Reviewerly/exHarmony huggingface dataset:
An Analysis Study of Vertical Selection Task in Aggregated Search

Aggregated Search (AS) paradigm is generally decomposed into two sub-tasks, the first one is Vertical Selection that focuses on predicting which verticals, if any, are relevant given a user query. The second is Results Presentation, that aim at deciding where to present the different results of the selected verticals. In this study, we have focused on vertical selection sub-task, which is considered as the most important task in the Aggregated Search process, and that because the task of presenting the relevant results can only be done after the selection of the relevant verticals. But despite its interesting role, there is no huge literature regarding vertical selection, as it is difficult to assess the open research problems and the real advancement in this research field. The purpose of this article is to assemble and analyses the different efforts made in Vertical Selection to the date. This includes different sources of evidences used to make relevant predictions, as well as various vertical selection approaches and evaluation methodologies exploited in the literature. Research in this field has shown an evolution from 2008 to 2013, in the next three years (2013-2016), this area didn't receive significant attention from researchers. In the last two years, the vertical selection research has been renewed. This analysis study aims at better understanding and clarifying the advancement of this task which can bring new ideas to improve its performance in future works.
Interactive Subjective Study on Picture-level Just Noticeable Difference of Compressed Stereoscopic Images

The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picture-level JND (PJND) of compressed stereo images. The study, which involves 48 subjects and 10 stereoscopic images compressed with H.265 intra coding and JPEG2000, includes two parts. In the first part, we determine the minimum distortion that the HVS can perceive against a pristine stereo image. In the second part, we explore the minimum distortion that each subject perceives against a distorted stereo image. Modeling the distribution of the PJND samples as Gaussian, we obtain their complementary cumulative distribution functions, which are known as Satisfied User Ratio (SUR) functions. Statistical analysis results demonstrate that the SUR is highly dependent on the image contents. The HVS is more sensitive to distortion in images with more texture details. The compressed stereoscopic images and the PJND samples are collected in a data set called SIAT-JSSI, which we release to the public.
SMART Virtual Dental Learning Environment

SMART Virtual Dental Learning Environment (VDLE) provides a sophisticated program that does not bind the user to have expensive gadgets and to incorporate new technology in everyday life. As we see every day that new technologies are being incorporated in redundant ways and those that are actually worthwhile are not common in our environment. In this paper, we aim to use the Virtual Reality VR concept and pitch the idea of evolving the day to day activities to a better. Designing of SMART VDLE used for Teaching and Learning environments for collaborative environment endorsing content delivery across the globe.
A Reconfigurable Memory-Based Fast VLSI Architecture for Computation of the Histogram

Histogram computation is a fundamental task often encountered in image processing systems. This plays an important role in various applications like image registration, show-through correction method in scanned image of duplex mode printed document, etc. Mutual information (MI) is one of the best metric for intensity-based multimodality image registration. Similarity measurement between the images consumes significant amount of the execution time in image registration. Computation of MI requires to obtain individual and joint histogram of two images. Typical joint histogram sizes range between 32 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 256 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The demand of hardware resource for computation of histogram increases substantially with increasing of histogram size. The array-based method may not be a suitable candidate in this application, because of large histogram size. Computation of histogram is inherently sequential in nature. But a parallel computation of histogram would reduce the processing time, which is going to help various imaging systems. In this paper, a memory-based parallel algorithm for histogram computation and its possible VLSI architecture have been presented. The architecture is mapped in field programmable gate array. The proposed architecture utilizes 99.66% less of the hardware compared to latest available architecture in the literature and consumes 8.78 mW power.
Examining the classification and evolution of novice users’ mental models of an academic database in the search task completion process

The main task of this article is to develop a classification system for novice users’ mental models of an academic database and to elaborate upon the evolution mechanisms of those mental models. In total, 83 undergraduate students, mainly sophomores, who were all novice users of the academic database China National Knowledge Infrastructure (CNKI), participated in the experimental study. Their mental models were measured from the diagrams or pictures and corresponding interpretations they produced to articulate their perceptions of CNKI at five time points. A bottom-up encoding approach and content analysis were used to analyse the research data. The results demonstrated that novice users’ mental models of the academic database can be classified as either system-oriented or user-oriented perspectives. Six categories were identified in the system-oriented perspective, and three were identified in the user-oriented perspective. It was also found that the evolution of users’ mental models can be facilitated by retrieval tasks, and it is noteworthy that task type can influence the evolution of users’ mental models. Furthermore, the evolution process of users’ mental models can be seen as learning behaviour, which includes a learning session and a forgetting session.
Integrating expert knowledge for Bayesian network structure learning based on intuitionistic fuzzy set and Genetic Algorithm

