Miracle-dz/UniFAR-FTUs huggingface dataset:
Target recognition based on phase noise of received signal

By using the effects of non-ideal oscillators, a technique is proposed to discriminate targets in continuous wave (CW) tracking radar. This method is based on the fact that each oscillator has an individual effect on the phase noise that helps to recognise real targets from the simulated ones. A simulated fake target, made through complex circuits, e.g. a digital RF memory, has a different phase noise from a real backscatter. Therefore, the transmitted and received signals are different in phase noise levels and patterns. To evaluate the performance of the proposed technique, the probability of detection (P D) is shown in different signal-to-noise ratios, ranges, and signal-to-jammer ratios based on an experimental setup.
Area Design of Keyboard Layout for Comfortable Texting Ability with the Thumb Jacobian Matrix

ABSTRACT The objective of this paper is to develop a theory for designing the keyboard area of smartphones, and endowing the users with comfortable texting ability via one thumb. In light of the fact that most users tend to hold the smartphone in one hand and manipulate with one thumb in daily life, this paper extracts the thumb kinematic chain from the thumb anatomy of the human hand, and defines the manipulation comfort ellipsoid of the thumb. Then some indexes such as the morphology and volume of the comfort ellipsoid, and the condition number of the thumb Jacobian are presented to evaluate the manipulation comfort of the thumb. Finally, the design problem of the smartphone’s keyboard area is transformed into an optimization problem that determines the most comfortable manipulation space for the human thumb. The proposed quantitative study method of the thumb manipulation comfort is verified by 12 subjects who hold a smartphone with one hand and manipulate the screen with one thumb, and the most comfortable keyboard layout of smartphone is determined for the 12 subjects. The proposed method for the manipulation comfort of the human thumb bridges the gap between the layout planning of soft keyboard of smartphones and the comfort of single thumb manipulation.
Expert Systems With Applications

use a limited number of features to classify COVID-19-related domain names as either malicious or legitimate. Our primary results show that a small set of carefully extracted lexical features, from domain names, can allow models to yield high scores; additionally, the number of subdomain levels as a feature can have a large influence on the predictions.
Non‐Electrode Components for Rechargeable Aqueous Zinc Batteries: Electrolytes, Solid‐Electrolyte‐Interphase, Current Collectors, Binders, and Separators

Rechargeable aqueous zinc batteries (AZBs) are one of the promising options for large‐scale electrical energy storage owing to their safety, affordability and environmental friendliness. During the past decade, there have been remarkable advancements in the AZBs technology, which are achieved through intensive efforts not only in the area of electrode materials but also in the fundamental understandings of non‐electrode components such as electrolytes, solid electrolyte interphase (SEI), current collectors, binders, and separators. In particular, the breakthroughs in the non‐electrode components should not be underestimated in having enabled the AZBs to attain a higher energy and power density beyond that of the conventional AZBs, proving their critical role. In this article, the recent research progress is comprehensively reviewed with respect to non‐electrode components in AZBs, covering the new‐type of electrolytes that have been introduced, attempts for the tailoring of SEI, and the design efforts for multi‐functional current collectors, binders and separators, along with the remaining challenges associated with these non‐electrode components. Finally, perspectives are discussed toward future research directions in this field. This extensive overview on the non‐electrode components is expected to guide and spur further development of high‐performance AZBs.
Introduction to Multilevel DC-Link Inverter andComparison of Switching Strategies for Cascaded Half Bridge Multilevel DC-Link Inverter

This paper describes a different class of multilevel inverter known as multilevel dc-link inverter (MLDCLI) which uses a multilevel dc-linkto provide a stepped dc voltage waveform approximating the shape of commanded sine wavewhich is then inverted after every alternate cycle by a single phase full bridge inverter to produce anacwaveform. The MLDCLI produces the same number of voltage levels as compared to that of conventional multilevel inverters but with less number of switches and other components. There are different square wave staircase and multicarrier sinusoidal pulse width modulated (SPWM) switching schemes. In this paper staircase and different phase shifted and level SPWMswitching schemes have been implemented for a ninelevel cascaded half bridge MLDCLI using simulation and the respective waveforms and results are compared.
Parallels between Manufacturing Systems in Industrial Production and Metabolic Systems in Biological Cells : A Network Based Unifying Model from a Production Logistics Perspective

