Reviewerly/exHarmony huggingface dataset:
Performance of OnPrem Versus Azure SQL Server: A Case Study

This paper presents a comparative study between on-premises databases and cloud databases regarding the response time of the database. It focuses on presenting the advantages of storing data and applications in the cloud and, of course, on managing it in comparison with managing the same data and applications locally on one or more physical machines. A Microsoft Azure account was created to manage the database that is stored in the cloud. To achieve comparative results, a specific testing architecture that uses a Universal Windows Platform app was created and used. The application is running locally on multiple physical machines and works with that database to extract data, operate, and upload new data. For local tests, the database was stored locally on a server, with and without replication, and for cloud tests, it was stored into a server in Central US. This paper provides a practical approach that could be used for examining the performance of basic database operations when dealing with a different number of user situations. As a result of tests carried out, we will highlight the many advantages of cloud data storage, such as data accessibility, speed, security, automation, and disaster recovery, and we will also try to offer an answer to the most common and important question: “Why cloud?”.
Research on the Tradeoff Between Privacy and Trust in Cloud Computing

Promoting cloud services must consider the privacy requirements between the user and the provider.Both privacy and trust are related to knowledge about an entity; however, there is an inherent conflict between trust and privacy.In this paper, we research the relationship between privacy and trust in the cloud computing.First, we construct a trust model based on multiple factors, such as direct trust, trust risk, reward-punishment, and feedback trust; the weight of trust factor is determined by class diversity and information entropy theory.Second, we propose a novel privacy metric model with multiple factors, such as privacy preference, credential attribute, interaction history, and privacy feedback, and the weight of privacy factor is based on the maximum dispersion.Third, we propose a tradeoff between privacy and trust; both user and the provider can choose privacy protection or trust establishment priority by personal preference and requirement.Fourth, we demonstrate and compare the tradeoff between privacy and trust, interaction success rate, trust evaluation accuracy, and privacy disclosure rate by different experiments; these simulation results show that the privacy of each partner can be effectively protected.
Document clustering for efficient and secure information retrieval from cloud

Summary Organizations prefer using cloud for storing their data due to availability of cost‐effective storage. The outsourced data include sensitive information, so data is encrypted as maintaining confidentiality and privacy of the documents is of paramount importance. Retrieving the desired information from the cloud requires efficient searching, which involves submission of search query to the cloud server by the end‐user. As the search terms may include sensitive information of an organization, it is desired that the search query should not reveal any confidential information. The existing works are not suitable for big‐data scenario due to high search time required for large document collections, thereby leading to increased cloud usage cost. Thus, an efficient approach to perform search on encrypted data using clustering is proposed in this paper. As the proposed technique clusters the documents based on the relationship between the keywords, the search method involves searching documents within the relevant cluster in contrast to searching the entire dataset. An efficient ranking method is incorporated to rank the documents according to the relevance to search query using Term Frequency‐Inverse Document Frequency ( tf‐idf ) value of the keywords in the documents, which leads to reduced communication overheads due to reduction in unnecessary documents being downloaded. Moreover, an efficient query randomization approach is proposed so that two or more queries involving the same search terms appear distinct. Experimental results using real datasets demonstrate that our proposed multi‐keyword ranked search scheme on encrypted cloud data significantly reduce the number of comparisons and search time in comparison to the existing techniques while maintaining recall of 100% and precision of 82%.
Recommendence and Fashionsence

The use of Artificial Intelligence (AI) techniques has become prevalent with almost all e-commerce sites. Following the trend, today's Fashion recommender systems are no exception, which widely use AI for catering to multiple use-cases. [3][4]. However this is still quite far from replicating the personalized shopping experience in a fashion store. Through this paper and demonstration we try and replicate the offline experience in an online world by using a Natural Language chatbot powered by multiple deep learning models. Athena, the personal shopping adviser in the presented system enables user interaction in natural language via a web-browser based application (extensible to a mobile application). The application is powered by two underlying machine learning components - Recommendence and Fashionsence. The recommendation system Recommendence is an ensemble of deep learning based and graph based collaborative filtering recommenders and is able to provide personalized fashion recommendations to the users. The Fashionsence component is a deep learning based model which can learn a fashion sense from online, trend and stylist data to recommend a trending fashionable set of items from the product inventory. This entire system is integrated via an orchestrating layer.
A Web Service-Oriented Geoprocessing System for Supporting Intelligent Land Cover Change Detection

