Reviewerly/exHarmony huggingface dataset:
An Innovative Method of Feature Extraction for Text Classification Using PART Classifier

With the advent of technology and thrust to create machines with human intelligence, the frontiers in machine learning are being stretched incessantly. With the increasing number of digital information in recent scenario, text classification has gained importance in machine learning task which needs to study texts thoroughly to achieve success. It is a task of assigning a random document to its predefined text class. This paper aims at presenting a methodology for developing an automatic system for solving the problem of classifying Bangla text document into their respective text categories. It introduces a hybrid approach (i.e., PART) for classification of Bangla text documents based on ‘term association’ and ‘term aggregation’ as baseline feature extraction methods. Comparison of results with other classification algorithms shows that this approach can elicit better results than the existing methods.
Improving Access to Internships with an On-Campus Software Development Center

Internships are widely recognized as a high-impact educational practice that effectively prepares students for successful careers in the computing industry. Unfortunately, not all students have equal access to computing internships. Students are often expected to find internships independently, but their classes generally do not teach them the skills needed to search for computing internships or to succeed in a technical interview. Furthermore, at a university in a rural area with little or no public transportation, students may not be able to accept an internship offer unless they have a car and enough time to travel to and from a work site in a nearby city. These problems are especially severe for students of opportunity (e.g., students with low socioeconomic status, first-generation students): the exact students for whom an internship can have the greatest impact. To address this problem, our university is starting a software development center to provide campus-based internships in which student teams develop software products for clients. Students hired by the center will benefit from dedicated workspace on or near our campus, ongoing training in common software development practices (e.g., version control, project management, requirements engineering) that are not covered consistently in a classroom setting, and on-site mentoring from experienced software professionals. We believe that this co-curricular experience will improve participants' ability to begin and persist in a career in software development after graduation. We will discuss our experience with several pilot projects and our vision and goals for the center.
Variability-intensive Software Systems

Product line engineering emerged from the software reuse and generative programming movements of the 70s. However, in today's competitive and fast-paced markets where users expect software to adapt to their specific needs, most modern software-intensive products and services are variability-intensive, regardless of whether they are part of a product line or not. In this talk, we explore how building variability-intensive software systems influences the various software product lifecycle stages. Furthermore, we look beyond variability in functional features: We explore the role of quality attributes in the design of variability-intensive software systems and discuss how to address the challenge of identifying and managing variability in quality attributes. Finally, based on the example of software product line research, we explore how research on variability has been changing over time and if research trends in industry and academia have diverged.
Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model

This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods.
Improvements in Everlasting Privacy: Efficient and Secure Zero Knowledge Proofs

Verifiable electronic voting promises to ensure the correctness of elections even in the presence of a corrupt authority, while providing strong privacy guarantees. However, few practical systems with end-to-end verifiability are expected to offer long term privacy, let alone guarantee it. Since good guarantees of privacy are essential to the democratic process, good guarantees of everlasting privacy must be a major goal of secure online voting systems. Various currently proposed solutions rely on unusual constructions whose security has not been established. Further, the cost of verifying the zero knowledge proofs of other solutions has only been partially analysed. Our work builds upon Moran and Naor’s solution—and its extensions, applications and generalisations—to present a scheme which is additively homomorphic, efficient to verify, and rests upon well studied assumptions.
The Problem With P-Hacking

Facts are stubborn things, but statistics are pliable.—Mark TwainFew things excite me quite as much as when the topic of statistics rises to the prominence of news headlines. Recently, an academic at a certain institution came under harsh scrutiny when he was discovered to be clandestinely P-hacking. 1 O’Connor A. No, chocolate probably isn’t a superfood. September 30, 2018: 10 Google Scholar P-hacking is as deceitful as the term implies. It is the practice of analyzing and reanalyzing a dataset to cherry pick results. 2 Simmons J.P. Nelson L.D. Simonshohn U. False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci. 2011; 22: 1359 Crossref PubMed Scopus (3873) Google Scholar It levers a well-known and accepted statistical outcome, the P value, to shortcut the scientific method.
Marginal Analysis of Ordinal Clustered Longitudinal Data With Informative Cluster Size

