Reviewerly/exHarmony huggingface dataset:
Statistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model

The Iranian plateau has been known as one of the most seismically active regions of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. Earthquakes are regularly felt on all sides of the region. Prediction of the occurrence location of the future earthquakes along with determining the probability percentage can be very useful in decreasing the seismic risks. Determining predicted locations causes increasing attention to design, seismic rehabilitation and evaluating the reliability of the present structures in these locations. No exact method has been approved for predicting future earthquake parameters yet. In recent years, more attention is paid to the earthquake magnitude prediction, but no study has been done in the field of probable earthquake occurrence hazard zonation. In this study, locations of future earthquakes in Iran were predicted by self-organized artificial neural networks (ANN). Then probable seismic risk zoning map was drawn by the statistical analyses, and the results indicated that the maps can properly predict future seismic events.
Learning Behaviors from a Single Video Demonstration Using Human Feedback

In this paper we present a method for learning from video demonstrations by using human feedback to construct a mapping between the internal state representation of the agent and the visual representation from the video. In this way, we leverage the advantages of both these representations, i.e., we learn the policy using agent centered state representations, but are able to specify the expected behavior using video demonstrations. We show the effectiveness of our method by teaching a hopper agent in the MuJoCo simulator to perform a backflip using a single video demonstration generated in MuJoCo as well as from a real-world YouTube video of a person performing a backflip.
Clustering in the presence of side information: a non-linear approach

Purpose Constrained clustering is an important recent development in clustering literature. The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge. The purpose of this paper is to suggest a new perspective for constrained clustering, by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must- and cannot-link constraints. Design/methodology/approach Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space. Unlike previous efforts, the author presents a non-linear method for constraint clustering, whose basic idea is to use different non-linear functions for each dimension in target space. Findings The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linear functions for each dimension in target space. The proposed method for a particular case is formulated and explained for quadratic functions. To reduce the number of optimization parameters, the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method. Originality/value This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions. This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.
Robot Learning by Collaborative Network Training: A Self-Supervised Method using Ranking

We introduce Collaborative Network Training -- a self-supervised method for training neural networks with aims of: 1) enabling task objective functions that are not directly differentiable w.r.t. the network output; 2) generating continuous-space actions; 3) more direct optimization for achieving a desired task; 4) learning parameters when a process for measuring performance is available, but labeled data is unavailable. The procedure involves three randomly initialized independent networks that use ranking to train one another on a single task. The method incorporates qualities from ensemble and reinforcement learning as well as gradient free optimization methods such as Nelder-Mead. We evaluate the method against various baselines using a variety of robotics-related tasks including inverse kinematics, controls, and planning in both simulated and real-world environments.
Applying Norms and Sanctions to Promote Cybersecurity Hygiene

Cybersecurity breaches cause enormous harm to the safety, privacy, and prosperity of individuals and organizations. Many security breaches occur due to people not following security regulations such as applying software patches, updating software applications, and so on. We term these regulations as cybersecurity hygiene. This paper investigates different sanctioning mechanisms with respect to the success in establishing these regulations for cybersecurity hygiene. Our findings have implications for workforce training to promote cybersecurity.
Investigating the Complexity of Computational Intelligence using the Levels of Inheritance in an AOP based Software

Aspect Oriented Programming (AOP) is a software development methodology that aims to improve the modularity by encapsulating the cross-cutting concerns into modular units called aspects. Inheritance of classes and aspects play a vital role while redefining the units of encapsulation. In this research, the impact of using multi-level inheritance in Aspect Oriented Soft-ware is quantitatively evaluated, with an extended and validated metric, namely the Weighted Average Depth of Inheritance. The metric has been applied on two equivalent OO and AO versions of a multi-level inheritance based Banking application case study which was developed using Java and AspectJ programming languages respectively. Based on the measurement it was found that the AspectJ version exhibits reduced cognitive complexity of computational intelligence compared to its equivalent Java version.
Performance Analysis of an Hyperconverged Infrastructure using Docker Containers and GlusterFS

The adoption of hyperconverged infrastructures is a trend in datacenters, because it merges different type of computing and storage resources. Hyperconverged infrastructures use distributed file systems (DFS) to store and replicate data between multiple servers while using computing resources of the same servers to host virtual machines or containers. In this work, the distributed file system GlusterFS and the hypervisor VMware ESXi are used to build an hyperconverged system to host Docker containers, with the goal of evaluate the storage performance of this system compared to traditional approach where data is stored directly on the server’s disks. The performance of the container’s persistent storage is evaluated using the benchmark tool Yahoo Cloud Service Benchmark (YCSB) against the NoSQL databases Cassandra and MongoDB under differents workloads. The NoSQL database’s performance was compared between the hyperconverged system with multiples disk configurations and a traditional system with local storage.
Better Than Optimal Huffman Coding?

