Reviewerly/exHarmony huggingface dataset:
Training Trees on Tails with Applications to Portfolio Choice

In this article, we investigate the impact of truncating training data when fitting regression trees. We argue that training times can be curtailed by reducing the training sample without any loss in out-of-sample accuracy as long as the prediction model has been trained on the tails of the dependent variable, that is, when ‘average’ observations have been discarded from the training sample. Filtering instances has an impact on the features that are selected to yield the splits and can help reduce overfitting by favoring predictors with monotonous impacts on the dependent variable. We test this technique in an out-of-sample exercise of portfolio selection which shows its benefits. The implications of our results are decisive for time-consuming tasks such as hyperparameter tuning and validation.
Digital signal processing on the cortex-M33 processor

This chapter covers a range of topics relating to the application of digital signal processing (DSP) for the Cortex-M33 processor, e.g., the data formats used for signal processing, DSP instruction set features, the handling of overflow in data processing, etc. Using a number of examples, including dot product, biquad filter, the finite impulse response (FIR) filter and fast Fourier transform (FFT), various optimization techniques are demonstrated.
Analysis of Query Optimizers

A query optimizer translates a query into a sequence of physical operators that can be directly carried out by the query execution engine.The goal of query optimization is to derive an efficient execution plan in terms of relevant performance measures, such as memory usage and query response time The query optimizer attempts to determine the most efficient way to execute a given query by considering the possible query plans. Importance: The goal of query optimization is to reduce the system resources required to fulfill a query, and ultimately provide the user with the correct result set faster. This is a feature of many relational database management systems and other databases such as graph databases.
LoS/NLoS Detection based Authentication for IoT Systems

The number of IoT devices is increasing significantly these days, especially in private spaces. The data collected in personal space may include private information. Because a lot of IoT devices use wireless channels to communicate with other devices, illegal devices (attackers) can easily access the channel. The attacker can access IoT devices and pretend to be legitimate devices. This paper proposes a novel LoS/NLoS detection based authentication scheme that aims to guarantee security while providing proper user convenience. We propose a new metric called Gap Difference of Phase (GDP) that helps to differentiate between LoS and NLoS channels. We evaluate the performance of the GDP-based LoS/NLoS detection method that also uses Support Vector Machine (SVM). As a result we confirm that our proposed method successfully differentiates between LoS/NLoS channels and achieves a balance between a high level of security and user convenience.
Reversible Data Hiding Based Bit-Plan Permutation and Absolute Moment Block Truncation Coding (AMBTC)

Data hiding is one of the branches of information security, and it is used in the field of communications and computer networks which be a solution when a secure channel is not available. In this paper, an information hiding method into codded images. A bit plan applied on the secret image and reshaped into the vector, while an Absolute Moment Block Truncation Coding (AMBTC) is used for coding color images (cover images). The coding is applied as a method for size reduction by representing each block with two means called Mb, Ma, and Bit-map that represent the location of them in binary form. The embedding of secret information is represented by modifying the bit-map. Additional complexity is added to the method by permutation the bit plane vector using a chaotic map called the logistic map. The proposed method was examined and proved its efficiency by obtaining a high PSNR and a low RMSE result, as well as the visual testing through the histogram test of the image before and after embedding.
A Semantic-Based Strategy to Model Multimedia Social Networks

The social facet of information has a deciding role in our quotidian life. An abstract representation and a proper management of Online Social Networks (OSNs) constitute a new challenge for communities of researchers. In addition, the need of extending OSNs to Multimedia Social Networks (MSNs)come from the fact that the vast majority of data is unstructured and heterogeneous, making the reuse and integration of information effortful. In this chapter we propose a general high-level model to represent and manage MSNs. Our approach is based on property graph represented by a hypergraph structure due to the intrinsic multidimensional nature of social networks and semantic relations to better represent the networks contents. Using the proposed graph structure is helpful to single out several levels of knowledge analysing the relationships defined between nodes of the same or different type. Moreover, the introduction of low-level multimodal features and a formalization of their semantic meanings give a more comprehensive view of the social network structure and content. Using this approach we call the represented network Multimedia Semantic Social Networks ( $$MS^2N$$ ). The proposed data model could be useful for several applications and we propose a case study on cultural heritage domain.
Ethical and Societal Issues of Automated Dark Web Investigation: Part 5

