Reviewerly/exHarmony huggingface dataset:
Human Detection In Dense Scene Of Classrooms

The intelligent classroom project has gained a lot of attention in recent years. One of the critical applications is to recognize the position and category of an object accurately in the classrooms. Object detector based on CNN is widely used to address this issue to realize more accurate performance. But in the scene of classrooms, dense distribution of people, like students and teachers, always leads to serious class-class or class-in occlusion and brings poor accuracy to current typical detectors. To solve this problem, we propose an original method named Dense Occlusion Object Detection network, which consists of Dense Anchor Generation Model (DAG) and Discriminative Part Selection Model (DPS). The DAG model could exploit crucial semantic information from feature map to generate accurate anchor boxes by locating center point and predicting box size of an object in the anchor-free manner. The DPS model aims to mitigate the occlusion problem through dividing the proposal into some smaller parts and processing them respectively so that we can select the most discriminative ones to update the confidence score of the input proposal. Experimental results show that the proposed method outperforms the state-of-the-art methods for human detection on our CScene dataset.
fogcached: DRAM-NVM Hybrid Memory-Based KVS Server for Edge Computing

With the widespread use of sensors in smart devices and robots, there is a growing expectation for edge computing that processes data not on distant cloud servers but also on or near interactive devices to store their data with low latency access. To satisfy these requirements, we consider a new edge computing system that consists of a hybrid main memory with a KVS (Key-Value-Store) server utilizing the DRAM and nonvolatile main memory (NVM). It provides large-capacity cache memory in a server, supporting high-speed processing and quick response for sensor nodes. However, since existing KVS servers are not designed for NVM, there are less satisfactory implementations that achieve low response time and high throughputs. We propose a novel hybrid KVS server that is designed and implemented on the Memcached distributed memory-caching system, which dynamically moves cached data between two types of memory devices according to access frequency in order to achieve a low latency compared to the existent approaches. We developed a Dual-LRU (Least Recently Used) structure for it. Evaluation was performed using a real machine equipped with NVM. The result showed the proposed method successfully reduced the response time and improves access throughputs.
A Mott Insulator-Based Oscillator Circuit for Reservoir Computing

In this work, we use a Cr-doped V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> based Mott oscillator circuit to build a reservoir computing system that has much smaller model size than an equivalent LSTM. In contrast to an LSTM, our reservoir computing system can be trained very efficiently and on-line, with very low latency. We demonstrate close to state-of-the-art performance with three benchmark tasks: speech recognition, handwritten digit recognition, and HDD channel decoding. We show that our Mott circuit-based reservoir computing system brings significant reduction in power consumption and inference speed compared to CPU, GPU, or FPGA based systems.
Internet of Value: A Risky Necessity

OPINION article Front. Blockchain, 15 September 2020Sec. Fourth Industrial Revolution Volume 3 - 2020 | https://doi.org/10.3389/fbloc.2020.00039
An automation API for authentication and security for file uploads in the cloud storage environment

Cloud Computing is widespread to support industrial and academy related applications worldwide. The archival data process in the cloud is one of the challenging tasks. To handle this issue, the authors addressed the model, which allows authenticating the cloud storage using the JOSS (Java Library for Open stack storage) through the authentication URL. To achieve the authentication of the heterogeneous data while uploading into the cloud storage, a proposed model developed using the JOSS. To address this research question and demonstrate our approach, we have created the API for the archival solution in order to authenticate the cloud storage using the JOSS authentication model, providing the authentication URL. It consists of the user id and password for the verification of the user while transmitting the data into the cloud storage. The API is becoming dynamic and complex, and one of the major challenges faced by the developer in order to develop appropriate standalone applicataion interface. The extensible mechanism for authentication requires a URL in order to connect to the cloud storage system. The objective of this paper is to enhance/optimize the rate of transmission to upload the data in the cloud storage using the rclone.
Graded Image Generation Using Stratified CycleGAN