Huffman coding is known to be optimal, yet its dynamic version may yield smaller compressed files. The best known bound is that the number of bits used by dynamic Huffman coding in order to encode a message of n characters is at most larger by n bits than the number of bits required by static Huffman coding. In particular, dynamic Huffman coding can also generate a larger encoded file than the static variant, though in practice the file might often, but not always, be smaller. We propose here a new dynamic Huffman encoding approach, that provably always performs at least as good as static Huffman coding, and may be better than the standard dynamic Huffman coding for certain files.
Collaborative Clustering Approach Based on Dempster-Shafer Theory for Bag-of-Visual-Words Codebook Generation

Feature encoding methods play an important role in the performance of the recognition tasks. The Bag-of-Visual-Words (BoVW) paradigm aims to assign the feature vectors to the codebook visual words. However, in the codebook generation phase, different clustering algorithms can be used, each giving a different set of visual words. Thus, the choice of the discriminative visual words set is a challenging task. In this work, we propose an enhanced bag-of-visual-words codebook generation approach using a collaborative clustering method based on the Dempster-Shafer Theory (DST). First, we built three codebooks using the k-means, the Fuzzy C-Means (FCM), and the Gaussian Mixture Model (GMM) clustering algorithms. Then, we computed the Agreement Degrees Vector (ADV) between the clusters of the pairs (k-means, GMM) and (k-means, FCM). We merged the obtained ADVs using the DST in order to generate the clusters weights. We evaluated the proposed approach for Remote Sensing Image Scene Classification (RSISC). The results proved the effectiveness of our proposed approach and showed that it can be applied for different recognition tasks in various domains.
Personal Identification by Human Motion Using Smartphone

There are two types of personal authentication methods, that is, biometric authentication method and password authentication method. Although biometric authentication has an advantage that security is higher than password authentication, there is also a disadvantage that biometric information can not be replaced. On the other hand, an authentication method using human motion has been proposed in an existing research. Human motion is a kind of changeable biometric information even when biological information leaks out. In the authentication method, biometric information for authentication depends on an acceleration sensor and a gyro sensor. DP matching is used for the comparison of users in the research. However, false acceptance rate is 10.71% and false rejection rate is 56.90% in a single action. There is no way to use this method for personal identification. In our research, we also obtained data from acceleration sensors and gyroscopes which are standard equipments on usual smartphones. We used convolutional neural networks for the comparison of individuals instead of DP matching. As a result of personal identification by convolutional neural network with data of “circle”, “rectangle” and “check” which are acquired from examinees, individuals can be identified with a rate of more than 88.5% on average in multi-level classification and 78.2% on average in binary classification.
IoT Integration in Manufacturing Processes

In manufacturing processes, simulation parameters such as arrival times have traditionally been drawn from statistical distributions or from empirical datasets. Although this approach may lead to relatively accurate parameters, there may be applications in which a more precise methodology is required. IoTInternet of Things (IoT) is a technology that enables for the processing of real time data through microcontrollers and servers. A simulation may ingest this real-time data to modify downstream simulation parameters towards values that will produce higher yield. This chapter will introduce two techniques that are made possible by the availability of real-time data in simulation. First, the chapter will discuss possible optimizations that may be made by selectively choosing parameters that lead to higher production based on real-time data input. Then, the chapter will focus on the ability of IoTInternet of Things (IoT) -based simulations to dispatch real-time instructions to robots placed in the manufacturing process. The chapter introduces these concepts by the model construction of a drug manufacturing process using a discrete-event simulation software called TaoTao .
Sentiment Analysis of Korean Teenagers’ Language Based on Sentiment Dictionary Construction

