Reviewerly/exHarmony huggingface dataset:
BPCM: A Flexible High-Speed Bypass Parallel Communication Mechanism for GPU Cluster

With the increasing complexity of computational tasks faced by artificial intelligence technology, the scale of machine learning models continues to expand, and the data volume and frequency of parameter synchronization also increase. This will cause the communication bandwidth within the GPU cluster to become the biggest bottleneck for distributed model training. Many existing solutions cannot be widely promoted due to the need for professional equipment support, high cost, and difficulty in use. To solve this problem, this paper proposes a multi-network card bypass parallel communication mechanism based on Intel DPDK technology to increase the bandwidth within the GPU cluster at a lower cost and make full use of the idle CPU resources of the GPU server to accelerate data transmission. Firstly, we propose a data transmission model based on multiple network cards, and design a port load balancing algorithm to ensure load balancing of multiple network cards. Secondly, the model and algorithm of CPU multi-core scheduling are implemented to reduce CPU energy consumption, resource occupation, and the impact on other applications. Furthermore, for multiple application scenarios, a rate adjustment model and algorithm are designed and implemented to ensure fair use of application bandwidth. Finally, the experimental results show that this mechanism can provide high bandwidth for GPU clusters with inexpensive multi-network cards, and provide superimposed bandwidth of multi-network cards in a single connection, which has high reliability and transmission efficiency, and is simple to use and flexible to expand.
Analyzing Large News Corpus Using Text Mining Techniques for Recognizing High Crime Prone Areas

For any developing country, crime rate analysis over different states and different districts for each state is an important task. For having a gross view of crime rates, online newspapers can be used as a useful resource for such tasks. Since all types of crimes are not reported to the police department and the reported crime data may not be easily accessible by the common people, the crime location information can be extracted from online local newspapers and/or national newspapers and the extracted information can be used for counting crime incidents for a location or an area. But, processing the huge collection of newspapers and extracting crime locations from them is not a trivial task. This task can be roughly divided into two main parts, the first part is to filter the crime news articles from the automatically downloaded news collection and the second part is the crime location extraction from each news article. Our proposed system does this task automatically through mining the collection of newspapers written in the Bengali language. To get the picture of high crime prone locations in a state, the proposed system visualizes the crime count per district in a state. Our system is tested on data related to the state of West Bengal. To generate the location wise crime histogram, this system can be used as a tool.
Conversational Question Answering over Passages by Leveraging Word Proximity Networks

Question answering (QA) over text passages is a problem of longstanding interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces a key research challenge: understanding the context left implicit by the user in follow-up questions. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
Neural Network Approach for 2-Dimension Person Pose Estimation With Encoded Mask and Keypoint Detection

We present an innovative approach for 2D person pose estimation by developing a convolutional neural network for human 2-channel mask prediction and human 2D pose estimation. Conceptually our idea is simple, inspired by prior image segmentation research in general. We establish a perception that explicitly encoded mask data can be served as a critical feature for person pose estimation. We propose a convolution neural network model by combining the image segmentation technique with the bottom-up approach for human pose estimation. We observe that the construction of a two stage-network for training in an end-to-end manner is beneficial to one another: for person mask prediction and 2D person pose estimation. At the pose estimation stage, we detect heat-maps against the person keypoints location from the mask information and their mutual connection relations. They are then used to estimate an ultimate pose in a way to remove the unwanted or occluded keypoints, as those keypoints may propagate across the network and lead to redundant pose estimation. We train and test our system on the MS-COCO dataset, and the experimental results validate the superior efficiency of the proposed methodology.
Prediction Model for Scheduling an Irregular Graph Algorithms on CPU–GPU Hybrid Cluster Framework