The Bayesian network (BN) structure learning from the observational data has been proved to be a NP-hard problem. The expert knowledge is beneficial to determine the BN structure, especially when the data are scarce and the related variables are huge in the researched domain. In this paper, we prop ose a new BN structure learning method by integrating expert knowledge. On the one hand, to improve the performance of expert knowledge usage, the intuitionistic fuzzy set (IFS) is introduced to express and integrate the expert knowledge. The determination of BN priori structure is transformed into the group decision making problem. On the other hand, the improved Bayesian information criterion score function and the Genetic Algorithm search algorithm are used to obtain the most suitable structure under the constraints from the priori structure. Some experiments demonstrate the validity of proposed scheme and compare the performance with the existing research results. The obtained BN structure owns better performance. The more the quantity of expert knowledge is, the better the performance of BN structure learning would be. Finally, the proposed method is applied to the thickening process of gold hydrometallurgy to solve the practical problem.
Half-Hearted Inception, Miserable Existence, and the Untimely Death of the Bloggers' Register in Russia

The Russian Federation joins a list of countries where violence and terrorism often have a religious and an ethnic basis, and where the authorities seek ways to deal with the issue of online hate speech. In this context, in May 2014 the Russian Parliament enacted Federal Law No 97-FZ (the Bloggers’ Law). This legislation required the compulsory registration of all popular bloggers with the country's internet regulatory agency, Roskomnadzor, thereby forcing the disclosure of their real identities. The Bloggers’ Law has been widely criticised, however, and although the internet community is still engaged in a heated discussion over whether there exists a legal right to online anonymity, the law made anonymous blogging in Russia a daunting undertaking. The Bloggers’ Law was repealed in the second half of 2017, and updating of the ‘Bloggers’ Register’ ceased. This article attempts to (i) analyse the context and the reasoning behind the introduction of the Bloggers’ Register; (ii) disentangle the relevant legal provisions; and (iii) assess its effectiveness, drawing conclusions based on events in the Russian blogosphere during the period 2014–17.
Questioning Authority

Abstract When a library adds a book to its collection, it adds a surrogate record for that book in the library's catalog. To get this record the library will either download it or create a record for the book from an international bibliographic record database. Authors have records too. These are known as name authority records. Recently the standards for creating these records changed to allow library catalogers to record more personal information about authors in authority records. This includes information about gender. There began a collective effort by a handful of catalogers to revise the new instructions so that binary gender was not encoded into the metadata of library records. This paper outlines the developments, results, and implications of this work.
Using Dynamic Software Product Lines to Implement Adaptive SGX-enabled Systems

In the light of computational outsourcing and external data storage, data protection and trusted execution become increasingly important. Novel hardware such as Intel's Software Guard extensions (SGX) attempts to provide a solution to protect data and computations from unauthorized access and manipulation, even against attackers with physical access to a machine. However, the current generation of SGX limits the protected memory space that can be efficiently used to 128 MiB, which must be shared between data and binary code. Thus, we propose to use a software product line approach to tailor an application's binary code in such a way that it can be updated during runtime, with the goal to only store relevant features in the protected memory at a given time. We provide a prototypical implementation that enables basic support for loading and unloading features during runtime and evaluate our prototype in terms of execution times against non-adaptive execution.
A 256 pixel, 21.6 μW infrared gesture recognition processor for smart devices

This paper presents a low-power digital signal processor for gesture detection using infrared (IR) sensors. IR compatible detecting algorithms are introduced to reduce power consumption and provide accurate detections for various types of gestures including sweeping in 8 directions, zooming in/out and a unique wake-up gesture. To further reduce the power, the proposed algorithm is capable of setting the processor into idle mode when there is no motion and resume active mode upon wake-up request. Test chips were fabricated in a 65 nm commercial TSMC process and occupy an area of 580 μm × 580 μm. Measurement results taking input from a 16 × 16 Heiman IR sensor array show that the proposed gesture recognition processor is able to detect correct gestures at 100 MHz maximum clock frequency. Under normal working condition, the processor consumes 21.6 μW in active mode and 7.2 μW in idle mode while processing 8-bit 16 × 16 input data array at 30 fps.
Efficient multi-resource scheduling algorithm for hybrid cloud-based large-scale media streaming

