Reviewerly/exHarmony huggingface dataset:
Hierarchical Decompositional Mixtures of Variational Autoencoders

Variational autoencoders (VAEs) have received considerable attention, since they allow us to learn expressive neural density estimators effectively and efficiently. However, learning and inference in VAEs is still problematic due to the sensitive interplay between the generative model and the inference network. Since these problems become generally more severe in high dimensions, we propose a novel hierarchical mixture model over low-dimensional VAE experts. Our model decomposes the overall learning problem into many smaller problems, which are coordinated by the hierarchical mixture, represented by a sum-product network. In experiments we show that our models outperform classical VAEs on almost all of our experimental benchmarks. Moreover, we show that our model is highly data efficient and degrades very gracefully in extremely low data regimes.
Revealing Abnormality Based on Hybrid Clustering and Classification Approach

Abnormality Detection is the process of locating abnormal instances within the data. In this work, we have applied Abnormality Detection to the domain of detection associated with Credit Card Fraud. This problem is actually attributed to demonstrating those credit card transactions which have occurred in the earlier times, with the presence of awareness related to those instances, which are actually fraud ones. Applying this model, we can use it to predict if a new transaction is a fraud based or not. In this proposed work, we have utilized a combination framework of data mining clustering algorithms so as to solve the problem of credit card fraud detection to a particular extent. The proposed work Revealing Abnormality Based on Hybrid Clustering and Classification Approach (RA-HC-CA) consists of two stages namely a clustering phase followed by a detection phase. In the clustering phase, we have employed a combined clustering approach initiated by k-means clustering algorithm followed by hierarchical clustering algorithm. Prior to Hierarchical/Agglomerative clustering, the whole data set is clustered into meaningful ‘k’ knots by k-means clustering procedure. The output of ‘k’ groups is then inputted to Agglomerative clustering algorithm to merge the already obtained ‘k’ clusters from the previous phase, into more meaningful clusters. This is continued until 70–75% of data falls on one large group, which is the Normal group. The remaining data instances may converge in various other abnormal groups. The strong assumption made here is that such clusters with less instances, than a particular threshold are considered to be groups pertaining to fraud ones. Then, so as to check for the presence of an instance as fraud one, we initially identify the proximate gathering to which it fit into. Then, within that identified cluster, LDA (Linear Discriminant Analysis) is carried out. It has been observed that the proposed approach (RA-HC-CA) achieved 80.5% accuracy in comparison with various other existing methods.
Scalable Training of Inference Networks for Gaussian-Process Models

Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an alternative approximation that employs stochastic inference networks for a flexible inference. Unfortunately, for such networks, minibatch training is difficult to be able to learn meaningful correlations over function outputs for a large dataset. We propose an algorithm that enables such training by tracking a stochastic, functional mirror-descent algorithm. At each iteration, this only requires considering a finite number of input locations, resulting in a scalable and easy-to-implement algorithm. Empirical results show comparable and, sometimes, superior performance to existing sparse variational GP methods.
Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection

This paper describes a method of pixel-level segmentation applied to parasite detection. Parasite diseases in most cases are detected by microscopic samples examination or by ELISA blood tests. The microscopic methods are less invasive and often used in veterinary, but they need more time to prepare and visually evaluate samples. Diagnosticians search the entire sample to find parasite eggs and to classify their species. Depending on the species of the diagnosed animal, the samples can contain various types of pollution, e.g. fragments of plants. Most of the objects in the sample by their transparency look similar, and some of parasites eggs might be unintentionally omitted. The presented method based on fully convolutional network allows processing the entire space of the sample and assigning a class to each pixel of the image. Our model was trained to classify parasite eggs and distinguish them from adjacent or overlapped pollution.
The Experiential Heterogeneous Earliest Finish Time Algorithm for Task Scheduling in Clouds

Task scheduling in cloud environments is the problem of assigning and executing computational tasks on the available cloud resources. Effective task scheduling approaches reduce the task completion time, increase the efficiency of resource utilization, and improve the quality of service and the overall performance of the system. In this paper, we present a novel task scheduling algorithm for cloud environments based on the Heterogeneous Earliest Finish Time (HEFT) algorithm, called experiential HEFT. It considers experiences with previous executions of tasks to determine the workload of resources. To realize the experiential HEFT algorithm, we propose a novel way of HEFT rank calculation to specify the minimum average execution time of previous runs of a task on all relevant resources. Experimental results indicate that the proposed experiential HEFT algorithm performs better than HEFT and the popular Critical-Path-on-a-Processor (CPOP) algorithm considered in our comparison.
Damage observation in glass fiber reinforced composites via μ-tomography

