Reviewerly/exHarmony huggingface dataset:
Optimizing RocksDB for Better Read Throughput in Blockchain Systems

Blockchain technology has recently gained a great attention from industry as well as academia because of its potential as transaction transparency. However, one of its pitfalls is the transaction throughput - the current transaction throughput available in contemporary Blockchain systems is criticized to be quite lower than that required in many real world applications. Many Blockchain systems are known to employ RocksDB as its storage engine for scale-out architecture and cost effectiveness, but use the RocksDB in its default configuration. While many tuning knobs are available in RocksDB, it is unfortunate that Blockchain systems cannot leverage them, thus resulting in low transaction throughput. In this vein, it is of great importance to develop a set of practical tuning techniques of RocksDB for higher transaction throughput in Blockchain systems. In this paper, we present an experience on tuning RocksDB read operations for Blockchain workloads. First, we make an observation about the characteristics of Blockchain workload and review RocksDB architecture. Then, based on the observation, we suggest two tuning techniques of parallel reads and read avoidance. In particular, the first tuning technique is very effective on the flash storage because it can exploit the abundant parallelism in the device. Our experimental results show that these tuning techniques, when used in combination, can increase the read throughput of a Blockchain benchmark, compared to the default setting of RocksDB, by more than 30 folds.
Application of Narrow Band Internet of Things Technology in Coalbed Methane Well Production

A low-power, low-cost and wide-coverage oil and gas production intelligent monitoring system based on NB-IoT is proposed. Currently, the monitoring data of the CBM wells is almost transmitted through the GPRS/GSM communication networks which cause high power consumption and high cost. With the introduction of the "Made in China 2025" plan, it become especially necessary to achieve a smooth upgrade of the existing industrial network. The overall idea of the design is to use the low-power STM32L431RCT6 as the MCU to collect the CBM well data, which this paper takes the liquid temperature, the hydraulic pressure and the liquid flow rate as examples, and sends the data to the NB-IoT module through the MCU. The NB-IoT module can utilize the existing telecom operators' NB base station to transmit data to the cloud server. Finally, the detection value is displayed to the user through the C# client program. The user can issue a command to the MCU in the well site through the client program to stop the work of the pumping unit when the monitoring data is abnormal. The result of experiment test indicates that the system has the characteristics of stable operation, high precision and safe information transmission.
The case-technology as a method of interactive practice-oriented education (the case study of the continuing professional education program)

Improving education process through efficient technologies to build essential job competences and increasing effectiveness of professionals is the relevant issue within the system of continuing professional education. The efficiency of the continuing professional education program depends on the student’s efforts as well on the methods to build new professional knowledge and skills. The case technology is one of the practice-oriented methods; it assumes that knowledge and skills building is a result of the proactive independent efforts aimed at conflict resolution, which results in acquiring professional knowledge and skills creatively and developing mental functions. The authors examine the case technology as a method of interactive practice-oriented education. The program “The Introduction into Bibliometrics” is the innovative one as it is integrated into the big-scale academic project. The methods and stages of case development in the area: “The publication ethics. Predatory journals” (4 stages) are described. The case application features are characterized. The authors conclude that the case method influences positively on students testing results within the array of the tests passed within the framework of the continuing professional education program.
Smart Surveillance: Chapter 3 - Quantifying Criminal Procedure

Since the inception of our criminal justice system, law enforcement officers and judges have relied primarily on experience, training, intuition, and common sense in making their predictions about whether reasonable suspicion or probable cause exists in a certain case. In response, courts have crafted broad standards to accommodate these subjective judgments and allow for flexibility in application. The broad, flexible nature of these standards is no accident: they have been intentionally left imprecise by generations of courts. One reason is the nearly infinite number of different facts that could arise in any criminal case, which make hard and fast rules rather impractical. But the main reason these rules have been kept ambiguous is that police and courts have historically lacked the necessary tools to evaluate the accuracy of their predictions with any precision. Thus, state actors have been forced to rely on their own subjective beliefs and anecdotal evidence in making their predictions.All of that is now changing. Big data analysis is providing police and judges with tools that can predict future behavior with greater precision than ever before. These tools hold out the promise of increased fairness and greater objectivity at many of the critical decision points in our criminal justice system. They also will be able to quantify the benefits of various forms of surveillance, allowing us to predict with much greater precision the success rate for each of these methods. This chapter argues in favor of adopting precise legal standards for different levels of surveillance in order to harmonize the analytical world of big data with the legal world of criminal justice.This chapter provides a road map for a transition from intuitive descriptive standards to empirically tested quantitative standards. It first explains why a move to quantified legal standards is an improvement over the descriptive standards now used by courts. It then examines the current set of descriptive standards and attempts to determine what level of certainty courts are applying when they use these standards – in other words, it seeks to translate the current standards of reasonable suspicion and probable cause into quantitative terms. In doing so, it calculates the de facto probability of reasonable suspicion and probable cause that has been applied by judges, and also draws on original empirical research into the views of federal magistrates. The chapter proposes that ultimately these descriptive standards should be jettisoned altogether in favor of a fully quantified system that could adjust more precisely to the productivity of different types of surveillance.
Normal-Basis Arithmetic

