Reviewerly/exHarmony huggingface dataset:
Attesting L-3 General Program Anomaly Detection Efficiency with SPADA

One of the main challenges for security systems is the detection of general vulnerability exploitation, especially when the exploit uses valid control flow. Thus, the detection of anomalous behavior provides an exciting research direction, as the research in this field tries to describe what is the standard program execution, to then detect as anomalous any behavior that does not fit that description.In this work, we compare two mechanisms that aim to detect general anomalies: SPADA and LAD. SPADA is an L-3 language mechanism that partitions phases and uses simple phase features to detect anomalies. LAD is a constrained L-1 language mechanism that applies complex clustering and machine learning models on specific functions to detect anomalies. In our experimental campaign with several real-world exploits, we show that SPADA’s detection mechanism performs better than LAD while being much simpler and easier to implement. We therefore show experimental evidence that further attests the efficiency of L-3 attack detection mechanisms for real attacks.
Game Data Analytics using Descriptive and Predictive Mining

The game industry is an industry that includes game development, marketing, and monetization. However, to be able to enter the game industry is not easy. Game developers must know how the market is going to be able to reap huge profits. By knowing the market situation, game developers can also determine whether the games made are in accordance with market conditions. Getting this information is not easy, especially for small game studios. In this research, we made a new application to find knowledge about games that are and will be trending. We used data mining is used to obtain this information. Data mining uses data from the Steam API to do clustering using the Hierarchical K-Means method and predictive using the Multiple Linear Regression method. The use of the Hierarchical K-Means method produces 3 clusters for the game's popularity level. The use of the Multiple Linear Regression method produces predictions of the game's popularity in the future. This new system will be able to help indie game studios to be able to obtain information about the condition of the gaming market thereby increasing the benefits that can be obtained.
Computational Performance of the Parameters Estimation in Extreme Seeking Entropy Algorithm

This paper is dedicated to the evaluation of the computational time performance of the algorithms that estimate the parameters of the generalized Pareto distribution, namely Method of Moments, Maximum likelihood estimator and Quasi-maximum likelihood algorithms. The generalized Pareto distribution is utilized by the Extreme Seeking Entropy algorithm to detect novelty in data. The algorithm is evaluating the weight increments of the simple adaptive filter that are obtained via incrementally learning algorithm. The computational time performance is examined in the experiment with the detection of step-change parameters of the signal generator. Its output contains also additive Gaussian noise.
Incorporating Autonomous Bargaining Capabilities into E-Commerce Systems

Bundling is a technique e-commerce companies have adopted from traditional retail stores to increase the average order size. It has been observed that bargaining helps increase customer satisfaction while increasing the average order revenue for retailers. We propose a mathematical framework to incorporate bargaining capabilities with the product bundles provided by e-commerce websites. Our method creates a virtual agent that uses the modular Bidding-Opponent-Acceptance model for its bargaining strategy and the Thomas-Kilmann conflict mode instrument to model buyer behavior. We incorporate bargaining capabilities with bundles in an e-commerce system by using a negotiation agent that uses business logic for better strategy. It uses real-time data generated during a negotiation session, since the buyer behavior during a negotiation is crucial. No requirement exists for data from past negotiation sessions of the buyer, which removes bias as well as allowing for rapid changes to buyer behavior. The agent behavior can be altered by various hyperparameters. Our model provides utility metrics to measure buyer and agent satisfaction. Our results show that the agent successfully negotiates with humans from diverse backgrounds.
Bitcoin Under Broken Crypto Primitives

Abstract Bitcoin’s security relies solely on cryptographic primitives, namely on digital signatures, hash functions and Merkle trees. This chapter discusses the security of the Bitcoin system if some primitives become weaker due to advances in cryptanalysis, an increasing computing power of the adversaries or improper software implementations. The chapter starts with a general overview of the primitives in use, explaining possible attack strategies against each of them, which is followed by combined attack strategies. The chapter closes by showing the consequences of Grover’s and Shor’s quantum algorithms for Bitcoin’s security.
Adaptive Weighted Fuzzy C-Means Clustering Algorithm Based on Density Peaks

