Reviewerly/exHarmony huggingface dataset:
Accelerating Atrous Convolution with Fetch-and-Jump Architecture for Activation Positioning

Recent advancements in abundant applications of computer vision (CV) have attributed to convolutional neural network, which plays the primary role in feature extraction. For specific CV tasks such as semantic image segmentation, aggregation of multi-scale contextual information is demanded which leads to the adoption of atrous convolution. However, atrous convolution introduces extra performance overheads for conventional CPU and GPU machines, mainly due to inconstant latency of memory access caused by holes in atrous kernels. Such deterioration gets exaggerated especially during parallel processing of pixels in the output feature map. In this work, we first assemble continuous datastream by overlapping adjacent atrous kernels to avoid frequent update of memory access locations. By referring various patterns of atrous kernels, we introduce distributed fetch pointers to extract individual elements from assembled datastream while adaptively update pointer positions. A prototype dataflow accelerator has been designed with a demonstration on MobileNet-SSD network involving atrous kernels. The benchmarking result proves that our design achieves even slight speed-up for atrous convolution compared to conventional convolution, other than visible performance degradation for mainstream CPU and GPU.
Design and implementation of a time-based Sun tracking solar system

Because of continuous decrease fuel of fossil reserve in earth, the whole world is now turning to renewable energy sources like solar, wind, geothermal, hydropower etc. In addition, it can be said that the ultimate source of energy on earth is sun with a few exceptions. And it is also the cleanest energy source without any pollution. The power value of 1.74 x 1017 watts receives by earth and the world consumes about 1.2 x 1013 watts of power per day. We are attempting to devour more energy from the sun utilizing the panel of solar. Changing of solar energy from electrical energy is expanding where the panels position of solar is situated vertically from the sun. In this way, the sun's tracking location and the panels position of solar is significant. In this paper, a new design of automatic tracking system to track the position of sun's has been focused. Though it is positioned vertically from the sun for the maximum conversion of energy in every times as move as solar panel with this system. In this tracking system, Light Depending Resistors (LDR) are applied as sensors. This system includes a microcontroller, LDR, gear motor system, and solar panels. This system is designed as dual axis tracking system. We also added a Real Time Clock and limit switch to reset the position of the solar panels at the end of the day.
Virtualized Resource Scheduling in Cloud Computing Environments: An Review

This paper summarizes the status and results of current research on cloud computing resource scheduling optimization problems. At the virtual resource layer, it focuses on analyzes and discussion pertaining to quality of service and efficiency-aware resource scheduling strategies, node task load balance and resource utilization-aware resource scheduling strategies, and multi-objective optimization resource scheduling strategies. At the physical resource layer, the paper focuses on data center load balance-aware resource scheduling strategies, energy consumption and cost-aware resource scheduling strategies, resource scheduling strategies with containers as virtual units, and reinforcement learning resource scheduling strategy. Finally, on the basis of the reviewed literature results, this paper proposes future research directions for cloud computing resource management and scheduling.
Decision Making Framework Based On Sentiment Analysis in Twitter Using SAW and Machine Learning Approach

One type of social media that is often used is Twitter. The development of social media is so fast that even users reach 326 million and produce 500 million tweets every day in July 2018. Users can send, change, and read short messages which have been called tweets. Tweets can contain facts or opinions so it is very beneficial to be analyzed. The results of this analysis can be in the form of stock market predictions, elections, reaction events or news and measuring subjectivity. The activity of analyzing tweet is a series of sentiment analysis activities. However, the results of sentiment analysis are cumulative in percentage of tweet polarity and only provide a overview for decision making. So, the intuitive aspect still plays a role in deciding the results of the Sentiment Analysis. Therefore, there is a need for more specific modeling of sentiment analysis results. In the decision making phase, the results of the Sentiment Analysis are still in the Intelligence phase or can be called the Problem Discovery. To proceed again to the Design phase until with a Choice, it is necessary to have a Decision Support System (DSS). In this study trying to propose a decision making framework based on the results of sentiment analysis from the Tweet dataset. Sentiment analysis is built using the machine learning approach. Furthermore, the results of this study indicate that the SAW method can accept the input polarity of the number of tweets and produce alternative decision weights.
Group Key Establishment in a Quantum-Future Scenario

