Reviewerly/exHarmony huggingface dataset:
Reverse travel time imaging of microseismic location

ABSTRACTThe data sources used in microseismic locating include mainly the arrival time or waveform (wavefield value) in the microseismic record, and the algorithms used are mainly inversion or reve...
Improving Performance in Software Defect Prediction Using Variational Autoencoder

Software defect prediction (SDP) is a beneficial task to save limited resources in the software testing stage for improving software quality. However, the imbalanced distribution in defect datasets could be a challenge for often machine learning algorithms, an effect on the performance of the algorithms. To overcome this issue, oversampling techniques from the minority class has been adopted. In this work, we suggest a new oversampling method, which trained a variational autoencoder (VAE) to generate synthesized samples aimed for output mimicked minority samples that were then combined with training dataset into an augmented training dataset. In the experiments, we explored ten SDP datasets from the PROMISE freely accessible repository. We measured the performance of the proposed method by comparing it with state-of-the-art oversampling techniques including Random Over-Sampling, SMOTE, Borderline-SMOTE, and ADASYN. Based on the investigation results, the proposed method provides better mean performance of SDP models between all examined techniques.
Video Recommendation System Based on Human Interest

In today's world watching online videos have become a popular trend and a daily habit of our new generation. Videos are a reliable source for gaining knowledge and it is easier to grasp information through videos than reading. The internet is flooded with billions of videos hence it is a time consuming task for user to find a relevant video. So to save time as well as efforts there is a necessity to build a strong, efficient and accurate recommendation system which will display appropriate videos for the users. Video recommendation system saves users from browsing lots of videos to choose the appropriate ones, and on the other hand, it also brings the video websites more network traffic and user stickiness. The main task of the system is to provide personalized recommendations using Web Crawler, Rating Factor Neural Network, Slope one, and Slope one based Map Reduce of two types, one is Content-based filtering, and the other is Collaborative Filtering. Presentation of recommendations is an important part of the overall user experience. Video recommendation algorithm is the core of the system. The proposed paper is about the system which allows user to search for their favorite videos and the system recommends videos relevant to their choice.
Concept Drift Adaptive Physical Event Detection for Social Media Streams

Event detection has long been the domain of physical sensors operating in a static dataset assumption. The prevalence of social media and web access has led to the emergence of social, or human sensors who report on events globally. This warrants development of event detectors that can take advantage of the truly dense and high spatial and temporal resolution data provided by more than 3 billion social users. The phenomenon of concept drift, which causes terms and signals associated with a topic to change over time, renders static machine learning ineffective. Towards this end, we present an application for physical event detection on social sensors that improves traditional physical event detection with concept drift adaptation. Our approach continuously updates its machine learning classifiers automatically, without the need for human intervention. It integrates data from heterogeneous sources and is designed to handle weak-signal events (landslides, wildfires) with around ten posts per event in addition to large-signal events (hurricanes, earthquakes) with hundreds of thousands of posts per event. We demonstrate a landslide detector on our application that detects almost 350% more landslides compared to static approaches. Our application has high performance: using classifiers trained in 2014, achieving event detection accuracy of 0.988, compared to 0.762 for static approaches.
Estimating distance threshold for greedy subspace clustering

Many approaches have been proposed to recognize clusters in subspaces. However, their performance is highly sensitive to input parameter values. The purpose and expected ranges of these parameters may not available to a non-expert user. The parameter setting producing optimal results can only be known after repeated execution of the clustering process every time with a different set, which is very time consuming. Most of the existing algorithms show high runtimes due to excessive data scans. In this work, we propose a subspace clustering technique that estimates the distance threshold parameter automatically from the data for each attribute and works on the basis of single linkage clustering, in bottom up, greedy fashion. The experimental results show that, the algorithm produces optimal results without accepting any input from the user, achieves up to 10 times better runtime and improved accuracy in a single run without requiring any tuning of parameter values.
Time Series Analysis for Efficient Sample Transfers