This paper is to introduce Korean teenagers’ sentiment analysis through composing the Korean sentiment dictionary for teenagers’ language which is using KoNLPy and an algorithm for text sentiment analysis based on N-Gram concept which is used to calculate sentiment score from sentences. We gathered the text data (sentences) by web crawling several Korean web sites and using the Korean sentiment dictionary and algorithm described above, we conducted an experiment to analyze sentiment of the public.
Word Matching Algorithm based on Relative Positioning of Letters

In this paper, we propose a word matching algorithm to find the valid shifts of a given word in a text string. The idea is based on using pre-calculated information about the likelihood of relative positioning of each pair of letters of the search word in the words of the language under consideration. This information helps in deciding an order in which the letters of the search word should be compared with the letters of the words in the text. This ordering eliminates mis-matching words in text in less number of comparisons than those needed in standard ordering of comparisons. Our algorithm showed better performance than KMP algorithm.
An Experiment for Background Subtraction in a Dynamic Scene

This paper aims to analyze a background subtraction algorithm. Different from tradition methods, we feed the trained network with the target and background images. The paper focuses on how to get background images without using the temporal median filter. We use Gaussian mixture models to produce background images. In this way, the accuracy of background images increases. We also study the difference between grayscale and RGB images, and adding the foreground masks from the convolutional Neural Networks to the Gaussian mixture models. Experiments lead on the 2014 ChangeDetection.net dataset show that our proposed method outperforms several state-of-the-art methods, including IUTIS-5, PAWCS, SuBSENSE and so on.
Automatic Feature Engineering by Deep Reinforcement Learning

We present a framework calledLearning Automatic Feature Engineering Machine (LAFEM), which formalizes theFeature Engineering (FE) problem as an optimization problem over aHeterogeneous Transformation Graph (HTG). We propose a Deep Q-learning on HTG to support efficient learning of fine-grained and generalized FE policies that can transfer knowledge of engineering good features from a collection of datasets to other unseen datasets.
IMA Commission on New Minerals, Nomenclature and Classification (CNMNC) NEWSLETTER 48

The Na analogue of jeppeite Monoclinic: C2
Fast Depth Decision in Light Field Compression

Pseudo-sequence based light field compression methods are a highly efficient solution to compress light field images. They use state-of-the-art video encoders like HEVC to encode the image views. HEVC exploits Coding Tree Unit (CTU) structure which is flexible and highly efficient but it is computationally demanding. Each CTU is examined in various depths, prediction and transformation modes to find an optimal coding structure. Efficiently predicting depth of the coding units can reduce complexity significantly. In this paper, a new depth decision method is introduced which exploits the minimum and maximum of previously encoded co-located coding units in spatially closer reference images. Minimum and maximum depths of these co-located CTUs are computed for each coding unit and are used to limit the depth of the current coding unit. Experimental results show up to 55% and 85% encoding time reduction with serial and parallel processing respectively, at negligible degradations.
Formation of Computing and Coding Competences of Computer Science Teachers in Ukraine

Coding in schools continues to be an increasing world trend. Many European countries are introducing coding and computational thinking as a core curriculum subject. The paper is devoted to the problem of formation of computing and coding competences of Computer Science teachers. The modern use of computing and coding competences by the Ukrainian teachers is deeply analysed in the article. In the service of the aim of the paper, a questionnaire is developed by the authors. The questionnaire is purposed to gain data on the Ukrainian teachers’ views and attitudes towards the use of computing and coding competences. The basis of this survey is taken from Programmer Competency Matrix, but adapted for the Ukrainian teachers. Besides that analysis of various standards of computing and coding competences is described. As a result, the authors suggest new standards of computing and coding competences of Computer Science teachers in Ukraine, and proposals for formation of such competences via new common syllabus are considered.
Corporate Diversification: A Fundamental Exploration of General Business Environments, Industry Environments and Firm Characteristics

