Reviewerly/exHarmony huggingface dataset:
Influence Minimization Algorithm Based on Coordination Game

Influence analysis is the basic technology for predicting potentially hazardous behavior and determining the traceability of the hazardous behavior in the public security domain. Previous research has focused on maximizing the diffusion of the influence; however, little research has been performed on the method of minimizing the influence of negative information dissemination in networks. This paper proposes an influence minimization algorithm based on coordinated game. When the negative information is generated in the network and some initial nodes have been infected, the goal is to minimize the number of the final infected nodes by discovering and blocking the K uninfected nodes. First, the algorithm assumes that the behavior of the node propagating information depends on the coordination game with its neighboring nodes. Second, based on the local interaction model between the nodes, this paper quantifies the level of the influence of a node that is affected by its neighbors. Finally, the heuristic algorithm is used to identify the approximate optimal solution. The results of experiments performed on four real network datasets show that the proposed algorithm can suppress negative information diffusion better than the five considered existing algorithms.
Microservice-Based Cloud Application Ported to Unikernels: Performance Comparison of Different Technologies

Microservice architecture is nowadays a popular design pattern for cloud applications. Usually microservices are launched on the servers of a cloud datacenter inside virtualization containers. This provides their isolation and performance comparable to virtual machines. However, security of virtualization containers remains a problem. They share the hosts machine operating kernel and thus their mutual isolations depends on proper implementation of kernel isolation features like cgroups or namespaces. Moreover the usage of kernel security modules like SELinux is hindered by different base Linux distributions used for containers creation. The concept of unikernels provides much better security for microservices. They are launched on a hypervisor instead of a full operating system kernel and contain only the base files needed to run the application. However their performance in realistic environments still remains a question. In this paper we port a microservice-based cloud application from Docker containers to Rumpkernel and OSv unikernels and analyse its performance. It is shown, that the performance of this unikernel-based port can match or exceed the more traditional approaches.
Vision-Based Vehicle Detection in Foggy Days by Convolutional Neural Network

Vehicle detection plays an important role in advanced driving assisted system and autonomous driving system. However, the existing vehicle detection methods are not robust in harsh environments, especially in foggy environment. To solve this problem, a vision-based vehicle detection structure using convolutional neural network is presented to detect the vehicle in foggy days. In our vehicle detection structure, a pair of encoders and decoders is used to estimate atmospheric illumination and transmissivity, and to establish the defogging image firstly. And then, the vehicle detection is implemented by a proposed vehicle detection method which predict the left-top key point as well as the right- bottom key point of the vehicle, thus get the bounding box of the vehicle. To verify the effectiveness of the new method, a data set based on the video generated from PreScan simulation platform is set up. And the new vehicle detection method is tested in multiple scenarios such as left turn, right turn, uphill, downhill. Experimental results show that our vehicle detection structure can effectively detect vehicles in foggy days.
Image Recommendation With Reciprocal Social Influence

Image recommendation plays an important role for exploring user potential interests in large-scale image sharing websites (e.g., Flickr and Instagram). Social relationships have been exploited to learn user preference, and shown their effectiveness. We argue that their performance improvement tends to be limited, as most existing approaches only consider the side of social influence from friends to a user. However, social influence is reciprocal per se as the preference of friends will be also influenced by the user herself. In this paper, we propose a deep neural network for image recommendation (dubbed RSIM) by leveraging reciprocal social influence, and optimize the preferences of users and friends simultaneously. Specifically, we split images into three types: positive image by an active user, social image by her social friends, and negative image by neither of them. We contend that a user prefers positive image to social image, which is in turn better than negative image for relative preference learning. Two neural networks are designed to capture user and image representations by tags and visual features, respectively. The proposed model is evaluated on a real dataset crawled from Flickr. The experimental results show that better performance can be reached than the state-of-the-art social image recommendation models in terms of precision.
Comparative Analysis of Smoothing Filters in Confocal Microscopy Images