Remotely sensed imagery-based change detection is an effective approach for identifying land cover change information. A large number of change detection algorithms have been developed that satisfy different requirements. However, most change detection algorithms have been developed using desktop-based software in offline environments; thus, it is increasingly difficult for common end-users, who have limited remote sensing experience and geographic information system (GIS) skills, to perform appropriate change detection tasks. To address this challenge, this paper proposes an online geoprocessing system for supporting intelligent land cover change detection (OGS-LCCD). This system leverages web service encapsulation technology and an automatic service composition approach to dynamically generate a change detection service chain. First, a service encapsulation strategy is proposed with an execution body encapsulation and service semantics description. Then, a constraint rule-based service composition method is proposed to chain several web services into a flexible change detection workflow. Finally, the design and implementation of the OGS-LCCD are elaborated. A step-by-step walk-through example for a web-based change detection task is presented using this system. The experimental results demonstrate the effectiveness and applicability of the prototype system.
TNPU

Training large scale convolutional neural networks (CNNs) is an extremely computation and memory intensive task that requires massive computational resources and training time. Recently, many accelerator solutions have been proposed to improve the performance and efficiency of CNNs. Existing approaches mainly focus on the inference phase of CNN, and can hardly address the new challenges posed in CNN training: the resource requirement diversity and bidirectional data dependency between convolutional layers (CVLs) and fully-connected layers (FCLs). To overcome this problem, this paper presents a new accelerator architecture for CNN training, called TNPU, which leverages the complementary effect of the resource requirements between CVLs and FCLs. Unlike prior approaches optimizing CVLs and FCLs in separate way, we take an alternative by smartly orchestrating the computation of CVLs and FCLs in single computing unit to work concurrently so that both computing and memory resources will maintain high utilization, thereby boosting the performance. We also proposed a simplified out-of-order scheduling mechanism to address the bidirectional data dependency issues in CNN training. The experiments show that TNPU achieves a speedup of 1.5x and 1.3x, with an average energy reduction of 35.7% and 24.1% over comparably provisioned state-of-the-art accelerators (DNPU and DaDianNao), respectively.
HW-CDI: Hard-Wired Control Data Integrity

Ensuring that a program follows an uncompromised control flow at the machine instruction level can provide sound protection from control flow attacks that transfer a control flow to the attacker's flow during program execution. This paper proposes an enhanced control data protection for control flow integrity called hard wired control data integrity (HW-CDI). The HW-CDI hides the control data via encoding with a key and requires proper decoding with the key for a correct control flow transfer. A unique aspect of HW-CDI is that this key changes in terms of not only the location but also the value of the control data. This paper describes the features necessary to make HW-CDI, an effective approach for securing program control flows with low-performance overhead. More specifically, this paper describes how to incorporate the HW-CDI into the processor's instruction pipeline so that it becomes an integral part of indirect branch instruction execution. It also provides information on how to generate the encoding/decoding keys without additional instrumented code. The HW-CDI is able to differentiate control flow transfer instances, providing context-based protection at negligible performance overhead.
Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.
Emerging Perspectives in Human-Centered Machine Learning

Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML systems is more relevant than ever. This area of work, dubbed Human-Centered Machine Learning (HCML), re-thinks ML research and systems in terms of human goals. HCML gathers an interdisciplinary group of HCI and ML practitioners, each bringing their unique, yet related perspectives. This one-day workshop is a successor of Gillies et al. 2016 CHI Workshop and focuses on recent advancements and emerging areas in HCML. We aim to discuss different perspectives on these areas and articulate a coordinated research agenda for the XXI century.
Game Theory-Based Reasoning of Opposing Non-functional Requirements using Inter-actor Dependencies

