Reviewerly/exHarmony huggingface dataset:
FINDINGS FROM A MOBILE TABLET PROJECT IMPLEMENTATION IN RURAL SOUTH AFRICA

The purpose of this paper is provide evidence of how the selected teachers at 24 schools in 7 provinces in South Africa reacted to questions pertaining to the extent of their prior training, attitudes towards technology, perceived self-confidence in integrating ICTs and finally using ICTs on a daily basis.The project is known as the Information Communications Technology for Education (ICT4E) project, funded by the Department of Rural Development and Land Reform (DRDLR) in South Africa, where teachers were trained in seven of the nine provinces in South Africa over a period of a year and a half to integrate mobile technology to support their teaching and learning at 24 schools.This project involved 197 teachers and 6895 learners.The methodology that was applied is a sequential explanatory mixed method approach where data was collected using firstly a survey (baseline survey), followed by one-to-one interviews.Data was analyzed using descriptive statistics (quantitative data) as well as coding through Nvivo (quantitative data).The main results indicated that more females (65.5%) than males were involved.Teachers mostly received training on the basics of computers in Education and are mainly using word processors for their work purposes.The average age of the teachers are 43 years and their mother tongue is mostly Setswana (21%).In general, they indicated a positive attitude towards getting training to use the mobile tablets but they did worry about their competence and knowledge in resolving technical issues if these may occur with the tablets.A high percentage indicated they use social media (96%) and 92% indicated that their schools have banned the use of a mobile phone.95.4% of teachers felt comfortable to use a tablet to enhance their teaching and learning and they can teach in more effective ways (98%).The teachers were convinced (2%) that the mobile device will assist the learners to learn more.
A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm

Particle swarm optimization (PSO) has proved fast convergence in many optimization problems but still has the main drawback - falling in a local minimum. This paper presents a new Hybrid Particle Swarm Optimization and Evolutionary algorithm (HPSO-E) to solve this problem by introducing a new population of children particles obtained by applying a mutation and crossover operators taken from the evolutionary algorithm. In this way, we connect the best properties of the algorithms: fast convergence of the PSO and ability to global search introduced by the evolutionary algorithm. The novel hybrid algorithm shows sufficient convergence for unimodal benchmark function and excellent convergence for selected hard multimodal benchmark functions.
WWW’19 Tutorial on Explainable Recommendation and Search

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, where the explanations can be either post-hoc or directly come from an explainable model. Explainable recommendation and search can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
A distributed feature selection scheme with partial information sharing

This paper introduces a novel feature selection and classification method, based on vertical data partitioning and a distributed searching architecture. The features are divided into subsets, each of which is associated to a dedicated processor that performs a local search. When all local selection processes are completed, each processor shares the features of its locally selected model with all other processors, and the local searches are repeated until convergence. Thanks to the vertical partitioning and the distributed selection scheme, the presented method is capable of addressing relatively large scale examples. The procedure is efficient since the local processors perform the selection tasks in parallel and on much smaller search spaces. Another important feature of the proposed method is its tendency to produce simple model structures, which is generally advantageous for the interpretability and robustness of the classifier. The proposed approach is evaluated and compared to other well-known feature selection and classification approaches proposed in the literature on several benchmark datasets. The obtained results demonstrate the effectiveness of the proposed approach, both in terms of classification accuracy and computational time.
Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

Dynamically dimensioned search (DDS) is a well-known optimization algorithm in the field of single solution-based heuristic global search algorithms. Its successful application in the calibration of watershed environmental parameters has attracted researcher’s extensive attention. The dynamically dimensioned search algorithm is a kind of algorithm that converges to the global optimum under the best condition or the good local optimum in the worst case. In other words, the performance of DDS is easily affected by the optimization conditions. Therefore, this algorithm has also suffered from low robustness and limited scalability. In this work, an improved version of DDS called DDS-POBL is proposed. In the DDS-POBL, two effective methods are applied to improve the performance of the DDS algorithm. Piecewise opposition-based learning is introduced to guide DDS search in the right direction, and the golden section method is used to search for more promising areas. Numerical experiments are performed on a set of 23 classic test functions, and the results represent significant improvements in the optimization performance of DDS-POBL compared to DDS. Several experimental results using different parameter values demonstrate the high solution quality, strong robustness, and scalability of the proposed DDS-POBL algorithm. A comparative performance analysis between the DDS-POBL and other powerful algorithms has been carried out by statistical methods by using the significance of the results. The results show that DDS-POBL works better than PSO, CoDA, MHDA, NaFA, and CMA-ES and gives very competitive results when compared to INMDA and EEGWO. Moreover, the parameter calibration application of the Xinanjiang model shows the effectiveness of the DDS-POBL in the real optimization problem.
Labeling Analysis in the Classification of Product Review Sentiments by using Multinomial Naive Bayes Algorithm