Confocal microscopy is a widely used tool in the biomedical area, allowing to obtain 3D images with a high spatial resolution. Despite having advantages over conventional microscopy, the analysis of cellular images through confocal microscopy is a complicated process due to the very low S/N, so the use of filters is necessary to reduce noise. However, this step normally affects the quality of the edges, making them more diffuse. In addition, images acquired in confocal microscopy are affected by distortions introduced by lenses and the acquisition system. Therefore, it is possible to improve the edges definition by eliminating distortions. This is done by means of deconvolution methods such as the Wiener filter. Furthermore, in recent years a new generation of smoothing filters have been developed that seek to reduce Gaussian-type noise, without losing edges. However, a study has not been carried out to determine if these filters can be used in the elimination of noise in confocal microscopy, which are contaminated with Poisson noise. Therefore, in this work we present a comparative study of ten filters for the elimination of noise in confocal microscopy: median, anisotropic diffusion, bilateral, propagated, improved propagated, Rudin-Osher-Fatemi (ROF), TVL1, non-local means, K-SVD, and Wavelet 'A trous' and Haar filters, with and without preprocessing images with the Wiener filter, taking as criteria the noise reduction and the conservation of edges.
Assessing Nepal’s higher education

Even though more than one-third of Nepal’s colleges are categorized as community colleges (UGC, 2012), the biggest missing piece in Nepal’s higher education is community engagement. The irony is rooted in the fact that most of the colleges are satellite campuses affiliated with a handful of universities, offering bachelor and master’s degrees on the basis of centralized curricula and externally administered high stake examinations. Now that the country is poised to harness economic potentials by embracing a federal structure, higher education needs to unleash itself from the tether of pseudo-elitist ivory-tower model in favor of community-engaged scholarly activities geared toward the development of skills and entrepreneurship. Most of the scholars we spoke to emphasized skills development through community-based colleges that promote accessible job-oriented skills for adult students through apprenticeship, professional education, and technical and vocational education and training. This chapter presents an overview of current Nepalese higher education. Results are based on quantitative and qualitative data collected over the past two years. Results support an argument for a strong need for a community-engagement model of higher education to allow youth an opportunity to maximize local resources, overcome high unemployment rates, and end socio-cultural discrimination. Because the higher education system in Nepal is criticized for producing the human resource that doesn’t fit the demand of the job market. It is an irony that there is a dearth of technical man power in the local market on one hand and at the same time thousands of students and youths are leaving the country citing that there is lack of employment opportunities in Nepal. I think this can be minimized with the establishment of technical community colleges that cater to the needs of the local market. — Study participant
An exploration and experiment tool suite for code to architecture mapping techniques

Reflexion modeling can be used to validate that source code conforms to an intended architecture. However, it requires a mapping of source code modules (e.g., classes) to (software) architecture elements. We have developed a tool suite that allows for evaluation and exploration of automatic techniques to map source code modules to architecture elements. The suite includes a reusable core component and tools to define the architecture, define and run experiments with mapping strategies, and explore the results of these experiments. The experiments can be executed locally or in a remote high-performance computing environment.
Marketing Pattern Risks Detection Based on Semi-Supervised Learning

Detecting potential marketing pattern risks and preventing them can help enterprises lift operation efficiencies and reduce outlay costs. In this paper, we elaborate an ingenious method based on semi-supervised learning to identify latent marketing pattern risks for enterprises.
Survival Support Vector Machines: A Simulation Study and Its Health-Related Application

The support vector machine (SVM) has become a state-of-the-art classification method. Extensive developments of SVM ensure that support vector regression (SVR) is employed in many fields. In particular, in health applications, one of the most popular methods is survival data analysis. This paper describes the use of survival least square support vector machine (SURLS-SVM) applied to cervical cancer (CC) data with the benchmark Cox proportional hazards model (Cox PHM). The Cox PHM has assumptions that, unfortunately, cannot always be met in real cases. The SURLS-SVM overcomes this drawback. The SURLS-SVM cannot inform which predictors are significant, as the Cox PHM does. To address this issue, the feature selection using backward elimination is employed utilizing a concordance index increment. Moreover, the simulation study was conducted to know the effect of the number of features, sample size, and censoring percentage on the performance of the SURLS-SVM.
Knowledge systematization through the law of energy conservation

Energy is not only an up-to-date global world problem but it is also an important integrating notion of school Physics. It gets a special meaning in connection with energy changes and the Law of conservation of energy. The Law of energy conservation has its place in sciences, both in forming a scientific mental picture about the nature and universe as well as in systematisation of higher school students knowledge. The paper points at one particular option how to systemise knowledge while studying potential curves both in macro and micro-world.
An Architecture for Distributed Explorable HMD-Based Virtual Reality Environments

