Reviewerly/exHarmony huggingface dataset:
HCloud: A Serverless Platform for JointCloud Computing

Most of today's cloud providers, including Amazon, Google and Microsoft, usually offer computing services with the abstraction of virtual machines (VMs), which are also known as instances. Even though these public clouds share similar infrastructures, they provide different service quality standards and price models for users, which fluctuate according to the dynamic loads. The JointCloud computing model empowers the cooperation among multiple public clouds, which can provide the users better service quality with reasonable prices. However, it is difficult to dynamically cooperate among clouds based on the traditional VM abstraction. Any computation migration among clouds will trigger costly VM states transfer, which makes it extremely hard for users to adjust their deployment models swiftly. Fortunately, serverless computing model is getting popular recently and has been supported by major cloud vendors. This model allows users to upload their computing tasks to the cloud in the unit of function. Users do not need to consider all of the tedious works like virtual server maintenance; instead, the cloud automatically instantiates function workers to handle incoming requests on the fly. In this paper, we propose a new JointCloud platform called HCloud, which efficiently manages computing resources of multiple clouds while offering the server-less model to the users. The fine-grained granularity of function enables HCloud to flexibly migrate workloads among clouds, thus brings better service qualities and lower prices at the same time. For example, HCloud can reduce the cost by requesting resource from a cheaper cloud for latency-insensitive jobs while routing the requests of latency-sensitive ones to the nearest and performant clouds. We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers. The evaluation results show that HCloud can greatly improve the performance of serverless workloads with small costs.
Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment

Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.
Trapdoor Delegation and HIBE from Middle-Product LWE in Standard Model

At CRYPTO 2017, Roşca, Sakzad, Stehlé and Steinfeld introduced the Middle–Product LWE (MPLWE) assumption which is as secure as Polynomial-LWE for a large class of polynomials, making the corresponding cryptographic schemes more flexible in choosing the underlying polynomial ring in design while still keeping the equivalent efficiency. Recently at TCC 2019, Lombardi, Vaikuntanathan and Vuong introduced a variant of MPLWE assumption and constructed the first IBE scheme based on MPLWE. Their core technique is to construct lattice trapdoors compatible with MPLWE in the same paradigm of Gentry, Peikert and Vaikuntanathan at STOC 2008. However, their method cannot directly offer a Hierarchical IBE construction. In this paper, we make a step further by proposing a novel trapdoor delegation mechanism for an extended family of polynomials from which we construct, for the first time, a Hierachical IBE scheme from MPLWE. Our Hierarchy IBE scheme is provably secure in the standard model.
Diagnosis Approaches for Colorectal Cancer Using Manifold Learning and Deep Learning

Data visualization is still a challenge for numerous fields. For metagenomic data, datasets are usually characterized by very high-dimensional data which are hard to interpret to humans. Among diseases using metagenomic data for prediction, deep learning usually yields a lower performance comparing to classical machine learning for colorectal cancer prediction. In this paper, we present an approach using manifold learning with t-distributed stochastic neighbor embedding (t-SNE) and spectral embedding to visualize numerical data into images and leverage deep learning algorithms to improve the performance in colorectal cancer diseases prediction. The work also provides promising potentials to improve the visualization quality and performance in prediction tasks on dense data. The analytical results of samples coming from five various regions including America, China, Austria, Germany, and France show promising in use of combination between these visualization approaches and deep learning to enhance the performance in colorectal cancer disease diagnosis.
Improved Metric Learning Algorithm for Person Re-Identification Based on Asymmetric Metric

Person re-identification(re-ID) is becoming a hot research topic because of its value in both machine learning and video surveillance applications. In order to improve the robustness of Metric Learning by Accelerated Proximal Gradient(MLAPG), a person re-ID algorithm, called Asymmetric -MLAPG, is proposed on the basis of asymmetric metric. Unlike traditional metric learning which ignores the taking environment of the person images, asymmetric metric learning aims to find the feature matrix of each camera and map the vectors of images to a common space. In this paper, we rebuilt MLAPG model by Asymmetric metric. In addition to adding a regularization term which controls the influence of inconsistency of metric, there are two more regularization terms in order to control the offset of feature matrix from the initial value. Experiments show that the Asymmetric -MLAPG algorithm can achieve the better recognition rate and wide applicability on commonly used person re-ID data sets.
An Improved Object Detection Algorithm Based on M2Det