Along with the development of technology, e-commerce also experienced a fairly rapid development. The existence of e-commerce becomes another consumer alternative to make it easier for them to fulfill their needs. After buying the goods, consumers are free to assess the products they buy. Product reviews and ratings provided by consumers are one means that can be used to increase sales and can also be used to determine the decision in purchasing a product by reading the product reviews. However, using ratings and reviews alone is not enough to summarize one's opinion. Therefore, in this Final Project built a system that can classify opinions on product reviews into positive and negative sentiments by utilizing the rating. The dataset used is Grocery and Gourmet Food from Amazon as much as 50,000 which will then be labeled using Labeling Methods Average and Binary. The classification of this opinion uses the approach of Supervised learning Algorithm Multinomial Naïve Bayes. The result of this research shows that labeling using Method Average is suitable for processing Grocery and Gourmet Food Dataset and proves that the best ratio of feature selection usage is 20% succeed to produce 80.48% accuracy.
Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories

System Testing of Single-Arm Robots (SAR) is challenging as typical SAR involve multiple coordinated software controlled subsystems, such as motion and action control, perception and anti-collision systems. Developing convincing test scenarios which place the SAR into highly CPU-demanding cases is complicated due to the huge number of possible robots' workspace configurations. In this paper, we introduce RobTest, a tool-supported method for stress testing of SAR, which generates automatically optimal collision-free trajectories. Initially specified by a cloud of points and a set of obstacles, these trajectories are piecewise linear paths in a cost-labelled oriented graph. By using advanced Constraint Programming (CP) techniques, such as constraint refutation over continuous domains and constraint optimization over graphs, RobTest can generate continuous trajectories which 1) avoid physical obstacles and 2) maximize the load of the various CPUs of the SAR. These trajectories result into automatically robot computer programs which place the SAR into high-demanding test scenarios. Our initial experimental evaluation of RobTest shows promising results.
An Efficient Content Based Image Retrieval Scheme with Preserving the Security of Images

In this paper, an efficient content based image retrieval scheme is proposed which incorporates the security of images in a retrieval system. The main contribution of the proposed scheme is two folded that is to develop an efficient image retrieval process, and to provide security to the retrieved images. To achieve this goal, features from the foreground region as well as background features are extracted, where more importance is given to the foreground region which is identified based on Itti Koch Saliency Map. From the obtained foreground region texture features along with color features are extracted. Simultaneously, the background is divided into four regions and color features are extracted. Finally, the foreground and background features are combined to perform image retrieval process. Now to prevent the feature vector from illegitimate users, suitable cryptographic approaches are used to provide security in terms of confidentiality and integrity. To protect the integrity, the hash value of the feature vector is computed and the obtained hash value along with feature vector are enciphered together. This enciphered value is then communicated to the user. The content owner verifies the integrity of the feature and performs the retrieval process. The retrieved results are then watermarked to protect copyright and are further enciphered so that unauthorized user's cannot access the images. This security mechanism ensures that the retrieved images are shared only to the authorized user. The retrieval process is performed on Corel dataset and the results are comparable to the existing state-of-arts methods. Finally, this process is suitable to protect the security of images in a retrieval system.
Optimized training of deep neural network for image analysis using synthetic objects and augmented reality