Interaction is one of the critical elements of VR environments. The use of VR in different domains by domain experts and average users, who are not IT-specialists, requires new approaches to represent interaction in either a domain-specific or a generic way, which is neither directly related to 3D graphics nor animation. In this paper, we propose an architecture for distributed VR environments with an explorable representation of interaction based on the semantic web. The architecture enables creation of logs of interactions between users and objects. Exploration of such logs can be especially useful in VR environments intended to gain knowledge about users’ and 3D objects’ behavior, e.g., in training and education. The architecture is suitable for lightweight VR devices with limited computational power, such as headsets and smartphones. We present an implementation of the architecture for an immersive service guide for household appliances, which is based on Unity and Samsung Gear VR.
A study on integrating IoT Applications with Blockchain

Every industrial sector has started analysing the ways of utilizing the advancements in cyber physical technologies in their operations. Internet of Things has emerged as a new way of connecting the physical world to the cyber world to obtain tremendous amount of information about the physical world and make it available in the Internet for advanced analytics and business decision making. Thus, IoT promises huge results in terms of revenue, safety, monitoring and management in every business unit of an Enterprise. Block chain has emerged as a key technology in storing data in distributed ledgers which is not possible or extremely difficult to modify or damage. Wherever data security is significant and ensuring the integrity of data is challenging, we may apply Block chain to resolve the issues. This paper analyses the suitability of using Block chain in IoT ecosystems in the first part. The consequences of integrating Block chain with IoT and the opportunities and challenges in this process are discussed in the next part of the paper with use cases. Based on the contemporary challenges and maturity of the technology, this paper concludes the most appropriate set of applications on which Block chain and IoT can function together and it proposes a way of making the best possible outcomes from both the domains.
Towards an Adaptive Approach that Combines Semantic Web Technologies and Metaheuristics to Create and Recommend Learning Objects

This work aims to present a proposal that combines semantic web and metaheuristic strategies to recommend web-based Learning Objects (LO) according to learners' preferences. The idea is to create a content recommendation process that uses existing resources from the Web to recommend LO in virtual learning environments. In this approach, knowledge level and Learning Styles are considered in the student model. Preliminary results have shown the possibility of creation and personalized recommendation using Wikipedia contents. At the end, some questions according to the recommendation process and the web content should be answered.
Age and Gender Estimation using Optimised Deep Networks

Age and gender estimation plays a fundamental role in intelligent applications such as access control, marketing intelligence, human-computer interaction etc. The advent of deep architectures have paved a way to improve the performance of estimation models, however, there is still a lack of optimized architectures. This paper focuses on the use of convolutional neural networks, and parameter modeling and optimization, and their effect on accuracy and loss term convergence. This paper first makes use of a generalized deep architecture based on literature and looks at ways of optimizing and reducing complexity without loss of accuracy. Different activation functions such as rectified linear unit (ReLU), linear function, exponential linear unit (ELU), hyperbolic tangent and Googles' proposed Swish function were tested along with the use of additional convolutional and fully-connected layers. Experiments resulted in a less complex architecture for gender classification and results were in line with that of benchmark accuracies found in literature, however, the same couldn't be achieved for age estimation. The inability to find a simpler architecture for age estimation is attributed to the complex nature of features that are associated with age than that of gender and also the multi-class classification nature of the age estimation problem.
Single-Stream Deep Similarity Learning Tracking

The deep similarity tracking via two-stream or multiple-stream network architectures has drawn great attention due to its strong capability of extracting discriminative feature with balanced accuracy and speed. However, these networks need a careful data pairing processing and are usually difficult to be updated for online visual tracking. In this paper, we propose a simple and effective discriminative feature extractor via a Single-Stream Deep Similarity learning for online visual Tracking, defined by SSDST. Different from the popular two-stream or multiple-stream architecture, the proposed method is built on a usual CNN architecture such as VGG-M network only with one branch. We design a contrastive loss layer, where the samples are implicitly paired, to directly learn discriminative feature on the large video dataset. The proposed network is easily applied to online tracking by adding a binary classification layer instead of contrastive loss layer for handling a specific video. The proposed SSDST is extensively verified on two representative benchmarks and shows better advantages over online trackers and the two-stream or multiple-stream trackers.
Two Phase Content Based Image Retrieval with relevance ranking on classified images