Production logistics is the planning, scheduling, and control of all activities in a manufacturing system. The management of operations and logistics in manufacturing systems faces numerous challenges, caused by growth, diversity, dynamics and complexity. At the same time, conflicting targets further detain of a favorable solution. Biological concepts, often developed under the term bionics, promise a new perspective on solution approaches, e.g., in terms of organization principles or heuristics. Metabolic systems have already been recognized by scientists as particularly suited systems for a transfer, because they share numerous properties with logistic systems regarding structure, functions, and goals. However, a detailed analysis of even a proof of the systemic similarity is missing. Additionally, there is currently no foundation for a systematic or structured way of transferring concepts from metabolism to logistics due to the lack of a matching of elements, functions, and goals between the two systems. The result of this work is a network-based model that is able to represent material flow and control procedures in manufacturing systems as well as metabolite flow and metabolic regulation in metabolic systems, the proof of its feasibility by modeling flow networks using real company and metabolic data sets, and the use of measures from complex network theory to identify fundamental commonalities and differences between the two systems that can help creating new ideas for a transfer of concepts from metabolic systems to manufacturing systems.
Assessing the accuracy of distance‐ and interview‐based measures of hunting pressure

In the Amazon Rainforest, the sustainability of hunting is difficult to model. Accurate sustainability models for hunting of mammal populations require a spatially explicit measure of hunting pressure. Because field‐based measures of hunting pressure are time and labor‐intensive, distance‐based proxies for hunting pressure are frequently used. In this study, we tested accuracy of distance‐based parameters in predicting measured hunting pressure obtained through interviews for a riverine community in the Peruvian Amazon. We examined the spatial accuracy of the interviews and investigated the minimum requirements for spatial assessment of hunting pressure based on interviews. Results illustrate that hunter‐reported animal kill locations were accurate to within a mean of 1 km. Interview effort results showed that approximately 4 months of interviews capturing at least 50% of hunts are necessary to obtain a complete measure of hunting pressure across the landscape. Generalized linear models identified a novel spatially explicit approach that explained 59% of the deviance in measured hunting pressure. Our model was based on distances from locations that are easily obtained through remotely sensed imagery, participatory mapping, and terrain characteristics, highlighting that biologically relevant and spatially explicit estimates of hunting pressure can be obtained without lengthy field‐based methods. Hunting pressure across a landscape can be accurately predicted from remote sensing, participatory mapping, and terrain characteristics.
The Hierarchical Information System for Management of the Targeted Advertising

The problems of target audience identification and user segmentation for managing a process of the targeted advertising to customers are considered. It has proposed to combine the solution of these two tasks within the framework of one information system. An overview of the existing methods for identifying and segmenting of the target audience are presented. In addition, the existing applications and tools for solving the given problem are considered. The formalization of these tasks is presented. The functional models of business processes corresponding to the identifying and segmenting of the target audience are developed. The architecture of the information system is proposed. It is presented in the form of a deployment scheme, a database model is developed. The results of numerical studies and evaluation of the effectiveness of the developed information system are presented.
Model for Finding Frequent Sets in FP-growth for Multimodal Data

The paper examines the issues of hidden connections and potentially useful information from large data sets. Theoretical knowledge about associative rules is substantiated, their influence on connections in multimodal data sets is investigated. The methods of application of associative rules in practice are analyzed. The following are considered in detail: the basic concepts of associative rules and their connection with the idea of logical regularity; ways to determine the "strength" of these connections; basic algorithms for finding patterns; practical implementation of the search for associative rules. The regularities in the "templates" are analyzed: support and confidence value. The correct choice of these values, which directly affect the results of the search for rules, is experimentally determined. Research in this paper aimed to consider the basic concepts and find the Associative Rules both in traditional ways and in heterogeneous data of semantic networks, which creates specific problems when using existing algorithms. The data of semantic networks are analyzed, which in most cases serve a particular field and are highly specialized. The research presents the process of finding associative rules through the work of the classical Apriori algorithm and an alternative algorithm for finding associative rules. Previously, this problem was considered only to a small extent. The results of experiments on accurate SW data showed promising results.
Heuristic-based Reasoning on Financial Knowledge Graphs

