Reviewerly/exHarmony huggingface dataset:
Particle Filtering Approach for Data Association

An initial work has been performed to implement a sequential Monte Carlo method to solve the data association problem. The main motivation is to overcome the incorrect association when the state estimates are inaccurate. The solution is based on modeling the data association as a stochastic variable and estimated with a bootstrap particle filter. Two variants of the proposal function are evaluated, one with the uniform distribution over possible associations, and the other one with the distribution depending on the measurements and state estimates. The performance of both proposals is evaluated on the small simulation example, and compared to a purely deterministic approach, Nearest-Neighbour, as well. The obtained initial results are quite promising, and more evaluation and expansion to more examples and real data sets is suggested for the future work.
Interactive sound art and animated notation as an ensemble performance platform in primary level music education

This article showcases excerpts of my artistic research in progress. In particular, I demonstrate how an interdisciplinary approach combining knowledge from the fields of artistic research, Human Computer Interaction (HCI) and game studies mayinspire primary level music education of today. I highlight examples of novel digital music technology and present an innovate approach to using these for group performance exercises in the music classroom. In particular, I report from a study where my interactive sound art exhibition LINES, in combination with animated music notation, was used as a digital ensemble music platform. The data of the study comprises five workshop sessions with pre-schoolers using this platform. An autoethnographic method and analysis of video documentation of the workshop sessions were used as methods for the study. The results showed that LINES was both engaging and easily accessed. Moreover, it allowed the majority of the target group to perform musical exercises as an ensemble. I argue that the use of traditional instruments and traditional notation creates a democratic issue in primary level music education. Furthermore, with support from the study and related research, I argue that platforms such as this may democratize music education involving pupils aged 5-15 years.
The Role of Working Memory in the Search-Sensemaking Process

The goal of this study is to advance our understanding of the cognitive mechanisms underlying search-sensemaking by examining the process through the lens of working memory (WM). I aim to extend the existing literature on search and sensemaking by incorporating the role of WM in the process. While WM plays a critical role in complex cognitive tasks, little is known about how it manifests in users' search-sensemaking while they are working with information in a complex information space such as the Web. A mixed-method design will be adopted, consisting of an experimental study and interviews. Adults with different levels of working memory capacity (WMC) will perform a learning assignment task in a lab. The combined data of screen activities and think-aloud comments will be analyzed to investigate the differential patterns in search-sensemaking between high- and low-WMC individuals (RQ1). An evolving working document will be considered an external representation of their knowledge structure and be analyzed to investigate the differential patterns in conceptual changes between high- and low-WMC individuals (RQ2). Finally, a post-session interview will be conducted to identify a range of cognitive strategies used during the task (RQ3). The proposed study will build upon the cognitive process model of sensemaking and the literature on executive functions and WM. The study will help us better understand the cognitive process of search-sensemaking from the perspective of WM. The findings can also inform design decisions for systems that can facilitate search-sensemaking while supporting users with varying levels of WMC.
A Time-varying Neural-network-based ARX Model of the Traffic in Mobile Networks

This paper presents a time-varying discrete-time neural-network-based auto-regressive model with exogenous inputs (NN-ARX), which is continuously updated using the online sequential extreme learning algorithm (OS-ELM). The model exhibited a consistently satisfactory performance when describing the dynamics of the payload as a function of users, from real trac data of two stations in a 4G-LTE network. Some advantages of the proposed NN-ARX model include: (i) it provides more modeling flexibilty than classical nonlinear-regression-approaches, (ii) it has adaptive capabilities to characterize the time-varying-behavior of dierent cells and (iii) it has low computational cost, which makes the model suitable for the implementation of optimization and control strategies to improve the performance of the network.
Crossing the Hermeneutic Gap

Design is often described as a synthesizing and solution-oriented discipline that deals with ‘wicked problems (Horst Rittel and Webber 1973). However, experiences from practice in design research tells and shows that analysis and synthesis are not two distinct separable modes of working. In this chapter we focus on one of the main activities of design, termed variously as ‘the creative leap’ (Verganti 2009), ‘jumping the hermeneutic gap’ (Hallnäs and Redström 2006), ‘frame creation’ (Dorst 2015), ‘building creative bridges’ (Cross 1997), ‘design is intentional change in an unpredictable world’ (Nelson and Stolterman 2003). They are all different notions and description of the same phenomena: how solutions in design practice escapes deductive reasoning and how design proposals will need to jump, leap, build a bridge and face insecurities that stem from making suggestions for the future we cannot fully predict. We propose drifting by intention. It is not black magic (Tracee Wolf et al. 2006), but basic human skill (Kees Dorst 2015; Nigel Cross 2018) that is exercised and trained in educational systems and brought to high levels of sophistication in research and professional practice.
Implementation of high speed rainbow table generation using Keccak hashing algorithm on GPU