Abstract The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, periodontal disease outcomes, including clinical attachment loss, are often assessed using ordinal scoring systems. In addition, participants may lose teeth over the course of the study due to advancing disease status. Here we develop longitudinal cluster-weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with participant-level health-related covariates, including metabolic syndrome and smoking status, and potentially decreasing cluster size due to tooth-loss, by fitting a proportional odds logistic regression model. The within-teeth correlation coefficient over time is estimated using the two-stage quasi-least squares method. The motivation for our work stems from the Department of Veterans Affairs Dental Longitudinal Study in which participants regularly received general and oral health examinations. In an extensive simulation study, we compare results obtained from CWGEE with various working correlation structures to those obtained from conventional GEE which does not account for ICS. Our proposed method yields results with very low bias and excellent coverage probability in contrast to a conventional generalized estimating equations approach.
EXPERIENCE OF CREATING AND IMPLEMENTING INFORMATION-MANAGING EDUCATIONAL ENVIRONMENT AT UNIVERSITY AND FEATURES OF ITS ADAPTATION AT LYCEUM

The article describes experience of creating and implementing information-managing educational environment at university. The model of creating information-managing educational environment of university with elements of artificial intelligence and indicative management is described. This environment contributes to improve quality of training and management of educational processes and resources. The stages of creating and implementing information-managing educational environment are considered systemically, as continuous process focused on a consumer. The inhibitory and facilitating conditions for introduction of the model at university are described. To provide subject-oriented approach to the use of information resources of environment, recommended clusters of information subsystems for each category of users are described. Practically implemented scientific and methodological recommendations for subjects of educational process to overcome resistance to innovations introduced in educational organizations are proposed. Features of adaptation of the developed information-managing educational environment for schools are presented.
Insights From Library Information and Resource Sharing for the Future of Academic Library Collections

The successes and challenges of library resource sharing offer important lessons for librarians considering the future of academic library collections. Library resource sharing specialists provide interlibrary loan services because neither the Internet, nor any one library, holds all of the information that people need. And, how well ILL departments can facilitate access to information, in traditional and innovative ways, depends on many factors, including copyright guidelines, license terms, publisher embargoes, high publisher paywalls for article access, the existence of both legal and illegal social sharing, and, especially, on what librarians continue to collect, maintain and preserve.
Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural Networks.

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesses in the classification manifold that attract attackers searching for adversarial examples. Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space. Our defense then identifies attacks by comparing neuron activation signatures of inputs to those of trapdoors. In this paper, we introduce trapdoors and describe an implementation of a trapdoor-enabled defense. First, we analytically prove that trapdoors shape the computation of adversarial attacks so that attack inputs will have feature representations very similar to those of trapdoors. Second, we experimentally show that trapdoor-protected models can detect, with high accuracy, adversarial examples generated by state-of-the-art attacks (PGD, optimization-based CW, Elastic Net, BPDA), with negligible impact on normal classification. These results generalize across classification domains, including image, facial, and traffic-sign recognition. We also present significant results measuring trapdoors' robustness against customized adaptive attacks (countermeasures).
A Language Identification System Based on Voxforge Speech Corpus

In this work, we address the problem of identifying languages based on Voxforge speech corpus. We downloaded corpora for three languages: English, German and Persian from Voxforge. In addition, we recorded two additional corpora, the first one for Modern Standard Arabic (MSA) and the other one for Kabyl, one of the Algerian Berber dialects. To tackle this task, we used three classifiers, namely: k-Nearest Neighbors (kNN), Support Vector Machines (SVM) and Extra Trees Classifier. We obtained an average precision of $$87.45 \%$$ for binary classification compared to $$44 \%$$ for the multi-class one.
Diversity, accuracy and efficiency in ensemble learning: An unexpected result

Empirical evidence suggests that ensembles with adequate levels of pairwise diversity among a set of accurate member algorithms can significantly outperform any of the individual algorithms. As a result, several diversity measures have been developed for use in optimizing ensembles. We show, howeve r, that there is natural tension between the pairwise diversity of ensemble members and their individual accuracy. While efficient ensembles can be built with stronger forms of diversity, they also suffer in overall accuracy. On the other hand, ensembles built with weaker forms of diversity can be very accurate, but tend to be significantly more computationally expensive. We discuss these findings in light of the notion of diversity space.
DBN Based Cloud Service Response Time Prediction Method