When reasoning over large knowledge graphs, Datalog+/- languages offer a good trade-off between expressive power and computational complexity. However, in case of considerably large inputs and in the presence of recursive rules, even state-of-the-art reasoners struggle to accomplish reasoning tasks, e.g. answering Boolean Conjuctive Queries (BCQs). To address this problem, we introduce the notion of heuristic-based reasoning, that is, evaluating Datalog+/- rules according to an order of the facts determined via a user-defined heuristic. We show that adopting a fitting heuristic that guides the reasoning process provides an optimized way to answer a BCQ. To achieve this behaviour in practice, we enrich Datalog+/- programs with a context-aware operator, which we name Dynamic Hint Operator. We apply our new methodology to efficiently solve the Close Link problem on the knowledge graph of Italian companies, a relevant financial problem that estimates the risk to grant a specific loan to a company that is backed by collateral issued by another company.
CSK-SNIFFER: Commonsense Knowledge for Sniffing Object Detection Errors

This paper showcases the demonstration of a system called CSK-SNIFFER to automatically predict failures of an object detection model on images in big data sets from a target domain by identifying errors based on commonsense knowledge. CSK-SNIFFER can be an assistant to a human (as sniffer dogs are assistants to police searching for problems at airports). To cut through the clutter after deployment, this “sniffer” identifies where a given model is probably wrong. Alerted thus, users can visually explore within our demo, the model’s explanation based on spatial correlations that make no sense. In other words, it is impossible for a human without the help of a sniffer to flag false positives in such large data sets without knowing ground truth (unknown earlier since it is found after deployment). CSK-SNIFFER spans human-AI collaboration. The AI role is harnessed via embedding commonsense knowledge in the system; while an important human part is played by domain experts providing labeled data for training (besides human commonsense deployed by AI). Another highly significant aspect is that the human-in-the-loop can improve the AI system by the feedback it receives from visualizing object detection errors, while the AI provides actual assistance to the human in object detection. CSK-SNIFFER exemplifies visualization in big data analytics through spatial commonsense and a visually rich demo with numerous complex images from target domains. This paper provides excerpts of the CSK-SNIFFER system demo with a synopsis of its approach and experiments.
NeuTraL: Neural Transfer Learning for Personalized Ranking

Personalized ranking continues to be an important aspect of many information systems and personalization systems. Neural networks and deep learning continue to gain popularity because of their success in different fields of artificial intelligence such as computer vision and natural language processing. Recently, researchers began to apply deep learning to personalized ranking with success. Most personalization systems exploit historical preference data for users and items in warm-start scenario. A major challenge in personalized ranking occurs in the cold-start scenario which arises when there is little to no historical preference information. Content information is sometimes available and it can be used to alleviate the cold-start problem.Wepropose a solution that involves transfer learning from a deep model to a shallow model for both warm-start and cold-start personalized ranking. We corroborate our proposal with experiments on publicly available datasets in comparison with other baseline and state-of-the-art techniques.
Towards Hybrid Logic-based and Embedding-based Reasoning on Financial Knowledge Graphs

Warded Datalog+/- is a Datalog-based KRR language that guarantees decidability and tractability of the ontological reasoning task, thanks to its favourable theoretical properties. The Vadalog reasoning system exploits Warded Datalog+/- to provide a practical implementation of different reasoning tasks via basic isomorphism checks. However, these can be prohibitive in space and time complexity especially in the economic and financial context which is characterised by extreme-scale data stores and complex societal network dynamics. Recently, Knowledge Graph Embeddings (KGEs) have gained great interest in the scientific community and have extensively improved learning and knowledge discovery techniques. In this paper, we present and provide an experimental evaluation of Vada-ER, a framework that jointly uses logic-based reasoning and KGEs to provide a scalable alternative to basic isomorphism checks in ontological reasoning. With our work, we aim to improve the synergy between the reasoning and the embedding technologies and communities.
Method for Processing and Assessing the Degree of Digital Image Compression Based on Haar Transformation

The article presents the specifics of the developed method for processing and assessing the degree of digital image compression based on Haar transformation. In order to ensure a sufficient level of quality for the presentation of digital images of different formats and classes in the process of their multi-threaded processing in real time, it is proposed to implement a number of orthogonal transformations. The mathematical representation of the created method reflects the stages of restoration of individual image indicators in the process of its reception and transmission. The proposed method is based on the principles of complex adaptive compression using an estimate of the specific global and local sensitivity of the Haar transform functions. The developed method can be used for adaptive compression of digital images of different saturation with support for dynamic scaling of their sizes based on the estimation of the energy index in the analysis of the image transform, providing detailing of the degree of its saturation.
Democratizing Financial Knowledge Graph Construction by Mining Massive Brokerage Research Reports

