Reviewerly/exHarmony huggingface dataset:
A Risk Assessment Method based on Software Behavior

Traditional risk assessment methods have problems with the subjectivity of assessors and inaccuracy of vulnerability detection, which leads to unreliable and non-quantitative assessment results. To address these problems, this paper proposed a method to assess system risk based on software behavior. The behavior of untrusted software calling is necessarily associated with system risk, specifically, the larger the number and scope of untrusted software called, the higher risk the system faces, and vice versa. In other words, illegal operation of computers is the specific form of system risk and there is a probabilistic correlation between them. Because the number and scope of untrusted software calling can be accurately measured while risk level cannot be observed directly, this paper used a quantitative analysis method (HMM) to assess the system risk level, which ensures objectivity and accuracy of results. Furthermore, this paper analyzed and explained the risk assessment method based on software behavior through experiments.
Blended Learning in Smart Learning Environments

It is already commonly accepted, that blended learning seems to be one of the modern and promising approaches to teaching and learning. Blended learning aims to integrate traditional learning with innovative means, such as e-learning, analytics, game-based learning, and open educational resources, in order to create a new learning environment aiming to enhance learning effectiveness, and enrich learning experience. In the meantime, as one important and applicable output of the smart environments research, the concept of smart learning environments has evolved with a number of successful applications. The aim of this paper is to argue, that blended learning concept can be viewed as a promising perspective for affective learning strategies inclusion into recently more and more popular smart learning environments.
Automatic S-Phase Picking for Volcano-Tectonic Earthquakes Using Spectral Dissimilarity Analysis

We present an S-phase picking algorithm for volcano-tectonic earthquakes (VTs), based on the changes of frequency and amplitude expected in the plane transverse to the ray direction at S-phase arrival. A measure of these changes, called spectral dissimilarity, is proposed. Picking is performed in a particular waveform transformation that underlines such variations foreseen: horizontal instant power. Then, the algorithm provides a measure of its reliability, grounded on the low or high fluctuations of the picking instant obtained when applied to other horizontal components of the seismogram. Experiments are performed to test the algorithm with a challenging database of volcano seismic earthquakes from Mt. Etna, carefully picked and labeled by a human expert. The technique is compared to two well-known S-phase pickers: one based on the damped predominant period analysis and the other based on polarization and kurtosis rate analysis. The algorithm improves these techniques for the particular scenario of VTs, providing interesting results and possibilities of the application.
Implementation of Big Data in Cloud Computing with optimized Apache Hadoop

Big Data has pulled in strong consideration due to its importance to huge handling of data. With the help of the examination group, information has been received for the most recent version of choices to the system. With various production in the diaries which was gained with respect to Hadoop have methods, that makes it extreme for the analysis to get this sort of humongous sort of research. In the arrangement we are going to lead a logical writing audit to survey various kind of commitments to Apache Hadoop. The goal is to spot any kind of loopholes to mark inspiration to start new investigation and make efforts to Apache Hadoop and its plan of measuring and grouping the points in the self-tended inside this paper. The investigation will light some important conclusion of a few consideration efforts which are pointed out inside this paper creating the most productive and satisfactory amount of methodology of implementing Big Data inside Cloud Computing.
Measurement of rotationally symmetric aspherical surfaces with the annular subaperture stitching method

The annular subaperture stitching method is an effective method for testing rotationally symmetric aspherical surfaces. To create a full aperture map of this kind of asphere, we propose an annular subaperture stitching algorithm. The algorithm is based on triangulation interpolation theory, least-square fitting method, and ray tracing method. We first show the principle of our stitching algorithm. Then, the performance of the proposed method is analyzed by simulation testing to evaluate the accuracy of the above algorithm. In the end, the experiment is given to demonstrate the correctness of our method. Both the simulation and experiment results show that the introduced method is quite effective for the testing of rotationally symmetric aspherical surfaces.
Technology-Enhanced Assessment at universities and in schools: An initiative