This chapter consists of three sections on arithmetic operations in the field GF(2m) with normal-basis representations and the implementation of those operations. The first section—a short one—is on addition and squaring; both are very simple operations with a normal basis. The second section is on multiplication, a more complicated operation than the preceding two. And the last section is on exponentiation, inversion, and division.
A New Method for Designing a Stochastic Robust Regulator on the Principles of Manifolds

The paper considers a problem statement for the synthesis of a robust stochastic regulator based on the principles of control on manifolds and a new algorithm for designing a regulator for stochastic discrete objects. The object of study is presented in the form of a system of stochastic difference nonlinear equations. The new algorithm for the analytical design of a stochastic nonlinear control system is based on the classical method for the analytical design of aggregated regulators, previously developed for a deterministic nonlinear object with a complete description. The robust stochastic nonlinear regulator provides the following characteristics of the control system: a) the minimal variance of the output variable; b) the minimal dispersion of the target invariant; c) the minimum of the average value of the quality functional.
Modular-Arithmetic Cryptosystems

This chapter consists of a few examples of cryptosystems that are based on modular arithmetic; the reader will find many more examples in the published literature. The descriptions of the various cryptosystems are not intended to be complete and are given only to provide a context for the arithmetic. The focus is on the essence of the algorithms, and the reader who requires them can readily find the details elsewhere.
Learning footstep planning on irregular surfaces with partial placements

We present two contributions built upon on a previous footstep planner based on the ARA* search. Firstly, we have developed an improved foothold selection method using support polygons, to increase foothold availability in rough terrain. Secondly, we present a footstep classification method using the C5.0 algorithm, that takes advantage of cost similarity between adjacent steps. This is intended to learn feasibility and approximate transition costs for the ARA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> planner.These contributions extend capabilities of the planner by increasing footstep availability and allowing to generate more complex plans, without compromising safety.
Face Identification System

Face Affirmation (FR), the methodology toward recognizing people with the help of their facial pictures, has various affordable applications within the zone of statistics, information security; get to control, law demand, savvy cards and observation framework. Convolutional Neural Networks (Connets), a form of profound systems has been incontestable to be fruitful for Face Affirmation (FR). For in progress frameworks, some pre-process methods like examining ought to be done before utilizing to Connets. Be that because it could, at that time likewise complete footage (all the constituent esteems) square measure passed as contribution to Connets and each one amongst the suggests that (highlight determination, embody extraction, preparing) square measure performed by the system. this can be the rationale that death penalty Connets square measure once during a whereas advanced and tedious. Connets square measure at the start stage and also the exactnesses got square measure extraordinarily high, in order that they have way to travel. The paper proposes another technique for utilizing a profound neural system (another quite profound system) for facial acknowledgment. During this methodology, instead of giving crude constituent esteems as data, simply the separated facial highlights square measure given. This brings down the multifarious nature of whereas giving the exactness of ninety seven.05% on Yale faces dataset.
A Prototype based Hybrid Approach to speed-up Kernel FCM-K