Abstract Goal-oriented requirements engineering frameworks are used to model stakeholders’ objectives and requirements using goals. In a real-time environment, stakeholders’ requirements may have opposing objectives. Hence, a novel framework is needed that captures the real issues in order to achieve multi-objective optimization of inter-dependent actors. For obtaining an optimum strategy for inter-dependent actors in the i* goal model, a multi-objective two-person zero-sum game theory-based approach is applied in this paper, by balancing the opposing goals reciprocally. The proposed approach involves the generation of each objective function based on the inter-dependency relationships, the creation of decision pay-off matrices based on the objective function values and their variation to a final decision pay-off matrix. A Maxmin solution is formulated for the multi-objective game model, in which the optimization problem for each player is a linear programming problem. Finally, the most desirable strategies and their proportion values are found. By integrating Java with the IBM CPLEX optimization tool, a simulation model based on the proposed method was developed. A successful evaluation was conducted on various case studies from the existing literature. Evaluation results indicate that the developed simulation model helps users to choose an optimal alternative design option feasible in real-time competitive environments that have goals with opposing objectives.
Research on video classification method of key pollution sources based on deep learning

China's environmental problems are not only related to the fundamental interests of the broad masses of the people, but also to China's national security and international image. At present, China's environmental protection work is facing a complex situation. Pollution sources can be divided into natural pollution sources and man-made pollution sources. Natural sources of pollution refer to places where nature releases harmful substances or causes harmful effects to the environment, such as active volcanoes. Man-made pollution source refers to the pollution source formed by human activities, and is also the main object of environmental protection research and control. Among the man-made pollution sources, air pollution sources, water pollution sources and soil pollution sources can be classified according to the main objects of pollution. Among them, air pollution sources and water pollution sources have the greatest impact on human life. Therefore, it has become an important subject worthy of in-depth discussion to take automatic and electronic measures for potential environmental pollution incidents, discover environmental pollution problems in time, reduce the probability of environmental pollution incidents, and even put some major environmental pollution incidents in their infancy. In this paper, deep learning method is used to classify the existing key pollution source video. Water pollution experiments show that the accuracy of video counting reaches 93.1%, which is better than other video processing schemes. The operation time of the system reaches acceptable range, and a solution to meet the real-time requirement is put forward.
3D human pose estimation from range images with depth difference and geodesic distance

Depth difference, as a popularly used feature for characterizing pairwise pixels of range images, fails to precisely capture skeleton joints when human body possesses a wild and complicated articulation. As the geodesic distance of pairwise pixels is able to present a global connected property and adjacent pixels often belong to the same body component, we propose an effective and efficient framework for pose estimation from range images. Firstly, all the pixels of a range image are grouped into superpixels using an improved Simple Linear Iterative Clustering algorithm. Secondly, those superpixels are labelled as the components of a human body using the hybrid feature. Thirdly, componentwise cluster feature extraction is undertaken on skeleton joints of body components with K-means clustering algorithm. Finally, the feature points of each component are then stacked as a compact representation of human poses and mapped to the skeleton joints of a human body. Experimental results demonstrate that the proposed framework outperforms several state-of-the-art pose estimation methods.
Complete and Nondestructive Atomic Greenberger–Horne–Zeilinger‐State Analysis Assisted by Invariant‐Based Inverse Engineering

