Reviewerly/exHarmony huggingface dataset:
Recommendation algorithm based on user score probability and project type

The interaction and sharing of data based on network users make network information overexpanded, and "information overload" has become a difficult problem for everyone. The information filtering technology based on recommendation could dig out the needs and hobbies of users from the historical behavior, historical data, and social network and filter out useful resource for users in accordance with the needs and hobbies from the accumulation of information resource. Collaborative filtering is one of the core technologies in the recommendation system and is also the most widely used and most effective recommendation algorithm. In this paper, we study the accuracy and the data sparsity problems of recommendation algorithm. On the basis of the conventional algorithm, we combine the user score probability and take the commodity type into consideration when calculating similarity. The algorithm based on user score probability and project type (UPCF) is proposed, and the experimental data set from the recommendation system is used to validate and analyze data. The experimental results show that the UPCF algorithm alleviates the sparsity of data to a certain extent and has better performance than the conventional algorithms.
QoE-Aware Resource Allocation for Multiple Cloud Gaming Users Sharing a Bottleneck Link

Cloud gaming offers the capability of delivering high-quality graphics games to any type of end user device, however at the cost of high bandwidth consumption and strict latency requirements. Meeting Quality of Experience (QoE) requirements under limited resource availability calls for efficient and dynamic service adaptation. In this paper, we formulate an optimization problem for QoE-aware resource allocation for multiple cloud gaming users sharing a bottleneck link. The optimization problem is solved by utilizing algorithms that employ QoE estimation models derived from subjective studies for different types of games. We specifically investigate the impact on the resource allocation outcome when jointly considering both quality and QoE fairness as optimization objectives. The QoE-aware algorithms are shown to achieve higher average and higher minimum MOS values compared to a baseline algorithm. Results also confirm that both a cloud gaming service provider and resource provider should consider game type when adapting video coding parameters and allocating resources.
Using a many-objective approach to investigate automated refactoring

Software maintenance is expensive and so anything that can be done to reduce its cost is potentially of huge benefit. However, it is recognised that some maintenance, especially refactoring, can be automated. Given the number of possible refactorings and combinations of refactorings, a search-based approach may provide the means to optimise refactorings. This paper describes the investigation of a many-objective genetic algorithm used to automate software refactoring, implemented as a Java tool, MultiRefactor. The approach and tool is evaluated using a set of open source Java programs. The tool contains four separate measures of software looking at the software quality as well as measures of code priority, refactoring coverage and element recentness. The many-objective algorithm combines the four objectives to improve the software in a holistic manner. An experiment has been constructed to compare the many-objective approach against a mono-objective approach that only uses a single objective to measure software quality. Different permutations of the objectives are also tested and compared to see how well the different objectives can work together in a multi-objective refactoring approach. The eight approaches are tested on six different open source Java programs. The many-objective approach is found to give better objective scores on average than the mono-objective approach and in less time. However, the priority and element recentness objectives are both found to be less successful in multi/many-objective setups when they are used together. A many-objective approach is suitable and effective for optimising automated refactoring to improve quality. Including other objectives does not unduly degrade the quality improvements, but is less effective for those objectives than if they were used in a mono-objective approach.
Comparative Analysis of a Deep Convolutional Neural Network for Source Camera Identification

In image forensics, there is widespread growing investigations for Source Camera Identification (SCI) to identify the legitimate source of an image by using different approaches including filtering-based techniques and deep neural networks. Therefore, the aim of this paper is to shed light on the application of source camera identification in digital forensic science via a comparative analysis of state-of-the art filtered-based technique and deep learning convolutional neural networks-based (CNN). To this end, the basic sensor pattern noise-based estimation technique using the Wiener filter in the wavelet domain is considered as well as deep learning CNN model in order to assess the effectiveness of SCI. Different experiments have been carried out on our database of images taken from eleven different cameras using the same set of training and test images with dimensions 128 × 128 and 256 × 256 for both approaches. From the results of the overall false positive rates, false negative rates and overall accuracy, this paper suggests that filtered-based PRNU approach for SCI outperforms the deep learning CNN approach in digital image forensics when limited number of images are available in forensic investigations.
Assessing the Effectiveness of using YouTube Videos in Teaching the Chemistry of Group I and VII Elements in a High School in Lesotho

