Reviewerly/exHarmony huggingface dataset:
Method of Structural Functional-Value Modeling of a Complex Hierarchic System

The method of structural functional-value modeling of complex system is offered. It is based on a polynomial approximation of the dependence of the value of subsystems on the level of their functional suitability. The initial data for calculations on the functional-value model of a complex system with serial or parallel interconnection of subsystems are formulated and determined. The choice of an indicator of the structural functional perfection of a complex system and the way of defining the function of the value function of its subsystems are grounded. The order and specific features of structural functional-value calculations of a complex system for serial and parallel subsystems interconnection are developed. The effectiveness of the developed approaches and algorithms is tested in the functional-value modeling of specific complex systems: an educational institution (serial subsystems) and an object monitoring system (parallel subsystems). The minimization of the value of a complex system, provided that it fulfilled its functional purpose at a given level, was carried out by the Lagrange multiplier method. The issues that are solved in the value rationalization of the proposed method include following: ensuring a given level of functional perfection of the system at its minimum value; structural improvement of a complex system due to the choice of a subsystem for which the improvement of the functional perfection of the whole system can be carried out with minimal value. The researches made it possible to formulate the rules of structural rationalization of a complex system. This method is adapted to application at different levels of a priori uncertainty of the source data and can be useful at all stages of the existence of a complex system: development, operation, utilization. In addition, it can be used to investigate poorly formalized and non-formalized complex systems, that is, it enables a qualitative analysis of a complex system to be translated into a quantitative one.
Complaint management on Twitter – evolution of interactional patterns on Polish corporate profiles

Abstract The study concerns complaint management on Polish brand profiles on Twitter. The aim was to investigate selected language properties of corporate tweets and trace potential changes in the interactional patterns on the profiles occurring between 2015 and 2017. The study focuses on the structure and frequency of the respective strategies, as well as formality, the use of non-standard structures and emoticons. The study indicates the following directions of change: an increase in the use of address terms and explanations, a greater degree of language formality and indirectness, among others. The changes point to increased formulaicity and conventionalisation of expression, as well as an increased use of fixed patterns and templates. The changes constitute evidence of standardisation of the means of expression in customer encounters and a transfer of the conventions typical of other channels of interaction with consumers to the online context.
Informing the On/Off-prem Cloud Discussion in Higher Education

The proliferation of external vendors providing public cloud services has resulted in a push to migrate away from on-premise compute and storage resources. The desire to move capital costs to operational costs, promises of lower maintenance and operational costs, lower failure rates, and ease of use have all played a part. However, for larger organizations with recurring and predictable usage patterns it needs to be investigated whether an on-premise and dedicated computational cluster might indeed remain more appropriate and cost-efficient. In this work, we assess various typical use-cases and provide a Total Cost of Ownership calculator to compare the costs of an on-premise cloud with that of an external cloud vendor.
Applications, Analytics, and Algorithms—3 A’s of Stream Data: A Complete Survey

Recent decades have shown tremendous growth in both hardware and software. Today, data is generated everywhere from schools, colleges, hospitals, institutions, industries, supermarkets, railway stations, traffic system, communication industry, and so on. Most of this data is generated through digital devices and electronic gadgets. This digital flooding paved the way for business analytics where the data can be analyzed on the business perspective to identify the needs and scope of the consumers and thereby to increase the profit margin. Particularly nowadays, streaming data is generated abundantly everywhere. Storing, processing, and analyzing stream data in real time are a major challenge today. Time-critical applications generate fast streams of temporal data. Analytics with stream data is meaningful only if there is quick and immediate response. Delayed response is of no use in the case of stream data analytics. This paper does an extensive study of the applications, analytic methods, and algorithms that can be applied on continuous streaming data to achieve better performance. A comparative analysis of the traditional, deep learning and reinforcement algorithms is also described. Finally, the challenges in handling stream data are analyzed and defined along with its future scope.
Implementation of Clustering and Similarity Analysis for Detecting Content Similarity in Student Final Projects