Abstract A protocol to realize complete and nondestructive atomic Greenberger–Horne–Zeilinger (GHZ)‐state analysis in cavity quantum electrodynamics (QED) systems is presented. In this protocol, the three information‐carrier atoms and the three auxiliary atoms are trapped in six separated cavities, respectively. After ten‐step operations, the information for distinguishing the eight different GHZ states of the three information‐carrier atoms is encoded on the auxiliary atoms. Thus, by means of detecting the auxiliary atoms, complete and nondestructive GHZ‐state analysis with high success probability is realized. Moreover, the driving pluses of operations are designed as a simple superposition of Gaussian or trigonometric functions by using the invariant‐based inverse engineering. Therefore, the protocol can be realized experimentally and applied in some quantum information tasks based on complete GHZ‐state analysis with less physical entanglement resource.
Identifying turning points in animated cartoons

Detecting key story elements such as protagonist, opponent, desire, turning points, battle, and victory, etc. is essential for various narrative work applications including content retrieval and content recommendation systems. The task of automatically identifying story elements is challenging because of its complexity and subjectiveness and currently, there are no available algorithms for this task. In this paper, we focus on identifying turning points in a story of a cartoon movie. The proposed methodology extends the novel two-clocks theory, originally validated on scripts of theatre plays, to video stories. The assumption behind the two-clocks theory is that the perception of time is different when some special event happens to a certain agent (e.g., time flows slower for a patient and quicker for a tourist). The story timeline is monitored with two clocks: an event clock, which measures the regular time flow of the story; and a weighted clock, which measures the timing of the story events. We have conducted an experiment on 28 episodes of a cartoon series and achieved promising results: 78.6% precision for turning points identification and 100% precision for key scene detection. The proposed approach is the first step towards development of intelligent systems for automated understanding of stories in narrative works such as cinema movies and even amateur videos uploaded to the Internet.
Multiresolution Cube Propagation for 3-D Ultrasound Image Reconstruction

Three-dimensional (3-D) ultrasound provides physicians with volume rendering of patient ultrasound data. This medical ultrasound technique carries important clinical value in image-guided surgery. However, 3-D ultrasound is not widely used because obtaining 3-D ultrasound images is difficult and costly, and the technology used is incapable of scanning large-volume organs. This paper proposes a novel method for 3-D freehand ultrasound reconstruction, which can accurately restore 3-D volumes from 2-D B-scans through cube propagation. The 3-D multi-resolution method creates a 3-D volume pyramid and a 3-D texture pyramid to preserve the structural and textural details of the imaged volume. Computation efficiency is improved with the 3-D approximate nearest neighbors (ANN) search technique, which is proposed to accelerate the nearest-neighbor search through the whole 3-D ultrasound volume. Texture information in the texture pyramid is added to the cube distance in the nearest-neighbors searching step to identify suitable nearest neighbors. Experiments performed on clinical and synthetic datasets demonstrate that the proposed algorithm is robust and can achieve quality results in a short execution time relative to current reconstruction techniques.
Using Variability as a Guiding Principle to Reduce Latency in Web Applications via OS Profiling

Request latency is a critical metric in determining the usability of web services. The latency of a request includes service time - the time when the request is being actively serviced - and waiting time - the time when the request is waiting to be served. Most existing works aim to reduce request latency by focusing on reducing the mean service time (that is, shortening the critical path).
Making semantics for essence

In this paper, I develop a truthmaker semantics for essence and use the semantics to investigate the explanatory role of essence.
Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.
SPAARC: A Fast Decision Tree Algorithm

Decision trees are a popular method of data-mining and knowledge discovery, capable of extracting hidden information from datasets consisting of both nominal and numerical attributes. However, their need to test the suitability of every attribute at every tree node, in addition to testing every possible split-point for every numerical attribute can be expensive computationally, particularly for datasets with high dimensionality. This paper proposes a method for speeding up the decision tree induction process called SPAARC, consisting of two components to address these issues – sampling of the numeric attribute tree-node split-points and dynamically adjusting the node attribute selection space. Further, these methods can be applied to almost any decision tree algorithm. To confirm its validity, SPAARC has been tested and compared against an implementation of the CART algorithm using 18 freely-available datasets from the UCI data repository. Results from this testing indicate the two components of SPAARC combined have minimal effect on decision tree classification accuracy yet reduce model build times by as much as 69%.