The improvement of innovations in science, technology and industry is happening in a current trend because of hybrid many-core Graphical processing units (GPUs), and multi-core central processing units (CPUs) based cluster. In this article, we have designed a prediction model using parameters such as computation and communication cost of irregular graph algorithms. To schedule irregular graph algorithms on the best set of processors of the hybrid cluster, we used our prediction models that predict the execution time of irregular graph algorithms and recorded as the performance history of the data. A reasonable set of data is used to educate the prediction model. The data forecasted by the model can be used and compared against actual runtimes to increase the accurateness of the model and decrease or eliminate any errors. We have tested our scheduling strategy using irregular graph algorithms Breadth-First Search(BFS) and Depth-first search(DFS) benchmark applications on the hybrid cluster. Our algorithm shows that up to 75.32% average performance improvement for BFS against TORQUE. Similarly, when compared to our predictive scheduling algorithm against TORQUE for DFS we achieved 89.68%. And 18.52% of average percentage prediction errors compared to the linear regression model.
Large Scale Abstractive Multi-Review Summarization (LSARS) via Aspect Alignment

In an active e-commerce environment, customers process a large number of reviews when deciding on whether to buy a product or not. Abstractive Multi-Review Summarization aims to assist users to efficiently consume the reviews that are the most relevant to them. We propose the first large-scale abstractive multi-review summarization dataset that leverages more than 17.9 billion raw reviews and uses novel aspect-alignment techniques based on aspect annotations. Furthermore, we demonstrate that one can generate higher-quality review summaries by using a novel aspect-alignment-based model. Results from both automatic and human evaluation show that the proposed dataset plus the innovative aspect-alignment model can generate high-quality and trustful review summaries.
Fused Recurrent Network Via Channel Attention For Remote Sensing Satellite Image Super-Resolution

Remote sensing satellite images often suffer from low spatial resolution. Image super-resolution plays an important role in remote sensing image processing. However, existing methods show that increasing network depth will inevitably lead to the dramatic increase of model parameters and the over-fitting problem. Besides, most methods treat different types of information (low-frequency and high-frequency) equally. Motivated by these observations, we propose a fused recurrent network via channel attention (CA-FRN) in this paper. The basic module, recursive channel attention block (RCAB), pays enough attention to the high-frequency information and diminishes the low-frequency information adaptively through channel attention. Based on RCAB, we render our model effective by retaining and fusing hierarchical local information of both low-resolution and high-resolution, and we enhance the network performance simply by increasing the number of RCABs without adding extra parameters. We evaluate the proposed model on satellite images from different datasets, and the proposed CA-FRN is superior to the state-of-the-art methods. Code is available at https://github.com/lxy0922/CAFRN.
Applying FAIR Principles to Improve Data Searchability of Emergency Department Datasets: A Case Study for HCUP-SEDD

Abstract Background There is a recognized need to improve how scholarly data are managed and accessed. The scientific community has proposed the findable, accessible, interoperable, and reusable (FAIR) data principles to address this issue. Objective The objective of this case study was to develop a system for improving the FAIRness of Healthcare Cost and Utilization Project's State Emergency Department Databases (HCUP's SEDD) within the context of data catalog availability. Methods A search tool, EDCat (Emergency Department Catalog), was designed to improve the “FAIRness” of electronic health databases and tested on datasets from HCUP-SEDD. ElasticSearch was used as a database for EDCat's search engine. Datasets were curated and defined. Searchable data dictionary-related elements and unified medical language system (UMLS) concepts were included in the curated metadata. Functionality to standardize search terms using UMLS concepts was added to the user interface. Results The EDCat system improved the overall FAIRness of HCUP-SEDD by improving the findability of individual datasets and increasing the efficacy of searches for specific data elements and data types. Discussion The databases considered for this case study were limited in number as few data distributors make the data dictionaries of datasets available. The publication of data dictionaries should be encouraged through the FAIR principles, and further efforts should be made to improve the specificity and measurability of the FAIR principles. Conclusion In this case study, the distribution of datasets from HCUP-SEDD was made more FAIR through the development of a search tool, EDCat. EDCat will be evaluated and developed further to include datasets from other sources.
Monte Carlo Gradient Quantization

We propose Monte Carlo methods to leverage both sparsity and quantization to compress gradients of neural networks throughout training. On top of reducing the communication exchanged between multiple workers in a distributed setting, we also improve the computational efficiency of each worker. Our method, called Monte Carlo Gradient Quantization (MCGQ), shows faster convergence and higher performance than existing quantization methods on image classification and language modeling. Using both low-bit-width-quantization and high sparsity levels, our method more than doubles the rates of existing compression methods from 200× to 520× and 462× to more than 1200× on different language modeling tasks.
Denoising real bursts with squeeze‐and‐excitation residual network