Cloud resources are taken the pay-as-you-go model, so cloud service providers expect to provide better cloud service performance at fewer virtual machine resources. Therefore, how to predict cloud service performance under given virtual machine resources and concurrent requests has become a hot research issue. Aiming at the problem of cloud service performance prediction, this paper takes response time as an important indicator to measure cloud service performance, and proposes a DBN-based cloud service response time prediction method. This method establishes a DBN based mapping model between cloud service response time with concurrent requests and virtual machine resources. The cloud service response time is predicted based on the mapping model. The experiments show the effectiveness of the proposed method.
Synthesizing Explainable Behavior for Human-AI Collaboration

As AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. This requires AI systems to exhibit behavior that is explainable to humans. Synthesizing such behavior requires AI systems to reason not only with their own models of the task at hand, but also about the mental models of the human collaborators. Using several case-studies from our ongoing research, I will discuss how such multi-model planning forms the basis for explainable behavior.
Improving the Sharpness of Digital Images Using a Modified Laplacian Sharpening Technique

Many imaging systems produce images with deficient sharpness due to different real limitations. Hence, various image sharpening techniques have been used to improve the acutance of digital images. One of such is the well-known Laplacian sharpening technique. When implementing the basic Laplacian technique for image sharpening, two main drawbacks were detected. First, the amount of introduced sharpness cannot be increased or decreased. Second, in many situations, the resulted image suffers from a noticeable increase in brightness around the sharpened edges. In this article, an improved version of the basic Laplacian technique is proposed, wherein it contains two key modifications of weighting the Laplace operator to control the introduced sharpness and tweaking the second order derivatives to provide adequate brightness for recovered edges. To perform reliable experiments, only real-degraded images were used, and their accuracies were measured using a specialized no-reference image quality assessment metric. From the obtained experimental results, it is evident that the proposed technique outperformed the comparable techniques in terms of recorded accuracy and visual appearance.
Deep Convolutional Neural Networks for DGA Detection

A Domain Generation Algorithm (DGA) is an algorithm to generate domain names in a deterministic but seemly random way. Malware use DGAs to generate the next domain to access the Command & Control (C&C) communication server. Given the simplicity of the generation process and speed at which the domains are generated, a fast and accurate detection method is required. Convolutional neural network (CNN) are well known for performing real-time detection in fields like image and video recognition. Therefore, they seemed suitable for DGA detection. The present work provides an analysis and comparison of the detection performance of a CNN for DGA detection. A CNN with a minimal architecture complexity was evaluated on a dataset with 51 DGA malware families and normal domains. Despite its simple architecture, the resulting CNN model correctly detected more than 97% of total DGA domains with a false positive rate close to 0.7%.
Improved Cooperative Multi-agent Reinforcement Learning Algorithm Augmented by Mixing Demonstrations from Centralized Policy

Many decision problems for complex systems that involve multiple decision makers can be formulated as a decentralized partially observable markov decision process (dec-POMDP) problem. Due to the computational difficulty with obtaining optimal policies, recent approaches to dec-POMDP often use a multi-agent reinforcement learning (MARL) algorithm. We propose a method to improve the existing cooperative MARL algorithms by adopting an imitation learning technique. For a reference policy in the imitation learning part, we use a centralized policy from a multi-agent MDP or a multi-agent POMDP model reduced from the original dec-POMDP model. In the proposed method, during the training process, we mix demonstrations from the reference policy by using a demonstration buffer. Demonstration samples from the buffer are used in the augmented policy gradient function for policy updates. We assess the performance of the proposed method for three well-known dec-POMDP benchmark problems -- Mars rover, co-operative box pushing, and dec-tiger. Experimental results indicate that augmenting the baseline MARL algorithm by mixing the demonstrations significantly improves the quality of policy solutions. With these results, we conclude that the imitation learning can enhance MARL algorithms and that policy solutions from MMDP and MPOMDP models are a reasonable reference policy to use in the proposed algorithm.
A Behavior-Based Proactive User Authentication Model Utilizing Mobile Application Usage Patterns