While terrorists have developed many ways to use the Internet for illicit purposes, their use of the Internet also provides opportunities for gathering intelligence to counter and even prevent acts of terrorism. Gathering evidence for the prosecution of such acts of terrorism could also improve. A significant amount of knowledge about the functioning, activities and sometimes the targets of terrorist organisations can be derived from websites, chat rooms and other Internet communications. Further, increased Internet use for terrorist purposes provides a corresponding increase in the availability of electronic data which may be compiled and analysed for counterterrorism purposes. This chapter is about the legal framework for developing a sophisticated tool for Law Enforcement Agencies (LEAs) to proactively prevent, detect and deter terrorist activity involving use of the Internet.
A Unique Extensible Framework Detection and Classification of Traffic Events Based on Deep Learning Approach

Social media, like Twitter, Instagram, YouTube, Facebook, Tumblr etc. is used to obtain valuable information relevant to traffic, which may be a commentary or user produced encounter. In this skeptical representation we mainly use Deep belief network (DBN) which was one of the major Deep Learning Techniques that will be utilized for public sentiment evaluation on traffic information through social networking. In the current system, the visitor's information may be decided to work with Google Maps or perhaps in FM Radio. Google Map won't provide the correct Traffic Information. Additionally, it won't supply some info regarding the visitor's functions. FM Radio won't provide the info 24*7. In addition, it is going to provide the information just to the community or maybe the coverage of theirs. Networks like Deep belief network (DBN) and Neural Networks has been used to test the model with many datasets. The performance valuation was completed and it is observed that it detects the text very accurately when a small amount of information is given to the proposed scheme. This system will overcome the drawbacks of the existing system and also achieves better accuracy rate on comparing.
내러티브 프레임 분석을 통해 본 한국 대학생 영어학습자의 동기적 자아

The purpose of the study is to explore how Korean college students view English and their Ideal L2 self and Ought-to L2 self in the process of learning English. As narrative inquiry has been recognized as a methodological tool to investigate how learners make sense of their personal experience, the study attempts to illustrate how learners narrate their relationship with English, and their Ideal L2 self and Ought-to L2 self in their stories of learning English. Using the narrative frames (Barkhuizen & Wette, 2008) and interviews, learner narratives were collected from 34 Korean college students. The findings show that the participants viewed themselves as unsatisfactory learners who have been struggling between their Ideal L2 self and Ought-to L2 self lacking specific strategies to narrow the gap between their two selves. The results imply that Korean learners of English need to be encouraged to establish a clear image of Ideal L2 self and to act upon their L2 learning environment to maintain their motivation in learning English.
Predicting Network Bandwidth Usage: Case Study at PT. Bank ABC

The development of the internet network is now comprehensive and an essential requirement for digital competition companies. All activities that used to be done manually are slowly transforming into digitization and automation. In this process, an internet network is needed, so it is necessary to fulfill internet networks' availability. In this paper, an analysis is conducted to optimize network bandwidth requirements at PT. Bank ABC uses data mining techniques. The data mining method is carried out using predictive techniques using three algorithms, namely Deep Learning, Generalized Linear Model and K-Nearest Neighbors, each of which is tested using split data models and cross-validation. Of these three algorithms, the Generalized Linear Model algorithm produces the highest accuracy value.
Big Data Scenarios Simulator for Deep Learning Algorithm Evaluation for Autonomous Vehicle

One of the challenges in developing autonomous vehicles (AV) is the collection of suitable real environment data for the training and evaluation of machine learning algorithms for autonomous vehicles. Such environment data collection via various sensors mounted on AV is big data in nature which require massive time and money investment and in some specific scenarios could pose a significant danger to human lives. This necessitates the virtual scenarios simulator to simulate the real environment by generating big data images from a virtual fisheye lens that can mimic the field of view and radial distortion of commercial available camera lens of any manufacturer and model. In this paper, we proposed the novelty of developing a fisheye lens with distortion system to generate big data scenarios images to train and test imaged based sensing functions and to evaluate scenarios according to EuroNCAP standards. A total of 10,123 RGB, depth and segmentation images of varying road scenarios were generated by proposed system in approximately 14 hours as compared to existing methods of 20 hours, achieving 42.86% improvement.
Database Traceability Algorithm Based On Zero Watermark

As database security issues become more and more important, copyright determination and database traceability technologies are becoming important requirements for database security. In order to trace the database at the same time of copyright determination, a database traceability algorithm based on zero watermark is proposed. In this paper, the database table is split by using the method of retaining the reference relationship, so that the database table can be traced efficiently. At the same time, a zero watermark method based on classification is applied to the split database table, so that the database is protected by copyright certification. Simulation experiments verify the good robustness and high traceability of the algorithm.
Bibliometrics and visualization analysis of China's Service Quality Development Trend

