Reviewerly/exHarmony huggingface dataset:
Perspectives of Consent Silence in Cyberbullying

Weather the silence of bystanders is consent or not in cyberbullying type incidents has risen a scientifically debate in the field of victim-aggressor-witness dynamics research.Silence implies consent represents a concept of social interaction, which generally states that people tend to assume lack of a response or opinion to an action as a tacit approval of that action.Our research team has developed the project Keeping youth safe from Cyberbullying, aiming to deeper understand the dynamics of different cyberbullying aspects in online environments among youth.Our focus was in analyzing the relationship between consent silence, seen as a core concept in bystanders' mindset and other psychological or behavioral traits like awareness, anonymity, fear, shame, school popularity and self-worth that fuel the tank of silence, in 140 Romanian high school students.Results show that consent silence represents the most significant predictor of future bystander, aggressor or victim type of response behavior when youth are directly or indirectly involved in cyberbullying incidents.Conclusions and implications are discussed.
Robust to Noise Models in Natural Language Processing Tasks

There are a lot of noisy texts surrounding a person in modern life.A traditional approach is to use spelling correction, yet the existing solutions are far from perfect.We propose a robust to noise word embeddings model which outperforms existing commonly used models like fasttext and word2vec in different tasks.In addition, we investigate the noise robustness of current models in different natural language processing tasks.We propose extensions for modern models in three downstream tasks, i.e. text classification, named entity recognition and aspect extraction, these extensions show improvement in noise robustness over existing solutions.
Learning Domain-Independent Deep Representations by Mutual Information Minimization

Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the distributions of source and target domain representations to be similar, which heavily relies on the characterization of the distributions of domains and the distribution matching criteria. In this paper, we proposed a novel framework for domain transfer representation learning. Our motive is to make the learned representations of data points independent from the domains which they belong to. In other words, from an optimal cross-domain representation of a data point, it is difficult to tell which domain it is from. In this way, the learned representations can be generalized to different domains. To measure the dependency between the representations and the corresponding domain which the data points belong to, we propose to use the mutual information between the representations and the domain-belonging indicators. By minimizing such mutual information, we learn the representations which are independent from domains. We build a classwise deep convolutional network model as a representation model and maximize the margin of each data point of the corresponding class, which is defined over the intraclass and interclass neighborhood. To learn the parameters of the model, we construct a unified minimization problem where the margins are maximized while the representation-domain mutual information is minimized. In this way, we learn representations which are not only discriminate but also independent from domains. An iterative algorithm based on the Adam optimization method is proposed to solve the minimization to learn the classwise deep model parameters and the cross-domain representations simultaneously. Extensive experiments over benchmark datasets show its effectiveness and advantage over existing domain transfer learning methods.
Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles

Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders.
Ease.ml/ci and Ease.ml/meter in action

Developing machine learning (ML) applications is similar to developing traditional software --- it is often an iterative process in which developers navigate within a rich space of requirements, design decisions, implementations, empirical quality , and performance . In traditional software development, software engineering is the field of study which provides principled guidelines for this iterative process. However, as of today, the counterpart of "software engineering for ML" is largely missing --- developers of ML applications are left with powerful tools (e.g., TensorFlow and PyTorch) but little guidance regarding the development lifecycle itself. In this paper, we view the management of ML development life-cycles from a data management perspective. We demonstrate two closely related systems, ease.ml/ci and ease.ml/meter, that provide some "principled guidelines" for ML application development: ci is a continuous integration engine for ML models and meter is a "profiler" for controlling overfitting of ML models. Both systems focus on managing the "statistical generalization power" of datasets used for assessing the quality of ML applications, namely, the validation set and the test set . By demonstrating these two systems we hope to spawn further discussions within our community on building this new type of data management systems for statistical generalization.
Lower Bounds for Adversarially Robust PAC Learning

In this work, we initiate a formal study of probably approximately correct (PAC) learning under evasion attacks, where the adversary's goal is to \emph{misclassify} the adversarially perturbed sample point $\widetilde{x}$, i.e., $h(\widetilde{x})\neq c(\widetilde{x})$, where $c$ is the ground truth concept and $h$ is the learned hypothesis. Previous work on PAC learning of adversarial examples have all modeled adversarial examples as corrupted inputs in which the goal of the adversary is to achieve $h(\widetilde{x}) \neq c(x)$, where $x$ is the original untampered instance. These two definitions of adversarial risk coincide for many natural distributions, such as images, but are incomparable in general. We first prove that for many theoretically natural input spaces of high dimension $n$ (e.g., isotropic Gaussian in dimension $n$ under $\ell_2$ perturbations), if the adversary is allowed to apply up to a sublinear $o(||x||)$ amount of perturbations on the test instances, PAC learning requires sample complexity that is exponential in $n$. This is in contrast with results proved using the corrupted-input framework, in which the sample complexity of robust learning is only polynomially more. We then formalize hybrid attacks in which the evasion attack is preceded by a poisoning attack. This is perhaps reminiscent of "trapdoor attacks" in which a poisoning phase is involved as well, but the evasion phase here uses the error-region definition of risk that aims at misclassifying the perturbed instances. In this case, we show PAC learning is sometimes impossible all together, even when it is possible without the attack (e.g., due to the bounded VC dimension).
When to use parametric models in reinforcement learning?