In cryptography, key establishment protocols are often the starting point paving the way towards secure execution of different tasks.Namely, the parties seeking to achieve some cryptographic task, often start by establishing a common high-entropy secret that will eventually be used to secure their communication.In this paper, we put forward a security model for group key establishment (GAKE) with an adversary that may execute efficient quantum algorithms, yet only once the execution of the protocol has concluded.This captures a situation in which keys are to be established in the present, while security guarantees must still be provided in the future when quantum resources may be accessible to a potential adversary.Further, we propose a protocol design that can be proven secure in this model.Our proposal uses password authentication and builds upon efficient and reasonably well understood primitives: a message authentication code and a post-quantum key encapsulation mechanism.The hybrid structure dodges potential efficiency downsides, like large signatures, of some "true" post-quantum authentication techniques, making our protocol a potentially interesting fit for current applications with long-term security needs.
Interactive Verifiable Polynomial Evaluation

Cloud computing platforms have created the possibility for computationally limited users to delegate demanding tasks to strong but untrusted servers. Verifiable computing algorithms help build trust in such interactions by enabling the server to provide a proof of correctness of his results which the user can check very efficiently. In this article, we present a doubly-efficient interactive algorithm for verifiable polynomial evaluation. Unlike the mainstream literature on verifiable computing, the soundness of our algorithm is information-theoretic and cannot be broken by a computationally unbounded server. By relying on basic properties of error correcting codes, our algorithm enforces a dishonest server to provide false results to problems which become progressively easier to verify. After roughly logd rounds, the user can verify the response of the server against a look-up table that has been pre-computed during an initialization phase. For a polynomial of degree d, we achieve a user complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> ), a server complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1+ϵ</sup> ), a round complexity of O(logd) and an initialization complexity of O(d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1+ϵ</sup> ).
Multi-granularity Recurrent Attention Graph Neural Network for Few-Shot Learning

Few-shot learning aims to learn a classifier that classifies unseen classes well with limited labeled samples. Existing meta learning-based works, whether graph neural network or other baseline approaches in few-shot learning, has benefited from the meta-learning process with episodic tasks to enhance the generalization ability. However, the performance of meta-learning is greatly affected by the initial embedding network, due to the limited number of samples. In this paper, we propose a novel Multi-granularity Recurrent Attention Graph Neural Network (MRA-GNN), which employs Multi-granularity graph to achieve better generalization ability for few-shot learning. We first construct the Local Proposal Network (LPN) based on attention to generate local images from foreground images. The intra-cluster similarity and the inter-cluster dissimilarity are considered in the local images to generate discriminative features. Finally, we take the local images and original images as the input of multi-grained GNN models to perform classification. We evaluate our work by extensive comparisons with previous GNN approaches and other baseline methods on two benchmark datasets (i.e., miniImageNet and CUB). The experimental study on both of the supervised and semi-supervised few-shot image classification tasks demonstrates the proposed MRA-GNN significantly improves the performances and achieves the state-of-the-art results we know.
Visualization as Intermediate Representations (VLAIR) for Human Activity Recognition

Ambient, binary, event-driven sensor data is useful for many human activity recognition applications such as smart homes and ambient-assisted living. These sensors are privacy-preserving, unobtrusive, inexpensive and easy to deploy in scenarios that require detection of simple activities such as going to sleep, and leaving the house. However, classification performance is still a challenge, especially when multiple people share the same space or when different activities take place in the same areas. To improve classification performance we develop what we call a Visualization as Intermediate Representations (VLAIR) approach. The main idea is to re-represent the data as visualizations (generated pixel images) in a similar way as how visualizations are created for humans to analyze and communicate data. Then we can feed these images to a convolutional neural network whose strength resides in extracting effective visual features. We have tested five variants (mappings) of the VLAIR approach and compared them to a collection of classifiers commonly used in classic human activity recognition. The best of the VLAIR approaches outperforms the best baseline, with strong advantage in recognising less frequent activities and distinguishing users and activities in common areas. We conclude the paper with a discussion on why and how VLAIR can be useful in human activity recognition scenarios and beyond.
FCOS Based Human Detection System Using Thermal Imaging for UAV Based Surveillance Applications

