Reviewerly/exHarmony huggingface dataset:
Test of English vocabulary recognition based on natural language processing and corpus system

English vocabulary recognition has certain applications in both learning and life. The existing English vocabulary recognition model is limited by a variety of factors, which will result in a more complicated recognition process and a low recognition accuracy. In order to improve the effect of English vocabulary recognition, based on natural language processing algorithms and corpus systems, this paper proposes a multi-feature fusion adaptive kernel-related filter tracking algorithm for the problems of kernel-related filtering algorithms. Moreover, based on the KCF algorithm, this paper improves the algorithm from three parts: feature fusion, adaptive change of update rate, and scale detection. In addition, this paper explores whether the vocabulary recognition of different rhythms will affect the reaction time and accuracy of the second language vocabulary recognition when the test subjects are in the experimental conditions with similar characters and different voices. The research results show that the model constructed in this paper performs well in the recognition of English words.
GEOLOGICAL EVALUATION OF LAKE VAN (TURKEY) FOR URANIUM-THORIUM DEPOSITION

Purpose of this study is to prove most probable existence of economical U-Th mineralisations within Tatvan Basin which have been hidden among foreign international research project manuscripts of Lake Van and to inform the importance of national exploration studies. Lake Van is a pull-apart basin formed within Quaternary Muş-Zagros suture zone with right lateral movement. Calculations on heat transfer below the Tatvan Basin indicate that a constant heat flow is about fourty times the continental average which have been only reported from some oceanic ridges. Data indicate the heat source below Tatvan Basin bottom represents a steadily collapsing cauldron subsidence of Nemrut volcano’s magma chamber. Lake Van surface water has mean 76ppb dissolved uranium content of hyrothermal and authigenic origin. It is calculated that there is at least 50.000 tons of dissolved uranium exists in the Lake Van waters. Ultimate deposition of U-Th mineralisation within euxinic Tatvan Basin have been expected to be a continuous process during geologic history of Lake Van as long as uranium resources remain and its NaHCO3 water functions as dissolving agent Sodic Lake Van waters continuously dissolve uranium from 1) high 3He/4He and U-Th containing hydrothermal fluxes of mantle origin coming up through cauldron subsidence faults of Tatvan Basin, 2) per-alkaline rhyolitic volcanic ash rain of Nemrut volcanism, 3) Bitlis granitoid Massive basement, 4) repeated authigenic disentegrations of U to (Th and 4He) within the sedimentary deposites of Tatvan Basin through its 600.000 years history. Tatvan Basin is the deepest basin with 450m depth, 300km2 flat area and constant unoxic basal water table undisturbed by currents and has the following verifications for Quaternary U-Th depositions in the unconsolidated porous sediments: 1) organic mass rich levels with reducing microbial activities, 2) evaporitic dolomites deposited during low stand lake levels with high U-Th concentrations, 3) varved, mixed-layered clays with high hectorite content, 4) sub-aquaeous, basic-intermediate volcanic basement intrusions with reducing properties, 5) measured very low redox potentials in basal environment, 6) very high density of U-Th. Drilling core sequence and the gamma ray logs from Ahlat Ridge have been used in the foreign literature published since 1974 until now while the existence or non-existence of uranium has not been mentioned. Gamma ray logs of drilling cores and their pore water analyses from Tatvan Basin were carried out abroad but not published yet. Thus gamma ray logs belonging only to shallow Ahlat Ridge sequence where uranium mineral precipitation is not possible is misleading.
Mathematical modeling of systems, its nature and limits of applicability

Purpose The purpose of this paper is to establish the nature of mathematical modeling of systems within the framework of the object-semantic methodology. Design/methodology/approach The initial methodological position of the object-semantic approach is the principle of constructing concepts of informatics proceeding from fundamental categories and laws. As the appropriate foundation, this paper accepts the system-physical meta-ontology is being developed in this paper. Findings The genesis of system modeling is considered in the aspect of the evolution of language tools in the direction of objectification. A new conception of formalized knowledge is being put forward as the mathematical form of fixing time-invariant relations of the universe, reflecting regularity of the dynamics of natural or anthropogenic organization. Object knowledge is considered as a key component of the mathematical model, and the solving of system information problems with its use is characterized as “work of knowledge.” The establishment of the meta-ontological essence of modern mathematical modeling allows us to formulate its fundamental limitations. Originality/value The establishment of system-physical limitations of modern mathematical modeling outlines the boundaries from which it is necessary to proceed in the development of future paradigms of cognition of the surrounding world, which presuppose convergence, synthesis of causal (physicalism) and target (elevationism) determination.
“Digital legitimation” of scientific knowledge: to articulation of the problem