Acquiring large amounts of data for training and testing Deep Learning (DL) models is time consuming and costly. The development of a process to generate synthetic objects and scenes using 3D graphics software is presented. By programming the path and environment in a 3D graphical engine, complex objects and scenes can be generated for the purpose of training and testing a Deep Neural Network (DNN) model in specific vision tasks. An automatic process has been developed to label and segment objects in synthetic images and generate their corresponding ground truth files. Performances of DNNs trained with synthetic data have been shown to outperform DNNs trained with real data.
An improved N-grams based Model for Authorship Attribution

Authorship attribution is the problem of studying an anonymous text and finding the corresponding author in a set of candidate authors. In this paper, we propose a method based on N-grams model for the problem of authorship attribution. Several measures are used to assign an anonymous text to an author. The different variants of the proposed method are implemented and validated on PAN benchmarks. The numerical results are encouraging and demonstrate the benefit of the proposed idea.
Annotation projection for temporal information extraction

Abstract Approaches to building temporal information extraction systems typically rely on large, manually annotated corpora. Thus, porting these systems to new languages requires acquiring large corpora of manually annotated documents in the new languages. Acquiring such corpora is difficult owing to the complexity of temporal information extraction annotation. One strategy for addressing this difficulty is to reduce or eliminate the need for manually annotated corpora through annotation projection. This technique utilizes a temporal information extraction system for a source language (typically English) to automatically annotate the source language side of a parallel corpus. It then uses automatically generated word alignments to project the annotations, thereby creating noisily annotated target language training data. We developed an annotation projection technique for producing target language temporal information extraction systems. We carried out an English (source) to French (target) case study wherein we compared a French temporal information extraction system built using annotation projection with one built using a manually annotated French corpus. While annotation projection has been applied to building other kinds of Natural Language Processing tools (e.g., Named Entity Recognizers), to our knowledge, this is the first paper examining annotation projection as applied to temporal information extraction where no manual corrections of the target language annotations were made. We found that, even using manually annotated data to build a temporal information extraction system, F -scores were relatively low (&lt;0.35), which suggests that the problem is challenging even with manually annotated data. Our annotation projection approach performed well (relative to the system built from manually annotated data) on some aspects of temporal information extraction (e.g., event–document creation time temporal relation prediction), but it performed poorly on the other kinds of temporal relation prediction (e.g., event–event and event–time).
The GDPR and Its Application in Connected Vehicles—Compliance and Good Practices

Symbol of the 20th century economy, the automobile is one of the mass consumer products that has impacted society as a whole. Commonly associated with the notion of freedom, cars are often considered as more than just a mean of transportation. Indeed, they represent a private area in which people can enjoy a form of autonomy of decision, without encountering any external interferences. Today, as connected vehicles move into the mainstream, such a vision no longer corresponds to the reality. In-vehicle connectivity is rapidly expanding from luxury models and premium brands to high-volume midmarket models, and vehicles are now massive data hubs. Not only vehicles, but drivers and passengers are also becoming more and more connected. As a matter of fact, many models launched over the past few years on the market integrate sensors and connected on board equipment, which may collect and record, among other things, the engine performance, the driving habits, the locations visited, and potentially even the driver’s eye movements, or other biometric data for authentication purposes. Such data processing is taking place in a complex ecosystem, which is not limited to the traditional players of the automotive industry, but is also shaped by the emergence of new players belonging to the digital economy. Aware of the issues at stake for the protection of motorists’ privacy in this ecosystem, the Commission Nationale de l’Informatique et des Libertés (CNIL)—the French data protection authority—developed a reference framework enabling professionals to comply with the General Data Protection Regulation (GDPR), applicable as from 25 May 2018, thus making the compliance simpler and ensuring that users enjoy transparency and control in relation to their data.
Newly-Coined Words and Emoticon Polarity for Social Emotional Opinion Decision