In medical imaging, CycleGAN has been used for various image generation tasks, including image synthesis, image denoising, and data augmentation. However, when pushing the technical limits of medical imaging, there can be a substantial variation in image quality. Here, we demonstrate that images generated by CycleGAN can be improved through explicit grading of image quality, which we call stratified CycleGAN. In this image generation task, CycleGAN is used to upgrade the image quality and content of near-infrared fluorescent (NIRF) retinal images. After manual assignment of grading scores to a small subset of the data, semi-supervised learning is applied to propagate grades across the remainder of the data and set up the training data. These scores are embedded into the CycleGAN by adding the grading score as a conditional input to the generator and by integrating an image quality classifier into the discriminator. We validate the efficacy of the proposed stratified CycleGAN by considering pairs of NIRF images at the same retinal regions (imaged with and without correction of optical aberrations achieved using adaptive optics), with the goal being to restore image quality in aberrated images such that cellular-level detail can be obtained. Overall, stratified CycleGAN generated higher quality synthetic images than traditional CycleGAN. Evaluation of cell detection accuracy confirmed that synthetic images were faithful to ground truth images of the same cells. Across this challenging dataset, F1-score improved from \(76.9\pm 5.7\)% when using traditional CycleGAN to \(85.0\pm 3.4\)% when using stratified CycleGAN. These findings demonstrate the potential of stratified Cycle-GAN to improve the synthesis of medical images that exhibit a graded variation in image quality.
Auction-Based Pricing in Cloud Environment

The rapid development of cloud computing requires improvement of pricing and allocation mechanisms of cloud resources. Dynamic pricing and allocation mechanisms are considered convenient, due to characteristics of cloud resources and the fact that demand for cloud resources is not uniform. The aims of such a mechanism are to optimize the utilization of cloud resources, to maximize cloud providers' revenues, and to minimize prices for cloud customers. Auction-based pricing and allocation mechanisms are often used since resources are allocated to the customers that value them the most, and prices are determined depending on the supply and demand conditions. Selection of an appropriate bidding strategy is a very important issue and requires the comprehensive approach. This chapter analyses the benefits of auction-based pricing and allocation mechanism in the cloud environment. In addition, the effects of different bidding strategies application are addressed.
Generation of Viewed Image Captions From Human Brain Activity Via Unsupervised Text Latent Space

Generation of human cognitive contents based on the analysis of functional magnetic resonance imaging (fMRI) data has been actively researched. Cognitive contents such as viewed images can be estimated by analyzing the relationship between fMRI data and semantic information of viewed images. In this paper, we propose a new method generating captions for viewed images from human brain activity via a novel robust regression scheme. Unlike conventional generation methods using image feature representations, the proposed method makes use of more semantic text feature representations, which are more suitable for the caption generation. We construct a text latent space with unlabeled images not used for the training, and the fMRI data are regressed to the text latent space. Besides, we newly make use of unlabeled images not used for the training phase to improve caption generation performance. Finally, the proposed method can generate captions from the fMRI data measured while subjects are viewing images. Experimental results show that the proposed method enables accurate caption generation for viewed images.
Exploring smart home and internet of things technologies for smart public buildings

Recent years have seen a rapid increase in the development and use of smart home technologies. Besides being used in private homes, these and other wireless Internet of Things (IoT) technologies may also complement classical building automation to retrofit existing stock. Especially public and commercial buildings in Germany and Europe have the potential to become more energy-efficient and to provide a healthier environment for their users. This paper introduces an ongoing feasibility study on Smart Public Buildings at the University of Applied Sciences Stuttgart. The study addresses the special requirements of public institutions for the utilization of smart home and other IoT technologies. To explore and validate these requirements, different use cases and prototypes based on an open-source platform for the home automation have been developed. Firstly, a preceding analysis of the special requirements of public buildings in Germany is outlined. Secondly, the basic installation of a smart building system at the university campus is introduced. Finally, some of the prototypes and use cases that have been carried out are presented.
One-Class Support Tensor Machines with Bounded Hinge Loss Function for Anomaly Detection

Traditional one class support tensor machine (OCSTM) is a popular classifier that is widely adopted for one class classification, however, outliers in the data negatively affects its performance. To improve the robustness of OCSTM against outliers, in this paper, we present OCSTM with bounded loss function rather than finding optimized support vectors with unbounded loss function. To solve the corresponding optimization problem, we have presented half quadratic optimization to drive the problem to traditional OCSTM, followed by solving a typical OCSTM optimization problem iteratively. We further demonstrate our algorithms through experiments on eight real-world benchmark datasets. Experimental results show that the proposed approach separates well most of the samples of interested class from origin even in the presence of outliers.
PWIDB: A framework for learning to classify imbalanced data streams with incremental data re-balancing technique

