Reviewerly/exHarmony huggingface dataset:
Joint Optimization of MapReduce Scheduling and Network Policy in Hierarchical Data Centers

As large-scale data analytic becomes norm in various industries, using MapReduce frameworks to analyze ever-increasing volumes of data will keep growing. In turn, this trend drives up the intention to move MapReduce into multi-tenant clouds. However, the application performance of MapReduce can be significantly affected by the time-varying network bandwidth in a shared cluster. Although many recent studies improve MapReduce performance by dynamic scheduling to reduce the shuffle traffic, most of them do not consider the impact by widely existing hierarchical network architectures in data centers. In this article, we propose and design a hierarchical topology (Hit) aware MapReduce scheduler to minimize overall data traffic cost and hence to reduce job execution time. We first formulate the problem as a Topology Aware Assignment (TAA) optimization problem while considering dynamic computing and communication resources in the cloud with hierarchical network architecture. We further develop a synergistic strategy to solve the TAA problem by using the stable matching theory, which ensures the preference of both individual tasks and hosting machines. Finally, we implement the proposed scheduler as a pluggable module on Hadoop YARN and evaluate its performance by testbed experiments and simulations. The testbed experimental results show that Hit-scheduler can improve job completion time by 28 and 11 percent compared to Capacity Scheduler and Probabilistic Network-Aware scheduler, respectively. Our simulations further demonstrate that Hit-scheduler can reduce the traffic cost by 38 percent at most and the average shuffle flow traffic time by 32 percent compared to capacity scheduler. In this article, we have extended Hit-scheduler to a decentralized heuristic scheme to perform the policy-aware allocation in data center environments. Many existing centralized approximation approaches are too complex and infeasible to implement over a data center, which typically include large amounts of servers, containers, switches, and traffic flows. In the extension, we have designed a decentralized heuristic scheme to perform the Policy-Aware Task (PAT) allocation by using existing centralize algorithm to approximately maximize the total gained utility. Finally, the simulation based experimental results show that the proposed PAT policy reduces the communication cost by 33.6 percent compared with the default scheduler in data centers.
Multi-Pooling Attention Learning for Melanoma Recognition

Melanoma is a type of skin cancer with high mortality rate. Early diagnosis of malignant melanoma usually relies on the judgement of dermatologists. Recently, more and more research paid attention to classify skin lesion automatically by dermoscopy images, which is able to assist clinicians to diagnose quickly and accurately. However, there still remain some challenges on extraction of discriminative feature due to the inter-class similarity and intra-class variation. In this paper, we propose a novel multi-pooling attention learning for skin lesions classification, which consists of multiple types of pooling operations, attention mechanism and feature fusion. The multi-pooling layer with flexible pooling patterns is designed to capture more representative features. The attention mechanism has the ability to help our model focus on the lesion regions. Meanwhile, in order to deal with the issues of inter-class similarity and intra-class variation of skin lesions, we adopt a joint loss function to optimize the final results. Our proposed approach has been validated on ISIC2017 dataset that provided by IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge for Skin Lesion Analysis Towards Melanoma Detection. The results show that our proposed network achieves superior performance over six top-ranking approaches in ISBI 2017 challenge leaderboard and five other methods.
Development of Low-Cost Autonomous Robot

Aim of this research is to develop the prototype of low-cost autonomous robot that could be used for experimental purpose at schools and research laboratories. The components used for robot development are Ultrasonic sensor, Camera module and Raspberry Pi as a main processor. The camera module is used to detect the target. It helps robot in a way that it captures image whenever any object appears in the path at specified distance range. The captured image is then sent to the raspberry pi for the image processing. For extracting useful information from the image, following image processing steps are carried out: importing the capture image, Colour conversion and resizing image, clean the image, filter by Colour and brightness, crop and compare. An overview table is developed that shows the cost, application, number of drives and the controller used in the previous developed robots. The total cost of developed robot reported in this research is around 6300 pkr. This type of inexpensive robot finds usefulness in schools for robotic studies where each student can work individually and understand the sensing, actuating and the dynamics of wheeled robot. It could be used in research laboratories for experimental purpose.
An Inverse Problem Solution in Semiotic Systems Under Uncertainty