This study, based on the theory of Technological Pedagogical Content Knowledge investigated the effectiveness of introducing open source YouTube videos in the teaching and learning of the Chemistry topic ‘Group Properties’ at a high school in Lesotho. A quasi-experimental design was used for two conveniently selected Form D classes; one class used as the experimental group ( n = 49) and the other as a control group ( n = 60). The questions in the pre-test and the similar post-test were formulated to establish the learners’ cognitive abilities in line with various levels of Bloom’s Cognitive Domain. The mean performance of the two groups before the intervention was not statistically different. After introducing the experimental group to YouTube videos the performance of the group was significantly better than that of the control group during the post-test. The performance of the experimental group was also significantly better than that of the control group at the higher cognitive levels of Bloom’s Cognitive Domain, namely, at application to evaluation. The incorporation of videos in teaching Chemistry provided a better option than the traditional method of science teaching. The use of the YouTube videos resulted in a doubling of the percentage of the experimental group learners passing the post-test (from 12 to 27%), while the percentage of control group learners passing the tests remained constant at 5% for both the pre-test and the post-test. In light of the significant benefits of using YouTube videos in this context of poor laboratory resources, it is recommended that teachers should be trained to integrate technology in their teaching in order to supplement practical work.
Relative velocity measurement using low cost single camera-based stereo vision system

Measurement of depth and relative velocity of a moving object from a camera system are the most important issues in different applications of computer vision such as robotic vision, intelligent vehicle etc. From the human stereo vision system, we know that, there is a slight difference between the two images captured by our two eyes at a particular instant due to the difference in positions of our eyes on our face. This difference is known as disparity and has a significant role in determining the distance of an object from our eyes. The same principle and disparity may be used as a parameter for measuring the relative velocity of an object which has relative motion towards or away from the camera system. This article presents a novel low cost, low resolution stereo vision system based on a single camera, which may be used to measure the position and relative velocity of a moving object. This new system is very simple in design, low cost and capable of measuring the depth and relative velocity with desirable accuracy.
Multimodal subspace support vector data description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.
Dynamic selection of fitness function for software change prediction using Particle Swarm Optimization

Over the past few years, researchers have been actively searching for an effective classifier which correctly predicts change prone classes. Though, few researchers have ascertained the predictive capability of search-based algorithms in this domain, their effectiveness is highly dependent on the selection of an optimum fitness function. The criteria for selecting one fitness function over the other is the improved predictive capability of the developed model on the entire dataset. However, it may be the case that various subsets of instances of a dataset may give best results with a different fitness function. The aim of this study is to choose the best fitness function for each instance rather than the entire dataset so as to create models which correctly ascertain the change prone nature of majority of instances. Therefore, we propose a novel framework for the adaptive selection of a dynamic optimum fitness function for each instance of the dataset, which would correctly determine its change prone nature. The predictive models in this study are developed using seven different fitness variants of Particle Swarm Optimization (PSO) algorithm. The proposed framework predicts the best suited fitness variant amongst the seven investigated fitness variants on the basis of structural characteristics of a corresponding instance. The results of the study are empirically validated on fifteen datasets collected from popular open-source software. The proposed adaptive framework was found efficient in determination of change prone classes as it yielded improved results when compared with models developed using individual fitness variants and fitness-based voting ensemble classifiers. The performance of the models developed using the proposed adaptive framework were statistically better than the models developed using individual fitness variants of PSO algorithm and competent to models developed using machine learning ensemble classifiers.
A robust and efficient framework for fast cylinder detection