Abstract To finish study, students are requested to submit final projects. In some universities, the final projects are not necessary to be submitted for publication. The final project reports are stored in a local database. As the number of final projects is growing in the local database, similar contents may exist among the documents. The commercial tools cannot be used to detect the content similarity since the documents are not published. This paper proposed a system to detect content similarity in documents that are stored in a local database. Considering the number of stored documents, this similar content detection system implements two step processes. First, clustering documents to find most related documents. Second, finding content similarity among the selected documents. The experiment results show that the system is successfully clustering documents and detecting content similarity by implementing TF-IDF and Cosine Similarity algorithms. This system is limited to proceed documents that are written in Bahasa.
Hybrid Quantum Interferometer in Bifurcation Mode as a Latching Quantum Readout

We develop a type of magnetometer consisting of a hybrid quantum interference device (HyQUID) that is set in a bistable state. We demonstrate its operation in a latching mode that can be employed to measure small changes in the applied flux. The device can be used to probe the flux state of a superconducting circuit using straightforward electrical resistance measurements, making it suitable as a simple qubit readout with low back action.
Single Image Super-Resolution via Similarity Between Spatially Scattered Features

The development of convolutional neural networks (CNN) has remarkably improved the current research on single image super-resolution (SISR). Several high-quality studies have been performed on reconstruction accuracy and perceptual quality, which are the two main issues in SISR. Nevertheless, numerous problems in SISR remain unsolved. SISR is inherently an ill-posed problem owing to insufficient information, and as the scale factor increases, the lack of information becomes even more pronounced. We have studied ways to solve the local characteristics of CNN to deal with additional useful information. A CNN uses a convolution layer designed based on local features, and repeatedly accumulates these features to expand a receptive field. We have explored network structures that can directly handle global information even at lower layers, which are not covered by the receptive field of a CNN. In this paper, we propose a non-local attention SISR network (NASR) that generates and utilizes the globally scattered similarity information of features. In addition, we propose a very deep architecture based on dense blocks that does not suffer from gradient vanishing without any normalization. Experimental results on standard benchmark datasets indicate the effectiveness of the proposed network, which exhibits state-of-the-art performance in terms of reconstruction accuracy and perceptual quality.
Defining language dependent post-editing guidelines for specific content

Abstract The rapid development of neural machine translation systems and the emergence of the e-book have broadened the scope of text types that can be translated by machines. At the early stage of the machine’s infiltration into the translation field, target texts were mainly technical texts such as patents, instruction manuals, etc. Literary texts have been considered as the last bastion of human translation because the machine translation (MT) has produced word-for-word translation, unsuitable for literary texts with distinct stylistic elements. However, it turns out that the field of literary translation was not immune to the rise of MT. Style is one of the critical elements in literary texts, but it has been dismissed in the existing MT post-editing guidelines. Therefore, this research attempts to provide methodological ideas about how to come up with a machine translation post-editing guideline (MTPE) for style improvement especially for language pairs with divergent syntax and semantics like English and Korean. First, the linguistic and cultural differences in writing styles are sorted out based on previous research. Second, the different ways in which human translators address writing style are investigated. Third, the strategies that human translators employ in their translations are applied to machine translation post-editing to demonstrate how the strategies can be incorporated into English-Korean MTPE to improve style. This preliminary research would lay the groundwork for refining post-editing style guidelines and for accumulating manually post-edited data for style improvement, which would be conducive to building and customizing automatic post-editing systems.
Objects Detection and Recognition in Videos for Sequence Learning

A key challenge to sequence learning for video comprehension is objects detection and localization in dynamic and real-time environment. This paper presents two methodological approaches to autonomous and generic object detection and localization in video sequences. Algorithms for both facial and non-facial object localization, as well as their integration, are developed. A set of experiments and case studies for practical video image processing is demonstrated for sequence learning. This work paves a way to sequence learning towards enhanced computer and robot vision technologies in applications of self-driving cars and real-time facial recognition.
Decentralizing AI Using Blockchain Technology for Secure Decision Making

Lately, Blockchain and artificial intelligence (AI) have become two of the most popular and revolutionary technologies. Blockchain, the key technology behind Bitcoin, is a distributed database that allows transactions to be recorded in an incorruptible and non-modifiable manner. With smart contracts, Blockchain can be used to provide a decentralized/distributed environment where multiple parties can interact with each other without any trusted third party. On the other hand, AI enables machines to have intelligence and decision-making capabilities just like humans. This paper presents a thorough survey on how Blockchain technology can be used for empowering AI. We review the literature and summarize how Blockchain can make AI secure and efficient.
Learning of the «Thinking and Writing» Сourse at a Pedagogical University: Concept and Forms of Work