This paper proposes an improved target detection algorithm based on M2Det, which improves the way to construct feature pyramids in M2Det, and improves the detection accuracy on the Apple dataset by stacking MLFPN modules. The experimental results prove that the two-layer MLFPN module achieves the best performance. Compared with the original M2Det model, mAP@0.5 and mAP@0.75 have improved by 11.8 and 10.5, respectively.
A Fuzzy Logic Based System for Cloud-based Building Information Modelling Rendering Optimization in Augmented Reality

In recent years, Building Information Modelling (BIM) has become the standard for managing the lifecycle of a building. The metadata embedded to BIM models can be used onsite to enhance field worker's view with relevant information about the task. Augmented Reality (AR) is a natural candidate for enabling onsite interactions with BIM models and data, due to its ability for overlaying digital information on top of real-world objects through a digital display. However, the complexity of BIM models and the limited hardware capabilities of AR-ready devices (e.g. head mounted and mobile devices), makes it difficult to provide reliable visualizations, decreasing considerably application's performance. This paper presents a type-1 Fuzzy Logic System (T1FLS) that helps optimizing loading of BIM 3D models in an AR application. Experiments results show that the proposed T1FLS has an average frame per second (FPS) rate of 16.33 in the selected AR headset (Microsoft HoloLens). The FPS rate when using the proposed T1FLS is in average 2.33 times better than using a fixed batch size when loading BIM 3D objects.
Joint Language Identification of Code-Switching Speech using Attention-based E2E Network

Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. In the existing works, the LID on code-switching speech involves modeling of the underlying languages separately. In this work, we propose a joint modeling based LID system for code-switching speech. To achieve the same, an attention-based end-to-end (E2E) network has been explored. For the development and evaluation of the proposed approach, a recently created Hindi-English code-switching corpus has been used. For the contrast purpose, an LID system employing the connectionist temporal classification-based E2E network is also developed. On comparing both the systems, the attention based approach is noted to result in better LID accuracy. The effective location of code-switching boundaries within the utterance by the proposed approach has been demonstrated by plotting the attention weights of E2E network.
A Technology for Automatically Counting Bus Passenger Based on YOLOv2 and MIL Algorithm

The bus passenger data are very important for urban bus dispatching management. When passengers get on or off the bus, they often hide from each other. It is a great challenge for automatically accounting passengers through camera. The traditionally video-based target detection algorithm or target tracking algorithm is difficult to accurately count the number of passenger on and off. In this paper, the YOLOv2 algorithm is combined with the MIL tracker so as to real-time account the number of passengers in the bus surveillance video. Experiment shows that the accuracy rate of bus passenger statistics proposed in this paper reaches over 99%, and it proves that our method has good real-time and high accuracy.
Local occluded face recognition based on HOG-LBP and sparse representation

Face recognition system in real environment is easily affected by posture, illumination, shielding and other factors, which makes the recognition effect of traditional face recognition algorithm not ideal. Because most of the current face recognition algorithms do not take into account the fact that there may be occlusion in the actual image, the result of face recognition is wrong. In view of the presence of face occlusion, this paper proposes a face recognition method of local occlusion based on the feature and sparse representation of block-oriented gradient histogram (HOG) and local binary mode (LBP). First, the algorithm segmented the face image and extracted HOG and LBP features from each block to obtain the HOG-LBP joint feature of each block of image, which was projected into the feature subspace by PCA to establish an occlusion dictionary, and then classified and identified by sparse representation reconstruction residual. Compared with the traditional algorithms on ORL and AR face data sets, the proposed algorithm is proved to have better recognition rate and robustness.
Block chain technology for protecting the banking transaction without using tokens