In this work, a complete solution is provided for detecting and identifying cylindrical shapes, which are commonly found in household and industrial environments, using consumer-grade RGB-D cameras. Most standard approaches to detect and identify cylinders are not robust to outliers (e.g. points on other objects in the scene), which limits their applicability in realistic scenes. In addition, these methods fail to benefit from environmental constraints, e.g. the fact that cylinders often lie or stand on flat surfaces. To tackle the aforementioned limitations, we introduce three main novelties: (i) a point cloud soft voting scheme with curvature information that reduces the influence of outliers and noise, (ii) a selective sampling of the orientation space that favorsorientations known a priori, and (iii) a deep-learning based classifier to filter out objects with non-cylindrical appearance in the 2D images, thus further improving robustness to outliers. A set of experiments with synthetically generated data are used to assess the robustness of our fitting method to different levels of outliers and noise. The results demonstrate that incorporating the principal curvature direction within the orientation voting process allows for large improvements on cylinders parameters estimation. Furthermore, we demonstrate that combining the 2D deep-learning cylinder classifier with the 3D orientation voting scheme allows for large speed-up and accuracy improvements on cylinder identification. The qualitative and quantitative results with real data acquired from a consumer RGB-D camera, confirm the advantages of the proposed framework.
Predicting Video-frames Using Encoder-convlstm Combination

Video generation is an active field of research. With the rise in the amount of available data and economically available processing power in the form of GPUs, deep Learning has been a go-to solution for many real life problems and similarly it is often attempted to solve the problem of video generation using deep learning. Predicting the next set of frames for a given set of frames in a video has seldom been taken up. Each video is composed of a consecutive closely related frames of images. If we consider these frames, the frame in each time-step seems to be related to the frames in the preceding time-steps. Therefore, we have both spatial and temporal data available from any set of consecutive frames in a video. Learning some sort of representation of the images that encodes the spatial data of the images (frames) can be combined with learning how these representations of a particular time-step is related with the next few time-steps is made possible, then prediction of the next few frames for a given set of frames is made possible. Our aim is to propose a simple yet effective model that can achieve this goal.
Prediction of reading difficulty in Russian academic texts

Education policy makers view measuring academic texts readability and profiling classroom textbooks as a primary task of education management aimed at sustaining quality of reading programs. As Russian readability metrics, i.e. “objective” features of texts determining its complexity for readers, a re still a research niche, we undertook a comparative analysis of academic texts features exemplified in textbooks on Social Science and examination texts of Russian as a foreign language. Experiments for 7 classifiers and 4 methods of linear regression on Russian Readability corpus demonstrated that ranking textbooks for native speakers is a much more difficult task than ranking examination texts written (or designed) for foreign students. The authors see a possible reason for this in differences between two processes: acquiring a native language on the one hand and learning a foreign language on the other. The results of the current study are extremely relevant in modern Russia which is joining the Bologna Process and needs to provide profiled texts for all types of learners and testees. Based on a qualitative and quantitative analysis of a text, the research offers a guide for education managers to help build consensus on selecting a reading material when educators have differing views.
SAQIP

Proposing an architecture that efficiently compensates for the inefficiencies of physical hardware with extra resources is one of the key issues in quantum computer design. Although the demonstration of quantum systems has been limited to some dozen qubits, scaling the current small-sized lab quantum systems to large-scale quantum systems that are capable of solving meaningful practical problems can be the main goal of much research. Focusing on this issue, in this article a scalable architecture for quantum information processors, called SAQIP, is proposed. Moreover, a flow is presented to map and schedule a quantum circuit on this architecture. Experimental results show that the proposed architecture and design flow decrease the average latency and the average area of quantum circuits by about 81% and 11%, respectively, for the attempted benchmarks.
Tracking Multiple Image Sharing on Social Networks