Manifestation of the new methods of scientific research based on computational processing of high volumes of data (experimental, statistical, secondary information, etc.) led to broadening of the methods of scientific cognition, as well as to changes in the processes of legitimation of new knowledge. The article examines separate aspects of the indicated changes, and raises a question on the need for more accurate and systematic monitoring and analysis over the processes of changing the mechanisms of legitimation of scientific knowledge, obtained within the framework of &amp;ldquo;science based on big data&amp;rdquo;. The conclusion is made that in the context of the Fourth Paradigm (Data-Intensive Science) is being formed the fourth strategy for legitimation of scientific knowledge &amp;ndash; &amp;ldquo;digital legitimation&amp;rdquo;. The knowledge generated by data-intensive science is legitimized virtually by complexity and scale of technologies used for its acquisition. The author underlines the need for thorough analysis of the &amp;ldquo;digital&amp;rdquo; type of legitimation due to the fact that the expansion of digital method for production of scientific results bears a number of risks. Particularly, alongside with accumulation of inaccuracies and deterioration in the quality of scientific examination, most severe risks consist in possibility of science being caught into the institutional development traps, as well as in exacerbation of the existing systemic crisis.
Comparison of the Response Times of MongoDB and PostgreSQL According to Type of Query in Geographical Databases

A hybrid and distributed geographical database is being developed which utilizes the database engines PostgreSQL and MongoDB for later implementation in Geoserver’s architecture. In the course of the study, it was necessary to establish whether there was difference between the response times of PostgreSQL and MongoDB per type of query and the use of geographical indexes, in order to appropriately select the Database Management System to be used by the Geoserver implemented Web Map Service. After a statistical analysis with 11 different types of query, the conclusion was that the type of query affects the response time of the Database Management Systems.
Aligning the Concepts of Risk, Security and Privacy Towards the Design of Secure Intelligent Transport Systems

Intelligent Transport Systems (ITS) play a key role in our daily activities. ITS development over the last decades has been based on the rapid evolution of information technologies, which include processing capabilities, availability of hardware and communication technologies. Moreover, ITS use Information and Communication Technologies (ICT) to improve sustainability, efficiency, innovation and safety of transportation networks helping towards better management of transportation networks with the use of advanced technologies, which facilitate monitoring, and management of information. However, as the development of ITS services increases so does the users’ awareness regarding the degree of trust that they show on adopting this kind of services. The later has brought light to several security and privacy concerns that ITS analysts should consider when implementing various IT related services. This paper moves into this direction by identifying how risk analysis can interact with security and privacy requirements engineering world, in order to provide a holistic approach for reasoning about security and privacy in such complex environments like ITS systems. The key contribution of the paper is the conceptual alignment of three well-known methods (EBIOS, Secure Tropos and PriS) as the first step towards the design of a complete assurance framework that will assist analysts in designing safe and trustworthy ITS services.
QuantumSim: A memory efficient simulator for quantum computing

Classical computing has been evolving, to help solve harder problems. Following Moore’s Law the miniaturisation of transistors has helped improve performance. However, this has led to a ”Power Wall”. The clock frequency of processors have not been making the leaps predicted by Moore’s law. This is simply because the power dissipation becomes too high after a certain frequency. This, along with a few other factors, led the industry to move to multi-core processors and continue to homogeneous multi-core systems, multiple cores that are identical to each other, and heterogeneous, multiple cores that are not identical to each other. To further improve performance of computing systems, hardware accelerators were introduced. The one we are all familiar with is the GPU (graphics processing unit). As the name suggests it is an accelerator to process graphics. It is used extensively for image processing and is much faster at doing this than a CPU. There are many more types of hardware accelerators that offers high speedups for certain applications. Quantum accelerators are one such example. There are a certain class of problems that cannot be solved, or rather will take too long to solve on a classical computer, such that it is practically infeasible. One such problem is prime-factorisation problem.
Trajectory Planning of Autonomous Driving Vehicles Based on Road-Vehicle Fusion