With the increase in the amount of dynamic and personalized content in media streaming systems, a solution that is based on only the cloud or content delivery network (CDN) cannot satisfy all application-specific requirements. Thus integrating the cloud with CDNs and private data centers can be advantageous. To efficiently deliver a huge amount of media content using a geographically distributed hybrid cloud, we propose a Multi-Resource Scheduling Algorithm for Hybrid Cloud-Based Large-Scale Media Streaming(MHLMS). The algorithm transforms resource scheduling into a Nash bargaining problem and solves it efficiently from a geometrical perspective. The simulation results show that compared to algorithms that use only the cloud or CDN, MHLMS can reduce the cost by approximately 60% and reduce the average delivery distance by up to 70%. Thus, compared with the existing video delivery methods, MHLMS can handle millions of channels effectively with much lower time complexity.
Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.
Do citation chimeras exist? The case of under‐cited influential articles suffering delayed recognition

In this study we investigate if articles suffering delayed recognition can at the same time be under‐cited influential articles. Theoretically these two types of articles are independent, in the sense that suffering delayed recognition depends on the number and time distribution of received citations, while being an under‐cited influential article depends only partially on the number of received (first generation) citations, and much more on second and third citation generations. Among 49 articles suffering delayed recognition we found 13 that are also under‐cited influential. Based on a thorough investigation of these special cases we found that so‐called authoritative citers play an important role in uniting the two different document types into a special citation chimera. Our investigation contributes to the classification of publications.
Exploring How Interactive Technology Enhances Gesture-Based Expression and Engagement: A Design Study

The interpretation and understanding of physical gestures play a significant role in various forms of art. Interactive technology and digital devices offer a plethora of opportunities for personal gesture-based experience and they assist in the creation of collaborative artwork. In this study, three prototypes for use with different digital devices (digital camera, PC camera, and Kinect) were designed. Subsequently, a series of workshops were conducted and in-depth interviews with participants from different cultural and occupational backgrounds. The latter were designed to explore how to specifically design personalised gesture-based expressions and how to engage the creativity of the participants in their gesture-based experiences. The findings indicated that, in terms of gesture-based interaction, the participants preferred to engage with the visual traces that were displayed at specific timings in multi-experience spaces. Their gesture-based interactions could effectively support non-verbal emotional expression. In addition, the participants were shown to be strongly inclined to combine their personal stories and emotions into their own gesture-based artworks. Based on the participants’ different cultural and occupational backgrounds, their artistic creation could be spontaneously formed.
Topological Map Construction Based on Region Dynamic Growing and Map Representation Method

In the human–machine interactive scene of the service robot, obstacle information and destination information are both required, and both kinds of information need to be saved and used at the same time. In order to solve this problem, this paper proposes a topological map construction pipeline based on regional dynamic growth and a map representation method based on the conical space model. Based on the metric map, the construction pipeline can initialize the region growth point on the trajectory of the mobile robot. Next, the topological region is divided by the region dynamic growth algorithm, the map structure is simplified by the minimum spanning tree, and the similar region is merged by the region merging algorithm. After that, the parameter TM (topological information in the map) and the parameter OM (occupied information in the map) are used to represent the topological information and the occupied information. Finally, a topological map represented by the colored picture is saved by converting to color information. It is highlighted that the topological map construction pipeline is not limited by the structure of the environment, and can be automatically adjusted according to the actual environment structure. What’s more, the topological map representation method can save two kinds of map information at the same time, which simplifies the map representation structure. The experimental results show that the map construction method is flexible, and that resources such as calculation and storage are less consumed. The map representation method is convenient to use and improves the efficiency of the map in preservation.
Understanding Optimizations and Measuring Performances of PBKDF2

