Reviewerly/exHarmony huggingface dataset:
Story Based Video Retrieval using Deep Visual and Textual Information

From the past few decades, popularity of online news videos have been increased day by day because of rapid advancement in technology and internet availability. At most all the news channels have been uploading their news in the form of headlines and bulletins on world wide web. To watch complete news headline and bulletins of a specific TV channel is very time consuming. Users want to watch the news of their interest instead of watching the whole news-headlines and news-bulletins. This paper deals with the retrieval of a specific NEWS category like World, political, weather etc from the news headlines based on user requirements. Our proposed system is based on deep convolution neural network. Accuracy and effectiveness of proposed architecture have been evaluated using 2D convolution neural networks. The dataset has been collected from different news channels and sorted according to the categories. We have collected 200 videos for eight categories of news. Our trained model is able to recognize the categories of a news with 91.7% accuracy. The distinction between news categories with such high accuracy on the locally prepared dataset shows the novelty in research. The dataset is collected from 6 Pakistani news channels. Dataset and live Web application of this methodology will be made publicly available for research community.
Coflow scheduling algorithm based density peaks clustering

Recently, there are many applications using coflow-based scheduling to improve performance in a cluster. However, most applications implement coflow-based scheduling by modifying their API. These applications also need to modify the third-party libraries. This is very complicated and inefficient. Therefore, prior work has proposed a coflow-based scheduling algorithm CODA, which can automatically identify coflows. CODA does not have to design the API for each application and it is transparent for the application. But CODA performs poorly on the unstable traffic and rapid traffic in datacenter. In this paper, we propose a coflow scheduling algorithm named CS-DP to solve this problem. First, we employ the density peak clustering algorithm to implement a fast, application-transparent coflow identifier. Then we employ MLFQ (multi-level feedback scheduling queues) for scheduling. Moreover, we add a threshold calculation module on MLFQ. The key point of threshold calculation is to get the appropriate threshold by historical traffic. Thus, CS-DP can accommodate the current traffic quickly and effectively. Finally, the simulation results show CS-DP enabling communication stages to complete 1.11×(1.17×) and 2.6×(3.0×) faster on average(95-th percentile) compared to CODA algorithm and per-flow fairness on normal traffic.
Phase transitions in quantum annealing of an NP-hard problem detected by fidelity susceptibility

Quantum annealing (QA) for the NP-hard maximum independent set problem with a unique solution is studied using the quantum Monte Carlo method. A fraction of the samples exhibit first-order phase transitions in terms of the spin-glass order parameter q. Moreover, while no singularities could be observed for the sample-averaged , a divergence in the sample-averaged fidelity susceptibility is found. Since all of the first-order transitions of q occur with smaller transverse field strength compared to the divergence point of , it is likely that the phase detected by induces the first-order transitions. This suggests that failures of QA due to exponentially small energy gaps are phenomena within this underlying phase.
Learning Vision-Based Quadrotor Control in User Proximity

We consider a quadrotor equipped with a forward-facing camera, and an user freely moving in its proximity; we control the quadrotor in order to stay in front of the user, using only camera frames. To do so, we train a deep neural network to predict the drone controls given the camera image. Training data is acquired by running a simple hand-designed controller which relies on optical motion tracking data.
i-VALS: Visual Attention Localization for Mobile Service Computing

Identifying and localizing the user's visual attention can enable various intelligent service computing paradigms in a mobile environment. However, existing solutions can only compute the gaze direction, but without the distance to the intended target. In addition, most of them rely on eye tracker or similar infrastructure support. This paper explores the possibility of using portal mobile devices, e.g., smartphone, to detect the visual attention of a user. i-VALS only requires the user to do one simple action to localize the intended object: gazing at the intended object and holding up the smartphone so that the object and the user's face can be simultaneously captured by the front and rear cameras. We develop efficient algorithms to obtain both the distance between the camera and user, the user's gaze direction and the object's direction from the camera. The object's location can then be computed by solving a trigonometric problem. i-VALS has been prototyped on commercial off-the-shelf (COTS) devices. The extensive experiment results show that i-VALS achieves high accuracy and small latency, effectively supporting a large variety of applications in smart environments.
Maximizing Profits in Crop Planning Using Socio Evolution and Learning Optimization