Background: Technology-enhanced assessment (TEA) is a broad term that encompasses the diverse methods, by which technology can be used to support the management and delivery of assessment in educational institutions, in workplace and lifelong learning, in professional training and development, and so on. Purpose of Study: There is a new initiative at the University of Belgrade, Serbia, to explore computing technology underpinnings of the practice of TEA (computing underpinnings of the practice [CUP] of TEA), at universities and in schools in the country. Sources of Evidence: The CUP of TEA initiative starts from the current insight into current practices of assessment processes in different educational institutions. The data collected include assessment process descriptions from these institutions, specification of technologies used for the purpose, and specification of the educational profiles of participants in the process. Main Argument: The CUP of TEA initiative develops: A novel and comprehensive framework for TEA to lay the foundation for systematic, accurate, effective, and innovative application and use of TEA in educational institutions; specifically designated sets of information and communication technology (ICT) tools and services – ICT toolsets – that support practical application of TEA; several application cases that implement the TEA framework and use different ICT toolsets for TEA in different educational contexts, showcasing the integration of best TEA practices into curricula; and a set of policies that enable educational institutions and their teachers to officially establish the use of the framework for TEA in their courses and daily work. The CUP of TEA initiative covers both formative and summative assessment and supports formal, non-formal, and informal learning and assessment. Conclusions: The ultimate goal of the initiative is not just better, faster, and more informative assessment. It is the creation of a robust teaching and learning assessment approach that improves and advances the quality and efficiency of education. Keywords: Technology Enhanced Assessment; assessment processes; application cases
From Ubiquitous to Ubiquitous Blended Learning Environments

As advances in information and communication technology increasingly transform learning and teaching; blended learning and ubiquitous learning concepts have gained attention and become pervasive in 21st century. With the help of recent advances in mobile learning, wireless networks, RFID tags, a new model of blended learning—ubiquitous blended learning—that takes advantage of increasing ubiquity of online devices in online phase of blended learning is considered to gain attention in designing online courses. In this chapter, the author presents a picture of ubiquitous and blended learning studies while focusing on the results of ubiquitous learning and suggesting a rationale for such designs. The author defines ubiquitous blended learning as an instructional design approach that integrates ubiquitous technologies involved on-line and/or virtual learning with face-to-face learning by decreasing seat-time in class and increasing outdoor learning activities to facilitate learning from not just the teacher but from peer to peer and on-line learning communities as well.
Discovery of Incremental Association Rules Based on a New FP-Growth Algorithm

In this paper, we propose a new FP-Growth algorithm for incremental association rule discovery. We also design a new FPISC-tree based on the FUFP-tree structure. The new FPISC-tree is more suitable for the task of incremental association rule discovery than FUFP-tree structure. The basic ideas of the proposed algorithm are to retrieve the frequent itemsets from the original database and to use their support count in the update of the new support count of the incremental database so that the original paths do not need to be reprocessed as well as to strategically use them to discover frequent itemsets from the FPISC-tree. Experimental results show that the proposed algorithm was able to reduce the number of constructed subtrees and the execution time was significantly less than those of the FPGrowth and FUFP-tree.
Intelligent Tennis Robot Based on a Deep Neural Network

In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%.
Sketch Based Image Retrieval using Transfer Learning

Sketch based image recognition is an approach where the system recognizes an image from the database based on the query sketch received from the user through an interface. In our implementation, we use transfer learning approach to learn the features of the sketch. Transfer learning is a technique used to reuse a model, which is pretrained, for a new problem thereby enhancing the model accuracy and reducing training time. Sketchy Database is used for training which contains 75,471 sketches of 125 categories. A pretrained VGG19 network is used to find the similarity between the sketch and image database using a cosine similarity function. Based on the result of the similarity function, SBIR recognizes a set of similar images corresponding to the sketch. Since the uses of touch devices are increasing rapidly even in a relatively small area, we focus on our final solution being scalable as well as distributed.
End-to-end let's play commentary generation using multi-modal video representations