The performance of classification algorithms with highly imbalanced streaming data depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied using static batch data to resolve the class imbalance problem, which is difficult if applied for massive data streams. In this paper, a new Piece-Wise Incremental Data re-Balancing (PWIDB) framework is proposed. The PWIDB framework combines automated balancing techniques using Racing Algorithm (RA) and incremental rebalancing technique. RA is an active learning approach capable of classifying imbalanced data and can provide a way to select an appropriate re-balancing technique with imbalanced data. In this paper, we have extended the capability of RA for handling imbalanced data streams in the proposed PWIDB framework. The PWIDB accumulates previous knowledge with increments of re-balanced data and captures the concept of the imbalanced instances. The PWIDB is an incremental streaming batch framework, which is suitable for learning with streaming imbalanced data. We compared the performance of PWIDB with a well-known FLORA technique. Experimental results show that the PWIDB framework exhibits an improved and stable performance compared to FLORA and accumulative re-balancing techniques.
A New Simple Direct Computation of Cubic Convolution Spline Interpolation

It has been demonstrated that the cubic convolution spline interpolation (CCSI) scheme is one of the best algorithms for image resampling or compression. In this paper, a new simple direct computation of CCSI is proposed, where the sampled data are directly calculated by the use of the original data. Moreover, the proposed algorithm provides a regular and simple structure based on linear correlation and thus is naturally suitable for VLSI implementation. Computer simulations on several standard images indicate that the proposed simple direct computation of CCSI scheme can achieve almost the same objective and subjective performance with lower complexity.
Safe Decentralized Applications Development Using Blockchain Technologies

Decentralized applications, architectures and Blockchain are very popular and progressive technologies that are definitely ahead of its time. Nowadays world has a lot of decentralized applications and proposals, however the approaches and concepts decentralized software development based on the Blockchain technology is only emerging. This process is a common thing for new technologies as far as its adaptation takes time and often requires to change a usual approach to it. This article suggests a new way of safe decentralized applications development using Blockchain technologies and Ethereum platform. Ethereum platform-it's a project that is undoubtedly second best after bitcoin for a quite long period of time. Ethereum is presented as a platform for various tasks, such as maintaining a stable application performance in case of any threats. The main object of the research are the decentralized applications, build on a Ethereum platform basis using the smart-contracts algorithm. The study helped to improve an existing crypto-token allocating model which goal is acquiring more stable performance. On the basis of improved model a new method of allocating cryptotokens in decentralized Ethereum applications was developed, defined a main stage of apps elaboration, offered an allocating crypto-token algorithm which is able to use associative tables and dictionaries.
A CycleGAN Accelerator for Unsupervised Learning on Mobile Devices

Cycle-consistent generative adversarial networks (CycleGANs) have been commonly used for unsupervised-learning applications, especially for image-to-image translation. A CycleGAN has more complex dataflow since it features two generator-discriminator pairs. Massive external memory access also results in a long latency for both training and inference. Data structure for transposed convolution also needs to be tailored. This paper presents the first dedicated CycleGAN accelerator for energy-constrained mobile applications. The numbers of external and internal memory accesses are reduced by 98.3% and 68.3% through spatial data reuse, input feature map reuse, and local data reuse. The computational complexity is reduced by 79.4% by skipping zeros in the transposed convolutional layers. An architecture with two processing cores is proposed to improve the utilization by 2×. Designed in a 40-nm CMOS technology, the proposed CycleGAN accelerator dissipates 445 mW at 227 MHz from a 0.9-V supply. It achieves a 38× higher throughput-to-area ratio and 127× higher energy efficiency than a GPU.
S-APIR: News-Based Business Sentiment Index

This paper describes our work on developing a new business sentiment index using daily newspaper articles. We adopt a recurrent neural network (RNN) with Gated Recurrent Units to predict the business sentiment score of a given text and aggregate the scores to define an index, named S-APIR. An RNN is initially trained on Economy Watchers Survey and then fine-tuned on news texts for domain adaptation. Also, a one-class support vector machine is applied to filter out texts irrelevant to business sentiment. Moreover, we propose a simple yet useful approach to temporally analyzing how much and when any given factor influences the predicted business sentiment. The validity and utility of the proposed approach are empirically demonstrated through a series of experiments on Nikkei Newspaper articles published from 2013 to 2018.
Calibrated Surrogate Maximization of Dice

In the medical imaging community, it is increasingly popular to train machine learning models for segmentation problems with objectives based on the soft-Dice surrogate. While experimental studies have showed good performance with respect to Dice, there have also been reports of some issues related to stability. In parallel with these developments, direct optimization of evaluation metrics has also been studied in the context of binary classification. Recently, in this setting, a quasi-concave, lower-bounded and calibrated surrogate for the \(F_1\)-score has been proposed. In this work, we show how to use this surrogate in the context of segmentation. We then show that it has some better theoretical properties than soft-Dice. Finally, we experimentally compare the new surrogate with soft-Dice on a 3D-segmentation problem and get results indicating that stability is improved. We conclude that the new surrogate, for theoretical and experimental reasons, can be considered a promising alternative to the soft-Dice surrogate.
Multigranularity Semantic Labeling of Point Clouds for the Measurement of the Rail Tanker Component With Structure Modeling

