Reviewerly/exHarmony huggingface dataset:
When the Virtual Machine Wins over the Container: DBMS Performance and Isolation in Virtualized Environments

Database management systems (DBMSs) are a common service in the cloud. Cloud providers employ virtualization to consolidate DBMSs for efficient resource utilization and to isolate collocated DBMS workloads. To guarantee the service-level agreement (SLA), the disk I/O performance and its isolation are critical in DBMSs because they are inherently disk I/O intensive. This paper investigates the disk I/O performance and its isolation of MySQL on two major virtualization platforms, KVM and LXC. KVM provides the hypervisor-based virtualization (virtual machines) while LXC provides the operating-system-level virtualization (containers). Containers are widely believed to outperform virtual machines because of negligible virtualization overheads, while virtual machines are to provide stronger performance isolation. Contrary to the general belief, our results show that KVM outperforms LXC by up to 95% in MySQL throughput without compromising the isolation. Furthermore, LXC violates the performance isolation; a container given 30% share of the disk bandwidth consumes the 70% share. Our analysis reveals that the file system journaling, which is mandatory to maintain file system consistency, has negative impact on both the performance and its isolation in the container. Because of the sharing of the journaling module in containers, the journaling activities are serialized and bundled with each other, resulting in poor performance and violation of the performance isolation.
A hybrid method for Arabic aspect-based sentiment analysis

Nowadays, sentiment analysis has been a very active research area with the increase of social media data. It presents a very useful task for product evaluation, social recommendation and popularity analysis. It constitutes also a crucial move towards natural language processing (NLP) domains. Our m ain goal is to identify sentiments towards aspect in the sentence. An aspect presents a specific entity or object features (price, product quality, etc.). Therefore, its analysis requires two primordial steps: extract entity aspects and identify the sentiments from all these aspects. In our research work, we propose a new hybrid method to detect sentiments towards aspects. We start with a machine learning method to detect the different aspects within a given sentence, followed by a rule based method to identify sentiments within these aspects. The evaluation of our hybrid system based on a reference dataset of Arabic Hotels’ reviews Semantic Evaluation-2016 shows that our system outperforms baseline research to achieve encouraging results (96% of F-score).
Pre-processing and data validation in IoT data streams

In the last few years, distributed stream processing engines have been on the rise due to their crucial impacts on real-time data processing with guaranteed low latency in several application domains such as financial markets, surveillance systems, manufacturing, smart cities, etc. Stream processing engines are run-time libraries to process data streams without knowing the lower level streaming mechanics. Apache Storm, Apache Flink, Apache Spark, Kafka Streams and Hazelcast Jet are some of the popular stream processing engines. Nowadays, critical systems like energy systems, are interconnected and automated. As a result, these systems are vulnerable to cyber-attacks. In real-world applications, the sensing values come from sensor devices contains missing values, redundant data, data outliers, manipulated data, data failures, etc. Therefore, our system must be resilient to these conditions. In this paper, we present an approach to check if there is any above mentioned conditions by pre-processing data streams using a stream processing engine like Apache Flink which will be updated as a library in future. Then, the pre-processed streams are forwarded to other stream processing engines like Apache Kafka for real stream processing. As a result, data validation, data consistency and integrity for a resilient system can be accomplished before initiating the actual stream processing.
Holonote: Text editor of augmented reality oriented to people with motor disabilities

The advance of technology in the 21st century has brought about fundamental changes in the fusion of physical and technological reality. Today, with virtual reality devices, it is possible to plan a visit to a museum without leaving home, even to perform highly complex surgeries. There are several benefits that have come with these technologies, it has improved the quality and experience of games, as well as it has managed to design and plan intelligent cities. Consequently, this work proposes to provide a better experience to the user with motor difficulties when using a text editor by creating a virtual reality environment that allows to alternate the entries to the system, that is to say, to add functionalities for the handling or control of the applications such as voice recognition or the fixed glance on some component. HoloLens is a growing technology that has become popular with the emergence of several applications and thanks to the combination of physical reality in a virtual environment. For this work, an analysis of related work has been carried out, a word processor has been developed, and promising results have been tested, evaluated and validated.
Learning multiple instance deep quality representation for robust object tracking