With the development of artificial intelligence and communication technology, road-vehicle fusion technology has become the trend of future traffic development. High-precision trajectory planning is required for the operation of autonomous driving vehicles. This paper focus on the trajectory planning problem for autonomous vehicles driving in the region where objects are occluded. At first, the space-time map and Frenet coordinate system of the road are established. Through cooperative perception between the autonomous driving vehicles and infrastructure system, the platform of road analyzes the potential risks. Hybrid A* path planning improved for the speed planning generates the optimal trajectory. The proposed framework is implemented through simulations in accident-prone scenarios in this paper. The simulation results show that the trajectory and speed smoothness of the autonomous driving vehicle is improved, and the driving safety of the autonomous driving vehicle is enhanced in the road-vehicle sensing cooperative system.
Parallel decoding of multiple logical qubits in tensor-network codes

We consider tensor-network stabilizer codes and show that their tensor-network decoder has the property that independent logical qubits can be decoded in parallel. As long as the error rate is below threshold, we show that this parallel decoder is essentially optimal. Holographic codes are interesting examples of tensor-network codes, so we first test out this parallel decoding strategy on the max-rate holographic Steane (heptagon) code. For holographic codes with a constant number of logical qubits, the tensor-network decoder was shown to be efficient with complexity polynomial in $n$, the number of physical qubits. Here we show that, by using the parallel decoding scheme, the complexity is also linear in $k$, the number of logical qubits. We then calculate the bulk threshold (the threshold for logical qubits a fixed distance from the code center) under depolarizing noise for the max-rate holographic Steane code to be $9.4%$. We also introduce some further holographic error-correcting codes and calculate their thresholds under depolarizing noise. One example is based on an 11-qubit code due to Gottesman, which has asymptotic rate 0.114 and threshold 13.8%. Another code we consider is an asymptotically zero-rate holographic code, which performs extremely well under dephasing noise in the $X, Y$, or $Z$ basis with a threshold of $50%$.
Clustering of Scientific Activity of Faculty Staff Based on the Results of Publication Activity

The article is devoted to the study of the publication activity of the faculty staff, which is reflected in the world’s leading scientometric systems. The report presents tools for comparative analysis of two main scientometric indicators – the Hirsch index in the RSCI system and the number of publications indexed in Scopus and Web of Science. Significant features that characterize the faculty staff and their publication activity were determined, on the basis of which the clustering of scientific activities was carried out. As a result of cluster analysis, the faculty staff was divided into eight meaningfully interpreted clusters with similar characteristics of employees and their publication activity. A structural model of teacher transitions between selected clusters is presented. The drivers of the University’s scientific activity are only two clusters out of eight, in which the publication activity is most pronounced. Employees in the other clusters should also fall into these two groups. From a practical point of view, the presented results can be useful for management to assess the current state of scientific activity and its improvement. This approach is universal and can be carried out in any educational organization of higher education, regardless of its specifics. This will help universities to conduct self-analysis and manage the performance of departments, in particular, to improve the results of publication activity.
Cascaded Denoising Convolutional Auto-Encoders for Automatic Recovery of Missing Time Series Data

This paper proposes a kind of supervised cascaded denoising convolutional auto-encoders (CDCAE), aiming to accurately recover the missing load data in electric power system. The one-dimensional load data are reshaped as two-dimensional image for data enhancement, which enables the convolutional neural network (CNN) to understand the semantics of load data. Numerical results in comparison with similar day filling (SDF) clearly validate the effectiveness of the proposed CDCAE in accuracy.
The Role of Knowledge Management in Creating Competitive Advantage in Small and Medium-size Enterprises inthe Republic of Iraq

Knowledge management (KM) is a vital component of organizational strategies to increase competitiveness. This study identifies the impact of KM in creating a competitive advantage. The main purpose is to determine the level of appreciation of the concept among SME stakeholders in Iraq. Both qualitative and quantitative research methods were explored, 102 respondents were reached, and the results of the field survey were analyzed using SPSS for descriptive statistics analysis. The result shows that the application of KM is appreciated in Iraq, but its implementation is not yet at an optimal level. The research provides evidence from a developing country on the application of knowledge management in the management of small and medium-sized enterprises.
Virtual Touring for the Puglia Regional Museum Directorate

The article describes the design process carried out to deliver a tool for the virtual fruition of the resources of the Puglia Regional Museum Directorate through multimedia content, within the frame of the Vi.S.T.A. (Virtual and Social heritage Touring Application) project. Identifying the virtual tour as the most suitable technology to achieve the promotion and exploitation needs of the Directorate’s museums, the project envisages the realization of an integrated system, conceived as a dedicated IT platform including a specific virtual touring section, for a selected pilot case. The article focuses on the design study for the virtual tour interface and the selection of the most appropriate functions for it, and describes the collaborative approach adopted. After the description of the project objectives and context, the design study and the related methodology are presented. Then, the results of the design activity are presented and discussed.
In-Bed Human Pose Classification Using Sparse Inertial Signals