With the growing trend in mobile and computing devices, large number of digital images are generated in day to day life. These generated images are stored in databases for later use. As enormous quantity of digital images are stored in database there is a need for retrieving the relevant image. The content - based image recovery (CBIR) that manages the recovery of images from the large database is one of the applications of computer vision. Content - based image recovery is carried out in two phases in this research work. In the first phase Convolutional Neural Network(CNN) is used for detecting the images. CNN are capable of detecting multiple objects each belonging to different class by processing the image in a single pass. Convolution Neural Network is used to solve the multiple class detection problem. In the second phase once the objects are detected, relevant images are retrieved by using relevance ranking where priority is assigned within same class of images.
Data visualization: foundations, techniques, and applications

The idea that there is no precedence for the amount of data that is being generated today, and that the need to explore and analyze this vast volumes of data has become anincreasingly difficult task that could benefit from using Data visualization is presented. It is pointed that the goals of data visualization are data-driven and depend largely on the type of application, but the final objective is to convey to people information that is hidden in large volumes of data. Finally, the visualization pipeline is presented to review aspects of visualization methodology and visualization tool design, to conclude that the true potential of visualization emerge from the interaction of the user with the visualization model. The paper concludes establishing that the current processes of digital transformation will increase the need for visual analytics tools.
Applying a quantum amplification over a system for image feature matching and image recommendation

A novel approach for improving the performance of a image feature matching and recommendation system is proposed that is a major continuation of the previous work in [3]. An advanced clustering method is suggested in order to deal with the binary image feature descriptors: a quantum variant of the k-majority algorithm over ORB descriptors. Jaccard-Needham dissimilarity measure is used as a distance measure step of the algorithm. Finally, the Grover's algorithm is used, providing the opportunity for a specific feature search in the database. The paper also provides the main steps in constructing a similar fast search system. The transformation from a classical to a quantum representation algorithm is described. Such approach can be applied in other applications. Both the computational complexity and the verification correctness are also indicated in the paper.
Research on measurement path planning of complex object

Abstract In the past automatic measurement system for three-dimensional shape of complex parts, there was no corresponding measurement scheme for the groove-shaped complex parts, which there existed problems such as low work efficiency and large measurement error. In order to solve this problem, this paper is based on the principle of raster scanning projection measurement, and explores the mathematical model of projection grating trigonometry theory, and analyzes the different characteristics of scattered geometric features such as walls and edges inside the groove in complex parts. Combined with the scanning constraints such as the inclination angle and depth of field of the 3D scanner, the method of measuring the path planning of the groove-shaped complex parts was proposed and studied. Finally, the measurement path planning verification experiment was carried out, which is based on the above analysis. The results show that the position deviation during the measurement process is within 0.001mm, the angular deviation is within 0.009°, which proves the feasibility of the measurement path for the groove-shaped complex parts.
Academic Network Representation Learning Based on Metapath Tree

Abstract Network representation learning aims to use low-dimensional dense vectors to represent nodes in the graph, which can reflect the graph structure and can be used in a variety of machine learning tasks. The academic network contains richer information, which most of the current methods are unable to capture. This paper proposes a method to get better vectors in the academic network. This method first uses the metapath tree to guide the random walk process, and adds a sampling process to preserve multiple metapath information. The vector representation of the nodes is obtained by training using the skip-gram model on the academic network. Experiment results show that the proposed model outperforms several traditional network representation learning models in multi-label classification and clustering tasks.
A Kernel Iterative K-Means Algorithm

In this paper Mercer kernels with certain invariance properties are briefly introduced and an apparently not well-known construction using certain cohomology groups is described. As a consequence some kernels arising from this are given. Hence a kernel version of an iterative k-means algorithm due to Duda et al. is exhibited. It resembles the usual k-means algorithm but relies on a different update procedure and allows an elegant computation of the target function.
Probing IoT-based consumer services: ‘insights’ from the connected shower

Abstract This paper presents findings from the deployment of a technology probe—the connected shower—and implications for the development of ‘living services’ or autonomous context-aware consumer-oriented IoT services that exploit sensing to gain consumer ‘insight’ and drive personalised service innovation. It contributes to the literature on water sustainability and the potential role and barriers to the adoption of smart showers in domestic life. It also contributes to our understanding of context, which enables user activity to be discriminated and elaborated thereby furnishing the ‘insight’ living services require for their successful operation. Problematically, however, our study shows that context is not a property of sensor data. Rather than provide contextual insights into showering, the sensor data requires contextualisation to discriminate and elaborate user activity. Thus, in addition to examining the potential of the connected shower in everyday life, we consider how sensor data is contextualised through the doing of data work and the relevance of its interactional accomplishment and organisation to the design of living services.