The initial clustering center and membership matrix of the traditional FCM algorithm are randomly selected, so if there are outliers or uneven distribution of the data set, the FCM algorithm will fall into a local optimum, which will affect the clustering result. In view of the above problems, this paper proposes an adaptive weighted FCM algorithm based on density peaks. This algorithm improves the FCM algorithm by two points: first, the algorithm uses the density peak idea of the DPC algorithm to determine the initial clustering center, so as to improve the shortcomings of the FCM algorithm to randomly select the clustering center and reduce the number of iterations of the algorithm; Secondly, the algorithm uses an improved inverse cotangent function to construct the sample weight of each sample point for the class and uses it to improve the membership matrix of the FCM algorithm. In this way, the algorithm improves the shortcomings of FCM algorithm to randomly obtain membership matrix, and improves the accuracy of clustering. The experimental results show that the proposed algorithm has good clustering effect, smaller number of iterations and better time performance.
A person‐agent and related information resources: A case study of complex relationships

ABSTRACT Identifying and mapping relationships is of increasing importance as many cultural heritage institutions work to model data in Linked Data (LD) format. As a pilot of a larger LD project, this study examines relationships between information resources, between person‐agents and resources, and between person‐agents, in order to improve description and access to information related to persons and resources. We used a case‐study approach to identify new and previously‐defined relationships in bibliographic records associated with a person‐agent. To find a case that exhibits complex relationships, we selected Lin Huiyin (林徽因), a famous Chinese architect, architectural historian, poet, artist, designer, and writer in the 20th century. We collected 903 records using WorldCat and China Academic Library &amp; Information System. We applied a coding scheme consisting of relationship categories from several existing taxonomies. In the analysis, additional relationships emerged, which were then added to the coding scheme. Our next step is to broaden the scope of the case by examining other types of resources outside libraries such as Lin's architectural works. Further studies of complex cases will allow us to refine our taxonomy and develop an LD model for relationships between resources and person‐agents.
Engineerpreneurship: Engineers can be Entrepreneurs

Engineers solve real life problems by inventing new products, discovering new methods and create new solutions, they also find new ways of dealing with technical and engineering issues effectively and efficiently. Many engineers talk about their ideas, while it is believed that the start up is just a simple step ahead, this is usually is not the case. Many engineers left their engineering profession to start up a business, but, there are only few successfully entrepreneurs who still practice engineering [4].The author, who is both a business owner and a practising engineer, will share his experiences on the transition from being an engineer to becoming an entrepreneur. While running the business, the author is also involved in the engineering work and speaks in international conferences, hence the term "ENGINEERPRENEUR". This paper will also discuss the contradicting attributes and the challenging route to sustain the business. Finally, the author will summarize with who an engineerpreneur is and how one should measure successes in an entrepreneurship.
Image Transmission Performance Over UHF Channel Using Real Time Processing in LOS and NLOS Transmission

Data transmission from the transmitter to the receiver is influenced by several parameters including distance, obstacle and data size. In general, the farther the distance of the transmitter to the receiver, the longer the data will be sent. This relationship is also applied to data size parameter, where the larger the data sent, the longer the data received at the receiver. Therefore, this research aims to measure transmission performance of big size data such as image in real time processing unit applied in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) UHF radio module. Since image data processing takes a significant time, the design considered a highspeed processor. Payload, designed using Raspberry Pi Zero W processors and Raspberry Pi Camera Rev1.3, has a function of capturing image data remotely. This payload will communicate with a receiver station over 3DR as Radio Transceiver Module. Receiver station set up to display the received image data. The proposed design performed that the process time of image data transmission is getting longer along with changes in data size and distance. The research results that NLOS data transmission takes longer time compared to LOS data transmission at around 18.483 seconds for 512×384 pixel image with quantization level of K=32. The test conducted to the same image showed that the data loss of NLOS transmission is 8% larger than LOS data transmission.
Wilt Dataset-based Comparative Analysis of Three Neural Networks

This paper carries out a comparative analysis of three neural networks and their specific kernel machine learning algorithms, namely Inverse Weighted K-means in Online mode (IWKO1), evolving Fuzzy Optimally Pruned Extreme Learning Machine (eF-OP-ELM) and Support Vector Machine (SVM). The presentation of IWKO1, eF-OP-ELM and SVM is given and associated with their mathematical background, and the steps of their implementations are next outlined. The comparative analysis involves two performance indices, prediction accuracy and execution time, measured after the implementation on the Wilt dataset. The analysis is important in the context of the need to handle big data volumes in terms of using special algorithms, which are specially designed to process appropriately such data and information.
The ISO/IEC 29110 Software Lifecycle Standard for Very Small Companies