This work presents a novel automatic financial knowledge graph (KG) construction framework by mining massive brokerage research reports without explicit financial expertise guidance and intensive manual rules. We propose a semantic-entity interaction module to construct the interaction feature between the entity and semantic context in the research reports and build a KG from scratch according to a predefined schema based on the obtained interaction features. We train the semantic-entity interaction module using a pre-extracted entity set as a remote supervision-based approach. We further introduce entity augmentation over this entity set from the inference samples of the semantic-entity interaction module to maintain the entity set.
Gender Differences in Early Career Performance Reviews: a Text Mining Study

It is well known that fewer women than men earn STEM degrees and persist in STEM careers. Since early career experiences affect career attrition, we investigate gender differences in early career performance reviews. Our analysis is enabled by a unique dataset, with nearly 6,000 performance reviews of undergraduate engineering students participating in co-operative internships. Text mining of workplace supervisor comments included in the reviews reveals several gender differences. Male students are more likely to be described as eager, efficient, and independent, whereas female students are perceived as thorough and collaborative. Moreover, male students are more likely to be asked to improve their interpersonal skills, whereas female students are more likely to receive suggestions to improve their business knowledge. Our results thus suggest that men and women are perceived differently in the STEM workplace from the beginning of their careers.
Building A Knowledge Graph for Audit Information

We present our insights from the experience of creating a knowledge graph (KG) for the auditing domain. We discuss the main challengesinbuildingsuchKGstartingfromtextandunstructureddataandpresentanoverviewofoursolution. Theproposed approach follows a standard pipeline when it first extracts entities from auditing documents and then finds relationships among them. However, the process is especially challenging because auditing entities are in most cases non-named entities, which are hard to model in the graph and to identify in text. From our experience, we finally derive a set of observations on the limits of automatic methods for the construction of audit KGs and a possible direction to address them.
Data Analytics Tools in Higher Education

Nowadays, data analytics tools play an important role in the improvement and the optimization of processes in different business areas. The wide range of the collected data has led to increased interest in the analysis of data to support data-driven decision making at all levels of educational institutions. In this regard, many companies, organizations and higher education institutions developed software tools that extract data from all systems used at the university and provide the summarized data in an appropriate format for each stakeholder group. The paper presents 35 data analytics tools that have been developed and deployed by higher education institutions. Further it offers a short analysis addressing whether those tools offer means in favor of typical university processes and corresponding stakeholders.
Thermal Stress Has Minimal Effects on Bacterial Communities of Thermotolerant Symbiodinium Cultures

Algae in the dinoflagellate family Symbiodiniaceae are endocellular photosymbionts of corals and other cnidarians. This close relationship is disrupted when seawater temperature increases, causing coral bleaching eventually affecting entire coral reefs. Although the relationship between animal host and photosymbiont has been well-studied, little is known about the bacterial community associated with Symbiodiniaceae in culture. We compared the microbial communities of three isolates from different species of the genus Symbiodinium (formerly known as Symbiodinium clade A) with different ecophysiology, levels of interaction with the animal host, and thermal adaptations. Two species, Symbiodinium microadriaticum and Symbiodinium necroappettens, exhibit intermediate thermotolerance, with a decrease of both growth rate and photochemical efficiency with increased temperature. The third species, Symbiodinium pilosum, has high thermotolerance with no difference in growth rate or photochemical efficiency at 32°C. Microbial communities were characterized after 27 days of growth under control (26°C) and high temperature (32°C). Data shows stronger grouping of bacterial assemblages based on Symbiodinium species than temperature. Microbial communities did not group phylogenetically. We found a shared set of fifteen ASVs belonging to four genera and three families that remained in all three Symbiodiniaceae species. These included Labrenzia, Phycisphaeraceae (SM1A02), Roseovarius, and Muricauda, which are all commonly associated with corals and Symbiodiniaceae cultures. Few ASVs differed significantly by temperature within species. S. pilosum displayed significantly lower levels of microbial diversity and greater individual variability in community composition at 32°C compared to 26°C. These results suggest that bacteria associated or co-cultured with thermotolerant Symbiodinium might play an important role in thermotolerance. Further research on the functional metabolic pathways of these bacteria might hold the key to understanding Symbiodinium’s ability to tolerate thermal stress.