A mathematical model of decision support in qualitative semiotic systems under uncertainty is proposed. The model is based on the description of a complex system in the syntactic, semantic and pragmatic aspects and the inverse problem solution in the semiotic system. The mathematical formulation of the inverse problem solution in semiotic systems is given. A method of decision-making support, based on the construction of a conceptual framework of decisions that organizes the search for new solutions, is proposed. Methods for determining the names of decision classes in the conceptual framework are considered. A method for constructing artificial names of solutions class is proposed. Artificial names of decision class are used as queries to Internet search engines, which allow you to find terms (sign-symbols) that describe the subject area in the form of vector spaces that characterize the frequency of the terms used in the different contexts of the subject area. A method for expert determination of the name of the prototype of decision classes in the conceptual framework of solutions is proposed.
Automatic Material Classification Using Thermal Finger Impression

Natural surfaces offer the opportunity to provide augmented reality interactions in everyday environments without the use of cumbersome body-mounted equipment. One of the key techniques of detecting user interactions with natural surfaces is the use of thermal imaging that captures the transmitted body heat onto the surface. A major challenge of these systems is detecting user swipe pressure on different material surfaces with high accuracy. This is because the amount of transferred heat from the user body to a natural surface depends on the thermal property of the material. If the surface material type is known, these systems can use a material-specific pressure classifier to improve the detection accuracy. In this work, we address to solve this problem as we propose a novel approach that can detect material type from a user's thermal finger impression on a surface. Our technique requires the user to touch a surface with a finger for 2 s. The recorded heat dissipation time series of the thermal finger impression is then analyzed in a classification framework for material identification. We studied the interaction of 15 users on 7 different material types, and our algorithm is able to achieve 74.65% material classification accuracy on the test data in a user-independent manner.
Method for Identifying and Counting Objects

The article presents an implementation of object recognition methods using the OpenCV library. The analysis of existing approaches, as well as recognition algorithms. The best methods were selected for the quality and number of recognizable objects in the image, taking into account the time to search. A recognition algorithm based on the analysis of brightness histogram (three color components of objects is proposed); a recognition algorithm based on the analysis of the contour length has been developed; programs for counting objects of the same type in the image are developed, taking into account their separate location on the image and the possibility of overlapping (merging contours). The above algorithms are implemented in the programming language C++ using the OpenCV library. The results obtained in the course of using the implemented methods, both individually and in combination, are presented.
An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.
A New Provably Secure Identity-Based Multi-proxy Signature Scheme

In a multi-proxy signature scheme, an original signer could delegate his signing power to a designated proxy group. Only the cooperation of all proxy signers in the proxy group could generate a legitimate proxy signature on behalf of the original signer. In this paper, we formalize the definition and security model of the identity-based multi-proxy signature, and we construct a new identity-based multi-proxy signature scheme using bilinear pairings. We show the security of our scheme in the random oracle model, and the security of our scheme is based on the hardness of the computational Diffie-Hellman problem.
Scalable reinforcement learning on Cray XC

Summary Recent advancements in deep learning have made reinforcement learning (RL) applicable to a much broader range of decision making problems. However, the emergence of reinforcement learning workloads brings multiple challenges to system resource management. RL applications continuously train a deep learning or a machine learning model while interacting with uncertain simulation models. This new generation of AI applications impose significant demands on system resources such as memory, storage, network, and compute. In this paper, we describe a typical RL application workflow and introduce the Ray distributed execution framework developed at the UC Berkeley RISELab. Ray includes the RLlib library for executing distributed reinforcement learning applications. We describe a recipe for deploying the Ray execution framework on Cray XC systems and demonstrate scaling of RLlib algorithms across multiple nodes of the system. We also explore performance characteristics across multiple CPU and GPU node types.
A Survey on Range Proof and Its Applications on Blockchain