We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric models and experience replay. Replay-based learning algorithms share important traits with model-based approaches, including the ability to plan: to use more computation without additional data to improve predictions and behaviour. We discuss when to expect benefits from either approach, and interpret prior work in this context. We hypothesise that, under suitable conditions, replay-based algorithms should be competitive to or better than model-based algorithms if the model is used only to generate fictional transitions from observed states for an update rule that is otherwise model-free. We validated this hypothesis on Atari 2600 video games. The replay-based algorithm attained state-of-the-art data efficiency, improving over prior results with parametric models.
Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.
Registration Algorithm Based on Line-Intersection-Line for Satellite Remote Sensing Images of Urban Areas

Image registration is an important step in remote sensing image processing, especially for images of urban areas, which are often used for urban planning, environmental assessment, and change detection. Urban areas have many artificial objects whose contours and edges provide abundant line features. However, the locations of line endpoints are greatly affected by large background variations. Considering that line intersections remain relatively stable and have high positioning accuracy even with large background variations, this paper proposes a high-accuracy remote sensing image registration algorithm that is based on the line-intersection-line (LIL) structure, with two line segments and their intersection. A double-rectangular local descriptor and a spatial relationship-based outlier removal strategy are designed on the basis of the LIL structure. First, the LILs are extracted based on multi-scale line segments. Second, LIL local descriptors are built with pixel gradients in the LIL neighborhood to realize initial matching. Third, the spatial relations between initial matches are described with the LIL structure and simple affine properties. Finally, the graph-based LIL outlier removal strategy is conducted and incorrect matches are eliminated step by step. The proposed algorithm is tested on simulated and real images and compared with state-of-the-art methods. The experiments prove that the proposed algorithm can achieve sub-pixel registration accuracy, high precision, and robust performance even with significant background variations.
Evaluating experiment design with unrepeated scenes for video quality subjective assessment

The conventional video subjective test design, in which subjects view and rate multiple versions of each source video sequence, was used for decades. New technology, like adaptive streaming, makes it almost impossible to use this design since much longer sequences are needed. In this paper we examine three experiment designs: the conventional design and two alternatives that use each source sequence only once. Based on data collected by three laboratories, we compare the accuracy and scoring behavior of these three designs. We check whether there is a significant difference in scoring behavior between the experiment designs. One of the proposed experiment designs is proposed for immediate use.
A Study on Sequential K-Nearest Neighbor (SKNN) Imputation for Treating Missing Rainfall Data

This paper demonstrates a novel application of a gene imputation model, Sequential K-Nearest Neighbor (SKNN) imputation model to address the issues of missing rainfall data in Kuching City.To determine the reliability and robustness of SKNN imputation model in treating the missing rainfall data, an experiment was done to compare the imputation performance of SKNN against a conventional imputation model, K-Nearest Neighbor (KNN).The experiment was conducted using datasets with different missing entries (1%, 5%, 10%, 15%, and 20% of missing data entries).The datasets were created by artificially introducing the missing entries into a complete rainfall dataset.The imputation performance of the imputation models was evaluated with respect to Bias (B S ), Root Mean Square Error (RMSE), Coefficient of Correlation (r), and Index of Agreement (d).The SKNN was found to be superior to KNN in terms of accuracy and imputation performance.It was also reported that RMSE and B S can express the relationship of missing data entries and imputation performance significantly.
Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Objective: The objective of this study is to develop a novel algorithm on a mobile system that can warn drivers about the possibility of a collision with a pedestrian. The constraints of the algorithm are near-real-time detection speed and a good detection rate.Method: Histogram of gradients (HOG)-based detection is widely used in pedestrian safety applications; however, it has low detection speed for real-time systems. Hence, it has no direct usage for mobile systems. In order to achieve near-real-time detection speed, partial Haar transform predetections are applied to an image before HOG detection. The partial and HOG detections are merged and a score-based confidence level is defined for the final detection phase. In this way, the outcome is prioritized and different warning levels can be issued to warn the driver before a possible pedestrian collision.Results: The proposed algorithm provides an increase in detection speed (from 46 to 76 fps) and detection rate (from 80 to 91%) with respect to HOG-based pedestrian detection. It also improves confidence of the results by multidetection merging and score assignment to detections.Conclusions: Performance improvement of the algorithm is compared with respect to state-of-the-art detectors/algorithms. Based on the detection rate and detection speed performance, it can be concluded that the proposed algorithm is suitable to be used for mobile systems to warn drivers about the possibility of collision with a pedestrian.
DevDocOps: Towards Automated Documentation for DevOps