Real-time transfer optimization approaches offer promising solutions as they can discover optimal transfer configuration in the runtime without requiring an upfront work or making assumptions about underlying system architectures. On the other hand, existing implementations suffer from slow convergence speed due to running many sample transfers with suboptimal configurations. In this work, we evaluate time-series models to minimize the impact of sample transfers with suboptimal configurations by shortening the transfer duration without degrading the accuracy. The results gathered in various networks with rich set of transfer configurations indicate that, in most cases, Autoregressive model can accurately estimate sample transfer throughput in less than 5 seconds which is up-to 4x improvement over the state-of-the-art solution. We also realized that while the most common transfer applications report transfer throughput at most once a second, decreasing the reporting interval is the key to further reduce the impact of sample transfers by quickly determining their performance.
Immersive mixed reality object interaction for collaborative context-aware mobile training and exploration

The recent generation of mobile devices brings the necessary processing power, sensorics and other hardware to enable highly interactive and immersive mixed reality experiences. However, existing applications mainly focus on single users or the individual experience, and are often limited to basic user-device interaction.
Towards Agent Organizations Interoperability: A Model Driven Engineering Approach

In the research and development of multiagent systems (MAS), one of the central issues is how to conciliate the autonomy of the agents with a desirable and stable behavior of the MAS as a whole. Agent organizations have been proposed as a suitable metaphor for engineering social order in MAS. However, this emphasis has led to several proposals of organizational models for MAS design, thus creating an organizational interoperability problem: How to ensure that agents, possibly designed to work with different organizational models, could interact and collectively solve problems? In this paper, we have adopted techniques from Model Driven Engineering to handle this problem. In particular, we propose an abstract and integrated view of the main concepts that have been used to specify agent organizations, based on several organizational models present in the literature. We apply this integrated view to design MAORI, a model-based architecture for organizational interoperability. We present a MAORI application example that has shown that our approach is computationally feasible, enabling agents endowed with heterogeneous organizational models to cooperatively solve a problem.
Resolving the Skills Paradox

Today, the demand for IS professionals is greater than the supply, and filling the IT pipeline with individuals with the right skills and knowledge is more important than ever. Gaining a better understanding of the technical skills and "soft skills" needed in IS jobs will enable industry professionals to focus their recruiting and development practices on these high-priority competencies. In addition academic professionals can better prepare graduates with the right skills and knowledge. The overall objective of this study of IS job descriptions was to determine the job skills in existing IS jobs. This is the first study which is based upon an analysis of actual job descriptions within a large IS operation. The study addressed three research questions, including the skills identified in the job descriptions, the skills weighted by the number of IS professionals in each job, and the skills relevant to job clusters consisting of comparable tasks. Our findings explain the paradox of survey studies showing a predominant need for interpersonal and communications skills while the job advertisements emphasize technical skills. Our analysis indicates that IS job skill and knowledge components include a cross-section of technical skills and "soft skills," and that both skill sets are important criteria in hiring, training, and career development.
A Generalized Multi-Objective Framework for UAV Mission Planning

We present a novel framework for mission/trajectory planning of electrically powered UAVs by formulating a multiobjective multi-constraints optimization problem. The problem considers a number of objectives such as, flight time, harshness and aging under maximum available energy, peak power, maximum allowed time and initial-final target state. In particular, we show that the power demand is related to the angular velocity, as well as we introduce the concept of harshness (inverse of trajectory smoothness), and aging of the mechanical structure induced by the use or by environmental effects. The multiobjective optimization problem (MOP) is solved by introducing a new decomposition approach where a number of scalar optimization sub-problems are jointly solved using an evolutionary algorithm. Numerical results are reported to both verify performance and mission feasibility as well as to determine computation time which is a key element to be considered for real time applications.
Dynamics of Quantum Coherence in Bell-Diagonal States under Markovian Channels*

Abstract We study the curves of coherence for the Bell-diagonal states including l 1 -norm of coherence and relative entropy of coherence under the Markovian channels in the first subsystem once. For a special Bell-diagonal state under bit-phase ip channel, we find frozen coherence under l 1 norm occurs, but relative entropy of coherence decrease. It illustrates that the occurrence of frozen coherence depends on the type of the measure of coherence. Also, we study the coherence evolution of Bell-diagonal states under Markovian channels in the first subsystem n times and find that coherence under depolarizing channel decreases initially then increases for small n and tends to zero for large n. The dynamics of coherence of the Bell-diagonal state under two independent same type local Markovian channels is discussed.
CuLDA

Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training process is very time-consuming, which prevents the adoption of LDA in many scenarios, e.g., online service. GPUs have benefited modern machine learning algorithms and big data analysis as they can provide high memory bandwidth and tremendous computation power. Therefore, many frameworks, e.g. TensorFlow, Caffe, CNTK, support GPUs for accelerating various data-intensive machine learning algorithms. However, we observe that the performance of existing LDA solutions on GPUs is not satisfying. In this paper, we present CuLDA, a GPU-based efficient and scalable approach to accelerate large-scale LDA problems. CuLDA is designed to efficiently solve LDA problems at high throughput. To this end, we first delicately design workload partitioning and synchronization mechanism to exploit multiple GPUs. Then, we offload the LDA sampling process to each individual GPU by optimizing from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA outperforms them by a large margin (up to 7.3X) on a single GPU. CuLDA is able to achieve an extra 7.5X speedup on 8 GPUs for large data sets.
A Framework for integrating software design patterns with game design framework

Object oriented design patterns (DP) are meant to provide a common structure for solving common software development problems. Applying those patterns is expected to improve the software systems maintainability, comprehensibility, and provide a common language for developers to understand and effectively communicate. However, this was not always the case, where some studies have shown that using design patterns may lead to a system harder to maintain and understand and more error prone; as compared to same systems without the use of design patterns. This variation is referred to the features of the system on hand, and the experience of the developers who are capable to know what design patterns to use and when. Considering the video game development, the use of software design pattern is still at low scale, and still; there is no recipe on what and when to use design patterns. In the meantime, there are different game design frameworks that define different aspects which need to be considered when developing a game. In this paper, we propose a framework that integrates some creational and behavioral DP, with a specific game design framework. In this way, we claim that the developers will have some hints on what DP to use with the main game aspects. This framework is suitable for games which can be developed after the first course of game design, regardless of the game engine used for development. The framework can be extended with more DP, for larger scale and more sophisticated game.
Workload-Aware Subgraph Query Caching and Processing in Large Graphs

A subgraph query q that finds as output all its subgraph-isomorphic embeddings from a data graph g has been core to modern declarative querying in large graphs. In this paper, we address subgraph queries with the availability of query workload information, W = {w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,..., w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> }, where w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ∈ W is a previously issued query with all its subgraph-isomorphic embeddings cached beforehand. We introduce a workload-aware subgraph querying framework, WaSQ, that leverages query workload for subgraph query rewriting, search plan refinement, partial results reusing, and false positive filtering towards facilitating the whole subgraph querying process. Experimental studies in real-world graphs demonstrate that WaSQ achieves significant and consistent performance gains in comparison with state-of-the-art, workload-oblivious solutions for large-scale subgraph querying.
Bullying Hurts

The contemporary period is scarred by the predominant place of social media in everyday life. Despite social media being a useful tool for communication and social gathering it also offers opportunities for harmful criminal activities. One of these activities is cyber-bullying enabled through the abuse and mistreatment of the internet as a means of bullying others virtually. As a way of minimising this occurrence, research into computer-based researched is carried out to detect cyber-bullying by the scientific research community. An extensive literature search shows that supervised learning techniques are the most commonly used methods for cyber-bullying detection. However, some non-supervised techniques and other approaches have proven to be effective towards cyber-bullying detection. This paper, therefore, surveys recent research on non-supervised techniques and offers some suggestions for future research in textual-based cyber-bullying detection including detecting roles, detecting emotional state, automated annotation and stylometric methods.
Weakly-Supervised Image Semantic Segmentation Based on Superpixel Region Merging

In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). Image annotation has always been an important role in weakly-supervised semantic segmentation. Most methods use manual labeling. In this paper, super-pixels with similar features are combined using the relationship between each pixel after super-pixel segmentation to form a plurality of super-pixel blocks. Rough predictions are generated by the fully convolutional networks (FCN) so that certain super-pixel blocks will be labeled. We perceive and find other positive areas in an iterative way through the marked areas. This reduces the feature extraction vector and reduces the data dimension due to super-pixels. The algorithm not only uses superpixel merging to narrow down the target’s range but also compensates for the lack of weakly-supervised semantic segmentation at the pixel level. In the training of the network, we use the method of region merging to improve the accuracy of contour recognition. Our extensive experiments demonstrated the effectiveness of the proposed method with the PASCAL VOC 2012 dataset. In particular, evaluation results show that the mean intersection over union (mIoU) score of our method reaches as high as 44.6%. Because the cavity convolution is in the pooled downsampling operation, it does not degrade the network’s receptive field, thereby ensuring the accuracy of image semantic segmentation. The findings of this work thus open the door to leveraging the dilated convolution to improve the recognition accuracy of small objects.
Deep (Un)Learning: Using Neural Networks to Model Retention and Forgetting in an Adaptive Learning System