Kernel versions of FCM have been proved to be better than FCM in identifying overlapping and linearly inseparable clusters in the input space. Kernel FCM-F (KFCM-F) and Kernel FCM-K (KFCM-K) are the two kernel versions of FCM. In KFCM-F the cluster centers are considered in the feature space, where as in KFCM-K the cluster centers are identified in the kernel space. KFCM-K is superior than KFCM-F w.r.t the clustering quality, but it is not applicable on large data sets because of its quadratic time complexity i.e., O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) where n is the size of the data set. This paper propose a new prototype based hybrid technique to speed-up KFCM-K for large data sets. The proposed method initially identifies some representative data items from the given data, say l where , in linear time. The conventional kernel FCM-K is then applied over these representatives. As the clustering time is reduced to O(n+l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Experimental study on several benchmark data sets shows that the proposed method converges in less time when compare to conventional KFCM-K, but with a negligible deviation in the clustering quality.
Design of Object Detection System Using Radar Device AWR1642 (Case Study of Human Object)

Radar Device is a system that uses radio waves to detect an object or determine the range of a detected object. Besides the range of the object, data captured by the radar can be processed to determine the angle and velocity of the detected object. In this paper, the detected objects supposed to be human by utilizing AWR1642 as the device to detect an object and DBSCAN as the clustering algorithm. This research using two configuration files. The result shows that the system can detects object(s) in real-time with minimum angle resolution 15 degrees.
Characterizing Search-Engine Traffic to Internet Research Agency Web Properties

The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. In this paper, we focus on IRA activities that received exposure through search engines, by joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine.
Dark Data: People to People Recovery

The Internet of Things (IoT) is exploding disruptively. The IoT is making life easier for ordinary consumers and workers, but it is also generating zettabytes of dark data. The real time analytics involving close interaction between humans and instruments on the Internet is the main commercial motivation behind the IoT revolution. This means the data that are consumed instantly and their interpretation that is filed in an indexed and structured form are the main productive outcomes, while dark data and haphazardly stored interpretations add to the tare, bringing down efficiency and increasing costs. Since out of all data stored in the world today, almost all are generated in the recent three years, and the phenomenal growth will soon lead to a crisis, we need to put in place a global framework that never lets dark data clog the information highways but actually harnesses the real time analytics for a better planned future. We propose here a plan to build a “Data Waste Management” or “Data Sewerage” and local “Data Reservoir” system.
BiT C Detector: Gallstones Detection for Ultrasound Images

Ultrasound images are captured in real-time. They can present the anatomy and activities of the body's internal organs. Ultrasound is useful to observe changes in the form, size or shape of organs, tissues, and vessels. It is also utilized to detect abnormal masses, such as tumors or cancer cells. Ultrasound imaging techniques can also be used to detect gallstones, hard particles that develop in the gallbladder. Ultrasound-based diagnosis may accurately detect gallstones as small as 2 mm in diameter. However, there are difficulties when ultrasound image is low in brightness or consist noise. Therefore, accurate gallstones detector by using only ultrasound image is still a challenging task. The existing edge detection techniques are basically sensitive to noise. There are two types of image will be tested to determine the result which is ultrasound images with gallstones and ultrasound images without gallstones. The BiT_C Detector intends to reduce the noises that available on each test image to facilitate the next threshold process. The threshold will be set to several values from 0.1 to 0.9 to determine the optimal threshold value for each image. There is also five comparisons to experimenting the performance of the BiT_C Detector and four other conventional methods. From the results, it can be proven that BiT_C Detector can detect gallstones ultrasound images.
Cloud-Based Healthcare Portal in Virtual Private Cloud

Healthcare system providing cloud-based storage has the potential to store the patient’s therapeutic records to the remote server than maintaining the files and radiological images on a hard drive or local storage device that enables the patient to access their medical records at any place by using the Internet through a Web-based application. The structure of cloud application guarantees the privacy and security of health-related data to preserve the sensitive health information. The architectural design of cloud computing aims to alleviate the privacy concern and to fulfill the confidence and trust of the cloud-based healthcare organization. The requirements, architecture design, software components, and validation methods of cloud-based healthcare system are discussed.
Pairwise Conditional Random Fields for Protein Function Prediction

Protein Function Prediction (PFP) is considered one of the complex computational problems where any protein can simultaneously belong to more than one class. This issue is known as Multi-label classification problem in pattern recognition. Multi-label data sets are kind of data where each instance belongs to more than one class. This feature differentiates multi-label classification from the standard types of data classification. One of the challenges in multi-label classification data is correlation between the labels. This feature makes the issue cannot be classified into distinct sets of classification divided. Another major challenge is the high dimensional data in some applications. This paper presents a new method for the Protein Function Prediction and classification of multi-label data using conditional random fields. More specifically, the proposed approach is a method based on Pairwise Conditional Random Fields which considered the relationship of the labels. After introducing the Pairwise Conditional Random Fields optimization problem and solving it, the proposed method is evaluated under different criteria and the results confirm higher performance compared to available multi-label classifiers.
Exploring the Combination of PReMVOS, BoLTVOS and UnOVOST for the 2019 YouTube-VOS Challenge

