transformation and digitalization against the background of changes in the format of social relations in the direction of greater pluralism, demassification of information sources, channels and recipients. The relevance of the study lies in the need to assess the consequences of the transition to online communication of teachers and students in order to identify new functional / dysfunctional effects of digitization of the educational environment. The paper represents the results of an online survey (spring – summer of 2020), when the pandemic of the new coronavirus led to the transition to distance learning. The survey was conducted among students of Russian and German universities to identify the attitude of students to online education. Possible unintentional consequences of digitalization of the educational environment were identified. On the basis respondents’ answers, the authors have analyzed emotional attitude of Russian and German students to the compulsory transition to online learning as well as their assessments of its positive and negative aspects. The authors provide ratio of the advantages and disadvantages of online learning in the perception of Russian and German students, highlight some side effects of online communication between teachers and students, as well as the concerns of students caused by the transition to a new communication format. According to students of Russian and German universities, online communication is not beneficial for the quality of education. The increased time spent in front of the computer screen is considered harmful. Besides, students in both countries are concerned with the weakening of social ties and the loss of social contacts.
Pains and Gains of Peer-Reviewing in Software Engineering (2)

The standard approach for evaluating scientific contributions by software engineering venues is peer-reviewing. Papers submitted for consideration by a venue are sent to peers (i.e., expert colleagues in the field), who carefully read them and provide corresponding evaluation reports, i.e., the peer reviews. Peer-reviewing comes in various alternative forms, as discussed in the teaser column of this series [1]. The teaser also pointed out some questions currently motivating discussion within the software engineering community: Which is the "best" among currently adopted peer-reviewing alternatives? Would it be better to have zero-blind, single-blind, or double-blind reviews? How many, and which stages should be involved in the process of peer-reviewing? Are currently adopted peer-reviewing processes sustainable in the long run? These are just a few examples of questions setting up the discussion, with many other questions outside there and yet to come.
Multilevel Classification of Pakistani News using Machine Learning

The availability of innumerable sources of online news has benefitted the masses as they have opportunity to gather news from a diverse set of sources. However, classification of this huge data being generated on regular basis has never been a simple task. This textual information can be invaluable only when it is processed to maximize its usefulness which is possible with automated text classification. Natural Language Processing (NLP) and Machine learning techniques have been extensively applied in this particular domain to address this challenge. Text classification is helpful in several scenarios such as product mining, emotions or sentiment analysis, etc. News classification is one of its applications through which content of news is processed and analyzed to assign predefined label(s). This research is focused on classification of Pakistani news obtained from dataset available on Open Data Pakistan. We have applied various machine learning algorithms including Logistic Regression, Random Forest, Support Vector Machine, and Naïve Bayes for first-level classification and Logistic Regression for multilevel classification. Comparative analysis of these algorithms is also presented. We achieved a maximum of 97.8% accuracy through Support Vector Machine in single-level classification and 83% through Logistic Regression in multilevel text classification.
An Attention Method to Introduce Prior Knowledge in Dialogue State Tracking

Dialogue state tracking (DST) is an important component in task-oriented dialogue systems. The task of DST is to identify or update the values of the given slots at every turn in the dialogue. Previous studies attempt to encode dialogue history into latent variables in the network. However, due to limited training data, it is valuable to encode prior knowledge that is available in different task-oriented dialogue scene. In this paper, we propose a neural network architecture to effectively incorporate prior knowledge into the encoding process. We performed experiment, in which entities belonging to the dialogue scene are extracted as the prior knowledge and are encoded along with the dialogue using the proposed architecture. Experiment results show significantly improvement in slot prediction accuracy, especially for slot types date and time, which are difficult to recognize by an encoder that is trained with limited data. Our results also achieve new state-of-the-art joint accuracy on the MultiWOZ 2.1 dataset.
Handwritten Mathematical Expression Recognition: An approach on data augmentation