The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, utilising similarities (or priors) within and across an image dataset helps recover clean images. As the noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this study, the authors propose a deep residual model with squeeze-and-excitation (SE) modules for the burst denoising. Unlike previous methods, the authors' model does not need an explicit aligning procedure, which is light-weighted and fast. The network contains a noise estimation convolutional neural network, which makes it capable of blind denoising. Besides, by inverting the image processing pipeline and simulating real noise in bursts, their model can suppress real noise blindly. Since denoising performance is closely related to the noise level, frame displacement, and the number of frames (burst length), intensive experiments including ablation study are performed. Quantitative results show that the proposed method performs significantly better than previous state-of-the-art methods V-BM4D and KPN in removing Gaussian noise. Qualitative results show that the proposed method is also effective in removing real noise using bursts and the SE module is key to reduce blur in results.
GoPi: Compiling Linear and Static Channels in Go

We identify two important features to enhance the design of communication protocols specified in the pi-calculus, that are linear and static channels, and present a compiler, named GoPi, that maps high level specifications into executable Go programs. Channels declared as linear are deadlock-free, while the scope of static channels, which are bound by a hide declaration, does not enlarge at runtime; this is enforced statically by means of type inference, while specifications do not include annotations. Well-behaved processes are transformed into Go code that supports non-deterministic synchronizations and race-freedom. We sketch two main examples involving protection against message forwarding, and forward secrecy, and discuss the features of the tool, and the generated code. We argue that GoPi can support academic activities involving process algebras and formal models, which range from the analysis and testing of concurrent processes for research purposes to teaching formal languages and concurrent systems.
COMTIS: Customizable touchless interaction system for large screen visualization

Large screen visualization systems have been widely utilized in many industries. Such systems can help illustrate the working states of different production systems. However, efficient interaction with such systems is still a focus of related research. In this paper, we propose a touchless interaction system based on RGB-D camera using a novel bone-length constraining method. The proposed method optimizes the joint data collected from RGB-D cameras with more accurate and more stable results on very noisy data. The user can customize the system by modifying the finite-state machine in the system and reuse the gestures in multiple scenarios, reducing the number of gestures that need to be designed and memorized. he authors tested the system in two cases. In the first case, we illustrated a process in which we improved the gesture designs on our system and tested the system through user study. In the second case, we utilized the system in the mining industry and conducted a user study, where users say that they think the system is easy to use.
Automatic Key Moment Extraction and Highlights Generation Based on Comprehensive Soccer Video Understanding

The massive growth of sports video has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by editors. Many methods have been applied to this task and have achieved some positive results. Unlike previous works ignoring the multi-modality information, we propose a novel system that leverages visual and audio information derived from the soccer video. The proposed system involves three crucial tasks which can be jointly used to produce highlights automatically, i.e. play-back detection, soccer event recognition and commentator emotion classification. We introduce a new dataset of 460 soccer games totaling 700 hours with a benchmark for three tasks. Making use of recent progress in deep learning, we further provide strong baselines on three tasks. The experiments on the proposed dataset demonstrate state-of-the-art performance on each independent task. The real-world deployment shows that this system can be useful for soccer games to find and extract soccer video highlights.
A fast graph-based data classification method with applications to 3D sensory data in the form of point clouds

Data classification, where the goal is to divide data into predefined classes, is a fundamental problem in machine learning with many applications, including the classification of 3D sensory data. In this paper, we present a data classification method which can be applied to both semi-supervised and unsupervised learning tasks. The algorithm is derived by unifying complementary region-based and edge-based approaches; a gradient flow of the optimization energy is performed using modified auction dynamics. In addition to being unconditionally stable and efficient, the method is equipped with several properties allowing it to perform accurately even with small labeled training sets, often with considerably fewer labeled training elements compared to competing methods; this is an important advantage due to the scarcity of labeled training data. Some of the properties are: the embedding of data into a weighted similarity graph, the in-depth construction of the weights using, e.g., geometric information, the use of a combination of region-based and edge-based techniques, the incorporation of class size information and integration of random fluctuations. The effectiveness of the method is demonstrated by experiments on classification of 3D point clouds; the algorithm classifies a point cloud of more than a million points in 1–2 min.
Implementation of Data Mining In Predicting the Study Period of Student Using the Naïve Bayes Algorithm