In this paper, we explore how multi-modal video representations can be applied in an end-to-end fashion for automatically generating game commentary based on Let's Play videos using deep learning. We introduce a comprehensive pipeline that involves directly taking videos from YouTube and then using a sequence-to-sequence strategy to learn how to generate appropriate commentary. We evaluate our framework using Let's Play commentaries for the game Getting Over It with Bennet Foddy. To test the quality of the commentary generation, we apply perplexity to evaluate our language models using different input video representations to highlight different aspects of gameplay that might influence commentary.
From caveman to gentleman

Even though modern video games present rich storytelling and high graphical fidelity, they are still lacking in rich non-scripted NPC social interactions. In this work we present an extension of the Comme il Faut (CiF) and CiF-CK social interaction models, where we added emotions and a emotion appraisal process based on the OCC model of emotion and also added a belief system that models the social network values that the NPCs expect regarding the other NPCs' relationships. A version of the new model, which we named Comme il Faut - Exiles (CiF-Ex), was implemented in the AAA game Conan Exiles using their modding tools and validated. The results were noteworthy: the users enjoyed the game more and tended to spend more time near CiF-Ex enabled NPCs. The majority of the users preferred CiF-Ex NPCs, versus the vanilla NPCs. CiF-Ex NPCs were perceived as more believable and less predictable.
Non-Markovian dynamics of collective atomic states coupled to a waveguide

When atoms are optically coupled to a one dimensional waveguide, they can interact through macroscopic distances. The retardation effects inherent to field propagation and the associated delay in information backflow between the atoms result in a departure from the familiar Markovian dynamics. We study the case of two two level atoms coupled along a waveguide. One remarkable feature of the dynamics in this regime is the formation of long-lived bound states in the continuum (BIC),<sup>30</sup> that refer to a hybrid diatomic molecule bound together by propagating modes of a field. In particular, we study the probability of reaching such a bound states of the system starting in an initially anti-symmetric state of the emitters.
Design-centric maze generation

A maze is a common structure in a game level. When we design game levels having a different purpose of each level, we may desire mazes with different topological properties, such as lots of branches or long straight-ways. Thus, we need the ability to design mazes based on our game mechanics. In this paper, we introduce our design-centric maze generation in which designers can input their desired properties to create their own mazes. Our method also enables the designers to control the topology of the solution path of a maze. Additionally, this method can provide several mazes which satisfy the given desired properties allowing designers to choose the best maze and use it to build game content for a game level. To demonstrate how useful our design-centric method is, this paper provides several use-cases of building actual game levels and shows that we can design the levels effectively using our method.
Query processing on large graphs: Approaches to scalability and response time trade offs

Graphs, being an expressive data structure, have become increasingly important for modeling real-world applications, such as collaboration, different kinds of transactions, social networks, to name a few. With the advent of social networks and the web, the graph sizes have grown too large to fit in main memory precipitating the need for alternative approaches for an efficient, scalable evaluation of queries on graphs of any size. In this paper, we use the time-tested “divide and conquer” approach by partitioning a graph into desired number of partitions (and possibly with appropriate characteristics) and process queries over those partitions to obtain all or specified number of answers. This entails correctly computing answers that span multiple partitions or even need the same partition more than once. Given a set of partitions, there are a number of approaches using which a query can be evaluated: (i) One Partition At a Time (OPAT) approach, (ii) Traditional use of Multiple Processors (TraditionalMP), and (iii) using the Map/Reduce Multi-Processor approach (MapReduceMP) approach. The first approach, detailed in this paper, has established scalability through independent processing of partitions. The other two approaches address response time in addition to scalability. For the OPAT query evaluation approach, necessary minimal book keeping has been identified and its correctness established in this paper. Query answering on partitioned graphs also requires analyzing partitioning schemes for their impact on query processing and determining the number as well as the sequence in which partitions need to be loaded to reduce the response time for processing queries. We correlate query properties and partition characteristics to reduce query processing time in terms of the resources available. We also identify a set of quantitative metrics and use them for formulating heuristics to determine the order of loading partitions for efficient query processing. For OPAT approach, extensive experiments on large graphs (synthetic and real-world) using different partitioning schemes analyze the proposed heuristics on a variety of query types. The other two approaches are fleshed out, analyzed, and contrasted with the OPAT approach. An existing graph querying system has been extended to evaluate queries on partitioned graphs. Finally all three approaches are compared for their strengths and weaknesses.
Center of mass and the optimal quantizers for some continuous and discrete uniform distributions