Blockchain is one of the hot research technologies recently. Blockchain-based distributed ledger requires that all the transactions are public and transparent such that the transactions information is recorded on the chain, which may easily cause the privacy disclosure and other problems. Therefore, in order to preserve users' privacy, the transaction amount should be hidden. The common method is homomorphic commitment. When verifying the legitimacy of a transaction, using the range proof protocol to verify that the transaction amount is non-negative. This paper studied and summarized the range proof protocol based on zero-knowledge proof. We discussed the definition of range proof, the research results, the classification of algorithm, the security and efficiency for each algorithm. This paper discussed further directions for the future research.
IMES: An Automatically Scalable Invisible Membrane Image Encryption for Privacy Protection on IoT Sensors

With the continuous changes in social needs, Smart City that become a new engine for traditional urban economic transformation, industrial upgrading and urban management. Around the popularity of Smart City, the combination of cloud computing, wireless mobile networks and IoT technology undoubtedly has become a perfect match. However, when the information obtained by the IoT devices are transmitted through the wireless network, people in the city are faced with a lot of personal information leaked without knowing it. This problem, which not only requires the introduction and improvement of relevant laws, but also brings new challenges to the cryptographic technology. This paper proposes an automatically scalable invisible membrane image encryption solution for image privacy issues for the smart city through IoT-based sensors environment. The scheme utilizes the integer vector homomorphic encryption algorithm (VHE) to flexibly generate invisible membrane based on the size of the privacy image to protect private information.
Pedestrian Detection - A Survey

Pedestrian detection is one of the important fields of computer vision. Computer vision is getting more information from the digital images and videos and distinguishes between objects and classifies the object. Pedestrian detection is being applied in a wide range of applications such as video surveillance, automated driving, etc. This pedestrian detection has been a growing research area and then so many techniques have been used for detection. This paper focuses on various techniques applied to pedestrian detection and discusses the outcome of every technique and its accuracy and miss rate.
Efficient SVM-based approach for CBIR

The fundamental aim of the research presented in the paper is to evolve general purpose clustering method that can efficiently handle large capacity image databases. In this paper a novel algorithm based on combination of K-means clustering and SVM was developed to improve standard general-purpose approach on clustering and to provide relevant results during retrieval process. Introduced technique of choosing preliminary number of clusters makes better use of K-means approach, while SVM-based technique improves clustering by correcting excessive generalization, that usually happens due to local optima problem. The experimental results show that the proposed method reaches high recall and precision.
Behavior Recognition On Multiple View Dimension

This paper proposes a behavior recognition pattern recognition method based on image recognition and applies it to the field of dance training. Dance is a performing art and graceful dance is inseparable from the dancers' good training mode. However, not everyone could enjoy high-quality dance education. We provide a dance training system based on 2D pose estimation and binocular stereo vision. The system relies on binocular imaging principle and deep learning model instead of wearable sensing devices or depth cameras to capture dancer's three-dimensional human movement in real time. Meanwhile, the learner's movements will be analyzed to get the difference between the movements and the standard dance, the goodness of the movements and the corresponding scores which are feedback to the learner in order to help them correct their wrong movements and show a better dance.
A TSGP-Based Tip Search Optimization Algorithm

Blockchain is an underlying technology that initially implemented for Bitcoin. Data consistency, decentralization, and non-tamperability of this technology provide unique proposition values in many applications. However, slow transaction speed, high demand of resources, and scalability have become the bottlenecks that limit the blockchain's applicability. Instead of a chain, IOTA is a network of tangle distributed ledger in order to offer higher scalability and quicker transaction time, compared to blockchain. In this paper, we propose a new TSGP (Tree Solve Graph Problem) tip selection algorithm for IOTA. Our experiments show that our algorithm performs better than tip selection algorithms in the literature. Moreover, increasing the number of transactions plays no impact on the transaction connection speed in our algorithm.
A Real-World Text Classification Application for an E-commerce Platform

This study aims to reflect the real-world utilities of machine learning applications by implementing a set of different text classification algorithms in terms of accuracy and performance. We developed a system capable of executing various text classification algorithms and generated models trained on real product catalog data collected from morhipo.com, an online fashion commerce platform. The highest mean accuracy rate was obtained as 96.08% (ranging between 85.44% and 99.99%) with a standard deviation value of 5.65% by Linear Support Vector Classifier (LinearSVC) algorithm.
Information Retrieval from Search Engine Using Particle Swarm Optimization