Crop planning is the strategy of choosing the appropriate crop and allocating the field area for cultivation. The objective of crop planning is to choose the suitable crop among various competing crops that canJaya Brindha,G. be grown during a particular season. A best cropping plan must satisfy the objectives like maximizing the profitability and productivity of the crop and allocating the scarce resources such as water for irrigation and field area. Crop planning must be optimized, so that it maximizesGopi,E.S. the returns from every part of the land used for cultivation which satisfies all the constraints. Social evolution and learning optimization (SELO)—a sociocultural inspired metaheuristic algorithm based on human social behavior is explored to solve this crop planning problem. SELO is initially tested on four benchmark functions out of which two are unconstrained and two are constrained. SELO is further explored to solve a benchmark optimization problem on crop planning in Vaalharts Irrigation Scheme (VIS) in South Africa. SELO increases the total profits by ZAR 4,689,569 (ZAR is the South African currency.) when compared with the current practices. To evaluate the performance of SELO, the results are compared with the existing swarm intelligence metaheuristic techniques such as Cuckoo search, Firefly algorithm, Glowworm swarm optimization, and Genetic algorithm. While comparing with Cuckoo search (most effective swarm technique in VIS crop planning), SELO increases the total profits by ZAR 255,489 (ZAR is the South African currency.). In addition, a case study on decision support systems for crop planning in Cauvery Delta Region are attempted. SELO is investigated for the real-time statistics obtained for Thanjavur, Thiruvarur, Nagapattinam, and Tiruchirapalli districts of Cauvery Delta Region in Tamilnadu, India. It is observed that SELO optimizes the land use among various available crops and increases the profits by 8.92, 1.39, 2.98, and 9.16 crores ( is the Indian currency.) when compared with the corresponding current practices in Thanjavur, Thiruvarur, Nagapattinam, and Tiruchirapalli districts, respectively. The obtained results reveal that SELO has a good potential to solve these crop planning problems.
Labelled Non-zero Particle Flow for SMC-PHD Filtering

The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter assisted by particle flows (PF) has been shown to be promising for audio-visual multi-speaker tracking. A clustering step is often employed for calculating the particle flow, which leads to a substantial increase in the computational cost. To address this issue, we propose an alternative method based on the labelled non-zero particle flow (LNPF) to adjust the particle states. Results obtained from the AV16.3 dataset show improved performance by the proposed method in terms of computational efficiency and tracking accuracy as compared with baseline AV-NPF-SMC-PHD methods.
Investigating User Perceptions of HRI in Social Contexts

In this paper, we present the results of a recent user study that investigates if user perception of HRI in social contexts may be affected by changing the interaction modality with the robot.Leveraging on Robot Social Attribute Scale (RoSAS) survey and on a statistical analysis, our results show that, in some interaction modalities, a greater feeling of discomfort is felt by users interacting with the robot.Interestingly, results also show the influence of users' gender on the user perception.
Improving a Design Space: Pregnancy as a Collaborative Information and Social Support Ecology

Pregnancy is a major life experience that changes relationships, identities, and home environments. It is a personal, collaborative, and domestic process of health changes, behavioral adaptations, and social adjustments that goes beyond the medical care of a pregnant woman. Using the lenses of information and support ecologies, we examine whether the complexities of pregnancy are reflected in the design of mobile technologies that support this life altering experience. To do this, we analyzed 191 iOS pregnancy applications ("apps") to understand the types of functionality they supported. We found that the majority provided static medical and birth event information but had shallow functionality for leveraging social support. Almost all apps excluded expectant fathers, used gendered interfaces and information choices, and focused primarily on fetal development or the pregnant woman's physical health. We call for less gendered and more meaningfully collaborative mobile health technologies to support pregnancy.
Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

This paper enhances the recognition capabilities of the facial component-based techniques using the concepts of better Viola–Jones component detection and weighting facial components. Our method starts with enhanced Viola–Jones face component detection and cropping. The facial components are detected and cropped accurately during all pose-changing circumstances. The cropped components are represented by the histogram of oriented gradients (HOG). The weight of each component was determined using a validation process. Combining these weights was done by a simple voting technique. Three public databases were used: the AT&amp;T database, the PUT database, and the AR database. Several improvements are observed using the weighted voting recognition method presented in this paper.
Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval

Given limited time and space, IR studies often report few evaluation metrics which must be carefully selected. To inform such selection, we first quantify correlation between 23 popular IR metrics on 8 TREC test collections. Next, we investigate prediction of unreported metrics: given 1–3 metrics, we assess the best predictors for 10 others. We show that accurate prediction of MAP, P@10, and RBP can be achieved using 2–3 other metrics. We further explore whether high-cost evaluation measures can be predicted using low-cost measures. We show RBP(p = 0.95) at cutoff depth 1000 can be accurately predicted given measures computed at depth 30. Lastly, we present a novel model for ranking evaluation metrics based on covariance, enabling selection of a set of metrics that are most informative and distinctive. A greedy-forward approach is guaranteed to yield sub-modular results, while an iterative-backward method is empirically found to achieve the best results.
Network-Based Feature Extraction Method for Fraud Detection via Label Propagation

Using machine learning to detect fraud has become a new mainstream in the online lending industry. In the process of learning, feature engineering is a key step to determine the performance of the final model. Traditional feature engineering is mostly based on the intrinsic features of dataset, especially the combination of them. However, fraud has gradually become a group behavior. In a related network, nodes represent users, divided into fraudulent nodes and non-fraudulent nodes. Fraudulent nodes are sparsely related with non-fraudulent nodes, and the links between fraudulent nodes are closely related. This paper presents a method based on Label Propagation Algorithm to extract network features from the related network. Based on the related network containing of known fraudulent users and relationship between them, a personalized label propagation algorithm is used to infer the unknown user's fraud probability, and the fraud probability is regarded as a network-based derivative feature to increase the information entropy of the feature engineering. Additionally, we changed the initialization method of the transition probability matrix and label distribution matrix, to avoid the performance degradation of label propagation algorithm caused by the unbalanced distribution of fraud data. By testing in real datasets, 17% precision score of detecting fraudster was achieved using only network feature.
Informal Governance on Cryptomarkets for Illicit Drugs

A global phenomenon has emerged in recent years: that of anonymous drug marketplaces on the Internet that usually combines anonymising software and virtual currencies as a means of payment, concealing the identity and location of its users. These technological innovations on the Internet allow users to proceed with illicit drug transactions with almost completely anonymous identities and physical locations and thus minimise the risk of law enforcement intervention. On cryptomarkets, vendors offer a wide range of psychoactive substances and other goods or services for sale. In addition, the purchased drugs are delivered by traditional postal services without their knowledge. This chapter aims to analyse the development of practices of resistance in relation to antidrug policies, practices that unfold through anonymous drug marketplaces. To explore social practices of informal resistance, the chapter draws on a digital ethnographic study on cryptomarkets. It observes that users on cryptomarkets follow a self-proclaimed libertarian ideology to systematically bypass drug control policies. The chapter also reflects upon how both economically disadvantaged, socially excluded, marginalised, technically non-skilled users and drug addicts, as well as drug producers of the global South are systematically excluded from expressions of informal resistance to prohibition-based drug policies as they do not participate in cryptomarkets.
Analysis and Text Classification of Privacy Policies From Rogue and Top-100 Fortune Global Companies

In the present article, the authors investigate to what extent supervised binary classification can be used to distinguish between legitimate and rogue privacy policies posted on web pages. 15 classification algorithms are evaluated using a data set that consists of 100 privacy policies from legitimate websites (belonging to companies that top the Fortune Global 500 list) as well as 67 policies from rogue websites. A manual analysis of all policy content was performed and clear statistical differences in terms of both length and adherence to seven general privacy principles are found. Privacy policies from legitimate companies have a 98% adherence to the seven privacy principles, which is significantly higher than the 45% associated with rogue companies. Out of the 15 evaluated classification algorithms, Naïve Bayes Multinomial is the most suitable candidate to solve the problem at hand. Its models show the best performance, with an AUC measure of 0.90 (0.08), which outperforms most of the other candidates in the statistical tests used.
System of interaction between universities, scientific organizations and industrial enterprises under conditions of digital economy in Russia