Social Networks (SN) and Instant Messaging Apps (IMA) are more and more engaging people in their personal relations taking possession of an important part of their daily life. Huge amounts of multimedia contents, mainly photos, are poured and successively shared on these networks so quickly that is not possible to follow their paths. This last issue surely grants anonymity and impunity thus it consequently makes easier to commit crimes such as reputation attack and cyberbullying. In fact, contents published within a restricted group of friends on an IMA can be rapidly delivered and viewed on a SN by acquaintances and then by strangers without any sort of tracking. In a forensic scenario (e.g., during an investigation), succeeding in understanding this flow could be strategic, thus allowing to reveal all the intermediate steps a certain content has followed. This work aims at tracking multiple sharing on social networks, by extracting specific traces left by each SN within the image file, due to the process each of them applies, to perform a multi-class classification. Innovative strategies, based on deep learning, are proposed and satisfactory results are achieved in recovering till triple up-downloads.
Continuous Bengali Speech Recognition Based On Deep Neural Network

Nowadays, deep learning is the most reliable approaches in the field of speech recognition to do the Acoustic modeling. Working with a language like “Bengali” that is not very resource-rich in terms of availability of parallel data (i.e. speech with aligned text) is a challenging problem. Also, there are lots of approaches going with deep learning to achieve better performance in Bengali Language without benchmarking a specific corpus. So, the achieved results are biased. In this paper, DNN-HMM and GMM-HMM based models have been used, which have been implemented in Kaldi toolkit, for continuous Bengali speech recognition benchmarking on a standard and publicly published corpus called SHRUTI. Previously, the best word error rate (WER) had been achieved on SHRUTI was 15% using eMU-SPHINX based GMM-HMM and this study has been shown that using Kaldi based feature extraction recipes with DNN-HMM and GMM-HMM acoustic models have achieved performances WER 0.92% and WER 2.02% respectively. Another finding of this study is, the WERs of both models are very close because the size of the corpus is small.
Improving Object Detection with Relation Graph Inference

Many classic object detection approaches have proven that detection performance can be improved by adding the object's context information. However, only a few methods have attempted to exploit the object-to-object relationship during learning. The reason for this is that objects may appear at different locations in an image, with an arbitrary size and scale. This makes it difficult to model the objects in a unified way within a network. Inspired by Graph Convolutional Network (GCN), we propose a detection algorithm that can infer the relationship among multiple objects during the inference, achieved by constructing a relation graph dynamically with a self-adopted attention mechanism. The relation graph encodes both the geometric and visual relationship between objects. This can enrich the object feature by aggregating the information from the object and its relevant neighbors. Experiments show that our proposed module can efficiently improve the detection performance of existing object detectors.
A Hierarchical Neural Summarization Framework for Spoken Documents

Extractive text or speech summarization seeks to select indicative sentences from a source document and assemble them together to form a succinct summary, so as to help people to browse and understand the main theme of the document efficiently. A more recent trend is towards developing supervised deep learning based methods for extractive summarization. This paper extends and contextualizes this line of research for spoken document summarization, while its contributions are at least three-fold. First, we propose a neural summarization framework with the flexibility to incorporate extra acoustic/prosodic and lexical features, for which the ROUGE evaluation metric is embedded into the training objective function and can be optimized with reinforcement learning. Second, disparate ways to integrate acoustic features into this framework are investigated. Third, the utility of our proposed summarization methods and several widely-used state-of-the-art ones are extensively compared and evaluated. A series of empirical experiments seem to demonstrate the effectiveness of our summarization methods.
AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.
A multilayered semantic framework for integrated forensic acquisition on social media

In recent years, examination of the social media networks has become an integral part of investigations. Law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process needs collection and analysis of the information which is immense, heterogeneous, and spread across multiple social networks. This process is technically intricate due to heterogeneous and unstructured online social networks (OSNs). Hence, creating cognitive challenges and massive workloads for the investigators. Therefore, it is imperative to develop automated and reliable solutions to assist investigators. Capturing the forensic information in the structured form is crucial for automation, sharing, and interoperability. This paper introduces the design of a multi-layer framework; from collection to evidence analysis. The central component of this framework is a hybrid ontology approach that involves multiple ontologies to manage the unstructured data and integrate various social media data collections. This approach aims to find the evidence by automated methods that are trustworthy and therefore admissible in a court of law.