In this paper, we propose an approach for generating Mathematical Expression (ME) images from the CROHME dataset. Our approach employs two methods. The first method transforms original ME images which belong to the CROHME dataset by geometric transformations. The second generates new ME images based on the dictionary of character patterns collected from the CROHME dataset. The generated ME images follow rules of math form. Based on the combination of both two methods, we introduce a much larger dataset for handwritten math expression recognition problem compared to original CROHME. That is the main contribution of this paper. To evaluate, we employ a sequential system containing a module for object detection- Single Shot MultiBox Detector (SSD)- and a module for parsing SSD’s outcome into $\mathrm{L}^{A} \mathrm{T}_{E}\mathrm{X}$ string- DRACULAE and focus on improving the detector. We trained and evaluated the system on CROHME 2013 training set combined with and without our own generated dataset to point out the impact of our generative approach. The experimental results indicate that the detector achieves 52.57% on mAP instead of 36.98% if we do not use added dataset.
A Dynamic Load-balancing Method for Distributed RDF Stream Processing Systems

This paper proposes a method for multi-query optimization for distributed RDF stream processing systems. Due to the recent advances of IoT (internet of things) and CPS (cyber-physical systems), the edge computing environment becomes rich in computational resources, and the popularity of RDF stream has been increasing due to its high expressivity. To deal with massive RDF streams on the server-side, distributed RDF stream systems have been intensively studied, where many user queries are processed simultaneously using multiple computing nodes. For making query processing more efficient, it is important to cope with the changes of RDF streams in data rate and data distribution. However, the state-of-the-art methods cannot cope with it. To this problem, we propose a cost model for distributed RDF query processing and a dynamic load-balancing method based on the model. The experimental study shows that the proposed method outperforms the existing method even when the data rate and data distribution of RDF stream changes.
Bayesian Inference for Inverse Gaussian Data with Emphasis on the Coefficient of Variation

This paper considers the Bayesian framework for inference on the parameters of an inverse Gaussian distribution parameterized in terms of the mean (μ) and coefficient of variation (δ). In the past literature, various parameterizations have been considered, some involving the coefficient of variation, where the prior distribution is assumed on the joint parameter (μ, d(δ)), d(.) being some function. However, in some practical applications, the researcher may possess direct prior information about (μ, d(δ)), from some previous studies. Here, we obtain the non-informative prior for (μ, δ) and consider informative priors in terms of (1) independent generalized inverse Gaussian (GIG) priors for μ and δ, and (2) a conditional prior for δ|μ generated by an inverse Gamma prior for δ2|μ and a GIG prior of μ. Posterior distributions of the parameters μ and δ are derived that are explicitly available except for a normalizing constant. An illustration in terms of a real data example is presented, where the computational algorithm is provided using R software. The resulting values are also checked using the Gibbs sampler using WinBUGS software.
Acceptance Analysis and ELSI-Aspects of Sensor-Based Care-Management: Certain Results from a Qualitative Study Addressing Dehydration Management

Healthcare Interventions are often based on a variety of components, e.g different products, processes and adaption of organizational rules that have to be considered. Dehydration can be one example for such a hybrid multi perspective innovation that has to be effectively implemented in a service workflow. A newly developed sensor-based dehydration system is aimed to enforce the monitoring of dehydration and concomitantly the associated care-management process. Hence, technology directly tackles the organizational skill mix of caregivers. A study employing mixed-methods shows that a dehydration sensor could enhance interface management for all stakeholders involved. In consequence, such an enhanced dehydration management could have fewer “revolving door” effects between nursing homes and clinics. However, the perceived impact of sensorics upon interpersonal relationship between caring and cared person seems to be relevant for acceptance of professional caregivers. The view on care-givers’ acceptance and their attitudes towards implementation of technologies into a typical personalized care-relationship raise further questions on personal, organizational as all as societal aspects of acceptability.
Natural Exponent Inertia Weight based Particle Swarm Optimization for Mining Serial Episode Rules from Event Sequences