This paper proposes the implementation of high speed rainbow table generation using Keccak hashing algorithm with the integrated development environment CUDA for GPU in the heterogeneous GPU+CPU system. Utilizing the GPU's powerful capacity, the algorithm greatly improves the performance of rainbow chain generation by dispatching the pre-computation of rainbow chain to each GPU thread. The table generation speed on GPU+CPU system and CPU was compared by the configuration of chain length and number of chains in this paper. In addition, the password coverage rate of table generated by the proposed reduction function was evaluated.The speed-up of pre-computation on GPU GeForce GTX 1080 outperforms that on CPU Xeon E5-1620 v4 3.5GHz by 239 times when the chain length is 200.
A new method for predicting the shale distribution of the Wufeng Formation in the Upper Yangtze Region, China

Taking the Late Ordovician Wufeng Formation (WFF) shale in the Upper Yangtze region as an example, we conducted a lithofacies distribution, thickness quantification, and paleo-topographic reconstruction of the Late Ordovician graptolite zones. Specifically, we focused on the Late Katian Dicellograptus complexus and the Early Hirnantian Metabolograptus extraordinarius within a chronostratigraphic framework, using the Geographic Information System (GIS) and 310 stratigraphic sections (incl. drilling) obtained from the Geobiodiversity Database (GBDB). Reconstruction of the geographic distribution indicates that the WFF and the synchronous sediments in the Upper Yangtze region contain 8 litho-stratigraphic units, which are geographically distributed across 7 provinces/municipalities and do not exhibit significant variations in lithofacies. The black graptolite shale extends in a broad swath from east to west within the basin, while the other lithofacies deposited during the same period are present on the periphery of the basin. All these strata were deposited in a normal neritic epicontinental sea environment, except for the flysch sediments in the southern Hunan area. The thickness reconstruction involves a comparison of three spatial interpolation methods, including Inverse Distance Weighting (IDW), Kriging, and the Radial Basis Function (RBF). Based on a general verification, IDW is considered to be the optimal method since it has the minimum standard deviation and variance. Based on the contours obtained from the IDW model, the WFF black shale is estimated to have an overall area of 0.67 × 10 6 km 2 , an average thickness of 6.2 m, and a total volume of 3902 km 3 . This shale was deposited over a 2.83 Ma period. Therefore, the volume of shale deposited per million years is estimated to be 1379 km 3 /my and the average thickness of shale deposited per million years is 2.37 m/my. The Hirnantian paleo-water-depth values obtained using 275 sections were used to infer the Late Katian paleo-topography. These results suggest that the Yangtze platform was surrounded by ancient highlands to the west, south, and north, exhibiting a paleo-geographic framework characterized by one uplift and four depressions. This setting blocked water circulation, causing the water to be contained and forming a closed and restricted marine environment, which was one of the major factors controlling the deposition of the organic-rich WFF shale. With the advent of the big data era of geology, the methodology of GIS-based technology is readily exportable to any resource play having spatial distribution pattern. Results can be provided rapidly and efficiently generated from geological data.
Modeling Content Interaction in Information Diffusion with Pre-trained Sentence Embedding

Social networks have become indispensable parts of our daily life, and therefore understanding the process of information diffusion over social networks is a meaningful research topic. Usually, multiple pieces of information do not spread in isolation; rather, they interact with each other throughout the diffusion process. This paper aims to quantify these interactions by modeling users' forwarding behavior after reading a series of information. Inspired by several successful components prevalent in recent research of deep learning, i.e., long short term memory (LSTM) network and bi-directional encoder representation from transformers (BERT), we designed IMM Enhanced model and InfoLSTM model. In our experiments on real-world Weibo dataset, both models significantly outperform baselines such as the prior IMM model and IP model, with IMM Enhanced model improving 23.52% and InfoLSTM model improving 32.56% in F1 score (absolute value) compared to that of baseline IMM model. In addition, we visualize the dataset and the parameters learned in IMM Enhanced model, which further enables us to discuss the relationship between text similarity and information diffusion interaction with case studies.
Application Research of Business Rules Engine Management System Based on Drools

This paper describes the design of a business rules engine management system based on Drools. Through the visual encapsulation of Drools, users can use a way similar to natural language to describe the originally complex, complicated and professional rules, and through the abstract configuration of business objects and parameter objects, the system can meet the business requirements of different business scenarios. In the whole life cycle management of rules, parameter table and parameter data, the system supports users to control the change of data state through three-level approval mode, so that users can conveniently carry out the configuration and management of various data in the system. At the same time, the system will record each key operation log, in order to facilitate enterprises and institutions to carry out system audit.
Referential Transparency of Motivated Public Authorities' Names in Translations