In this paper, we first consider a flat plate (called a lamina) with uniform density ρ that occupies a region of the plane. We show that the location of the center of mass, also known as the centroid, of the region equals the expected vector of a bivariate continuous random variable with a uniform probability distribution taking values on the region . Using this property, we prove that the Voronoi regions of an optimal set of two-means with respect to the uniform distribution defined on a disc partition the disc into two regions bounded by the semicircles. Besides, we show that if an isosceles triangle is partitioned into an isosceles triangle and an isosceles trapezoid in the Golden ratio, then their centers of mass form a centroidal Voronoi tessellation of the triangle. In addition, using the properties of center of mass we determine the optimal sets of two-means and the corresponding quantization error for a uniform distribution defined on a region with uniform density bounded by a rhombus. Further, we determine the optimal sets of n-means, and the nth quantization errors for two different discrete uniform distributions for some positive integers n ≤ card(supp(P)).
Anomaly Detection in Time Series data using Hierarchical Temporal Memory Model

In this data-driven world, the Anomaly detection technique plays a key role in various domains. Since the amount of generated data is huge, conventional anomaly detection techniques using batch processing are inefficient since the cost of storing and processing large amounts of data incurs various costs. Therefore, the best algorithm for this task should be able to detect anomalies in a real-time manner and with no human intervention. In this paper, we discuss the application of the Hierarchical Temporal Memory algorithm to detect anomalies in real time and in an unsupervised manner. We applied this algorithm to the stock market dataset and analyzed the performance. We also applied the algorithm on an artificially created dataset with a known point anomaly. This work tests the performance of the algorithm for real-world applications and detects anomalies.
Atmospheric horizontal extinction determined with a single digital camera-based system in the scope of solar power tower plants

A methodology based on the use of a single digital camera has been developed to determine the horizontal attenuation of solar radiation between the field of heliostats and the receiver in a solar tower plant. For this purpose, only the measurement of ambient light and a dark object at known distance is needed. The scenario for this work has been the Plataforma Solar de Almería (PSA, SE Spain), where a large black surface is available. This surface has been designed to be used for a horizontal attenuation measurement system, developed and implemented at the PSA, which is based on the use of two identical digital cameras. Specifically, the horizontal atmospheric attenuation values provided from the single camera-based system and the two camera-based system were compared during several days of July 2018, in conditions of medium values of atmospheric turbidity. A good agreement between the attenuation values obtained with both methods, within their respective uncertainty margins, has been found.
Extreme learning machine based classification for detecting micro-calcification in mammogram using multi scale features

In the human body, there are some genes that are lead to the growth of the cells. The mutation of these genes are called cancer. Breast cancer is higher in women, and which will causes largest number of cancer related deaths among women. Breast cancer rates are higher among women in many countries. To increase the results of breast cancer and survival, early diagnosis is crucial. There are two early screening plans for breast cancer: early detection and screening. Limited resource parameters with low health systems where most women are diagnosed in the late stages and should organize early diagnosis programs based on knowledge of the first signs and symptoms. Many methods are used to test women to identify cancer before all symptoms appear. Mammography is one of the methods in which an image of the breast used to detect and diagnose breast cancer tumors. Micro-calcification can be found in mammogram and it will indicate the presence of breast cancer. Preprocessing, feature extraction and classification are the three important steps to detect the micro calcification in mammogram. And there are different classifiers used for the classification of micro calcification. In this paper we analyze the performance of different classifiers and find out the best one for the classification using multi scale features.