Robustly tracking various objects within a video stream with complex objects and backgrounds is a useful technique in next generation computer vision systems. However, in practice, it is difficult to design a successful video-based object tracking system due to the varied light conditions, possible occlusions, and fast-moving objects. In this work, a novel weakly-supervised and quality-guided visual object tracking model is proposed, wherein the key is a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) that captures the feature sequence and predicts the quality score of each candidate window. More specifically, given a rich set of training videos annotated with the target objects, a weakly-supervised learning algorithm is first used to project all the candidate window features onto the semantic space. Next, we propose a two-stage algorithm to select the key frames from the video sequences, where both the shallow and deep filtering operations are conducted. Subsequently, the so-called BLSTM-RNN is proposed to characterize the feature sequence temporally, based on which the maximally possible object window can be calculated and finally output. In our experiment, a large video dataset containing 2045 NBA regular seasons and playoff basketball games was compiled. Based on this, a comparative study is conducted between the proposed algorithm and state-of-the-art video tracking methods. Extensive visualization results and comparative tracking precisions show the competitiveness of the proposed method.
Using positional sequence patterns to estimate the selectivity of SQL LIKE queries

Sequence patterns are frequently employed in many expert system applications in a wide range of domains from bioinformatics to smart homes and stock market analysis. Regular sequence patterns fail to express whether two consecutive items in a pattern are occurring right after each other in all pattern occurrences in an item database or not. Such a differentiation may be important for many intelligent system applications, for instance, to better address business questions like "should two frequently-bought-together items be located right next to each other on a retail store shelf, or is it ok to place them at some distance as long as they are in the same aisle?". In this paper, we propose a novel type of sequence pattern, called "positional sequence patterns", and illustrate its application on a special expert system, i.e., the query planner/optimizer of a database management system. Positional sequence patterns allow to accommodate extra information regarding whether a frequent ordered item pair always occurs next to each other without any gap in between in all pattern occurrences. Since positional sequence patterns are not considered by the existing sequence pattern mining algorithms, we also propose an algorithm to mine them. Next, we integrate the positional sequence patterns into the selectivity estimation component of the query optimizer as an expert system application. More specifically, in the knowledgebase of the query optimizer, a histogram-like structure of positional sequence patterns are created and stored. Then, during query optimization time, these histograms are utilized to infer the selectivity of flexible text queries that are enabled by the SQL LIKE operator. In particular, the proposed selectivity estimation method employs redundant pattern elimination based on pattern information content during histogram construction, and a partitioning-based matching scheme. The experimental results on a real dataset from DBLP show that the proposed approach outperforms the state of the art by around 20% improvement in error rates.
Performance Analysis of Secure Floating-Point Sums and Dot Products

Privacy-preserving collaborative applications enable groups of parties to run joint computations with private inputs, using cryptographic protocols that protect data privacy through-out the computation. The developers of these applications need a collection of protocols that provide efficient secure computation with all basic data types and secure protocol composition. We focus in this paper on protocols for multioperand floating-point addition and dot products, to provide a more comprehensive analysis of their complexity and performance, as well as improved solutions. These protocols are part of a framework that supports all basic data types. The analysis shows that adding protocols optimized for multiple inputs can substantially improve the performance of secure floating-point arithmetic.
COMPARING BUILDING SURFACES’ ORIENTATIONS TO OPTIMIZE SOLAR ENERGY COLLECTION

ABSTRACT Net-zero and other high performance green buildings normally do or should include optimized solar energy systems. While detailed computer-based energy simulations of buildings’ energy systems are becoming near-commonplace for many projects, simple, easy-to-use data tables are beneficial earlier in the design process to help guide preliminary decisions in all projects. Practical lookup tables, and then comparison of the data they contain, are also very useful for teaching new concepts, in this case for learning about solar orientations in sunny locations. Engineers, architects, design-build contractors, students, and other designers of green buildings can benefit through knowing, in advance, how exterior surfaces’ orientations increase or decrease the total annual solar energy arriving upon those surfaces. For example, maximizing the incoming energy on a particular roof is advantageous for gathering solar energy for heat or for conversion of that sunlight to electricity, but various requirements often limit designers’ choices for surfaces’ orientations. This paper presents simple tables that form a tool for making initial decisions on surfaces’ directions and slopes; the user can then study various effects further, such as local factors including cloudiness and shading, with detailed software. The classical solar geometry equations utilized are documented here for repeatability of the research, but are not necessary for use of this paper’s tables. Practical examples are given too to help readers use the tables.
Design and Implementation of A Scrum Project Training Platform Supporting Virtual Enterprise Environment