Abstract The paper focuses on adequacy and clarity of motivated public authorities' names in translation of constitutional acts. In order to assess the adequacy level of identification of the authorities' name references in the source legal system, a survey has been conducted. Its results allowed to conclude, that literal translation of authorities' names in incompatible systems can be referentially empty, even for persons with field knowledge. However, it can generally be assumed, that the sufficient identification is determined by an adequate level of knowledge about the given subject (entity) in the source legal system. It is functional translation that offers a significant simplification in the process of decoding the terms used to denote authorities' names.
Relation between Entropy and Peak Signal to Noise Ratio in Prediction Error Expansion Considering Region of Interest

Infusion of maximum information with minimum distortion in the message to be transmitted is one of the major intent of a communication system as well as digital watermarking. This paper presents a relation between Entropy (Information Theory) of the message to be inserted (payload) and the Peak signal to noise ratio (PSNR) for reversibly watermarked gray scale medical images. A performance evaluation using prediction error expansion in accordance with Region of interest (ROI) is implemented. A direct as well as an inverse liaison between the Entropy and Peak signal to noise ratio for different black is to white pixel ratio is concluded from the results.
A Survey on Recurrent Neural Network based model for solving the Assignment Problem

Assignment problem refers is the way of calculating a problem by assigning certain quantity of work to equal quantity of work handles inorder to obtain utmost optimized results. It occurs when one manages to solve a large problem to obtain optimized results and in the meantime fulfilling all the related constraints. Numerous ways on how assignment problem is solved has already been found but with the use of Recurrent Neural Network is a complete game changer. Although assignment problem is a method for optimizing a problem, incorporating Recurrent Neural Network leads to a more reliable, efficient and faster way of finding the most optimized result of the problem. This review is based on how assignment problem is solved effectively by using Recurrent Neural Network and discusses the various types of Recurrent Neural Network used in depth.
Video Classification Based On the Improved K-Means Clustering Algorithm

Abstract To solve the problem of low accuracy of video classification, this paper proposes an improved k-Means algorithm, which is used as a classifier to realize video classification. Firstly, video segmentation is carried out to extract the visual features of the video and form a group of visual features. The traditional k-Means clustering algorithm is improved to form initial clustering points with labeled video samples, optimize the objective function, and further optimize the clustering results of the video. Several experiments show that the proposed classification algorithm has high classification accuracy.
Recommender systems effect on user' choice behaviour

Recommender systems (RSs) research is so far focused mainly on improving recommendations' quality and precision. However, it is also important to discover the effect of RSs on users' behaviours. So far, very few researches have analysed the effect of RSs on users' behaviours. Therefore, not much knowledge about this essential topic is obtained. Hence, in this PhD researchwe focus on RSs' effect on users' behaviours. In order to investigate this effect, we propose a simulation procedure of users' choices under the influence of a RS. Then we measure metrics on users' choices that capture RSs effect. Moreover, we propose to conduct online experiments to study the effect of RSs by designing web-based platforms tracking the choices of the users in systems that offer also recommendations to the users.
Information Security Meets Adversarial Examples

Machine learning (ML) plays a pivotal role in supporting a variety of research problems at the core of information security and forensics (IFS). ML-based systems have improved overall performance in many applications, replacing more and more deterministic rule-based approaches with data-driven ones. Yet, enforcing their dependability in typical IFS scenarios that include strategic adversaries is a challenging open problem.This special session aims at strengthening the link between concepts and methods developed in the adversarial ML and IFS domains. To this end, it gathers contributions exploring implications of and countermeasures to adversarial attacks against learning systems. Special emphasis is placed on media security applications.
Using a Neural Network to Convert a Radio Signal from an RTL-SDR Receiver to Text

Voice recognition technology is now becoming increasingly popular. By translating an oral speech into text, people receive information for study, presented in a convenient format for working with it. This article deals in detail with interaction with RTL-SDR to obtain a digital signal with recording radio conversations and further converting them into a text document. The aim of the study is to find and implement a method that effectively solves the above-mentioned tasks. The problems associated with a large amount of audio information, which takes up a lot of memory resources for processing, are analyzed. The use of a neural network as a means of recognizing audio information is considered: this method provides the optimal way to obtain a finished text document while minimizing the steps of work and its execution time. The results obtained are analysed and conclusions are drawn on the further development of the study.
Using gelatin dactyloscopic foils and computer program Adobe Photoshop in expert analysis of crossed lines