Abstract The period of study of students is one of the parameters in the assessment of accreditation of university. Pembangunan Panca Budi University as one of the best institutions in North Sumatra needs to predict the period of study of students in order to know the strategies implemented so that students graduate on time. This study predicts the period of study using the Naïve Bayes Data Mining Algorithm method to students of the Pembangunan Panca Budi University of Computer System study program bachelor program with a study period of 4 years. Application designed using Naïve Bayes algorithm that works based on the shortest distance between two objects by determining the value of k. The value of k is a parameter to determine the closest distance between a new object and an old object. The data used as variables are student academic data, GPA for the first from 4 semesters, the average value of the National Examination during high school, and majors during high school. The results of the study showed that the data mining application that was designed could predict the period of study of students at the Pembangunan Panca Budi University.
Research on E-Commerce Potential Client Mining Applied to Apriori Association Rule Algorithm

The Apriori algorithm is an algorithm based on association rules. According to the association rules, it can recommend the products to the users, thereby saving the user's shopping time and making the user's shopping more convenient and faster, thereby attracting more users. However, the traditional The Apriori algorithm has a high degree of complexity when dealing with massive amounts of data. It is necessary to improve the Apriori algorithm so that it can be applied to the era of big data. The Apriori algorithm is a classical algorithm in the association model, but when the database is large, the Apriori algorithm The complexity will increase exponentially, so this paper improves the way the Apriori algorithm scans the database. The improved algorithm effectively reduces the complexity of the algorithm. The Apriori algorithm is applied to the recommended links in e-commerce.
Artisan: a Meta-Programming Approach For Codifying Optimisation Strategies

This paper provides a novel compilation approach that addresses the complexity of mapping high-level descriptions to heterogeneous platforms, improving design productivity and maintainability. Our approach is based on a co-design methodology decoupling functional concerns from optimisation concerns, allowing two separate descriptions to be independently maintained by two types of programmers: application experts focus on algorithmic behaviour, while platform experts focus on the mapping process. Our approach supports two key requirements: (1) Customisable optimisations to rapidly capture a wide range of mapping strategies, and (2) Reusable strategies to allow optimisations to be described once and applied to multiple applications. To evaluate our approach, we develop Artisan, a meta-programming tool for codifying optimisation strategies using a high-level general-purpose programming language (Python 3), offering full design-flow orchestration of key components (source-code, third-party tools, and platforms). We evaluate Artisan using three case study applications and three reusable optimisation strategies, achieving at least 24 times speedup for each application on CPU and FPGA targets with little application developer effort.
Lightweight Single Image Super-Resolution Through Efficient Second-Order Attention Spindle Network

Recent years have witnessed great success of applying deep convolutional neural networks (CNNs) to single image super-resolution (SISR). However, most of these algorithms focus on increasing modeling capability through developing deeper and wider networks, improving the performance but at a cost of huge computation. Targeting at a better trade-off between efficiency and effectiveness, we propose ESASN, an efficient second-order attention spindle network for lightweight SISR. ESASN is built upon efficient second-order attention spindle (ESAS) blocks, each of which contains two well-designed new modules, efficient multi-scale (EMS) module and second-order attention (SOA) module. EMS reduces a considerable number of parameters while retaining the multi-scale structure to explore rich features. SOA further rescales the multi-scale feature maps, capturing the inter-dependencies among channels pixel-wisely with little additional cost. Both qualitative and quantitative experimental results demonstrate that the combination of EMS and SOA works out favorably for SISR, lifting the performance with fewer parameters. Code is available at https://github.com/yiyunchen/ESASN.
Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements

This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.