It is important to ensure every component of the rail tanker with standard dimensions for guaranteeing transportation safety. Point clouds are widely used in the dimension measurement, and the key of the measurement is to segment the point cloud into different components. Currently, the segmentation is realized manually, which is labor-intensive and has high uncertainty. Different from this, we model this problem as semantic segmentation based on point clouds and present an automatic and accurate method for solving it. Previous semantic segmentation methods cannot capture characteristics of inherent geometric structures well, which leads to unexpected segmentation results. Therefore, we explore the inherent geometric structures hidden in the point cloud and propose a new two-stage framework to achieve automatic and accurate segmentation. Especially, the framework consists of structure modeling and semantic labeling. First, we model the inherent geometric structures of the tanker. To this end, the point cloud is partitioned into a few geometrically homogeneous regions, and each region contains points with semantic consistency and geometric continuity. An edge-informative graph is introduced to describe the characteristics of regions and the adjacency relationships between regions. Second, we design a new graph neural network to process the graph with modeling information for component segmentation. The network extracts the deep features for each region and models the interaction between regions. Besides, it obtains the final segmentation by labeling points at different granularity levels. We evaluate our method on the point clouds of different types of tankers. The results show that the overall accuracy of our method can reach 97.53%, and our method shows better performance compared with other baseline methods in almost all metrics.
Distribution sliders

Sliders are often used to enable users to easily enter preferences for continuous data. Although efforts have already been made to enrich and improve these interaction tools with additional information and visualizations, only rather basic variants of sliders are commonly used in online shops or databases. However, these sliders often provide users only with very limited information about underlying data.
Stochastic Curiosity Maximizing Exploration

Deep reinforcement learning (RL) is known as an emerging research trend in machine learning for autonomous systems. In real-world scenarios, the extrinsic rewards, acquired from the environment for learning an agent, are usually missing or extremely sparse. Such an issue of sparse reward constrains the learning capability of agent because the agent only updates the policy when the goal state is successfully attained. It is always challenging to implement an efficient exploration in RL algorithms. To tackle the sparse reward and inefficient exploration, the agent needs other helpful information to update its policy even when there is no interaction with the environment. This paper proposes the stochastic curiosity maximizing exploration (SCME), a learning strategy explored to allow the agent to act as human. We cope with the sparse reward problem by encouraging the agent to explore future diversity. To do so, a latent dynamic system is developed to acquire the latent states and latent actions to predict the variations in future conditions. The mutual information and the prediction error in the predicted states and actions are calculated as the intrinsic rewards. The agent based on SCME is therefore learned by maximizing these rewards to improve sample efficiency for exploration. The experiments on PyDial and Super Mario Bros show the benefits of the proposed SCME in dialogue system and computer game, respectively.
DisGCo

Graph algorithms are widely used in various applications. Their programmability and performance have garnered a lot of interest among the researchers. Being able to run these graph analytics programs on distributed systems is an important requirement. Green-Marl is a popular Domain Specific Language (DSL) for coding graph algorithms and is known for its simplicity. However, the existing Green-Marl compiler for distributed systems (Green-Marl to Pregel) can only compile limited types of Green-Marl programs (in Pregel canonical form). This severely restricts the types of parallel Green-Marl programs that can be executed on distributed systems. We present DisGCo , the first compiler to translate any general Green-Marl program to equivalent MPI program that can run on distributed systems. Translating Green-Marl programs to MPI (SPMD/MPMD style of computation, distributed memory) presents many other exciting challenges, besides the issues related to differences in syntax, as Green-Marl gives the programmer a unified view of the whole memory and allows the parallel and serial code to be inter-mixed. We first present the set of challenges involved in translating Green-Marl programs to MPI and then present a systematic approach to do the translation. We also present a few optimization techniques to improve the performance of our generated programs. DisGCo is the first graph DSL compiler that can handle all syntactic capabilities of a practical graph DSL like Green-Marl and generate code that can run on distributed systems. Our preliminary evaluation of DisGCo shows that our generated programs are scalable. Further, compared to the state-of-the-art DH-Falcon compiler that translates a subset of Falcon programs to MPI, our generated codes exhibit a geomean speedup of 17.32×.