This work is related to building a Human Detection system based on Fully Connected One Shot (FCOS). It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multithreaded GPU environments. Since our fundamental research is concentrated on object classification related to Unmanned Aerial Vehicle (UAV) applications, as a first step we choose to detect the humans from thermal dataset. Therefore, we used thermal images and videos possessed from thermal cameras of UAV lm to 50m above ground level as our dataset in building the model and testing. The FCOS extracts the features of an object using its efficient per-pixel fashion. Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using FCOS performs in a promising way and it can be used in automatic human detection systems.
Controllable stroke-based sketch synthesis from a self-organized latent space

Learning to synthesize free-hand sketches controllably according to specified categories and sketching styles is a challenging task, due to the lack of training data with category labels and style labels. One choice to control the synthesis is by self-organizing a latent coding space to preserve the similarity of structural patterns of the observed data. A practical way is introducing a Gaussian mixture prior over the latent codes, where each Gaussian component represents a specific categorical or stylistic pattern. As a result, we can generate sketches by sampling the latent variables from the Gaussian components or continuously manipulating the latent representations by interpolation. To achieve robust controllable sketch synthesis, it is critical to determine an appropriate Gaussian number. An underestimated Gaussian number cannot fully represent all the sketch patterns, i.e., some clusters have to contain sketches with more than one pattern. An overestimated one introduces redundant components, usually representing a chaotic collection of sketches with diverse patterns featured by other components. Both cases disturb pattern clustering over the coding space and make the internal code generation difficult to control for specific patterns. However, the Gaussian number is unavailable in this unsupervised task. In this paper, we present Rival Penalized Competitive Learning pixel to sequence (RPCL-pix2seq) to automatically determine the Gaussian number. Both quantitative and qualitative experimental results show RPCL-pix2seq can partition the codes for the sketches into an approximate stable number of clusters. Hence, we are able to do synthesis reasoning over the latent space, generating novel but reasonable sketches which neither appear in the training dataset nor exist in real life.
Forensic analysis of open-source XMPP multi-client social networking apps on iOS devices

In this paper, we present forensic analysis of Monal and Siskin IM, two decentralized open-source XMPP multi-client social networking apps on iOS devices that provide anonymity and privacy using OMEMO end-to-end encryption. We identified databases maintained by each app and storage locations within the iOS file system that stores the local copies of user information and metadata. We analyzed the databases and storage locations for evidential data of forensic value. The results in this paper show a detailed analysis and correlation of data stored in each app's database to identify the local user's multiple IM accounts and contact list, contents of messages exchanged with contacts, and chronology of conversations. The focus and main contributions of this study include a detailed description of artifacts of forensic interest that can be used to aid mobile forensic investigations.
Research on Coal Mine Risk Control Technology and Platform Base on Big Data

Abstract In order to improve the risk management and control ability, this paper studies coal mine risk management technology and big data platforms. First, BP neural network was optimized through data mining, and correction parameter technology. Based on this, a coal mine risk data prediction model was constructed. Then, combined with the control chart analysis, the coal mine risk early prevention method was proposed. At last, a big data platform to manage coal mine risk was developed. The results of this paper provide implementation methods and tools for coal mine risk management and control, and have reference significance for other enterprises.
An Occlusion-Aware RGB-D Visual Object Tracking Method Based on Siamese Network

In recent years, Siamese trackers have been extensively studied because of their high accuracy and speed. However, when the target is occluded by other objects, the result will be greatly drifted, which affects the quality of the tracking results. This study based on the RGB-D data proposes an object tracking method integrating a target occlusion estimation module and a target location correction module, called Siamese-Occlusion-Correction (SiamOC). When a target is occluded, these modules can help the Siamese tracker correct the target location. In this paper, experiments demonstrate that the method which is the real-time tracker has achieved competitive results on the CDTB dataset.
Face Mask Invariant End-to-End Face Recognition

This paper introduces an end-to-end face recognition network that is invariant to face images with face masks. Conventional face recognition networks have degraded performance on images with face masks due to inaccurate landmark prediction and alignment results. Thus, an end-to-end network is proposed to solve the problem. We generate face mask synthesized datasets by properly aligning the face mask to images on available public datasets, such as CASIA-Webface, LFW, CALFW, CPLFW, and CFP. Then, we utilize those datasets as training and testing datasets. Second, we introduce a network that contains two modules: alignment and feature extraction modules. These modules are trained end-to-end, which makes the network invariant to face images with a face mask. Experimental results show that the proposed method achieves significant improvement from state-of-the-art face recognition network in face mask synthesized datasets.
Integrating Local Outlier Factor with Radial Basis Function Interpolation for Small Sample Size Problems