Scrum is one of the most common development models in project management today. But its working mode is obviously different from the traditional software development process. Therefore, no effective online training platform has been found. This paper mainly introduces the system architecture and implementation details of a software project training platform. It perfectly integrates the development concept of Scrum. Students can have a good experience of this popular development mode in the industry. ctatistical results show that this system is reliable and can meet the demand of software development training.
Forecasting Sea Surface Temperature in Java Sea Using Generalized Regression Neural Networks

Sea surface temperature is an important factor that influences climate change and productivity in the sea. Sea surface temperature is measured manually every day. The method of observation is considered to be less efficient because the process is not easy and requires relatively expensive costs. In addition, consideration is taken when extreme weather can hinder the observation process and be able to endanger the observer. In this case, a prediction system is needed that can predict the current conditions and the next few days from sea surface temperature. Before predicting sea surface temperature, the first thing to do is to forecast each parameter of sea surface temperature. Parameters of sea surface temperature consist of air temperature, air humidity, rainfall, duration of solar radiation, and wind speed. Forecasting parameters of sea surface temperature are carried out using TS-GRNN. The best GRNN models for forecasting air temperature, air humidity, rainfall, duration of solar radiation, and wind speed were obtained by RMSE of 0.1421.
Automatic Parallelization of Probabilistic Models with Varying Load Imbalance

As scientists are designing increasingly complex and intricate models, the prominent way today to achieve acceptable execution time without sacrificing accuracy is through parallel computing. These techniques can improve execution time either on the level of the optimization methods or on the level of the model evaluations. This paper outlines an automatic parallelization approach for the latter. Processor specific procedures with embedded communication primitives are generated from static schedules produced by an evolutionary algorithm. These are passed to an optimizing compiler to avoid the overhead of typical task runtime systems. The two key insights are that the parallel structure of probabilistic models is revealed when the data is combined with the model and that static schedules can be combined into more robust schedules that can deal with varying load imbalance. For this, LogP model parameters and execution time of each computational task are measured and fed into a discrete event simulator to estimate the running time on the target parallel system. Performance is evaluated with three pharmacological models with different characteristics. The first model lacks enough exploitable parallelism while up to approximately 6x and 8x improvements are achieved for the other models. Compared to a theoretical system with infinite processors and no communication delay, this equates to exploiting 66% and 99% of the available parallelism. Performance improves even when load imbalance varies.
Learning Depth With Very Sparse Supervision

Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded geometrical constraint. This paper explores a new approach to learning depth perception requiring neither of those. Specifically, we train a specialized global-local network architecture with what would be available to a robot interacting with the environment: from extremely sparse depth measurements down to even a single pixel per image. From a pair of consecutive images, our proposed network outputs a latent representation of the observer's motion between the images and a dense depth map. Experiments on several datasets show that, when ground truth is available even for just one of the image pixels, the proposed network can learn monocular dense depth estimation up to 22.5% more accurately than state-of-the-art approaches. We believe that this work, despite its scientific interest, lays the foundations to learn depth from extremely sparse supervision, which can be valuable to all robotic systems acting under severe bandwidth or sensing constraints.
Selection of a machine learning algorithm for OSHA fatalities

Traditional programming has already cleared its path to machine learning. With the abundance of data that is available in different applications and fields, it has grown exponentially in the past years. With the available data, machine learning models can predict the output. The different models that have been used have transformed a wide range of real-world applications. Workplace safety is one of the most important factors that make an organization successful. Every organization has a plan that describes what employees should do in order to prevent injury, but still, accidents happen at the workplace. Accident related data is available with the Occupational Safety and Health Administration (OSHA). There are different machine learning algorithms that are used to predict the required output. In this paper, the authors are investigating different machine learning algorithms and their accuracy to the predicted results.
Blind Super-Resolution for Single Remote Sensing Image via Sparse Representation and Transformed Self-Similarity