The article is devoted to conceptual and methodological provisions of the «Thinking and Writing» course, introduced into the curriculum of first-year students of all subjects at the Moscow Sity University. The сourse was created at the intersection of such disciplines as contemporary Russian language, speech culture, philosophy, logic, rhetoric, methodology, psychology, and cultural studies. This determined the principles of material selection and its unconventional combination, as well as the forms of practical assignments. Based on the methods of analysis, comparison and experiment, we identified two fundamental principles of the course organization: positioning within the framework of the thought activity and reflection. Five blocks of topics are practiced at interactive seminars, where a student can take the position of a workshop author, due to a system of varied research, analytical and creative tasks of different competency zones, the trajectories of which students determine independently. Students' reviews of the course after its testing confirmed it's significance. Shifting away from traditional lectures to various interactive forms of working with students, during which they are involved in a collective intellectual search, provides great opportunities for the development of thinking, as well as for self-development through reading, writing and public speaking, which corresponds to the global educational model of free arts and sciences currently implemented in Russian education.
Sequent-Type Calculi for Three-Valued and Disjunctive Default Logic

Default logic is one of the basic formalisms for nonmonotonic reasoning, a well-established area from logic-based artificial intelligence dealing with the representation of rational conclusions, which are characterised by the feature that the inference process may require to retract prior conclusions given additional premisses. This nonmonotonic aspect is in contrast to valid inference relations, which are monotonic. Although nonmonotonic reasoning has been extensively studied in the literature, only few works exist dealing with a proper proof theory for specific logics. In this paper, we introduce sequent-type calculi for two variants of default logic, viz., on the one hand, for three-valued default logic due to Radzikowska, and on the other hand, for disjunctive default logic, due to Gelfond, Lifschitz, Przymusinska, and Truszczyński. The first variant of default logic employs Łukasiewicz’s three-valued logic as the underlying base logic and the second variant generalises defaults by allowing a selection of consequents in defaults. Both versions have been introduced to address certain representational shortcomings of standard default logic. The calculi we introduce axiomatise brave reasoning for these versions of default logic, which is the task of determining whether a given formula is contained in some extension of a given default theory. Our approach follows the sequent method first introduced in the context of nonmonotonic reasoning by Bonatti, which employs a rejection calculus for axiomatising invalid formulas, taking care of expressing the consistency condition of defaults.
Dense Crowd Counting with Capsule Networks

In this paper, we proposed and evaluated the adoption of a capsule network-based (CapsNet-based) model rather than the convolutional neural network-based (CNN-based) models which are predominant in crowd counting tasks. The aim is to join the task of generating a high-quality density map from a single image along with producing a more precise estimate of the number of people. CapsNet-based model has a strong capacity of representation and a powerful dynamic routing mechanism that could address the drawback of a limited number of training samples. The replacement of the scalar values of CNN by vectors when using a CapsNet allows learning more discriminative features, which contributes to generating high-quality density maps, and thus a more precise number of individuals in crowd scenes. Experimental results show that our proposal presents competitive results concerning state-of-the-art, but with a 59.2% reduction in the number of parameters.
Initial clinical evaluation of image fusion based on rigid registration and supporting percutaneous liver tumor ablation

The purpose of this article is to present and evaluate a proposed rigid registration method in the application of real-time fusion of pre-operative 3DCT images and intraoperative 2DUS images. A universal hybrid automatic rigid registration method was proposed, which allows registration of preoperative contrast enhanced CT with intraoperative 2D ultrasound images (neither CEUS nor 3DUS is required), with the possibility of manual correction. The method is based on automatically detectable markers, clearly identified in preoperative CT images, and by optical position tracking system during the procedure. A two-step fusion accuracy assessment was used. The first stage, the initial fusion assessment, was carried out at the time of the procedure. The second, objective stage (the final fusion assessment) was carried out after the procedure, and was based on the image and location data collected during the procedure. The clinical evaluation of the method was performed on 20 patients. For IFA and for Fusion Stability Assessment evaluation steps the following results were obtained, respectively: no fusion disorder: 10, good overlay: 8, permanently wrong fusion: 2 and no fusion disorders: 8, short-term fusion disorders: 9, frequent fusion disorders:3. The RMSE descriptive statistics (presenting order: median (first quartile third quartile) [min max]) was 8.87 (6.46 12.76) [5.04 18.84] mm. The results are qualitatively comparable with results obtained in other independent research, quantitatively comparable in accuracy, achieving mostly better results in terms of preparation time consumed and operating in real time, which justifies the possible clinical usage of the proposed method.
Self-tuning serverless task farming using proactive elasticity control