For many small and start-up software companies, implementing controls and structures to properly manage their software development activity is a major challenge. It is commonly agreed that very small software companies, implementing management procedures, and controls to appropriately administer their software development activity is a significant challenge. To help meet the need for VSE-specific systems and software lifecycle profiles and guidelines, the ISO/IEC jointly published ISO/IEC 29110 “Lifecycle profiles for Very Small Entities” series of standards and guides, with the overall objective being to assist and encourage very small software organization in assessing and improving their software. The purpose of this chapter is to provide a primer on the ISO/IEC 29110 standard focusing on two main process areas of Project Management and Software Implementation.
Self-Collaborative Unsupervised Hashing for Large-Scale Image Retrieval

Learning based hashing approaches have achieved considerable success in large-scale image retrieval due to the query effectiveness and efficiency. However, most studies highly rely on supervised knowledge like data labels, thus might fail in unsupervised setting. To address this issue, we propose a self-collaborative unsupervised hashing method (SCUH), which jointly learns hashing function and virtual labels in a self-collaborative manner. Different from existing unsupervised counterparts, SCUH treats sample features and the learned virtual labels as two groups of different features in a fair view. This results in two groups of transformation matrices, which have proven beneficial for capturing diversity information of different features to better advance binary codes. Moreover, we devise an alternate optimization algorithm to solve SCUH, in which each sub-problem has a closed-form solution as a byproduct. Experiments of image retrieval on three datasets show that SCUH performs favorably over eight comparative methods including unsupervised counterparts.
Research on HMM-Based Speech Retrieval Algorithm

Speech retrieval refers to the data mining technology that delivers results after retrieving in massive speech data according to the query items entered by users. This paper briefly discusses the reasons for the development of voice retrieval and the current development of voice retrieval technology, mainly explains the basic principle of Hidden Markov Model, and analyzes the three basic problems to be solved by its algorithm. On this basis, HMM is adopted for speech recognition, and simulated by MATLAB. Afterwards, the performance of HMM is analyzed in accordance with the simulation results.
Hand-eye Coordination for Textual Difficulty Detection in Text Summarization

The task of summarizing a document is a complex task that requires a person to multitask between reading and writing processes. Since a person's cognitive load during reading or writing is known to be dependent upon the level of comprehension or difficulty of the article, this suggests that it should be possible to analyze the cognitive process of the user when carrying out the task, as evidenced through their eye gaze and typing features, to obtain an insight into the different difficulty levels. In this paper, we categorize the summary writing process into different phases and extract different gaze and typing features from each phase according to characteristics of eye-gaze behaviors and typing dynamics. Combining these multimodal features, we build a classifier that achieves an accuracy of 91.0% for difficulty level detection, which is around 55% performance improvement above the baseline and at least 15% improvement above models built on a single modality. We also investigate the possible reasons for the superior performance of our multimodal features.
A Data-Driven Approach for Automatic Classification of Extreme Precipitation Events: Preliminary Results

Even though there exists no universal definition, in the South America Andean Region, extreme precipitation events can be referred to the period of time in which standard thresholds of precipitation are abruptly exceeded. Therefore, their timely forecasting is of great interest for decision makers from many fields, such as: urban planning entities, water researchers and in general, climate related institutions. In this paper, a data-driven study is performed to classify and anticipate extreme precipitation events through hydroclimate features. Since the analysis of precipitation-events-related time series involves complex patterns, input data requires undergoing both pre-processing steps and feature selection methods, in order to achieve a high performance at the data classification stage itself. In this sense, in this study, both individual Principal Component Analysis (PCA) and Regresional Relief (RR) as well as a cascade approach mixing both are considered. Subsequently, the classification is performed by a Support-Vector-Machine-based classifier (SVM). Results reflect the suitability of an approach involving feature selection and classification for precipitation events detection purposes. A remarkable result is the fact that a reduced dataset obtained by applying RR mixed with PCA discriminates better than RR alone but does not significantly hence the SVM rate at two- and three-class problems as done by PCA itself.
Research on Interactive Storytelling Method Using Artificial Intelligence and Physics Engine