Abstract Super-resolution (SR) reconstruction is one of the effective ways to improve the spatial resolution of remote sensing images, but the blur kernel estimation without any additional prior information is the key to the quality of SR reconstruction. Facing the above problem, a blind super-resolution method via sparse representation and transformed self-similarity is proposed in this paper. In this method, the blur kernel as well as the reconstructed high-resolution image are estimated at the same time using the structural self-similarity from a single image, a down-sampled version of the observed image is used as the training samples for the dictionary learning, and the internal patch search space is expanded using the transformed self-similarity to improve the quality of estimation. Experiment results show that, our method performs well both in quality of kernel estimation and SR reconstruction.
A Two-stage Demand Response Strategy for Datacenters in the Smart Grid Environment

Abstract As a large load of the smart grid, datacenters usually have a huge demand for power. Datacenter participation in demand response programs of smart grids can help the grid to adjust the load while reducing its own power costs so as to save electricity costs. In this paper, we designed a two-stage demand response participation strategy to alleviate the peak load pressure of the gird and reduce the power cost of the datacenter. The first stage determines whether to participate in demand response according to the real-time electricity prices variation of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by auction. Then the datacenter selects the tasks of the winning users to postpone processing with awards, which reduces the electricity costs of the datacenter and effectively meets the demand response requirements of the smart grid. In this way, the goals of both parties could be achieved.
A research of surgical navigation system of mixed reality based on image registration

This paper studies a registration method that is more suitable for using Microsoft HoloLens as a surgical navigation instrument. The most important step in the surgical navigation process is to register the virtual model with the corresponding lesion. Our registration method is mainly as follows: first use the 3D model recognition in vuforia for coarse registration; then extract the point cloud data of model and real scene scanned by HoloLens; finally use the normal vector geometric features to extract the feature points of the point cloud data, and then use the ICP algorithm Perform fine registration. Our registration method can solve the problem that the traditional ICP algorithm is easy to fall into a local optimum, and it can also improve the registration accuracy to a certain extent.
Convex Hulls in Solving Multiclass Pattern Recognition Problem

The paper proposes an approach to solving multiclass pattern recognition problem in a geometric formulation based on convex hulls and convex separable sets (CS-sets). The advantage of the proposed method is the uniqueness of the resulting solution and the uniqueness of assigning each point of the source space to one of the classes. The approach also allows you to uniqelly filter the sourse data for the outliers in the data. Computational experiments using the developed approach were carried out using academic examples and test data from public libraries.
Detection of Taxpayers with High Probability of Non-payment: An Implementation of a Data Mining Framework

Due to limitations in tax administrations, such as: staff, tools, time, etc., tax administrations seek to recover debts in the early stages of control, where the cost of collection is lower than in the subsequent stages. This work proposes a framework based on deep learning techniques to predict debts of taxpayers with high probability of non-payment in a short period of time. A group of debts of a tax administration was used to generate the model to estimate the risk of non-payment. A concordance index metric was used to measure the performance. The performance obtained was 90%.
Input-Dependent Error Sketching Model Enabled Information Theoretical Secure Sketch

Secure sketch conceals any random string w by generating a helper string ss (known as a sketch). It allows the exact recovery of w from ss given another value w' that is close to w. A secure sketch can be utilized to protect any error-prone secret, e.g., biometrics, stored in secret storage to promote secure authentication. When error tolerance is demanded, a secure sketch can be used as an error correction code to tolerate the noise over an unreliable, noisy communication channel. However, when both security and error tolerance are of interest, the error tolerance property of a secure sketch imposes entropy loss. It leads to a weak security guaranty on a low entropy input string. Recent work by Fuller et al. (2016) has exploited the structure of the input string. They showed that having precise knowledge over the input strings' distribution is essential to construct a secure sketch for an input string of low entropy. We formalized a new model for secure sketch construction to realize precise knowledge of the input distribution setting. With the formalized new model, we devised an explicit secure sketch construction to a large family of noisy sources. The devised secure sketch can tolerate an error rate close to 1/2 in polynomial time, i.e., O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ), and meets the best possible secure sketch's security bound with optimal entropy loss.