Reviewerly/exHarmony huggingface dataset:
God and Governance: Reflections on Living in the Belly of the Beast

In this critical rage article, Peter McLaren unleashes his revolutionary critique aimed at capitalist injustice behind postdigital socio-technological developments, historical forms of injustice such as racism and colonialism, and recent political events and developments including but not limited to US interventions in Latin America and the presidency of Donald Trump. Rising from two important prongs of McLaren’s work—revolutionary critical pedagogy and liberation theology—the article connects myth, religion, science, politics, technology, and humanity. The article reveals McLaren’s most intimate thoughts and experiences and aligns them with sophisticated theory and philosophy. It dances between the individual and the collective, the realistic and the utopian, the scientific and the mystical. This uniquely written article, which constantly hovers between extremes in the dialectic of love and rage, simultaneously develops theoretical discourse and practical action of revolutionary critical pedagogy.
Image Vectorization With Real-Time Thin-Plate Spline

The vector graphics with gradient mesh can be attributed to their compactness and scalability; however, they tend to fall short when it comes to real-time editing due to a lack of real-time rasterization and an efficient editing tool for image details. In this paper, we encode global manipulation geometries and local image details within a hybrid vector structure, using parametric patches and detailed features for localized and parallelized thin-plate spline interpolation in order to achieve good compressibility, interactive expressibility, and editability. The proposed system then automatically extracts an optimal set of detailed color features while considering the compression ratio of the image as well as reconstruction error and its characteristics applicable to the preservation of structural and irregular saliency of the image. The proposed real-time vector representation makes it possible to construct an interactive editing system for detail-maintained image magnification and color editing as well as material replacement in cross mapping, without maintaining spatial and temporal consistency while editing in a raster space. Experiments demonstrate that our representation method is superior to several state-of-the-art methods and as good as JPEG, while providing real-time editability and preserving structural and irregular saliency information.
Multi-Objective PSO Based Task Scheduling - A Load Balancing Approach in Cloud

Cloud computing is becoming one of the most disruptive forces that are attracting more and more customers towards it for various kinds of services. The increasing demand for cloud computing technology has given rise to network traffic also, hence it is important to balance the workload arising in the network. Load balancing is necessary for the efficient working of cloud services. Load balancing in the cloud can be both static as well as dynamic in nature. In the current scenario, the ability of cloud systems to adapt to changing conditions is necessary, hence dynamic load balancing is emerging as a hot topic. Load balancing can be either done by task scheduling or virtual machine migration. In this paper, a multi-objective particle swarm optimization for task scheduling is proposed. The objectives taken are makespan time, deadline and cost of communication. The experimental result has shown that the proposed method helped in reducing the makespan time, cost of communication and also completing the task within the deadline.
Enhanced Homomorphic Encryption Scheme with PSO for Encryption of Cloud Data

Cloud computing can be described as a distributed design that is accessible to different forms of security intrusions. An encoding technique named homomorphic encoding is used for the encoding of entities which are utilized for the accession of data from cloud server. The main problems of homomorphic encoding scheme are key organization and key allocation. Because of these issues, effectiveness of homomorphic encryption approach decreases. The encoding procedure requires the generation of input, and for this, an approach named Particle swarm optimization is implemented in the presented research study. PSO algorithms are nature encouraged meta-heuristic algorithms. These algorithms are inhabitant reliant. In these algorithms, societal activities of birds and fishes are utilized as an encouragement for the development of a technical mechanism. Relying on the superiority of computations, the results are modified with the help of algorithms which are taken from arbitrarily allocated pattern of particles. With the movement of particles around the searching area, the spontaneity is performed by utilizing a pattern of arithmetical terminology. For the generation of permanent number key for encoding, optimized PSO approach is utilized. MATLAB program is used for the implementation of PSO relied homomorphic algorithm. The investigating outcomes depicts that this technique proves very beneficial on the requisites of resource exploitation and finishing time. PSO relied homomorphic algorithm is more applicable in terms of completion time and resource utilization in comparison with homomorphic algorithm.
Leveraging Additional Resources for Improving Statistical Machine Translation on Asian Low-Resource Languages