ALEKS, which stands for “Assessment and LEarning in Knowledge Spaces”, is a web-based, artificially intelligent, adaptive learning and assessment system. Previous work has shown that student knowledge retention within the ALEKS system exhibits the characteristics of the classic Ebbinghaus forgetting curve. In this study, we analyze in detail the factors affecting the retention and forgetting of knowledge within ALEKS. From a dataset composed of over 3.3 million ALEKS assessment questions, we first identify several informative variables for predicting the knowledge retention of ALEKS problem types (where each problem type covers a discrete unit of an academic course). Based on these variables, we use an artificial neural network to build a comprehensive model of the retention of knowledge within ALEKS. In order to interpret the results of this neural network model, we apply a technique called permutation feature importance to measure the relative importance of each feature to the model. We find that while the details of a student’s learning activity are as important as the time that has passed from the initial learning event, the most important information for our model resides in the specific problem type under consideration.
Segmentation of Crowd into Multiple Constituents Using Modified Mask R-CNN Based on Mutual Positioning of Human

Automatically segmenting groups and individuals from crowd images can be important for surveillance purpose. In this paper, we present a simplified crowd detection and segmentation system. Crowd scene images can be divided into different regions: group, individual, crowd and row. Mask R-CNN with modified backbone architecture is employed for crowd image segmentation. The proposed system is tested on BIWI dataset and self-generated dataset to achieve accuracy of 83%. Furthermore, a crowd dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (CVML <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">**</sup> crowd dataset) having nearly four thousand images are annotated for training the system.
High Throughput Implementation of SMS4 on FPGA

The SMS4 algorithm is a block cipher algorithm, which has the characteristics of high security and easy implementation. However, the optimization and implementation schemes proposed for FPGA platform currently use multi-channel parallel and pipelined architectures to improve performance, which results in a large consumption of resources, and the clock cycles taken to process a single data block is not reduced. This paper proposes a novel implementation scheme of SMS4 on FPGA. This scheme separates the generations of 32 round keys and encryption operations, 32 round keys are generated on the host computer in advance, and the encryption operations completed on the FPGA. At the same time, for the 32-round iterative structure of the SMS4, this paper proposes a dual-cascade implementation architecture that can compress 32 rounds of iterative operations from 32 clock cycles to 16 clock cycles. This greatly improves the performance of the SMS4. To compare with the previous works needing 32 cycles or more, which greatly reduces the clock cycles spent on processing each data block. The throughput achieves 1.9 Gbps at a frequency of 286 MHz on Xilinx FPGA.
MultiBoot Vsr: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution

To make the best use of the previous estimations and shared redundancy across the consecutive video frames, here we propose a scene and class agnostic, fully convolutional neural network model for 4× video super-resolution. One stage of our network is composed of a motion compensation based input subnetwork, a blending backbone, and a spatial upsampling subnetwork. We recurrently apply this network to reconstruct high-resolution frames and then reuse them as additional reference frames after reshuffling them into multiple low-resolution images. This allows us to bootstrap and enhance image quality progressively. Our experiments show that our method generates temporally consistent and high-quality results without artifacts. Our method is ranked as the second best based on the SSIM scores on the NTIRE2019 VSR Challenge, Clean Track.
End-to-end semantic-aware object retrieval based on region-wise attention

Image representations based on pre-trained Convolutional Neural Networks (CNNs) have achieved the new state of the art in computer vision tasks such as object retrieval. Such methods usually encode the activations of convolutional layers to produce highly competitive global or local representations, as they contain the spatial information of the input image. In this work, we propose the region-wise attention mechanism to generate a semantic-aware encoding of convolutional features by two different methods. One is to re-weight the convolutional features according to the pixel-wise label from the semantic segmentation CNNs, and the other is to design a spatial attention block that adaptively recalibrates region-wise weights by explicitly modelling interdependencies between channels. We further build an end-to-end semantic-aware object retrieval pipeline based on off-the-shelf models and assess the performance of our proposed approach on the public available datasets Oxford5k and Paris6k, including large-scale datasets Oxford105k and Paris106k. As a result, we significantly improve the current state of the art.