An episode rule mining to extract useful and important patterns or episodes from large event sequences represents the temporal implication of associating the antecedent and consequent episodes. The existing technique for mining precise-positioning episode rules from event sequences, mines serial episodes resulting in enormous memory consumption. To resolve this issue, the proposed work ensures the generation of fixed-gap episodes and parameter settings through the use of Particle Swarm Optimization mechanism. Fixed-gap episodes are generated using Natural Exponent Inertia Weight-based Particle Swarm Optimization algorithm. In this paper, a new technique called Mining Serial Episode Rules (MSER) is proposed, which utilizes the correlation between episodes and the generation of parameter selection where the occurrence time of an event is specified in the consequent. Further, a trie-based data structure to mine MSER along with a pruning technique is incorporated in the proposed methodology to improve the performance. The efficiency of the proposed algorithm MSER is evaluated on three benchmark data sets Retail, Kosarak, and MSNBC where the experimental results outperform the existing methods with respect to memory usage and execution time.
Human Pose Detection Based on Multi-scale and Multi-stage Structure Network

Human pose detection is essential for human behaviour recognition. In this paper, an end-to-end network is proposed to improve the precision of pose detection by fusing multi-scale features, detected parts of the human body at coarse stages and the detected human positions. The network parameters are trained uniformly by Adam optimization algorithm. A large quantity of experiments is carried out on MPII dataset and the images captured by ourselves to explore the relationship between the detection rate and the network structure. The experimental results show that the network can achieve a high detection rate.
LCTL: Lightweight Compression Template Library

For fast and efficient data processing, a common approach in many application domains is to mainly store and process data in form of arrays of integers offering several benefits. For example, with the help of some additional lightweight computations for lossless integer compression, the necessary memory space can be dramatically reduced. Moreover, compressed integer values offer advantages for data processing such as increasing the effective bandwidth to reduce the memory wall effect. Thus, a large corpus of lightweight integer compression formats has been developed. Unfortunately, there is no approach available that allows to define a specific format in a consistent way and to generate executable code for compression and decompression from it. To overcome that, we present the Lightweight Compression Template Library (LCTL) in this paper. As we are going to show, LCTL allows (i) the implementation of a variety of compression formats in an abstract way, (ii) the generation of efficient executable code for compression, and (iii) the automatic derivation of the decompression routines out of a compression format. Thus, LCTL offers a unique approach to comprehensively and systematically implement the large corpus of lightweight integer compression formats.
Doc2Vec-based Approach for Extracting Diverse Evaluation Expressions from Online Review Data

This paper proposes a method for extracting diverse expressions from online movie review texts for a given keyword query. When people watch a movie that makes them cry, they generally do not say “I cried.” Instead, they use such euphemistic language as “I needed a handkerchief” or “My makeup was running.” To enable information retrieval based on audience reactions such as “movies that make me cry” using review texts, a variety of paraphrased expressions must be collected for arbitrary queries. Our proposed method extracts such expressions from review datasets by applying two extensions to Doc2Vec: 1) it changes the granularity of the training sentences to mitigate a lack of context, and 2) it applies query expansion for similarity calculation in advance. We conducted a large-scale experiment using crowdsourcing with 1.29 million actual sentences taken from Yahoo! Movies, Japan. The experimental result revealed that changing the training data granularity and adding the query expansion are both effective to accurately collect more diverse expressions that have a meaning similar to the given query.
Intra- and Inter-frame Iterative Temporal Convolutional Networks for Video Stabilization

Video jitter is an uncomfortable product of irregular lens motion in time sequence. How to extract motion state information in a period of continuous video frames is a major issue for video stabilization. In this paper, we propose a novel sequence model, Intra- and Inter-frame Iterative Temporal Convolutional Networks (I3TC-Net), which alternatively transfer the spatial-temporal correlation of motion within and between frames. We hypothesize that the motion state information can be represented by transmission states. Specifically, we employ combination of Convolutional Long Short-Term Memory (ConvLSTM) and embedded encoder-decoder to generate the latent stable frame, which are used to update transmission states iteratively and learn a global homography transformation effectively for each unstable frame to generate the corresponding stabilized result along the time axis. Furthermore, we create a video dataset to solve the lack of stable data and improve the training effect. Experimental results show that our method outperforms state-of-the-art results on publicly available videos, such as 5.4 points improvements in stability score. The project page is available at https://github.com/root2022IIITC/IIITC.