The accuracy of data-driven models requires large number of training samples. However, training samples in many applications are usually difficult to collect. Thus, learning from small training sets is still an unprecedented challenge. In this paper, we proposed an effective virtual sample generation approach integrating local outlier factor with radial basis function interpolation for solving small samples size problems. To capture underlying distribution of data points, local outlier factor is first employed to indicate the degree of outlierness for data points. Based on the degree, interesting points are over-sampled via a deliberately designed roulette wheel. Then virtual samples are synthetized by the RBF interpolation on the lines between an interesting point and its corresponding nearest neighbors. The experimental results over two benchmark function datasets and a real-world dataset demonstrate that the proposed approach outperforms the existing state-of-the-art methods.
UAV geofencing: navigation of UVAs in constrained environments

Geofencing is a technique that defines virtual boundaries in a specific geographical area. This technique is very useful in navigation of unmanned aerial vehicles (UAVs), which is called UAV geofencing. In this context, most of the works focus on defining the boundaries of admissible geographical regions, without addressing control issues and boundary handling problems. More precisely, in the presented geofencing systems, once the defined boundaries are violated, the reaction is limited to some predefined alerting actions. From a technical viewpoint, preventing UAVs from violating the boundaries defined by the geofencing systems can be considered as a control problem. In particular, by using a constrained control scheme, one can address the problem of enforcing geofence satisfaction at all times, while ensuring that control objectives are achieved and the UAV can fulfill the assigned mission. The aim of this chapter is to collect and to systematize in a common framework the numerous contributions dispersed regarding navigation of UAVs in constrained environments. In this chapter, the most recent results are discussed, and a perspective on some open research directions is provided.
Case Study of the Ukraine Covid Epidemy Process Using Combinatorial-Genetic Method

The article presents the modelling results of the Ukraine Covid-19 epidemy process using the combinatorialgenetic method based on official statistical data.A comparison with some other known methods for model construction and the process prediction is also given.This research is important for defining tendency of coronavirus evolvement in time and predicting its future activity in order to take some protective measures.
Quantum Cryptography

History repeats itself. Quantum cryptography has started a revolution of quantum computing and has repeated the days of Einstein's research paper on “Theory of Relativity,” which changed the world's perceptions of physics completely written based on Newton's laws. Quantum cryptography is going to change the definition of computers right from scratch. The last century was witness of a space race between US and USSR. This century would be witness of quantum computing race between US and China. It has changed the dimensions of operating systems, software, hardware, databases, and applications. Quantum computers are in a phase to replace the conventional computers and they are reaching to the level called quantum supremacy. The chapter covers the details of the basic principles and work methodology of quantum cryptography, the contribution of various pioneers, advantages over classical cryptography, its applications, future scope, and limitations simultaneously. The chapter covers the contribution of leading countries and organizations in quantum revolution.
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty

We consider the problem of simultaneously estimating a dense depth map and camera pose for a large set of images of an indoor scene. While classical SfM pipelines rely on a two-step approach where cameras are first estimated using a bundle adjustment in order to ground the ensuing multi-view stereo stage, both our poses and dense reconstructions are a direct output of an altered bundle adjuster. To this end, we parametrize each depth map with a linear combination of a limited number of basis “depth-planes” predicted in a monocular fashion by a deep net. Using a set of high-quality sparse keypoint matches, we optimize over the per-frame linear combinations of depth planes and camera poses to form a geometrically consistent cloud of keypoints. Although our bundle adjustment only considers sparse keypoints, the inferred linear coefficients of the basis planes immediately give us dense depth maps. RidgeSfM is able to collectively align hundreds of frames, which is its main advantage over recent memory-heavy deep alternatives that are typically capable of aligning no more than 10 frames. Quantitative comparisons reveal performance superior to a state-of-the-art large-scale SfM pipeline.
Neural Network Stealing via Meltdown

Deep learning services are now deployed in various fields on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multitenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep learning service with 92.875% accuracy and 1.325kB/s extraction speed.