The proliferation of DevOps enables significant acceleration and automation of the delivery and deployment of massive software products. Unfortunately, the development of supporting documents that is vital for large scale software systems in many cases does not keep pace with the rhythm of feature delivery using DevOps in practice, which becomes the bottleneck for many software organizations to deliver full value to the customers as claimed by DevOps. This paper proposes, implements, and evaluates a new approach, DevDocOps, for continuous automated documentation, in particular for DevOps. With DevDocOps, developers are able to create the documents simultaneously with their working versions of software, which largely guarantees the accuracy and integrity of documents as well as significantly increases their delivery speed. Within an established delivery chain, a set of templates are created to collect and transform the required information from its origin to the target documents for delivery. A real system, iDoc, is implemented to map, collect, and synthesize the information from document templates and automate the documentation process. The iDoc system supports the generation of documents in minutes and the instant feedback loop as well. DevDocOps has been successfully adopted in over 30 large software projects in a top tier global telecommunication enterprise. The lag time between the releases of the product version and its supporting document has been shortened from 1-2 months on average to less than 2 days. DevDocOps extends the scope of DevOps and enhances the value delivery by supporting continuous documentation and bridging the gap between feature delivery and document delivery with automation.
Advantages and Disadvantages of the Webometrics Ranking System

Today, there are several well-known global ranking lists for ranking universities in the world.While some of them ranked only a few hundreds of best and most influential universities, there are those that include a much larger number of ranking scientific institutions.One such global list which ranks the largest number of scientific institutions and scientists in the world is called Webometrics list.This list is very important for less developed economies and developing countries which have not established a sufficient quality control system of higher education quality, so this list serves as a corrective to the international evaluation of a wide range of universities in the world.In such a complex IT system of ranking an extremely large number of institutions and scientists, this system shows some disadvantages when ranking, which of course can be overcome by introducing certain improvements within the system of ranking.Systems that perform the collection, analysis, and indexing data have their advantages and disadvantages, which can sometimes lead to a misinterpretation of the data collected.Among other things, we will consider the possible solutions which would improve the rating system and prevent possible manipulation and uncertainty in the presentation of current and final results ranking.
Gender Prediction from Classified Indoor Customer Paths by Fuzzy C-Medoids Clustering

Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths.
Use of Clustering Sentiments for Opinion Mining: An Experimental Analysis

In E-commerce, the social media network analytics are in a key position for the extraction of a service or product information. Opinion mining is now the key ingredient for social network analytics. In the current study, we study opinion mining process in networks while different kinds of documents are dealt with that are opinionated and a formal discussion on the challenges for sentiment analysis via social networks is being updated [1]. The social network Twitter has now become a huge online platform which has millions of people with their opinions shared as a social activity [2]. One major concern is how to get reviews for products on the basis of its features. The application of the K-means algorithm on a dataset sample from twitter and twitter dataset which will be clustered by different opinions in the context of various product features and has been evaluated along with explanation by the hand of a machine learning tool.
Multi Sampling Random Subspace Ensemble for Imbalanced Data Stream Classification

The classification of data streams is a frequently considered problem. The data coming in over time has a tendency to change its characteristics over time and usually we also encounter some difficulties in data distributions as inequality of the number of learning examples from considered classes. The combination of these two phenomena is an additional challenge. In this article, we propose a novel MSRS (Multi Sampling Random Subspace Ensemble) a chunk-based ensemble method for imbalanced non-stationary data stream classification. The proposed algorithm employs random subspace approach and balancing data using various sampling methods to ensure an appropriate diversity of the classifier ensemble. MSRS has been evaluated on the basis of the computer experiments carried out on the diverse pool of the non-stationary imbalanced data streams.
Encounters with Self-Monitoring Data on ICT Use

Abstract This article elaborates on the prospects for research interventions that repurpose the means of datafication to create possibilities for people to reflect on what it means in their daily lives. The research data consist of qualitative research interviews (n=13) in which media diaries and tracking data from the participants’ smartphones and computers served as prompts for reflection. The experiences from the self-monitoring and the encounters with tracked data by self-identified avid ICT users are analysed to gain a better understanding of the kinds of possibilities for reflexivity that are enabled when people have access to data that are rarely available to them.
41st European Conference on Visual Perception (ECVP) 2018 Trieste

Free accessAbstractFirst published online May 2, 201941st European Conference on Visual Perception (ECVP) 2018 TriesteVolume 48, Issue 1_supplhttps://doi.org/10.1177/0301006618824879View correction