Science and education are playing an undoubtedly key role in shaping the innovation economy referred to as 'knowledge-based economy'. Development of academic science is the basic condition of achieving the strategic goals of innovative transformations in the Russian Federation economy. Each link in the 'fundamental triangle' of national economy development (university — scientific organization — industrial enterprise) requires particular analysis, specific set of support tools and mechanism enabling proper interaction between 'fundamental triangle' parts. Purpose of activities performed by universities and scientific organizations is supplying real economy enterprises with high-end professionals and innovative developments. This article presents key findings of investigation of the Russian Federation higher school's science-and-innovation resources development for the period 2006–2015/2016. The paper is addressing the matters related to planned key indicators of Federal targeted program entitled 'Research and development works in the priority areas of RF science-and-technology complex for 2014–2020' acting in capacity of key mechanism intended to stimulate interactions within the 'fundamental triangle'.
The Use of the Teachers Professional Competencies Research Results in Improving the Quality of the Supplementary Vocational Education

This article discusses the issues of improvement of professional skill of pedagogical workers; substantiates the role of additional professional education in the formation of the continuous professional growth of the teacher; the diagnostics of the level of professional teachers’ competence research is given.
Autonomous Segmentation and Modeling of Brain Pathological Findings Based on Iterative Segmentation from MR Images

This paper deals with the design of an automated algorithm for segmentation and modeling pathological areas of MR brain imaging data. For segmentation purposes was used namely active contouring method in MATLAB. The proposed algorithm was tested on a dataset of 21 MR frames. This work also deals with the comparison efficiency of preprocessing image to improve segmentation results and subsequently testing and verifying the proposed algorithm for real image data.
Compaction of Topological Quantum Circuits by Modularization

A topological quantum circuit is a representation model for topological quantum computation, which attracts much attention recently as a promising fault-tolerant quantum computation model by using 3D cluster states. A topological quantum circuit can be considered as a set of “loops,” and we can transform the topology of loops without changing the functionality of the circuit if the transformation satisfies certain conditions. Thus, there have been proposed many researches to optimize topological quantum circuits by transforming the topology. There are two directions of research to optimize topological quantum circuits. The first group of research considers so-called a placement and wiring problem where we consider how to place “parts” in a 3D space which corresponds to already optimized sub-circuits. The second group of research focuses on how to optimize the structure and locations of loops in a relatively small circuit which is treated as one part in the above-mentioned first group of research. This paper proposes a new idea for the second group of research; our idea is to consider topological transformations as a placement and wiring problem for modules which we derive from the information how loops are crossed. By using such a formulation, we can use the techniques for placement and wiring problems, and successfully obtain an optimized solution. We confirm by our experiment that our method indeed can reduce the cost much more than the method by Paetznick and Fowler.
Neural Variational Matrix Factorization with Side Information for Collaborative Filtering

Probabilistic Matrix Factorization (PMF) is a popular technique for collaborative filtering (CF) in recommendation systems. The purpose of PMF is to find the latent factors for users and items by decomposing a user-item rating matrix. Most methods based on PMF suffer from data sparsity and result in poor latent representations of users and items. To alleviate this problem, we propose the neural variational matrix factorization (NVMF) model, a novel deep generative model that incorporates side information (features) of both users and items, to capture better latent representations of users and items for the task of CF recommendation. Our NVMF consists of two end-to-end variational autoencoder neural networks, namely user neural network and item neural network respectively, which are capable of learning complex nonlinear distributed representations of users and items through our proposed variational inference. We derive a Stochastic Gradient Variational Bayes (SGVB) algorithm to approximate the intractable posterior distributions. Experiments conducted on three publicly available datasets show that our NVMF significantly outperforms the state-of-the-art methods.
Block-based fuzzy-image authentication method

Fragile watermarking methods have been widely used for image authentication. A novel image-authentication method based on fuzzy coding is presented in this work. This method is a block-based method and it consists of watermark generation, watermark embedding, watermark extraction, tamper detection, and image recovery phases. First, the cover image is divided into nonoverlapping blocks. In the watermark-generation step, fuzzy sets and fuzzy coding are used. To generate fuzzy sets, three threshold points are selected from a histogram of the cover image and fuzzy sets are created by using these threshold points. Memberships of pixels are calculated to generate features. Fuzzy coding is used to generate the watermark. The watermark-embedding function uses ±1 data hiding function for each block. Therefore, high visual quality is achieved. The generated watermark is compared with the extracted watermark in the tamper-detection phase. Finally, a new image-recovery algorithm is proposed for recovering tampered areas. To evaluate the proposed method, visual quality, tamper detection ability, image recovery capability, and computational complexity were used. The experimental results clearly indicated that the proposed method was a success.