Phrase-based machine translation (MT) systems require large bilingual corpora for training. Nevertheless, such large bilingual corpora are unavailable for most language pairs in the world, causing a bottleneck for the development of MT. For the Asian language pairs—Japanese, Indonesian, Malay paired with Vietnamese—they are also not excluded from the case, in which there are no large bilingual corpora on these low-resource language pairs. Furthermore, although the languages are widely used in the world, there is no prior work on MT, which causes an issue for the development of MT on these languages. In this article, we conducted an empirical study of leveraging additional resources to improve MT for the Asian low-resource language pairs: translation from Japanese, Indonesian, and Malay to Vietnamese. We propose an innovative approach that lies in two strategies of building bilingual corpora from comparable data and phrase pivot translation on existing bilingual corpora of the languages paired with English. Bilingual corpora were built from Wikipedia bilingual titles to enhance bilingual data for the low-resource languages. Additionally, we introduced a combined model of the additional resources to create an effective solution to improve MT on the Asian low-resource languages. Experimental results show the effectiveness of our systems with the improvement of +2 to +7 BLEU points. This work contributes to the development of MT on low-resource languages, especially opening a promising direction for the progress of MT on the Asian language pairs.
Optimization of Resource Allocation in Cloud Computing by Grasshopper Optimization Algorithm

Cloud computing system due to Pay-Per-Use Model has been popular among Cloud resource users. However, large volume of resource and requests from users have made the issue of resource allocation challenging in this kind of system. Therefore, the present paper aims to recognize the role of innovative Grasshopper Optimization Algorithm (GOA) and strongly highlights the significance of such an algorithm for optimized resource allocation in a Cloud computing environment. To do so, the proposed algorithm (i.e., GOA) was simulated with MATLAB and eight datasets were used. Moreover, GOA was compared with GA and SEIRA algorithms in order to have precise evaluation of its performance. Results strongly acknowledged the application of the proposed GOA and highlighted its high ability to solve the resource allocation problem in Cloud computing. Findings also revealed that the functions designed for the basic operators of the GOA could appropriately look into the space of the problem response, resulting in optimization of the discovered responses, and finally providing opportunities to obtain an acceptable response regarding the allocation problem. It was undeniably recommended that other optimization algorithms can be investigated and compared with GOA in order for the users and service providers to be armed with practical solutions concerning the resource allocation problem.
Personalized Persuasion to Promote Positive Work Attitudes in Public Workplaces

This paper investigates how mobile persuasive system targeting African audience could be designed and tailored to promote employee's commitment to the ideals, visions, and mission of an organization. We conduct a qualitative study with two categories of workers to uncover core factors that influence employee's attitudes to their jobs and map our findings to their matching social influence persuasive techniques. We propose that a persuasive system (PS) employing the social influence strategies could motivate workers towards acceptable positive pro-workplace behaviors and etiquette. The PS allows workers too compare their behaviors against set goals and acceptable standards, compete and compare performances with peers, view and respond to peers' activities, and receive recognition for accomplishing a target task. The system ensures the security of worker's data via the authentication of login credentials while showing them a personalized persuasive display of essential workplace information. We present a prototype persuasive system called "PAULApp" for motivating pro-workplace behaviors and plans for evaluation. PAULApp was designed using the iterative design process and was informed by the findings from the user studies.
Generalizability of Methods for Imputing Mathematical Skills Needed to Solve Problems from Texts

Identifying the mathematical skills or knowledge components needed to solve a math problem is a laborious task. In our preliminary work, we had two expert teachers identified knowledge components of a state-wide math test and they only agreed only on 35% of the items. Previous research showed that machine learning could be used to correctly tag math problems with knowledge components at about 90% accuracy over more than 100 different skills with five-fold cross-validation. In this work, we first attempted to replicate that result with a similar dataset and were able to achieve a similar cross-validation classification accuracy. We applied the learned model to our test set, which contains problems in the same set of knowledge component definitions, but are from different sources. To our surprise, the classification accuracy dropped drastically from near-perfect to near-chance. We identified two major issues that cause of the original model to overfit to the training set. After addressing the issues, we were able to significantly improve the test accuracy. However, the classification accuracy is still far from being usable in a real-world application.
Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks

Feature extraction is a critical stage of digital speech processing systems. Quality of features is of great importance to provide a solid foundation upon which the subsequent stages stand. Distinctive phonetic features (DPFs) are one of the most representative features of the speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme's DPF element reflects unique articulatory information about that phoneme. Therefore, there is a need to discover and investigate each DPF element individually in order to achieve a deeper understanding and to come up with a descriptive model for each one. Such fine-grained modeling will satisfy the uniqueness of each DPF element. In this paper, the problem of DPF modeling and extraction of modern standard Arabic is tackled. Due to the remarkable success of deep neural networks (DNNs) that are initialized using deep belief networks (DBNs) in serving DSP applications and its capability of extracting highly representative features from the raw data, we exploit its modeling power to investigate and model the DPF elements. DNN models are compared with the classical multilayer perceptron (MLP) models. The representativeness of several acoustic cues for different DPF elements was also measured. This paper is based on formalizing DPF modeling problem as a binary classification problem. Because the DPF elements are highly imbalanced data, evaluating the quality of models is a very tricky process. This paper addresses the proper evaluation measures satisfying the imbalanced nature of the DPF elements. After modeling each element individually, the two top-level DPF extractors are designed: MLP- and DNN-based extractors. The results show the quality of DNN models and their superiority over MLPs with accuracies of 89.0% and 86.7%, respectively.
Analysis of hand-crafted and learned feature extraction methods for real-time facial expression recognition

This paper presents an analysis of hand-crafted and learned feature extraction methods for real-time facial expression recognition (FER). Our analysis focuses on methods capable of running on mobile devices, including traditional algorithms such as Gabor transform, HOG, LBP, as well as two compact CNN models, named Mobilenet V1 and V2. Additionally, we test the performance of MOTIF, a highly efficient texture feature extractor algorithm. Furthermore, we analyze the contribution of the mouth and front-eyes regions for recognizing the seven basic facial expressions. Experimental results are evaluated on two publicly available datasets. KDEF database which was captured under controlled conditions and RAF database which represents more naturalistic expressions captured in-the-wild. Under the same experimental conditions, MOTIF presents the fastest performance by sacrificing accuracy, while Mobilenet V2 presents the highest results with considerable speed and model size.
End-to-End Speech Recognition Sequence Training With Reinforcement Learning

End-to-end sequence modeling has become a popular choice for automatic speech recognition (ASR) because of the simpler pipeline compared to the conventional system and its excellent performance.However, there are several drawbacks in the end-to-end ASR model training where the current time-step prediction on the target side are conditioned with the ground truth transcription and speech features.In the inference stage, the condition is different because the model does not have any access to the target sequence ground-truth, thus any mistakes might be accumulated and degrade the decoding result over time.Another issue is raised because of the discrepancy between training and evaluation objective.In the training stage, maximum likelihood estimation criterion is used as the objective function.However, the ASR systems quality is evaluated based on the word error rate via Levenshtein distance.Therefore, we present an alternative for optimizing end-to-end ASR model with one of the reinforcement learning method called policy gradient.The model trained the proposed approach has several advantages: (1) the model simulates the inference stage by free sampling process and uses its own sample as the input, and; (2) optimize the model with a reward function correlated with the ASR evaluation metric (e.g., negative Levenshtein distance).Based on the result from our experiment, our proposed method significantly improve the model performance compared to a model trained only with teacher forcing and maximum likelihood objective function.
Tangiball

Tangible interaction in virtual reality (VR) is known to offer several benefits. Although using tangible objects as controllers in VR is common, incorporating real-life tangible objects for dynamic embodied interactions is not a well-researched area. To that end, we propose a tangible soccer game system (?Tangiball') in room-scale VR, in which users interact with a tangible ball with their feet in real-time by only seeing its virtual representation inside a head mounted display. Tangiball includes a custom-built transparent tangible ball, inside which motion tracking markers are placed using a custom 3D printed attachment. The uniqueness of the Tangiball lies in the dynamic embodied tangible VR interaction it offers. This paper includes the design decisions and iterations, system details and lessons learned. The results of the pilot testing are promising, as users engaged with the Tangiball effortlessly and intuitively and mentioned that it was playful.
Positive Deviance as Design Thinking: Challenging Notions of Stasis in Technical and Professional Communication

In design thinking, extreme users have found work-arounds for common problems, but they are few in number and often overlooked in toolkits and write-ups. This article posits that positive deviance, an approach to social and behavioral change that is compatible with design thinking, offers technical and professional communicators an accessible and innovative methodology for engaging extreme users. The authors analyze a case study of how the positive deviance approach was used to address federal recidivism on the U.S.–Mexico border. They conducted a positive deviance inquiry to arrive at the everyday replicable behaviors that enabled released individuals to complete their terms of supervised release successfully, despite the odds against them and without access to special resources. The authors conclude by discussing the value and implications of focusing on extreme users.
No-reference image quality assessment focusing on human facial region