From the aspect of bibliometrics and social network analysis, this paper studies the content characteristics and network evolution of service quality in China, form visual analysis charts and summarizes the research hotspots and development trends. Taking CNKI database as the data source, data mining and association analysis of sample data were carried out by means of manual judgment, keyword co-occurrence, bibliometric software BICOMB and Pajek. 872 related literatures were retrieved from 1999 to 2019, and the tendency of the number of published papers in each year was close to the inverted U type. This study expands the application fields of bibliometrics and social network software, and analyzes the development trend of service quality. The theoretical and practical research on service quality has achieved rapid development in the past 20 years. The library service quality and service quality satisfaction have always gotten a high level of attention, and the characteristics of network platforms for service quality have gradually become apparent because of the development of the Internet and computer technology.
Artificial Intelligence optimization For Low-Light Image Enhancement

Image processing requires quality image data input to fulfill the desired final objective. Several pieces of research have been conducted in the effort to improve the image quality which is the initial process in the image processing to avoid poor output image quality. For example, a too dark image will make an unclear display of the image. The image occurs when shooting with low or dark lighting intensity. One of the ways to improve the image quality is by improving the contrast of the said image. This research is aimed to improve the image quality in the image by using the proposed method namely histogram equalization optimized using Particle Swarm optimization (PSO) algorithm to handle excessive image contrast. Several testing scenarios are conducted in this research to find out how optimal the influence of the PSO algorithm in the optimization process of improving image quality by handling excessive image contrast using the ExDark dataset. The results of testing conducted by comparing the average value of PSNR from Histogram Equalization and Histogram Equalization methods with the application of the PSO algorithm have obtained an average for the Histogram Equalization method of the three channels. The conducted research results in the enhancement of image quality with bigger accuracy compared to the ordinary Histogram Equalization method.
Physical Modeling Approach for a Differential Guide Mobile Robot Using Simscape

In comparison to the classical modeling approach of energy consumption in mobile robots, the physical modeling approach offers advantages in terms of design, system realization, adaptivity, and coast. In this paper, an energy model has been developed in Simscape software. The power of this environment is shown by simulating the energy model for a differential guide mobile robot as well as the motors. The model is tested with typical motor energy, mixed energy models, and a trapezoidal velocity as input.
Bitcoin-Energy Markets Interrelationships - New Evidence

The annual electricity consumption of cryptocurrency transactions has grown substantially in recent years, partially driven by the increasing difficulty in mining, but also driven by the large number of new market participants that have been attracted by the elevated prices of this developing financial asset. Total carbon production from mining now likely exceeds that generated by individual developed nations. This is now a prevailing and accepted feature in cryptocurrency markets, however unsustainable it may be. This paper investigates as to how Bitcoin's price volatility and the underlying dynamics of cryptocurrency mining characteristics affect underlying energy markets and utilities companies. Further analysis of potential side-effects within the market for Exchange Traded Funds are considered. The results show a sustained and significant influence of cryptocurrency energy-usage on the performance of some companies in the energy sector as separated by jurisdiction, emphasising the importance of further assessment of environmental impacts of cryptocurrency growth. Robustness testing presents evidence that dynamic correlations peaked during the sharp Bitcoin price appreciation of late-2017 as investors re-evaluated how this increased energy usage would influence the profitability of utility companies.
Selfie Sign Language Recognition with Shape Energy Features and Mahalanobis Distance Classifier

This paper introduces an idea of bringing sign language into real time using mobile phone. Smart phone front camera captures selfie sign videos. The hand shape is extracted from the captured video frames using sobel edge operators and morphological gradients. Two methods of feature extraction are proposed. In first feature extraction method, basic region properties height of hand shape, centroid and area of hand shape and distance of the centroid of hand shape from origin of the frame are used as features. In the second feature extraction method, hand contour DCT treated with PCA is generated as feature vector. Mahalanobis distance classifier is used to test the word matching score (WMS) of the selfie sign language recognition system (SSLR) for the proposed feature vector models. The average WMS with first feature vector is 74 and 85% for second feature vector.
Enabling Crosscutting Visualization for Geoscience

Our world is a complex ecosystem of interdependent processes. Geoscientists collect individual datasets addressing hyperspecific questions, which seek to probe these deeply intertwined processes. Scientists are beginning to explore how investigating relationships between disciplines can foster richer and more holistic research, but visualization tools are conventionally designed to address hyperspecific, rather than holistic, analysis. Bridging the vast wealth of available data will require new tools. Visualization has the potential to support holistic cross-disciplinary analysis to understand the complex innerworkings of our world, but doing so requires a paradigm shift to understand how visualization might enable lines of inquiry transcending traditional disciplinary boundaries. We present challenges for visualization in fostering such holistic geoscience analyses.
What is Mobile Operation System Quality?