The present study deals with a three dimensional analysis of a cyclic uniaxial experiment on a glass fibre reinforced epoxy resin composite. From monotonic and cyclic uniaxial mechanical experiments and by using a 2D digital image correlation optical setup, the macroscopic material behaviour is revealed. Based on the observed response, an ex situ cyclic tomography experiment was defined and conducted. The investigated dog-bone sample was loaded at four different load levels. At each subsequent unloaded stage, the sample was scanned in a laboratory tomograph. The 3D reconstructed volumes were analyzed with FE-based digital volume correlation in order to measure displacement fields in the region of interest. Since very low permanent strains were reported, the gray level correlation residuals were studied to observe the damaged zones in the bulk of the sample. Overlaying the correlation residuals with the corresponding microstructure accurately depicts damage mechanisms occurring in the investigated material.
Cross-Domain Spam Detection in Social Media: A Survey

Social media is now an integral part of everyone’s life. Due to its exponential growth with rising interest of users, they have become the source of the abundant amount of data prevailing on the internet. This tremendous amount of data is not only useful for researchers but also fascinates spammers. To reach more users, to increase monetary gain and to disseminate malicious activities, spammers are using multiple content sharing platforms. Conventional spam detection techniques have focused more on spam detection on one platform only. This paper discusses cross-domain detection techniques of email and web spams, social spams, opinion spams, and their comparisons. This is an attempt to provide various challenges in this area. As far as our knowledge concerned, this is the first detailed literature study in the field of cross-domain spam detection.
Study of Asian RDR based on re3data

Purpose RDR has become an essential academic infrastructure in an atmosphere that facilitates the openness of research output granted by public research funds. This study aims to understand operational status of 152 Asian data repositories on re3data and cluster repositories into four groups according to their operational status. In addition, identify the main subject areas of RDRs in Asian countries and try to understand what topic correlations exist between data archived in Asian countries. Design/methodology/approach This study extracts metadata from re3data and analyzes it in various ways to grasp the current status of research data repositories in Asian countries. The author clusters the repositories into four groups using hierarchical cluster analysis according to the level of operation. In addition, for identifying the main subject areas of RDRs in Asian countries, extracted the keywords of the subject field assigned to the each repository, and Pathfinder Network (PFNET) analysis is performed. Findings About 70 per cent of the Asian-country repositories are those where licenses or policies are declared but not granted permanent identifiers and international-level certification. As a result of the subject domain analysis, eight clusters are formed centering on life sciences and natural sciences. Originality/value The research output in developing countries, especially non-English-speaking countries, tends not to be smoothly circulated in the international community due to the immaturity of the open-access culture, as well as linguistic and technical problems. This study has value, in that it investigates the status of Asian countries’ research data management and global distribution infrastructure in global open-science trends.
Vehicle Detection in Aerial Images Using Selective Search with a Simple Deep Learning Based Combination Classifier

Contrary to the growing zest for bringing in place a complex detection methodology, aiming at improvement in performance of existing methodologies for detecting vehicles in aerial images, this novel piece of work sets forward a much simpler approach with superior results. It was found that methods that showed exemplary performance on common benchmark datasets otherwise, their performance dropped remarkably on aerial images. To achieve performance at par or comparable with the state-of-the-art methods on common benchmark datasets, several adaptations have been suggested in literature to existing methods, for detecting small vehicle instances in aerial images. This ranges from adaptations to the object proposal methods to introduction of more complex deep learning-based classifiers such as fast RCNN and faster RCNN. However, these methods have their own limitations along with the growing increase in system complexity. In this work, a novice, simple and accurate method has been proposed for the detection of small vehicles from aerial images. The experiments have been performed on the publicly accessible and diverse Vehicle Detection in Aerial Imagery (VEDAI) database. This novice technique utilizes Selective Search algorithm as the object proposal method in combination with a deep learning-based framework for classification, which comprises of a simple Convolutional Neural Network (CNN) architecture proposed in combination with a simple Deep Neural Network (DNN) architecture. The DNN utilizes Histogram of Oriented Gradients (HoG) feature input to generate output features that combine with the CNN feature map for final classification. This method is much simpler and achieves a significant accuracy of 96% in vehicle detection, which is much superior to any of the methods tried for aerial images in literature so far.
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation.