Access to smart home networks is mostly achieved through end-user devices, especially mobile phones, but such devices are susceptible to theft or loss. In this paper, we present a user authentication model based on application access events, using only a small amount of information, thus reducing the computation time. To validate our model, we utilize a public real-world dataset collected from real users over a long period of time, in an uncontrolled manner. The model is evaluated by differentiating between users who utilize shared apps at the same daily intervals. In addition, we evaluate various classification approaches regarding legitimate user classification in compliance with the history of app usage events. The results demonstrate the capacity of the presented model to authenticate users with high true positive and true negative rates.
Temporal Copy-Move Forgery Detection and Localization Using Block Correlation Matrix

One of the drawbacks of the current revolution on media is that tampering with images and videos is an increasingly easy task which brings a situation where digital media cannot be trusted. Digital video forensics study the effects of attacks and tampering techniques on videos and has arisen as a solution to the problem of lack of trustworthiness on digital media. Copy-Move tampering is one of the most common attacks, with variants for delete and duplicate objects in videos, and has been studied in several video forensics works with different approaches. Despite that, there is not yet a simple method to determine multiple variants of Copy-Move attacks. This work proposes a simple yet effective method do detect Copy-Move for both subregion and full-frame duplication.
Getting the Central RDM Message Across: A Case Study of Central versus Discipline-Specific Research Data Services (RDS) at the University of Cambridge

Abstract RDS are usually cross-disciplinary, centralised services, which are increasingly provided at a university by the academic library and in collaboration with other RDM stakeholders, such as the Research Office. At research-intensive universities, research data is generated in a wide range of disciplines and sub-disciplines. This paper will discuss how providing discipline-specific RDM support is approached by such universities and academic libraries, and the advantages and disadvantages of these central and discipline-specific approaches. A descriptive case study on the author’s experiences of collaborating with a central RDS at the University of Cambridge, as a subject librarian embedded in an academic department, is a major component of this paper. The case study describes how centralised RDM services offered by the Office of Scholarly Communication (OSC) have been adapted to meet discipline-specific needs in the Department of Chemistry. It will introduce the department and the OSC, and describe the author’s role in delivering RDM training, as well as the Data Champions programme, and their membership of the RDM Project Group. It will describe the outcomes of this collaboration for the Department of Chemistry, and for the centralised service. Centralised and discipline-specific approaches to RDS provision have their own advantages and disadvantages. Supporting the discipline-specific RDM needs of researchers is proving particularly challenging for universities to address sustainably: it requires adequate financial resources and staff skilled (or re-skilled) in RDM. A mixed approach is the most desirable, cost-effective way of providing RDS, but this still has constraints.
Robust correlation filter tracking via context fusion and subspace constraint

Abstract In the trackers based on correlation filter, the limitation of tracking samples and the efficiency of the solution affect the performance of the trackers. Based on the Discriminative Scale Space Tracking(DSST), we proposed a new framework to improve correlation filter tracking via context fusion and subspace constraint. First, the target and background are jointly modeled to enhance the ability to distinguish between target and background. Second, to achieve higher robustness, subspace constraint is applied to DSST, and it can be solved efficiently by subspace-based alternating direction method of multipliers(SADMM). Finally, through the model update strategy, high-quality samples are selected to update. The proposed algorithm is validated on the OTB2013, OTB2015 and TC128. Experimental results show that compared with fast DSST(fDSST), success rate and accuracy of the proposed method are increased by 4.2% and 6.8% respectively. Compared with the state-of-the-art trackers, the proposed method achieves a competitive result with real-time speed.
Fine-Grained Color Sketch-Based Image Retrieval

We propose a novel fine-grained color sketch-based image retrieval (CSBIR) approach. The CSBIR problem is investigated for the first time using deep learning networks, in which deep features are used to represent color sketches and images. A novel ranking method considering both shape matching and color matching is also proposed. In addition, we build a CSBIR dataset with color sketches and images to train and test our method. The results show that our method has better retrieval performance.