Nowadays, based-on mobile devices and internet, social network services(SNS) are common trends to everyone. Thus, decision of social and public opinions, and polarity about social happenings, political issues, government policies and decision, or commercial products is very important to the government, company, and a person. But, SNS are basically making newly-coined words and emoticons. Especially, emoticons are made by a person or companies. Newly-coined words are mostly made by communities. The SNS big data mainly consists of his kinds of newly-coined words and emoticons so that newly-coined words and emoticons analysis are very important to understand the social and public opinions, and polarity about social happenings, political issues, government policies and decision, or commercial products. Social big data is unstructured data and contains many newly-coined words and various emoticons. Therefore, there is a limitation to guarantee the accuracy and analysis range of social data of emotional analysis. The newly-coined words contains the social phenomena and trends of modern society implicitly. And the emoticons are electronic quasi-languages made up of letters and symbols, and express the emotional state more implicitly. Although the newly-coined words and emoticons are an important part of the emotional analysis, they are excluded from the emotional dictionary and analysis. In this research, newly-coined words and emoticons extracted from the raw twit messages include polarity and weight with pre-built dictionary. The polarity and weight would be calculated for emotional classification. The proposed emotional classification equation adds up the weights among the same polarity(positive or negative) and sums the negative weight value with the positive weight values. The polarity summation result is recorded in the variable. If the polarity summation result is more than threshold value, the twit message is decided as positive. If it is less than threshold value, it is decided as negative and the other values are decided as neutral. The accuracy of social big data analysis is improved by quantifying and analyzing emoticons and new-coined words.
Regularized Fuzzy <i>c</i>-Means Clustering and its Behavior at Point of Infinity

This study shows that a general regularized fuzzy c -means (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.
New Adaptive Multi-Memetic Global Optimization Agorithm

This paper deals with the Simple MEC (SMEC) algorithm which belongs to a class of MEC algorithms. The algorithm was selected for investigation due to the following reasons: nowadays this algorithm and its modifications are successfully used for solving various optimization problems; the algorithm is highly suitable for parallel computations, especially for loosely coupled systems; the algorithm is not sufficiently studied --- there are relatively few modifications of SMEC (while, for instance, tens of various modifications are known for particle swarm optimization). Authors proposed an adaptive multi-memetic modification of SMEC algorithm, which includes a stage of landscape analysis for composing a set of basic adaptation strategies; software implementation of the algorithm is also presented. Performance investigation was carried out with a use of multi-dimensional benchmark functions of different classes. It was demonstrated that the concept of multi-population along with the incorporated landscape analysis procedure allows making a rough static adaptation of the algorithm to the objective function at the very beginning of evolution process at the cost of small computational expenses. Utilization of memes, in turn, helps the algorithm to correct possible errors of static adaptation during the evolution due to a closer investigation of search sub-domains
How Empowering Is Citizen Science? Access, Credits, and Governance for the Crowd

Web-based crowdsourced citizen science is an efficient method for scientists to collect and process data. Although lay persons obtain the opportunities to participate in research and engage with scientists, these crowdsourced projects generally maintain the traditional hierarchy of academic science. Lay persons have little say in project or platform governance, and institutional tools to hold project investigators accountable are almost nonexistent. This article examines how existing institutional policies address the question of distribution in crowdsourced citizen science, as it may further affect lay participants’ role in the institution of scientific knowledge production and their access to research resources. This article begins by comparing the norms developed by citizen-science institutions. It then discusses examples from Galaxy Zoo to see how the results of research projects are distributed, both in the form of access to research outcome and in authorship. The article also discusses the potential conflicts that arise when crowdsourced projects are organized by for-profit companies and why citizen-science platforms should develop institutional norms to avoid such conflicts.
Relational Neurogenesis for Lifelong Learning Agents

Reinforcement learning systems have shown tremendous potential in being able to model meritorious behavior in virtual agents and robots. The ability to learn through continuous reinforcement and interaction with an environment negates the requirement of painstakingly curated datasets and hand crafted features. However, the ability to learn multiple tasks in a sequential manner, referred to as lifelong or continual learning, remains unresolved. Current implementations either concentrate on preserving information in fixed capacity networks, or propose incrementally growing networks which randomly search through an unconstrained solution space. This work proposes a novel algorithm for continual learning using neurogenesis in reinforcement learning agents. It builds upon existing neuroevolutionary techniques, and incorporates several new mechanisms for limiting the memory resources while expanding neural network learning capacity. The algorithm is tested on a custom set of sequential virtual environments which emulate meaningful and relevant scenarios.