Multi-person pose estimation is a fundamental yet challenging task in computer vision. Both rich context information and spatial information are required to precisely locate the keypoints for all persons in an image. In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information. Specifically, we design a Context Aware Path with structure supervision strategy and spatial pyramid pooling strategy to enhance the context information. Meanwhile, a Spatial Aware Path is proposed to preserve the spatial information, which also shortens the information propagation path from low-level features to high-level features. On top of these two paths, we employ a Heavy Head Path to further combine and enhance the features effectively. Experimentally, our proposed network outperforms state-of-the-art methods on the COCO keypoint benchmark, which verifies the effectiveness of our method and further corroborates the above proposition.
Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents

In the context of system testing on hardware-in-theloop test benches, performing functional tests on electrical control units comes with various challenges. Especially planning the execution of test cases for a test cycle and nightly batch runs is cumbersome. In order to support the test managers, this letter proposes an adaptable and flexible test management assistance system. Therefore, a set of requirements for such an assistance system has been identified and proposed solutions from the literature have been recalled. Out of those, software agents appear to be a suitable method for building up the framework of the assistance system. The framework proposed in this letter is divided into two phases: static prioritization of test scripts and dynamic execution ordering. The agents of the former phase aggregate relevant available data from various sources to calculate a test script priority. In the latter, the agents negotiate to solve the resource allocation problem for test scripts on test benches. In addition, test logs are taken into account at runtime for reprioritization. Finally, an industrial use case from the automotive industry is taken as an example to discuss the applicability and current obstacles on the way to application and qualitative evaluation of the expected benefits are discussed.
E-healthcare delivery solution

Proper health-care delivery in the rural area is one of the biggest challenge faced by the health professionals and the government. Majority of the Indian residents live in the rural areas where the condition of medical facilities is deplorable. It is understood that, there is a dire need of new practices and procedures to ensure that quality and timely health care resources reach the deprived corners of the Indian villages. We have developed a modular, easy re-configurable, touch screen based remote health care application for delivering primary health care in an efficient manner in the rural areas, where even the minimal health care facilities are not available. The application can capture patients symptoms using a knowledge base provided by the Doctors and send the symptoms and other important information to the Doctors over the Internet. As the application is running in the rural areas, it is often found that the Internet connectivity is a problem. Thus, we use short message services to send the patients’ vital. This paper describes the different steps which are taken to connect the rural patients with urban located doctors using SMS. The paper provides evidence that even when the Internet connectivity is not available in the villages, it is still possible to actively use our e-healthcare service for treatment of the rural patients.
A Cloud Computing Framework for Higher Education Institutes in Developing Countries (CCF_HEI_DC)

In order to satisfy the rapid development in ICT, high educational institutes including members who are involved and interested in research’s field began to investigate and seek new tools, ways, and technologies to support their mission. On the other hand, cloud computing technology, as a new technology have emerged the education and learning scene lately and the cloud computing service providers (CSPs) are getting more interested in this field to offer their productions and services. Nevertheless, the association of cloud computing within high educational institutes is mostly directed to learning, teaching, and other student’s activities area of interest. In most developing countries, there are countable obstacles and problems in their high educational institutes, above-mentioned one before, such as shortness in funds and necessary IT infrastructure and resources which are required to establish, develop and maintain an ICT environment for their students, lecturers and researchers in such institutes. In this paper, we propose a Cloud Computing Framework model for Higher Education Institutes in Developing Countries (CCF_HEI_DC) of six layers which could be implemented and developed in any developing country besides a modified hybrid cloud model which is suggested to host and support the proposed cloud computing framework model.
Multi-Agent Path Finding for UAV Traffic Management

