Reviewerly/exHarmony huggingface dataset:
Smart City Data Modelling using Semantic Web Technologies

In modern Smart City ecosystems, the wave of Big data has offered the potential for cities to obtain actionable insights from a large amount of data, collected from heterogeneous sources. To cope with data variety and to support querying facilities and navigation, domain experts, who possess the knowledge required to understand data sources structure and content, resort to the definition of metadata layers, for reconciling terminological differences and resolving ambiguity of data sources attributes. To this aim, the adoption of Semantic Web technologies assures a unified view of heterogeneous data sources and improves data access, leveraging open standards for knowledge representation. In this paper, we propose an approach to create a semantic data layer over Smart City data sources. The approach is grounded on two main pillars: (i) a procedure, to build and manage the semantic layer; (ii) a tool, implementing the steps of the procedure, devoted to support domain experts. Results of an experimentation conducted on representative Smart City data sources demonstrated the effectiveness of the approach. In particular, the presence in our approach of a preliminary step regarding lexical enrichment of data source attributes helped increasing the retrieval of useful concepts from domain ontologies for attribute annotation. In addition, allowing domain experts to provide additional ontological knowledge by specialising or creating new concepts and semantic relationships fostered flexibility in the annotation procedure, facilitating the evolution of the semantic layer. Finally, the developed semi-automatic tool helped domain experts in the semantic annotation process by also providing validation support.
Experimental Study and Numerical Analysis of Crane Operability in Floating Crane Catamaran

Crane vessel is a floating structure equipped with accommodation facilities and one or more cranes to do work in the field. Crane vessel continue to be developed so that they can lift large structures and operate safely in certain environmental conditions. The work that is usually done by crane vessel is to lift heavy or light loads from land to ship, from ship to land, from ship to sea, from sea to ship, and from ship to ship. The process of moving loads carried out by a crane vessel usually called the lifting process. In carrying out its work, a crane vessel is limited by certain criteria for safety reasons. One of them is the criterion that limits the heave motion at the end of the crane boom and roll and pitch motion of the crane vessel. In this final project, an experimental study and numerical analysis of crane operability were carried out on catamaran hulled crane vessel. Experiments were carried out at the Balai Teknologi Hidrodinamika BPPT and numerical analysis using the MOSES software. According to the criteria used, namely Operational Limitations of Offshore Crane Vessels, cranes on floating crane catamaran have the highest operability when the vessel heading is 0 o to the direction of the incoming waves, which is up to H s 1.45 m.
UPAYA MENINGKATKAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN MODEL COOPERATIVE LEARNING TIPE NUMBER HEAD TOGETHER PADA SISWA KELAS III SDN 38/II PAUH AGUNG

The background of this research is the low student learning outcomes caused by an inappropriate learning approach. The process carried out in the classroom is also not pleasant, causing the learning outcomes and learning process of students in class III of SD Negeri No.38/II Pauh Agung to be low. So with this problem, the researchers conducted research using the Number Head Together model.&#x0D; This type of research is classroom action research which consists of four stages, namely planning, action, observation and reflection, which is carried out collaboratively between practitioners and observers. The research was conducted in SD Negeri No. 38/II Pauh Agung. The subjects of this study were 20 third grade students. The research was carried out for 2 cycles. Each cycle consists of 2 meetings. Data collection techniques using the test method, observation sheets and documentation. Data analysis techniques are descriptive qualitative and quantitative.
Extracting the Implicit Search States from Explicit Behavioral Signals in Complex Search Tasks

Abstract In a complex search task, users often go through different search states during search interactions under complex search tasks. Automatically identify these search states from observable behavioral measures will enhance our understanding of users' cognitive variations and serve as the basis for state‐aware adaptive search recommendations and evaluations. To achieve this goal, this state seeks to extract implicit search states from explicit behavioral signals and explore the possible connection between automatically clustered search states and the existing state typologies developed based on user labeling and expert annotations. The results from our preliminary work indicate that search states and the associated behavioral patterns can be extracted through clustering analysis in multiple datasets, and that the identified clusters/states can be mapped to the states identified via qualitative coding. Our study demonstrates the feasibility of automatically extracting search states and pave the path towards state‐aware adaptive search systems.
Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation

Recently, abstract argumentation-based models of case-based reasoning (AA-CBR in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios. However, the formal properties of AA-CBR as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of AA-CBR (that we call AA-CBR_&gt;). Specifically, we prove that AA-CBR_&gt; is not cautiously monotonic, a property frequently considered desirable in the literature. We then define a variation of AA-CBR_&gt; which is cautiously monotonic. Further, we prove that such variation is equivalent to using AA-CBR_&gt; with a restricted casebase consisting of all "surprising" and "sufficient" cases in the original casebase. As a by-product, we prove that this variation of AA-CBR_&gt; is cumulative, rationally monotonic, and empowers a principled treatment of noise in "incoherent" casebases. Finally, we illustrate AA-CBR and cautious monotonicity questions on a case study on the U.S. Trade Secrets domain, a legal casebase.
Data-driven identities

Digital identity schemes have been framed by providers as routes to secure provision of essential services. The theoretical link between digital identity and development is unpacked in two ways: firstly, digital identity is seen as capable to provide the form of user recognition that public services require, making it possible to reduce exclusion errors in service provision. Secondly, digital identification can in theory minimise the unlawful inclusion of non-entitled beneficiaries into social welfare schemes, thus reducing inclusion errors within such programmes. But while linked to “development benefits” in multiple forms, digital identity schemes have been associated to issues of exclusion, data justice and perverse policy, reflecting broader and longer standing discussions in the information and communication technology for development (ICT4D) field. This chapter is articulated into three sections. The first section conceptualises data-driven identities and their theoretical links with socio-economic development. The second section proposes a framework for conceiving data-driven identities, focusing on aspects of access to services, back-end monitoring of service provision and the social policies underlying planning and delivery of services. The third section applies such a framework to diverse cases of digital identity schemes across the globe, from India’s Aadhaar to refugee schemes. In the conclusion, we draw a theoretical contribution to the literature on digital identity and socio-economic development and practical implications for countries embarking upon digital identity programmes.
Effects of Knowledge Management on Organizational Performance

The main purpose of this paper is to evaluate the impact of knowledge management (KM) on the organizational performance of Microsoft Corporation. The study will also inform global entrepreneurs regarding the importance and application of KM in workplaces. The study will discuss the modification and technological integration that are required to derive desired outcomes from KM. The research will also evaluate the contribution of KM to career development and opportunities for the employees. Modern organizations need to enhance their products and services to continue to be the first choice of customers. The KM has enabled organizations to determine the changing demands and requirements of customers. Therefore, it can be opined that the research aims to analyze the significance of KM in modern organizations, employees, and competitiveness.
On Music and Computability: Contrasts between Harry Partch’s Bitter Music and Guerino Mazzola’s Topos of Music

The clash of ideas between classical music and several avant-garde movements in the previous century, not only found its way into twentieth century musicology, it also lead to a number of new developments in music digitalization. This paper reflects on the inscription of these opposing ideas about the concept of music into the contemporary views on the human condition and the notion of computability of human interactions. Harry Partch, the American iconoclast broke away from the classic, predominantly European traditions in music, and contested the abstract architecture and well-tempered tonal system of it. The mathematician Guerrino Mazzola constructed an even bigger, abstract formalization system, that allegedly allows for a complete digitalization of music, from the mind and inner ear of the composer towards the scores, the gestures and sounds produced by the performers up to the auditory cortex of the listeners. In this paper we will mainly investigate the philosophical and musicological basis of this formalization system, which is essentially based on the Denotator system and a number of concepts from algebraic topology applied to music. Finally, we will unravel the typical de-humanizing aspects that are followed in the digitalization system as used by Mazzola, and what this approach implicates for humanity (and the human sciences).
Secret Key Establishment Using Modified Quantization Log For Vehicular Ad-Hoc Network

Traditional cryptographic approaches such as symmetric and asymmetric cryptography are commonly employed to solve network security issues. The Secret Key Generation (SKG) system has the advantage of extracting secret keys from a wireless channel's physical layer information. It allows two wireless devices within the transmission range to extract a shared symmetric key without the use of a fixed key distribution infrastructure, allowing vehicular ad hoc networks to exchange information (VANET). This study aims to create a secure data communication system on the Vehicular Ad-Hoc Network using RSS Key Generation. Starting from the Modified Quantization Log, the results of the Modified Quantization Log show that the average KDRM between Alice and Bob is the average KDRM between Alice and Bob is 9.4%; meanwhile, the average KGR is 71.4 bps. This shows that the number of bit mismatches after the Modified Quantization Log process between the two valid users is already small, because they have used the pre-processing process in front of them, namely using the Kalman Filter and from the results of the BCH Code to be matched again so that it becomes the key. The next process is Universal Hash which is tested with the NIST test. The NIST Test parameters used are approximately entropy, frequency, block frequency, longest run, cumulative sum forward, and cumulative sum reverse. The existing results are appropriate; namely, the threshold in p whose value is above 0.01 is achieved. From the results of the Average Approximate Entropy, it is found that the largest value is obtained by the 40k10ms scheme, which is 0.7352.
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks

We study a security threat to reinforcement learning where an attacker poisons the learning environment to force the agent into executing a target policy chosen by the attacker. As a victim, we consider RL agents whose objective is to find a policy that maximizes reward in infinite-horizon problem settings. The attacker can manipulate the rewards and the transition dynamics in the learning environment at training-time, and is interested in doing so in a stealthy manner. We propose an optimization framework for finding an optimal stealthy attack for different measures of attack cost. We provide lower/upper bounds on the attack cost, and instantiate our attacks in two settings: (i) an offline setting where the agent is doing planning in the poisoned environment, and (ii) an online setting where the agent is learning a policy with poisoned feedback. Our results show that the attacker can easily succeed in teaching any target policy to the victim under mild conditions and highlight a significant security threat to reinforcement learning agents in practice.
The Goals and Directions of Historical Education in the Era of the Fourth Industrial Revolution

There are two new problems in the 2022 Curriculum Revision that did not exist in the previous one. The first is how the older generation of “digital immigrants” can educate the future generation who are “digital natives.” The second is that it is necessary to design a curriculum that responds to the age of AI, where big data has surpassed the “human collective memory,” and AI has overtaken “human collective learning.” In the Fourth Industrial Revolution e ra, changes are taking place so rapidly that we cannot solve future problems using past examples. History education needed for students living in the era of hyper-acceleration should focus not on learning knowledge about the past but on transfer learning based on conceptual knowledge.BR The COVID-19 pandemic has had the effect of achieving the Fourth Industrial Revolution just in one year, which would have taken ten years otherwise. In the Covid-19 situation, where teachers cannot teach students in the classroom, digital transformation has taken place all over the place. Now, we have crossed the “tipping point” where we cannot go back to the past. The 2022 National History Curriculum Revision should seek a “new normal” curriculum, in which AI takes over the roles of textbooks and teachers to a certain extent and converts classroom and online classes into blended learning.
An Analysis on Low Cost and Performance of Hardware and Software Oriented Lightweight Block Ciphers for IoT Applications

The number of equipment or devices that are connected to the internet for data sharing/ storage analysis increases abundantly with the advancement in the field of Internet-of-Things, where security, especially the confidentiality of sensitive data shared by the resource constrained devices is one of the major concerns. Lightweight cryptography is currently evolving as the major security field for low energy devices. This paper made an analytical survey on various hardware and software-oriented lightweight block ciphers with respect to their cost and performance, security aspects of lightweight ciphers are not considered. The evaluation metrics used for analysis are throughput, area, cycles per block, energy, and power consumption. As per the analysis SPECK and SIMON are assessed as the best cipher in in terms of throughput. Whereas the lightweight encryption algorithms SPECK and GIFT shows increased efficiency with respect to low-cost analysis.
Morpheus II: A RISC-V Security Extension for Protecting Vulnerable Software and Hardware

• Software protects data • All software is (eventually) hackable • Finding/fixing vulnerabilities doesn’t scale • E.g., Malicious 7: buffer errors, code injection, numeric errors, permissions, resource mgt
Structures of THE, QS and ARWU Top 100 universities’ websites in different languages and social media accounts: Binary matrix approach

The aim of the study is to understand how the diversity of university language websites and social networks influence the universities’ reputation in the World University Rankings and how this diversity can be described. Combining the union of sets of university names in Times Higher Education (THE), QS World University Ranking (QS), and Academic Ranking of World Universities (ARWU) 2018, the World’s Top 100 Universities Rankings enabled us to obtain a set of 146 universities. From October 18, 2018, through November 1, 2018, the availability of website versions in different languages and social media applications was checked. The study enabled a 146x15 binary matrix to be built in the first case, and a 146x21 one in the second, with 15 meaning the number of website versions in foreign languages, and 21 standing for respective number of various social media accounts. The binary matrices were clustered with a view to obtaining dense submatrices consisting of units only. The study shows that approximately 47 percent of the universities surveyed have their websites in more than one language. All the 146 universities have websites in English, those in Chinese coming second and equalling to 21. Most popular social networking sites have been revealed, with over 84 percent of universities having Facebook, Twitter, YouTube and Instagram accounts. On the whole, 18 universities form a dense binary submatrix for 6 social media, including four of the above, as well as LinkedIn and Google+. The binary matrices are proved to be effective for higher education managers and experts focusing on specific regions and social media. Correlational dependence calculations on comparative analysis of traditional and altmetrics rankings are also performed.
The Needs Analysis of Faculty members and Students about Faculties' Teaching Competencies for Online Classes in University