Computer games with the digital characteristics of a non-linear approach have implemented interactive storytelling from the beginning, and today, with high game engine performance, users lead the story with minimal control, and there is an interactive movie genre that allows users to enjoy real-time stories such as movies. Appeared. It requires a lot of manpower and time to produce 3D resources that meet the increased expectations, and a more effective method than the existing production method of predicting, directing, producing, and recording users' needs is needed. Therefore, an advanced sub-unit interaction method was proposed as a way to realize interaction in various situations while reducing the amount of game engine resource production. This is a generation method that actively applies the improved physics engine and artificial intelligence reinforced by deep learning to make the character's movement, dialogue, and natural environment effects in real time in the game engine. As free deep learning libraries such as TensorFlow are activated and GAN learning methods along with CNN and RNN learning methods become generalized, the results of artificial intelligence show a quality that is indistinguishable from the original. In addition, by actively applying the improved physics engine, it is possible to create detailed movements of surrounding objects. The contents considered in the study are technologies that are still implemented, and if they are introduced, 3D resources can be created in real time without directly producing them, and the context setting and dialogue can induce users to naturally follow the flow of the main story. There will be.
Green Cloud Computing Strategies for Load Balancing

Cloud computing nowadays has emerged as one of the important aspect to distributed computing. Organizations are adopting cloud computing (Escalnte, D. and Korty, A.J., 2011. Cloud services: Policy and assessment. EDUCAUSE Review 46 (4).) for performance upgrading as one of parameter and also for cost effective computing. In same context to increase resource utilization and energy efficient techniques are developed using green cloud. These green datacenters provide overall efficiency to cloud database and thereby will be beneficial to distribute load to virtual machines. Green cloud therefore provides environmental benefits by reducing energy consumption by creating virtual machines and balancing there load. This paper will include detailed review for various load balancing strategies as algorithm which are applicable in any environment with respect to cloud and variety of parameter for load balancing.
Big Data Security Based Framework Using Metaheuristic Approaches in Cloud Environment

Security is the main concern in Big Data analysis. In the security analysis, most of the approaches that provide an effective security yet uses high amount of resources like memory or storage and time delay in order to increase the cost of processing. This paper emphasis on improvement based on utilization of resources by means of optimization of encryption approach. The optimisation approach uses a convex optimization with minimum amount of time or storage and such factors effects the total cost of processing. The proposed approach uses GWO optimization of Blow fish slices. The experiment involves the use of two types of dataset such as tweet and scientific workflow. The exper- imental analysis shows how effectively blow fish improves Grey wolf optimization (GWO). In addition, the other experiment uses particle swarm optimization (PSO) and flower pollination approach (FPA). Although the analysis represents that these approaches are not very much effective as compared to GWO.
Experimental Evaluation of Motor Skills in Using Jigsaw Tool for Carpentry Trade

Carpenters are key members and carpentry trades have a major role in the construction industry. Different hand and power tools are used to complete the jobs in this area. Various skill parameters are required for the proper usage of different tools, which are used in the construction industry. Based on the survey, choose one of the important tools used in this field that has conducted the experimentation. This paper describes the skill parameters involved in the proper use of the jigsaw tool and conducted an experimental study to analyze those skills varied between novices and experts. Based on the expert’s data, we could easily teach the novice for the proper usage of the tool. The expert’s data are set as a reference data for skilling the novices. This paper also discusses a comparison of expert’s data with the novice for proposing an assistive training system for jigsaw tools, which will accelerate the learning of use of the jigsaw tool.
Some Technical Challenges in Designing an Artificial Moral Agent

Autonomous agents (robots) are no longer a subject of science fiction novels. Self-driving cars, for example, may be on our roads within a few years. These machines will necessarily interact with the humans and in these interactions must take into account moral outcome of their actions. Yet we are nowhere near designing a machine capable of autonomous moral reasoning. In some sense, this is understandable as commonsense reasoning turns out to be very hard to formalize.In this paper, we identify several features of commonsense reasoning that are specific to the domain of morality. We show that its peculiarities, such as, moral conflicts or priorities among norms, give rise to serious challenges for any logical formalism representing moral reasoning. We then present a variation of default logic adapted from [] and show how it addresses the problems we identified.
BMGC 2020 Grand Challenge: Multi-Aspect Analysis of the MeToo Movement on Twitter

The grand challenge for the 2020 International Conference on Multimedia Big Data (BigMM'20) introduced a multi-aspect analysis of the MeToo movement, using Twitter to analyze five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. The challenge focused on accurately assigning multiple categorical labels to tweets from the #MeTooMA dataset. We received submissions from 10 different teams with 25 participants. The results provide progress and insight into the value of language signals in helping to analyze tweets related to the MeToo movement.