Increasing digital technology has revolutionized the life of people. There are many threats and frauds detected in banking system. A centralized database is used by banking system which makes the attacker easy to get access to data and this makes the system insecure. The drawback of this centralized system can be reduced by reforming the system by implementing blockchain technology without using tokens. Blockchain uses decentralized architecture for storing and accessing data over the database. This reduces attacks on database hacked. Transactions done through blockchain technology are verified by each block in the chain, which will make the transaction more secure and help banking system work faster.
Quantum Agile Development Framework

The interest in quantum computing has grown exponentially in recent years, with large technology companies engaging in the creation of computers and quantum technologies. The number of qubits skyrockets and makes the future practical implementation of mathematical algorithms and the creation of commercial systems increasingly viable. However, the growth of hardware is not being accompanied by programming and engineering methodologies adapted to this new paradigm, with new needs and risks. Associated with this, we are also in a world in which current development projects are carried out in multiple paradigms associated with different needs. In this paper, our findings and proposals are defined to anticipate the future needs of quantum software project management, taking into account the new roles, requirements and deficiencies of this new technology. Specifically, we focus on defining a classical-agile hybrid project management framework that can be adapted to the needs of these new programming paradigms, taking into account not only quantum programming, but projects that integrate classical-quantum hybrid developments.
An IoT Based Wearable Device for Healthcare Monitoring

Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications.
Unique Face Identification System using Machine Learning

The recognition of human faces plays an important role in many applications, for example in video surveillance and the management of facial image databases. This paper will design and implement a security system based on a machine learning algorithms. Principal Component Analysis (PCA) is the algorithm that represents the faces economically. It extracts the most dominant Eigenfaces from the present set of the faces. Comparison of video frames can be done by using this technique. Faces can be recognized in frames using the haar cascade to extract the characteristics of a human face. The SVM algorithm is used to classify between data sets using the kernel. The performance of the identification system also depends on the extraction of the attributes and their classification in order to obtain accurate results. These algorithms give different accuracy rates under different conditions, as observed experimentally. The precision and efficiency with which the model identifies people is the real added value of this paper.
Sampling Unknown Decision Functions to Build Classifier Copies

Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the decision behavior of the targeted classifier throughout the input space. In this article we propose two sampling strategies to produce such sets. We validate them in six well-known problems and compare them with two standard methods in terms of both their accuracy performance and their computational cost.
An Efficient Method for Moving Vehicle Detection in Real-Time Video Surveillance

Surveillance systems are important to monitor and control critical situations for the field of traffic flow. Our aim is to enhance the ITS system about highway traffic and retrograde vehicle detection from surveillance video. We review the literature about vehicle detection in various countries and major highways, day to day vehicle transportation face critical situations then instant accident from different locations. This paper proposes an efficient method of detecting traffic surveillance in moving vehicles for various road conditions. Kalman filtering predicts the area where to search for each corner feature. This system is used to detect moving vehicles effectively in complicated situations such as pedestrians crossing, weather conditions, roadside trees, etc. The tracking algorithms are carried out over a various range of vehicles. It is to determine the regions where significant motion has occurred.
A Survey on Different Consensus Mechanisms for the Blockchain Technology

The invention of blockchain as the niche technology for cryptocurrencies has an opportunity to be used in other application domains as well. This is promoted due to privacy and security offered by blockchains. The blockchain uses a consensus mechanism for accepting any new information. Conventional consensus mechanisms used in the blockchain need high computational power that restricts its usage in resource constrained systems. This paper, focuses on the survey of different consensus methods available for the blockchain. An IoT system consists of a number of interconnected sensors/actuators, such systems have limited resources including compute, storage and energy. This survey aims to study on the consensus methods which are relevant for the IoT networks.
A Longitudinal Study of Pervasive Display Personalisation