The purpose of this study was to analyze the needs of university faculty members and students for teaching competencies in online classes. To achieve the purpose, first of all, the teaching competencies of faculty members in online classes were determined through a review of previous studies. The final online classes teaching competencies were composed of 23 of 6 domains. Second, a survey was conducted with 208 participants from faculty members(103) and students(105) at universities in Busan Metropolitan City and Gyeongsangnam-do Province, Korea. Third, in order to draw the needs and priorities, collected data were analyzed by the three-steps analysis method of t-test, Borich need assessment formula, and the Locus for Focus model. As a result of t-test, there were significant differences in importance and performance value through all competencies about both faculty members and students. Finally, 4 competencies[① facilitation of attention and motivation, ② self-improvement, ③ development of instructional materials, ④ interaction] were identified as prior needs to faculty members while 6 competencies[① class evaluation and analysis, ② development of instructional materials, ③ open-mind and positive attitude, ④ evaluation achievement, ⑤ self-improvement, ⑥ communication of contents] to students. It was difference between faculty members and students for needs of teaching competencies in online classes. Therefore, based on the results, prior teaching competencies for faculty members should be considered and applied on deciding the support policies to improve the quality of online classes in university.
LATTICE FILTRATION THEORY. PART II: TWO-VARIATE AND MULTIVARIATE LATTICE FILTERS

Various space-time adaptive signal processing (STAP) systems allow a block representation of input signals and their covariance matrices. This is a convenient way to describe possible specifics of space and time parts of a STAP system and take this a priori information into account in subsequent signal processing. Such a block representation allows synthesizing multidimensional (quasi-)whitening and (quasi-)inverse filters, including their lattice variants. This second part of the series of articles about lattice filtration theory deals with two- and multidimensional lattice filters and continues the first part that focuses on the one-dimensional filters. For synthesizing the two-dimensional lattice filters, we derive a Generalized Block Levinson Algorithm (GBLA) and consecutively a Generalized Block Levinson Factorization (GBLF). We demonstrate that both the Generalized Levinson Algorithm and Factorization presented in the first paper are particular cases of the GBLA and the GBLF, respectively. A fundamental relation between lattice filters of different dimensionalities is revealed, which is that all lattice filters of a lower dimensionality (starting from one-dimensional) are nested in the filters of a higher dimensionality. We describe a nesting of one-dimensional lattice filters in two-dimensional ones, which exemplifies the overall nesting principle that keeps same for any dimensionality. In virtue of this fundamental relation between multi- and one-dimensional lattice filters, we propose to synthesize the former via the synthesis of the latter. Unlike the multi-dimensional lattice filters, the one-dimensional ones have a uniform structure and are significantly simpl
Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O

This chapter executes and appraises a tree-based method (the decision tree method) and an ensemble method (the gradient boosting trees method) using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, XGBoost, PySpark, and H2O). To begin, the chapter clarifies how decision trees compute the probabilities of classes.
Reversing General 2-Trailer Vehicles Using a 2D Steering Function Model and a Novel Mesh Search Algorithm

Reversing general 2-trailers is often necessary, but challenging and time-consuming even for professional drivers. Existing approaches in literature attempt to automate such maneuvers based on ideas from control theory. In this work, we propose a fundamentally novel approach for finding steering functions to arrive at a given configuration of vehicle internal angles. Our method is based on a simple steering function model with only two degrees of freedom, and we assume that it models paths of optimally short length for every reachable configuration. To find such a solution, a novel search algorithm is presented as well. Inspired by bisection, it applies a similar idea in 2D parameter space using a mesh graph which is transformed into a topologically equivalent graph in configuration space. The solution is found by an iterative refinement process. The algorithm can be executed multiple times per second on a Raspberry Pi 4, and the resulting steering functions and paths are simple and short.
Polymorphic Malware Detection by Image Conversion Technique

This model implements ways to detect polymorphic malware. This model uses a dynamic approach to detect the polymorphic malware. The objective is to increase the accuracy and efficiency of the detection as this malware can morph themselves, making it difficult to trace through anti-malware systems. As the tracing is going to be difficult the detection and classification system needs to be flexible that can able to detect the malware in every possible environment. This objective can be achieved by giving the system a superintelligence, this can be done by using the Convolutional Neural Networks (CNNs) in our system. This method records the pattern or the traces made by the polymorphic malware. The pattern is in the form of the image which is formed by converting the binary format of the hash codes. The generated images are then sent to the training module, based on this training module the Convolutional Neural Networks gives the result for any testing data.