Diversification has been central in the area of strategic management. Businesses in many countries have used diversification as the means to advance its economic progress. For Saudi Arabia to reach their vision by 2030, corporate diversification will be central to achieve a ‘short-cut’, if you will, in fulfilling its ambition. Researches in various disciplines are growing to propose creative solutions and approaches to achieve specific areas of the vision. However, the challenge should begin at solidifying corporate relationships that will soon connect various industries towards the common goals that have been presented before all stakeholders. Therefore, the purpose of this paper is to develop a framework for corporate diversification, based on general business, industry environments and the firm’s characteristics, and its effects on performance, to prepare firms to better understand its environments and characteristics in the context of developing countries in general and Saudi Arabia in particular. Data was collected from 62 managers representing their respective firms in Saudi Arabia’s petrochemical, construction, banking and financial sectors, retail sectors, telecommunications and ICT, and the transportation sector. A combination of parametric and non-parametric tests was carried out using SPSS 22.0. The findings of this study suggest that the most used expansion strategy by far is internal capacity expansion. Additionally, businesses in Saudi Arabia mostly diversify by adding new products to their product line. Based on ResourceBased View (RBV), Skilled and competent technical staff, highly trained operatives, ability to access and retain highly qualified subcontractors and specialist labor factors were managed in most of the surveyed corporations. Further, almost all the surveyed corporations identified their strengths to be the skill and competency of their technical staff. Interestingly, however, communications and information technology were identified as the main weaknesses of these corporations. In the current state of things, a positive note is the indication that there have been improvements in the utilization of resources by these corporations, amongst some other benefits. However, the main barriers revealed the biggest concern being that there have been too many activities unrelated to the main business. This framework has never been tested on businesses in Saudi Arabia.
The Early Reading Curriculum

Claire McLachlan makes a case for a comprehensive approach to early-grade reading instruction that is based on research into how children acquire and improve reading skills and how children are motivated to use reading for pleasure, learning, and interaction within their culture and with the wider world culture. She also makes a case for adapting the practices developed in the cultures of OECD countries to the cultures in African countries to ensure the effectiveness of these imported teaching and learning programmes.
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text

George C. G. Barbosa, Zechy Wong, Gus Hahn-Powell, Dane Bell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
Explaining Reward Functions in Markov Decision Processes.

Rewards in Markov Decision Processes (MDP) define the behavior of the model. Without a clear interpretation of what the reward function is and is not capturing, one cannot trust their model nor diagnose when the model is giving incorrect recommendations. Increasing complexity of state-of-the-art models used to represent the reward function and model-free methods that attempt to avoid representing this function make trusting the model much more difficult. We map these reward functions onto a standard classification problem where we can explain what factors the model considers in making decisions in local and global contexts and quantify whether the fit of the reward function is likely to be good for explaining the behavior of the model. We evaluate our proof-of-concept on both the standard version and a modified version of the Object World domain to add more nonlinearity.
ICS/SCADA Device Recognition: A Hybrid Communication-Patterns and Passive-Fingerprinting Approach

The Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) systems are the backbones for monitoring and supervising factories, power grids, water distribution systems, nuclear plants, and other critical infrastructures. These systems are installed by third party contractors, maintained by site engineers, and operate for a long time. This makes tracing the documentation of the systems’ changes and updates challenging since some of their components’ information (type, manufacturer, model, etc.) may not be up-to-date, leading to possibly unaccounted security vulnerabilities in the systems. Device recognition is useful first step in vulnerability identification and defense augmentation, but due to the lack of full traceability in case of legacy ICS/SCADA systems, the typical device recognition based on document inspection is not applicable. In this paper, we propose a hybrid approach involving the mix of communication-patterns and passive-fingerprinting to identify the unknown devices’ types, manufacturers, and models. The algorithm uses the ICS/SCADA devices’s communication-patterns to recognize the control hierarchy levels of the devices. In conjunction, certain distinguishable features in the communication-packets are used to recognize the device manufacturer, and model. We have implemented this hybrid approach in Python, and tested on traffic data from a water treatment SCADA testbed in Singapore (iTrust).
QoE Performance for DASH Videos in a Smart Cache Environment

During the past decade, Internet has seen dramatic increase in video traffic. Users expect a high quality of experience with online video streaming. For video delivery, the DASH (Dynamic Adaptive Streaming over HTTP) standard is one of the common approaches for streaming used by content providers. In order to give users a higher quality of experience, in-network caching and prefetching are useful to reduce video delivery latency with DASH-generated videos. In this paper, we present a Smart Cache framework that uses a cache prefetching scheme that prefetches segment bitrate based on forecasted throughput at the cache entity by using previous throughput values from clients. For our study, we have implemented our framework on the GENI testbed, and our results for single-client and two-client interactions show that Smart Cache increases the byte-hitrate and reduces the number of unused prefetches for cache. We also consider the impact on Quality of Experience (QoE) for each client during contention.