Widespread sensing devices enable a world in which physical spaces become personalised in the presence of mobile users. An important example of such personalisation is the use of pervasive displays to show content that matches the requirements of proximate viewers. Despite prior work on prototype systems that use mobile devices to personalise displays, no significant attempts to trial such systems have been carried out. In this article, we report on our experiences of designing, developing and operating the world’s first comprehensive display personalisation service for mobile users. Through a set of rigorous quantitative measures and 11 potential user/stakeholder interviews, we demonstrate the success of the platform in realising display personalisation, and offer a series of reflections to inform the design of future systems.
Simplification method for 3D Terracotta Warrior fragments based on local structure and deep neural networks

The emergence of the three-dimensional (3D) scanner has greatly benefited archeology, which can now store cultural heritage artifacts in computers and present them on the Internet. As many Terracotta Warriors have been predominantly found in fragments, the pre-processing of these fragments is very important. The raw point cloud of the fragments has lots of redundant points; it requires an excessively large storage space and much time for post-processing. Thus, an effective method for point cloud simplification is proposed for 3D Terracotta Warrior fragments. First, an algorithm for extracting feature points is proposed that is based on local structure. By constructing a k-dimension tree to establish the k-nearest neighborhood of the point cloud, and comparing the feature discriminant parameter and characteristic threshold, the feature points, as well as the non-feature points, are separated. Second, a deep neural network is constructed to simplify the non-feature points. Finally, the feature points and the simplified non-feature points are merged to form the complete simplified point cloud. Experiments with the public point cloud data and the real-world Terracotta Warrior fragments data are designed and conducted. Excellent simplification results were obtained, indicating that the geometric feature can be preserved very well.
Improving the visibility of nighttime images for pedestrian recognition using in‐vehicle camera

Abstract There is a need for methods to recognize night pedestrian to reduce pedestrian traffic accidents at night. In this paper, we proposed the method that converts images using continuous nighttime images from in‐vehicle camera. The proposed method performs feature extraction that does not depend on one's own vehicle speed because inputting continuous nighttime images, and the function of convolution layer that performs dimensionality reduction. In order to confirm the effectiveness of the proposed method, we prepared the images of simulation and camera. The nighttime images were conversed with the proposed method. After conversion, we calculated the recognition performance by applying object detection which is an object recognition method. We showed the proposed method is more robust against the own vehicle speed change.
Tactical Level of Intelligent Geometric Control System for Unmanned Aerial Vehicles

This study considers the tactical level of the intelligent geometric control system designed to solve the cutting-edge scientific problem of controlling unmanned aerial vehicles (UAVs) in unstable conditions. Intelligent geometric theory combines geometric control methods (methods of optimal control, complex motion control and stabilization, formation control, trajectory and target tracking, differential pursuit-evasion games, etc.) with intelligent control methods using tools of artificial intelligence (productions, semantic networks, fuzzy logic, frame-based behavioral microprograms, frame-based operations, machine learning, genetic algorithms, methods of knowledge acquisition, etc.) and provides reliable and high-performance control techniques for operating in uncertain environments under wind disturbances. Hierarchical architecture of intelligent geometric control system is designed for joint application of precise geometric and adaptive intelligent control methods as parts of a single robotic system. The solution to the problem of controlling a UAV group taking into account mathematical models of an aircraft and wind loads was simulated in MATLAB system.
Affine Template Matching Based on Multi-Scale Dense Structure Principal Direction

Reliable and robust matching of multimodal images is a challenging task because of the nonlinear grayscale distortion caused by radiometric differences and the geometric deformations caused by viewpoint changes between multimodal images. To address these problems, we propose a dense descriptor that uses multiscale structure principal direction to capture the structural features of the image, as well as a novel dissimilarity measurement. The proposed descriptor adapts to nonlinear gray distortion effectively and is robust to image noise. The dissimilarity measurement addresses the reversal of direction caused by intensity inversion. In addition, we propose an improved pixel selection method to speed up the algorithm. Experimental results show that the proposed matching algorithm is offers a superior performance relative to the current state-of-the-art methods.