There are some modern mobile operation systems. The main two of them are iOS and Android. However, in the past, there were two more commonly used ones: Windows Mobile and Symbian. Each of these systems has its own pros and cons, whereas none of them is the best or the worst one in different criterions. In this paper the main criterions of operation system quality are discussed. The paper defines what the mobile operating system quality is.
Remote sensing as a method of predicting PGM mineralization based on the results of the study of the Eastern Sector Bushveld complex

In this paper, a study of the differentiated Bushveld complex (South Africa) is carried out on the basis of remote cosmic and gravimagnetic methods. The article shows the possibility of using Landsat 7 ETM+ multi-zone space imagery and brightness characteristics to predict platinum mineralization in new promising areas. Analysis of the values of the gravitational field showed that in the area under study, platinum mineralization is concentrated in a fairly narrow range of values, which makes it possible to use this data as a search feature. The main conclusions have been formulated and the possibilities of the Landsat 7 ETM + multi-zone space survey to forecast platinum mineralization in new promising areas have been clearly demonstrated. The conducted studies prove the efficiency of the integration of remote (space and geophysical) to improve the approach to prospecting for platinum-coppernickel mineralization.
Memory Abstraction and Optimization for Distributed Executors

This paper presents a suite of memory abstraction and optimization techniques for distributed executors, with the focus on showing the performance optimization opportunities for Spark executors, which are known to outperform Hadoop MapReduce executors by leveraging Resilient Distributed Datasets (RDDs), a fundamental core of Spark. This paper makes three original contributions. First, we show that applications on Spark experience large performance deterioration, when RDD is too large to fit in memory, causing unbalanced memory utilizations and premature spilling. Second, we develop a suite of techniques to guide the configuration of RDDs in Spark executors, aiming to optimize the performance of iterative ML workloads on Spark executors when their allocated memory is sufficient for RDD caching. Third, we design DAHI, a light-weight RDD optimizer. DAHI provides three enhancements to Spark: (i) using elastic executors, instead of fixed size JVM executors; (ii) supporting coarser grained tasks and large size RDDs by enabling partial RDD caching; and (iii) automatically leveraging remote memory for secondary RDD caching in the shortage of primary RDD caching on a local node. Extensive experiments on machine learning and graph processing benchmarks show that with DAHI, the performance of ML workloads and applications on Spark improves by up to 12.4x.
Market Reaction to Large Transfers on the Bitcoin Blockchain - Do Size and Motive Matter?

Cryptocurrency markets are often deemed inefficient. This paper explores how the market reacts to a specific form of public information: large Bitcoin transactions. The event study examines the price effects of 2,132 transactions involving at least 500 Bitcoins over the period September 2018 to November 2019. Across all transactions, we find significant negative price effects for the minutes closely around the events. Yet the effects are positive over the period from the event to 15 minutes thereafter. We analyze further effects of the size and the probable motive of the transfers by dividing the sample into quartiles and by clustering specific Bitcoin addresses of cryptocurrency exchanges involved in the transfers. We find that the price effects can differ significantly depending on the size and type of a transaction. The results indicate that the market recognizes the nature of the transfer and prices in new information. While this constitutes evidence of efficiency, yet more efficient reactions remain conceivable.
Effects of Uber and Ola on SVM and Naïve Bayes

Twitter is a microblogging site that helps in opinion mining of tweets (reviews) from people []. Understanding the reviews from their tweets helps in determining what a business needs to improve on. This paper focuses on the algorithms used that are SVM and Naïve Bayes, their functionality, accuracy, precision and recall value. The effect of the two machine learning algorithms gives us the idea of which algorithm can fetch the best suitable results. To determine this, we use a medium to understand it which is ‘confusion matrix’. The datasets used are ‘Uber’ and ‘Ola’. Uber and Ola have a large number of users who use the cab service regularly. We extract tweets that vary in number and the algorithms check the words which represent sentiments of people’s review. The algorithms then train the data from the tweets to identify which tweets can be categorized as positive, negative and neutral.
Buzzing Lips: Sound Generation in Brass Instruments

This chapter deals with the vibrating lips as a sound source: this is a defining feature of all labrosones (brass instruments). The main processes through which the air flow generated by the player’s lungs can initiate and sustain the vibration of the lips are covered. The facial muscles which control the embouchure of a brass player are described, and the complex motion of the lips when sounding a note is illustrated by the results of experimental studies using both human players and artificial lips. Simplified descriptions of lip motion are examined, including sliding door and swinging door models, and an equation representing the lips as a single degree of freedom mechanical oscillator is derived. The application of the Bernoulli equation to the flow of air through the lip channel, taking into account flow separation and jet formation at the exit, leads to a second equation describing the relationship between the flow velocity and the pressure difference across the lips.