Recent studies on sleep reveal its impact on the well-being of humans. Monitoring of in-bed body postures can provide clinicians with early indicators of a wide range of musculoskeletal disorders. Current work on sleep pose classification is directed at non-wearable technologies, with issues associated to limited body observability and concerns over personal privacy; or on wearable sensors that consider only a small number of sleep poses and thus have limited generalisation. This paper proposes a novel method for wearable-based human pose classification capable of classifying twelve benchmark sleeping poses. To overcome the scarcity of labelled inertial data, a new data augmentation technique is proposed to generate realistic synthetic datasets emulating real-world conditions. An Error-Correcting Output Codes model is used to employ a multi-class classifier based on an ensemble of Support Vector Machine based classifiers. For system validation, a computer graphics simulator was used to accurately emulate data recording of in-bed body postures, leveraging on a standard articulated body file format commonly used by commercial motion-capture technologies. Experiments show superior performance (as high as 100% classification accuracy), and resilience to noise contamination beyond what could be encountered in reality.
Understanding Knotted Tensions in Purveying Pandemic Public Monitoring Technologies

Pandemic public monitoring technologies (PPMT) could be an integral building block of pandemic management. Yet their development and operation warrants reconciling the privacy vs. pandemic containment trade-off, which poses serious challenges to PPMT purveyors. In this paper, we decompose this trade-off into a complex problem comprising 10 knotted logical tensions (i.e., contradictory poles). To do so, we identify these tensions via a dialectical interrogation of 30 articles on PPMT purveyance, and we explicate the possible rationale behind each pole of each tension by playing devil’s advocate. We discuss several avenues for future research, including conceiving PPMT as transactional digital commons.
Chat Message Security Enhancement on WLAN Network Using Hill Cipher Method

Security is very important for the owner of the information, therefore the owner information must have good security so that the information they have cannot be owned by unwanted people. Information exchange can happen anywhere with various media exchanges. Exchange information using various networks. Among LAN networks, WLANs, WAN and so on. In exchange for WLAN networks, WLAN networks have many disadvantages and lack both in speed of delivery data and data security. In exchanging data using WLAN, Information thieves can infiltrate IP addresses on the WLAN network can steal and have the same access as WLAN network users another. Therefore we need a technique that can securely exchange information (chat messages) on the WLAN network. The author makes a system that can be used to secure message exchange information chat on WLAN networks with cryptographic techniques namely Security Of Chat Message On WLAN Network By Using Hill Chiper Method With this system the security of chat messages can be better.
COVID-19 and Cloud Computing, the Leadership Paradoxes of Decision Making Acceleration and Data Regulation

During Covid-19, decision makers realized the stressing need to transform their businesses in challenging setups characterized by urgency and need of rapid innovation and adaptation.Moreover, and specifically in emerging markets, leaders face challenges that are paradoxical in nature; those challenges affect society and businesses.As a consequence, reaching the level of a data driven and technology enabled high performance organization becomes critical and urgent.In this context, the leadership decision making to quickly adopt cloud paradigm while considering paradoxical factors becomes a flagship to survive businesses during Covid-19.Based on an inductive qualitative research, we try to clarify what do decision makers do to accept cloud computing in light of data sovereignty, and in contexts characterized by paradoxical factors.We try to identify what are these factors and how they relate to each other; and we try to answer the question of how Covid-19 effect the decision-making process.The research is relevant because we don't know much about the relationship among decision making, data and technology (Chaara et al 2019) and how they relate to pandemic challenges.Additionally, the research will help organizations accelerate their digital transformation during Covid-19 in light of the current policies and regulation about data sovereignty.
VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.
Variant Satisfiability of Parameterized Strings

Two “knowingly incomplete,” yet useful, variant-based satisfiability procedures for QF formulas in the instantiations of two, increasingly more expressive, parameterized data types of strings are proposed. The first has four selector functions decomposing a list concatenation into its parts. The second adds a list membership predicate. The meaning of “parametric” here is much more general than is the case for decision procedures for strings in current SMT solvers, which are parametric on a finite alphabet. The parameterized data types presented here are parametric on a (typically infinite) algebraic data type of string elements. The main result is that if an algebraic data type has a variant satisfiability algorithm, then the data type of strings over such elements has a “knowingly incomplete,” yet practical, variant satisfiability algorithm, with no need for a Nelson-Oppen combination algorithm relating satisfiability in strings and in the given data type.