The World Wide Web provides an ample amount of information to the users, however, this leads to difficulty in the identification of relevant content. Web mining could be considered as a cure to this problem. It includes the application of machine learning and data mining techniques, which helps in the automatic extraction of meaningful patterns and relationships from a huge cluster of web data. Web mining is categorized into three areas: (i) web content mining, digging out knowledge from the content (i.e., text and graphics) of web pages, (ii) web structure mining, which extracts information from data describing the organization of web content, and (iii) web usage mining, in which we gather patterns by looking at the interactions of the users with the web. There is no sharply defined variation among these categories, and all the three mining tasks can be combined. So our paper focus on the very different type of technique for searching the web content, where we perform mining of the web content efficiently and give the fruitful results for the users. We planned to apply the support vector machine technique and the Particle Swarm Optimization (PSO) algorithm for searching the web content and giving the efficient and best results.
CoPASample: A Heuristics Based Covariance Preserving Data Augmentation

An efficient data augmentation algorithm generates samples that improves accuracy and robustness of training models. Augmentation with informative samples imparts meaning to the augmented data set. In this paper, we propose CoPASample (Covariance Preserving Algorithm for generating Samples), a data augmentation algorithm that generates samples which reflects the first and second order statistical information of the data set, thereby augmenting the data set in a manner that preserves the total covariance of the data. To address the issue of exponential computations in the generation of points for augmentation, we formulate an optimisation problem motivated by the approach used in $$\nu $$-SVR to iteratively compute a heuristics based optimal set of points for augmentation in polynomial time. Experimental results for several data sets and comparisons with other data augmentation algorithms validate the potential of our proposed algorithm.
Dialog Management of Healthcare Consulting System by Utilizing Deceptive Information

In the past few years, there has been an increasing number of works on negotiation dialog. These studies mainly focus on situations where interlocutors work cooperatively to agree on a mutual objective that can fulfill each of their own requirements. However, in real-life negotiation, such situations do not happen all the time, and participants can tell lies to gain an advantage. In this research, we propose a negotiation dialog management system that detects when a user is lying and a dialog behavior for how the system should react when faced with a lie. We design our system for a living habits consultation scenario, where the system tries to persuade users to adopt healthy living habits. We show that we can use the partially observable Markov decision process (POMDP) to model this conversation and use reinforcement learning to train the system’s policy. Our experimental results demonstrate that the dialog manager considering deceptive states outperformed a dialog manager without this consideration in terms of the accuracy of action selection, and improved the true success rate of the negotiation in the healthcare consultation domain.
Mutual authentication‐based RA scheme for embedded systems

To improve the security and efficiency of remote attestation (RA) for embedded systems, this study proposes mutual authentication-based RA scheme for embedded systems. Especially, the authors design an RA framework based on authentication agents and measurement agents, which combines the mutually anonymous identity authentication scheme with the platform integrity attestation. During the identity authentication period, based on the traditional direct anonymous authentication scheme, the time-stamping mechanism and the mutual direct anonymous attestation mechanism are proposed to achieve bidirectional anonymous authentication of both parties in the communication. During the platform integrity attestation period, combining with the locality principle, they improve the data structure for storing the integrity measurements of the module and propose an RA mechanism based on locality principle-based hash tree. This mechanism can shorten the length of the certification path and improve the verification efficiency of platform configuration integrity certification. Furthermore, experimental results and analysis show that the efficiency of the proposed scheme is superior to the existing schemes.
Transfer Learning Models on Brain Age Prediction

The early diagnosis and intervention for neurological diseases such as Alzheimer's disease has been becoming more and more important in medical treatment. Some state-of-the-art studies have shown that people with an older brain-age, i.e., degenerating faster, than control people of the same age, may have a larger risk for cognitive decline. In this paper, we propose a method to build a model to predict brain age by end-to-end training of preprocessed MRI scans, of which the backbone of this model is a 3D-CNN. We firstly train on a large-scale dataset: UkBiobank, and conduct transfer learning on specific small datasets. We illustrate the performance on small datasets such as NKI and Cambridge that this transfer learning model can achieve better performance than the baseline methods reported in literature, with a MAE of 4.20 years at the top model.