A person’s face is the part that we are most interested in when we look at images. If the quality of the human facial regions in focus is poor, even if the quality of other regions is quite better, we are likely to badly evaluate the quality of the image. Thus, to exactly predict perceptual quality of images, the facial regions have to be specially tackled. However, the previous image quality assessment (IQA) methods did not specifically address the facial area. In this paper, we propose a no-reference (NR) IQA metric that uses global features of an image and local features extracted from detected facial regions by a convolutional neural network (CNN) based tiny face detector. Two regressors are used for the global features and the local facial features, respectively. The computed two scores are combined into a total quality score. Promising six IQA databases are utilized for experiments, and it is demonstrated that the performance of the proposed algorithm is competitive with the previous 2D NR IQA metrics.
DOLAP data warehouse research over two decades: Trends and challenges

This paper introduces to the Information Systems special issue, including the four best papers submitted to DOLAP 2018. Additionally, the 20th anniversary of DOLAP motivated the analysis of DOLAP topics, as follows. First, the recent 5-years DOLAP topics were confronted with those of VLDB, SIGMOD, and ICDE. Next, the DOLAP topics were analyzed within its 20 years of history. Finally, the analysis is concluded with the list of the most frequent research topics of the aforementioned conferences and still open research problems.
Face hallucination via multiple feature learning with hierarchical structure

In the past few years, neighbor-embedding (NE) based methods have been widely exploited for face hallucination. However, the existing NE based methods in spatial domain just employ single type of features for data representation, ignoring the compensatory information among multiple image features, resulting in bias in high resolution (HR) face image reconstruction. To tackle such problem, this paper presents a novel Multiple feature Learning model with Hierarchical Structure (MLHS) for face hallucination. Compared with conventional NE based methods, the proposed MLHS makes full use of multi-level information of face images, which can effectively remedy the flaw caused by just using single type of spatial pixel features, and adopts hierarchical structure to better maintain the manifold consistency hypothesis between the HR and low resolution (LR) patch spaces. The multiple learning strategy and hierarchical structure admit the proposed MLHS to well reconstruct the face details such as eyes, nostrils and mouth. The validity of the proposed MLHS method is confirmed by the comparison experiments in some public face databases.
Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns

Recent studies of student problem-solving behavior have shown stable behavior patterns within student groups. In this work, we study patterns of student behavior in a richer self-organized practice context where student worked with a combination of problems to solve and worked examples to study. We model student behavior in the form of vectors of micro-patterns and examine student behavior stability in various ways via these vectors. To discover and examine global behavior patterns associated with groups of students, we cluster students according to their behavior patterns and evaluate these clusters in accordance with student performance.
Practical Combinatorial Testing for XSS Detection using Locally Optimized Attack Models

In this paper, we present a combinatorial testing methodology for automated black-box security testing of complex web applications. The focus of our work is the identification of Cross-site Scripting (XSS) vulnerabilities. We introduce a new modelling scheme for test case generation of XSS attack vectors consisting of locally optimized attack models. The modelling approach takes into account the response and behavior of the web application and is particularly efficient when used in conjunction with combinatorial testing. In addition to the modelling scheme, we present a research prototype of a security testing tool called XSSInjector, which executes attack vectors generated from our methodology against web applications. The tool also employs a newly developed test oracle for detecting XSS which allow us to precisely identify whether injected JavaScript is actually executed and thus eliminate false positives. Our testing methodology is sufficiently generic to be applied to any web application that returns HTML code. We describe the foundations of our approach and validate it via an extensive case study using a verification framework and real world web applications. In particular, we have found several new critical vulnerabilities in popular forum software, library management systems and gallery packages.
Data Protection on Fintech Platforms

The security of data has been challenged by the incorporation of new services into the digital world. Data protection has become essential to continue operating in the new financial environment, especially due to the advent of Financial Technology (Fintech). This article reviews how data protection is applied to financial recommendation platforms identifying current trends in this area. Moreover, it looks at the evolution of computer security in the field of Fintech due to the security level that it requires. In addition, it examines the solution techniques for data storage issues in cloud security and encryption methods that assure data protection. Also, the European Union's data protection regulation is considered; it not only affects the entities based in the European territory but also those that are outside of it but manage the data of European citizens.