Abstract The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in science and industry. Whereas Infrastructure as a Service (IaaS) offerings have been commonly employed, more recently, serverless computing emerged as a novel cloud computing paradigm with the goal of freeing developers from resource management issues. However, as of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other and benefit from on-demand and elastic compute resources as well as per-function billing. In this work, we discuss how to employ serverless computing platforms to operate parallel applications. We specifically focus on the class of parallel task farming applications and introduce a novel approach to free developers from both parallelism and resource management issues. Our approach includes a proactive elasticity controller that adapts the physical parallelism per application run according to user-defined goals. Specifically, we show how to consider a user-defined execution time limit after which the result of the computation needs to be present while minimizing the associated monetary costs. To evaluate our concepts, we present a prototypical elastic parallel system architecture for self-tuning serverless task farming and implement two applications based on our framework. Moreover, we report on performance measurements for both applications as well as the prediction accuracy of the proposed proactive elasticity control mechanism and discuss our key findings.
A secured TPM integration scheme towards smart embedded system based collaboration network

To achieve more powerful task processing capabilities, the smart embedded systems (SES) are interconnected via wireless network to form a collaboration system. However, due to the limitations on system hardware, the SES device are usually built with the consideration of software functions instead of enough security mechanisms, that exposes the SESs under the security threats from malware or malicious users, such as software or data tampering. To address this issue, a Trusted Platform Module (TPM) is brought in the SES device to guarantee the integrity of the system, with which any unauthorized modifications towards the SES system can be detected by measurement operations of TPM. However, from the perspective of the external visitors, a SES collaboration network performs as a complete system. Thus, to unify the root-of-trust of the network, all the TPMs need to be integrated into a logical one, which can provide more efficient way to attest the external visitors. This brings two distinct advantages: (1) any nodes of the network can be the access node for the visitor, and (2) once a visitor has been successfully attested, it can access the network via any nodes without extra attestation. To achieve TPM integration, we have proposed five protocols to orchestrate the distributed TPMs, including Synchronization Protocol (SYNP), Node Accessing Protocol (NAP), Crossing-Node Access Protocol (CNAP), Updating Protocol (UPDP) and Node-Removing Protocol (NRP). We have built a prototype system composed of Raspberry Pis and Infineon TPM2.0 chips, in which these protocols are implemented and deployed. Then, we evaluate the protocols’ performance on time consumption, and the results show the feasibility and availability of these protocols. Finally, our analysis on experimental results gives the guidance for appropriate use of these protocols.
Using the concept of instance typicality in instance-based learning environments involving nominal attributes

Instance-Based Learning (IBL) is a machine learning research area with focus on supervised algorithms that use the given training set as the expression of the learned concept. Usually the training instances in the set are described by vectors of attribute values and an associated class. The general ization process conducted by an instance-based algorithm happens during the classification phase, when a class should be assigned to a new instance of unknown class. Attributes that describe instances can be of different types, depending on the values they represent and, usually, can be of discrete or continuous type. A subtype of the discrete type is known as nominal. An attribute of nominal type usually represents categories and there is no order among its possible values. This paper proposes and investigates an alternative strategy for dealing with nominal attributes during the classification phase of the well-known instance-based algorithm NN (Nearest Neighbor). The proposed strategy is based on the concept of typicality of an instance, which can be taken into account as a possible tiebreaker, in situations where the new instance to be classified is equidistant from more than one nearest neighbor. Experiments using the proposed strategy and the default random strategy used by the conventional NN show that a strategy based on the concept of instance typicality can be a convenient choice to improve accuracy, when data instances have nominal attributes among the attributes that describe them.
Inferring Political Preferences from Twitter

Sentiment analysis is the task of automatic analysis of opinions and emotions of users towards an entity or some aspect of that entity. Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate and improvise their weaknesses far before the actual elections. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. The amount of data generated is much large to study, analyze and draw inferences using the latest techniques. Twitter is one of the most popular social media platforms enables us to perform domain-specific data preparation. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning. The tweets related to the Delhi Elections in 2020 are extracted and employed for the task. Among the several algorithms, we observe that Support Vector Machines portrays the best performance.