Password-based key derivation functions (KDFs) are used to generate secure keys of arbitrary length implemented in many security-related systems. The strength of these KDFs is the ability to provide countermeasures against brute-force/dictionary attacks. One of the most implemented KDFs is PBKDF2. In order to slow attackers down, PBKDF2 uses a salt and introduces computational intensive operations based on an iterated pseudorandom function. Since passwords are widely used to protect personal data and to authenticate users to access specific resources, if an application uses a small iteration count value, the strength of PBKDF2 against attacks performed on low-cost commodity hardware may be reduced. In this paper we introduce the cryptographic algorithms involved in the key derivation process, describing the optimization techniques used to speed up PBKDF2-HMAC-SHA1 in a GPU/CPU context. Finally, a testing activity has been executed on consumer-grade hardware, and experimental results are reported.
A Robus Method for Wheelchair Detection: a Combination of the Gaussian Mixture Models and Histogram of Oriented Gradients

An important function for a smart health care system, aiming to maximize safety and comfort to elderly or people with dysfunctional legs, is the automatic detection of a wheelchair captured from a visual surveillance system. In this paper, we proposed a method for detecting a two-dimensional wheelchair image using a combination of the Gaussian Mixture Models (GMMs) and the Histogram of Oriented Gradients (HOG). The proposed method consists of three main steps: (i). foreground segmentation, (ii). feature vector extraction, and (iii). wheelchair detection. The GMMs technique was used to extract a moving object from a background, while the underlying feature vectors of the moving objects were obtained using the HOG method. Finally, the Support Vector Machines (SVM) was implemented to classify a wheelchair object. We implemented 1,217 images for evaluating the performance of our proposed method which results in 86.01% of the accuracy rate. The advantage of our proposed approach is that it can detect a wheelchair effectively without any knowledge or prior information of the previous frames.
Compact Storage of Superblocks for NIPoPoW Applications

Blocks in proof-of-work (PoW) blockchains satisfy the PoW equation $$H(B) \le T$$. If additionally a block satisfies $$H(B) \le T2^{-\mu }$$, it is called a $$\mu $$-superblock. Superblocks play an important role in the construction of compact blockchain proofs which allows the compression of PoW blockchains into so-called Non-Interactive Proofs of Proof-of-Work (NIPoPoWs). These certificates are essential for the construction of superlight clients, which are blockchain wallets that can synchronize exponentially faster than traditional SPV clients. In this work, we measure the distribution of superblocks in the Bitcoin blockchain. We find that the superblock distribution within the blockchain follows expectation, hence we empirically verify that the distribution of superblocks within the Bitcoin blockchain has not been adversarially biased. NIPoPoWs require that each block in a blockchain points to a sample of previous blocks in the blockchain. These pointers form a data structure called the interlink. We give efficient ways to store the interlink data structure. Repeated superblock references within an interlink can be omitted with no harm to security. Hence, it is more efficient to store a set of superblocks rather than a list. We show that, in honest executions, this simple observation reduces the number of superblock references by approximately a half in expectation. We then verify our theoretical result by measuring the improvement over existing blockchains in terms of the interlink sizes (which we improve by $$79\%$$) and the sizes of succinct NIPoPoWs (which we improve by $$25\%$$). As such, we show that deduplication allows superlight clients to synchronize $$25\%$$ faster.
An intelligent interconnected network with multiple reservoir computing

In this paper, considering the decomposition and composition mechanism of the traditional reservoir, an intelligent interconnected network with multiple reservoir computing in series–parallel configuration, called MRC-IIN, is proposed for multivariate time series classification and prediction. Firstly, according to the unsupervised learning of restricted Boltzmann machine and the Pearson correlation of multivariate inputs, the number of 1st column reservoirs of MRC-IIN can be determined, and each row interconnected reservoirs of MRC-IIN can also be determined. Secondly, according to the input autocorrelation of 1st column reservoirs, the number of each row reservoirs of MRC-IIN can be determined, such that the dynamic characteristics of the corresponding input can be fully reflected. Thirdly, through using the advantages of principal component analysis, each reservoir size of MRC-IIN can be determined, such that the feature information of each row reservoir states can be reduced layer by layer, and the most essential feature information can be retained. Fourthly, in order to ensure the stable application of MRC-IIN, a sufficient condition for global echo state property of MRC-IIN is given. Finally, the simulation results show that the MRC-IIN can improve classification and prediction performance.