This paper presents the method of crossed lines expert analysis developed and ap­plied in Serbia and other countries in the region by Miroslav Busarčević, a court expert with a degree in physical chemistry and MSc in criminalistics. The authors of the paper explain the procedure of lifting the prints of crossed lines using gelatin dactyloscopic foils, the scanning and visualization of the obtained print in the Adobe Photoshop com­puter program; presentation and interpretation of the results.
A method to reduce the computational cost of Modified Hausdorff Distance in Face Recognition

The Hausdorff distance (HD) is a metric to measure the similarity between two sets of points. Modified Hausdorff distance (MHD) method in face recognition is the applying of the Hausdorff distance to find the correlation between an input image and the model images in the database, thereby giving the best-matched image. It gives high accuracy in comparing with prevalent methods. However, the calculated volume in this method is very large, not suitable for high-speed identification systems with huge databases. In this paper, we propose a new method to reduce the computational complexity of MHD in face recognition. Firstly, we vectorized the dominant points (DPs) extracted from the face image based on the correspondence between them. Then, instead of completely scanning all points, our method quickly computes the component distances of MHD using the angles of representative vectors corresponding with DPs. Experimental results indicate that the complexity of the proposed method has improved by 8.41-8.75 times decrease. Besides, the accuracy of the proposed method in face recognition is also compared with the MHD method under varying conditions of the faces: 1) ideal condition, 2) various lighting conditions and 3) different poses of the head. The performance of the proposed method has been higher than the initial method under the ideal condition and various lighting conditions, and as equivalent to the original method under another condition.
Efficient Implicit Content-based Image Re-ranking Approach

This paper presents a new image re-ranking approach that can implicitly improve the retrieved images based on the file’s contents and some user-specific actions. In more detail, multiple descriptors are used to describe image files accurately and they do not require user intervention or tuned parameters. Furthermore, each of these descriptors has a weight, which affects the file rank. Unlike existing approaches, descriptor weight is assigned dynamically and changes from one file to another based on the percentage of differences found by the descriptor. Hence, the developed weight mechanism improves the chance of getting the required files significantly. The performance of the developed approach is investigated through several experiments and it has been observed that the approach has the ability of showing the most relevant files at the top of the query results and increases the percentage of the retrieved relevant files.
Technologies of visualisation in the modern school (an example of natural sciences and mathematics)

The article discusses the methodological aspects of the modernization to the subjects of nature-mathematical cycle of pre-service teachers to the use of visualization technologies. The specificity of the implementation of classical didactic principles (visibility, science, systemacy and consistency, the connection of theory with practice, mindfulness and strength of learning) in the professional training of specialists in terms of the use of visualization technologies is considered. The specific didactic principles of preparing the future teacher for the use of visualization technologies are highlighted. The principle of innovation, which provides for training students to use the newest capabilities of visualization technologies in their future professional activities; the principle of aesthetics, which consists in teaching students to create didactic visual aids with a pedagogically effective use of color and typographic solutions, the laws of composition in accordance with the psycho-physiological characteristics of the child for the effective assimilation of educational material and its most attractive perception; The principle of orientation to cloud services, due to the rapid development of hardware and software, which is based on teaching students to create digital didactic visual materials using modern online tools.
Cyber Harassment and the Scope of Freedom of Speech

Cyber harassment in its various forms imposes substantial costs on many persons. Discussion of those costs is plainly important in mobilizing support for legal and cultural responses to cyber harassment. It is important to recognize, however, that most forms and instances of cyber harassment do not implicate any of the commonly cited reasons for constitutionally protecting speech in the first place. Where any such reasons are in fact implicated in the cyber harassment cases, it is typically the meaningful autonomy and self-realization interests of the targets, rather than those of the harassers, that are at significant risk.
Toward Quality Multimorbid Health Questions for Online Information Seeking in Q&amp;A Sites

There is an increasing population of health information consumers (HIC) with multimorbidity. Previous studies explored the online information behavior of patients in general or HIC with a specific disease; however, the online information behavior of multimorbid HIC remains poorly researched. The focus of my dissertation research is on the online information behavior of HIC with multimorbidity. Specifically, I aim to explore the online interaction and information seeking behaviors of HIC across different types of Q&A sites. In addition, my dissertation aims to investigate the distinctive features of questions quality posed in Q&A sites from multiple aspects including content, language, and affect. The findings provide implications for designing online Q&A sites to better support health information seeking for HIC with multimorbidity. Further, I aim to develop guidelines for assisting HIC in improving their question quality to meet their health information needs.