Video Object Segmentation is the task of tracking and segmenting objects in a video given the first-frame mask of objects to be tracked. There have been a number of different successful paradigms for tackling this task, from creating object proposals and linking them in time as in PReMVOS, to detecting objects to be tracked conditioned on the given first-frame as in BoLTVOS, and creating tracklets based on motion consistency before merging these into long-term tracks as in UnOVOST. In this paper we explore how these three different approaches can be combined into a novel Video Object Segmentation algorithm. We evaluate our approach on the 2019 Youtube-VOS challenge where we obtain 6th place with an overall score of 71.5%.
Hardware and Software Methods for Restoring Input Signals of Dynamic Systems with Distributed Parameters. Reduction to the Perfect Device

The paper presents a brief review of the authors' results on methods for reconstructing input signals of dynamic systems. Continuous and discrete systems with distributed and lumped parameters are considered. Mathematical models of these systems are differential and difference equations, as well as integral equations. The main focus of the work is on methods for reconstructing input signals based on the results of preliminary parametric identification of systems. In this regard, a separate section is devoted to the authors' results review on the methods of parametric identification of dynamic systems. Methods for the precise determination of the impulse response functions of continuous and discrete stationary systems using two input signals are considered. A number of approximate methods are presented. Methods of parameters identification and restoration of input signals in systems modeled by Volterra series are investigated. A separate section is devoted to the results application in thing of specific technical systems.
Learning LASSO Regularized and Time-Aware Continuous Convolution Operator Correlation Filter for Visual Tracking

Discriminant correlation filters (DCF) have proven to perform well in visual tracking. However, the underlying discriminant correlation filter only utilizes the single-resolution features of the image block, which limits the performance of its algorithm. And there is still a problem with the existing DCF, that is, the degradation of the filter over time. The continuous convolution operator filter (C-COT) uses implicit interpolation to combine multi-resolution features to construct learning problems in a continuous domain. This efficiently integrates multi-resolution depth feature maps to solve the problem of targeting on the different resolution features. In order to improve the above problems, based on the continuous convolution operator filter (C-COT), the filter is LASSO regularized and adaptive spatial feature selection can be realized, and the filter maintains a certain sparsity, and secondly A time-aware optimization term is added to the objective function so that the learned filter can learn near its historical value to maintain the global structure of the feature. In order to make the learned filter anti-occlusion, this paper adopts a high confidence update strategy to avoid the model being polluted. The algorithm in this paper is called LASSO regularized time-aware continuous convolution operator filter (LTCOT). The algorithm can not only combine the effective features of multi-resolution, but also make the learning filter fully smooth transition between two frames and have certain sparsity, and has stronger discriminative power and anti-interference ability. The video in the OTBIOO database was used for testing with OPE (one pass evaluation), the area under the success line (AUC), average overlap accuracy (OP), and center pixel error distance (DP). The results show that the LTCOT algorithm's AUC score on the OTBIOO dataset is 2.4% higher than C-COT, OP is 85.9%, better than C-COT(81.4%), and DP is 90.1%, which is better than 89.3% of C-COT. At the same time, LTCOT's speed is six times higher than C-COT. The LTCOT algorithm can adapt to the visual tracking of complex situations such as occlusion, rotation and deformation, and has good robustness and accuracy.
The Layout of Sentiment Lexica in Word Vector Space

Sentiment lexicons play an important role in opinion mining systems and cognitive linguistics. Previous work aimed mostly at creating sentiment lexicons, but not thorough research into their fundamental properties. In this chapter, the arrangement of sentiment lexica in the multidimensional space of distributed word representations is studied. A hypothesis on the existence of sentiment lexica concentration areas is introduced, and it is tested on the basis of the joint analysis of the distribution of sentiment words and general lexica. The results of the test allow to confirm the proposed hypothesis for universal and domain-oriented sentiment lexicons. Also, the experiments discover the words which more than 80% of the sentiment lexica is concentrated around.