Unmanned aerial vehicles (UAVs) are expected to provide a wide range of services, whereby UAV fleets will be managed by several independent service providers in shared low-altitude airspace. One important element, or redundancy, for safe and efficient UAV operation is pre-flight Conflict Detection and Resolution (CDR) methods that generate conflict-free paths for UAVs before the actual flight. Multi-Agent Path Finding (MAPF) has already been successfully applied to comparable problems with ground robots. However, most MAPF methods were tested with simplifying assumptions which do not reflect important characteristics of many real-world domains, such as delivery by UAVs where heterogeneous agents need to be considered, and new requests for flight operations are received continuously. In this paper, we extend CBS and ECBS to efficiently incorporate heterogeneous agents with computational geometry and we reduce the search space with spatio-temporal pruning. Moreover, our work introduces a batching method into CBS and ECBS to address increased amounts of requests for delivery operations in an efficient manner. We compare the performance of our batching approach in terms of runtime and solution cost to a first-come first-served approach. Our scenarios are based on a study on UAV usage predicted for 2030 in a real area in Japan. Our simulations indicate that our proposed ECBS based batching approach is more time efficient than incremental planning based on Cooperative A*, and hence can meet the requirements of timely and accurate response on delivery requests to users of such UTM services.
Design for Eliminating Operation Specific Power Signatures from Digital Logic

Conventional digital logic operations have distinguishable power signatures. Side channel power analysis combined with classification algorithm can reveal unknown logic operations. Revealing the underlying operations is the main task in reverse engineering an application. In this paper, we propose an unconventional way of overcoming this vulnerability by using chaos based reconfigurable logic operations. The chaos gate used in this paper is built from a simple 3 transistor chaotic oscillator capable of generating aperiodic states starting from a suitably chosen initial condition. We propose a design methodology using chaos gate to implement different logic operations with very similar power profiles. Therefore, it becomes significantly harder to distinguish logical operations built with chaotic logic gates in contrast to the conventional static CMOS logic gates. In addition, we show a mixed implementation of bitwise logic operations using different proportions of chaos and conventional gates resulting in a significant reduction of total overhead.
Using Ontology and Rule-Based Reasoning for Conceptual Data Models Synonyms Detection

Conceptual data models can change during the information system development and teamwork phases, which require constantly monitoring with synonyms detection. This study elaborates on an approach for detecting synonyms in an entity-relationship model based on mapping with ontological elements. The use of a specific data model validator (DMV) tool enables formalization of the ontology and ER models, as well as their integration with the set of reasoning rules. The reasoning rules enable mapping between formalized elements of the ontology and ER model, and the extraction of synonyms. Formalized elements and reasoning rules are processed within Prolog for the extraction of synonyms. An empirical study conducted by using university student exams demonstrates usability of the proposed approach. The results show effectiveness in extraction of synonyms in all types of conceptual data model elements.
A Switching Approach that Improves Prediction Accuracy for Long Tail Recommendations

Recommender systems are software tools that play an important role of generating a list of recommendations for unseen items based on the past users experience and interactions. One of the most popular approaches is Collaborative Filtering (CF) that considers the users similarities to generate the recommendation. Although, recommender systems have been discovered in many aspects, the popularity bias is still one of the challenges that need to be considered. Therefore, we proposed a novel model that applies a switching technique to solve the long tail recommendation problem (LTRP) when collaborative filtering fails to find the target case using a multi-level method. We evaluate the results using the public dataset 100K Movielens. Our result outperforms all the existing methods through reducing the recommendation error rates for the items in the long tail.
Expanding Academic Librarians’ Roles in the Research Life Cycle

Abstract Research depends on prior results. The cycle of transforming research output to disseminated knowledge is changing to engage more researchers to openly discover and thereby shape future contributions to scholarship. No established framework helps librarians understand the opportunities that transition offers librarians. However, through four propositions, this paper addresses some of the changes facing academic librarians as they expand their roles: 1) Research cycles embrace interactive sharing and reuse of data; 2) Managing open research data expands librarians’ roles; 3) Intellectual entrepreneurship roles provide a model to empower others; 4) Librarians demonstrate their entrepreneurial leadership by creating partnerships outside the library. Now academic librarians have opportunities to strengthen their role in how higher education shapes research by shifting greater focus toward research data management [RDM]. Two seasoned administrators and librarians illustrate pathways to prepare academic librarians for these new roles. They offer two practitioners’ impressions of the demands and opportunities for librarians to extend their expertise to support RDM, and illustrate how academic librarians have begun doing so through professional association work (through the Association of College and Research Libraries (ACRL)) and at one academic library (at Drexel University). They urge academic librarians to step out of their comfort zones of organizing, preserving and servicing discovery of information resources and embrace emerging roles for which their values and expertise have prepared them. If librarians ignore these opportunities, they risk being bypassed in efforts to ensure that managing research data and scholarship are central to research protocols.