Reviewerly/exHarmony huggingface dataset:
Modeling of text and discourse worlds

The article deals with the author's algorithm for modeling both text and discourse worlds.The importance of modeling in the contemporary linguistic paradigm is proved.The model of the text world is interpreted as a combination of three components, such as: (1) the literary conceptual domain, (2) the readers' conceptual domains, (3) the interpretive field of the text world model.The model of the discourse world is interpreted as a triad of components such as: (1) the literary conceptual domain, (2) the conceptual domains of communicants, (3) the communicative situation of the informative's existing.The specific features of each component's architectonics in the models of both text and discourse worlds are revealed.Particular attention is drawn to concepts-dominants in the conceptual domain of the informative as the architectonics' part in the models of both text and discourse worlds.The significance of cognitive attractors in the conceptual domain of the informative is emphasised.A close relationship is established between the informative's conceptual domain and reader's conceptual domain.The relationship of two components in the worlds models is realised by cognitive attractors.It is determined that the third component in both text and discourse worlds models is fundamentally different from each other.In the model of the text world, there is a mental interpretative projection as the third component's architectonics.In the model of the discourse world, there is an extralinguistic reality as the third component's architectonics.The high verification coefficient of author's algorithm for modeling both text and discourse worlds are substantiated.
Voice Biometric Systems for User Identification and Authentication – A Literature Review

Purpose: Biometric trends are used in many systems because of security aspects. The cryptosystem is such an example which uses a biometric. But due to stored biometric data for the authentication, this can be a dangerous issue. Therefore, in comparison to conventional used biometric system, voice biometric system provides an efficient safety, security and unique identity. Among various speech recognition or processing methods, there is one called automated speech conversion methods, which also used to convert the recorded voice into text format. The overall concept of voice reorganization and voice biometric system is based on the acoustic modelling. Therefore, for getting the perfect speech detection, robust acoustic modelling is required. Our analysis describes the advancement and usage of voice biometric system for user identification and authentication. This paper provides a descriptive review of different voice biometric systems, their advancement and applications in different fields. Methodology: The core principles of the research issue have been well discussed in the literature review on speech biometrics. During this process, selected journals from a variety of secondary data sources, such as research papers published in a variety of reputed journals periodicals that are related to the topic are studied in the methodology. Findings/Result: A vocal biometric system is a biological system that captures an individual's voice and assigns it a unique characteristic for authentication purposes. This speech biometric method is primarily used to provide secure, quick, and frictionless access to various electronic devices. In the last three years, rapid technological advancements in neural networks have improved the deployment of speech biometric systems in a variety of industries. The majority of speech biometric system designs are based on the CPU, necessary power, and memory concepts. The advancement of software and hardware interface has been dramatically enhanced and implemented for many applications in the last few years, including smart watches, mobile phones, and car locking systems, where the interface between humans and electronics devices is critical. Banking security, attendance system, file access system, security control, and forensic development system are some of the other commercial applications. Originality: Following the literature study, the findings were utilized to conclude that, despite advances in biometric technology, there is still a significant gap in practical application, particularly for voice biometric systems. When building and developing a voice biometric system, it is necessary to integrate it with an IoT system. Paper Type: Literature Review.
An Architecture Using Payment Channel Networks for Blockchain-based Wi-Fi Sharing

Enabling Internet access while taking load of mobile networks, the concept of Wi-Fi sharing holds much potential. While trust-based concepts require a trusted intermediary and cannot prevent malicious behavior, for example, conducted through fake profiles, security-based approaches lack adequate accounting mechanisms and coverage. Against this backdrop, we develop a Wi-Fi sharing architecture based on blockchain technology and payment channel networks. Our contribution is twofold: First, we present a comprehensive collection of design principles for workable Wi-Fi sharing networks. Second, we propose and evaluate a reference architecture that augments current approaches with adequate accounting mechanisms and facilitates performance, scalability, security, and participant satisfaction.
Augmented Reality In-Field Observation Creation and Visualization in Underperforming Areas

To precisely diagnose and address farming issues that led to crop yield underperformance, agronomists need to conduct field trips at the targeted underperforming areas and record the observed issues to support farming decisions. Traditional field data visualization tools may not provide intuitive visualizations to support field trip investigations at targeted underperforming areas. This paper presents an Augmented Reality in-field observation tool to navigate agronomists to annotate the observed issues at precisely targeted underperforming areas. These recorded observation AR annotations are then synchronized in a Web-based Interactive Multi-Layer Mapping Tool for a complete precision farming decision-making process.
Penerapan Digitalisasi pada Koperasi Budi Utomo Desa Batu Ampar

Budi Utomo's Savings and Loans Cooperative is one of the cooperatives in Indonesia that has not made optimal use of information systems and technology. It can be seen from the absence of an integrated information system with one another, so that there are still several obstacles in processing cooperative data, including inconsistencies and data redundancies, lack of data accuracy, the length of the data search process, and the difficulty of disseminating information about cooperatives to members. To solve this problem, we need a web-based Cooperative information system. This cooperative information system is designed using the PHP programming language and to describe the results of analysis and system design, structured modeling techniques are used in the form of DFD and ERD. The output of this research is a web-based information system prototype design (case study: Budi Utomo Savings and Loan Cooperative). With this cooperative information system, it is hoped that it can provide a forum in the form of a web that can perform data processing properly and is integrated, and can help disseminate information to all interested parties.
AliExpress-A Collaborative Recommendation Algorithm

The objective of using Recommender systems in e-commerce is to guide or assist a customer in his purchase by providing personalized suggestions through the large product assortments, and thus helping him in better decision making. We try to investigate the reason for item-based collaborative filtering technique being preferred over popularity-based recommendation system. In order to do so we developed a personalized collaborative recommendation system based on item-based and popularity based techniques. Item-based collaborative filtering technique involved construction of a co-occurrence matrix to determine user-item interactions. Popularity-based recommendation system was developed to determine if popular features played an important role in consumers’ purchasing decisions. From the results obtained, we inferred that item-similarity model provided a better performance as it had higher precision and recall values in comparison to the popularity model.
Behavioral biometrics dentification based on machine learning methods

This essay aims to develop a machine learning model that is able to build an initial advantage for behavioral biometrics. While behavioral biometrics have theoretically shown to be advantageous over traditional identification methods like passwords and physical tokens, more research needs to be conducted to prove that behavioral biometrics can ensure high accuracy in order to be applied in real life situations. This essay conducts an experiment that builds and compares three models that can potentially be used in this field. We use the data posted on the UCI Machine Learning Repository, which contains a set of motion data collected by phone accelerometers. This experiment has proved the feasibility of behavioral biometrics and set up the foundation for subject-wise studies.
Architectures Based on Deep Learning for the Detection of Invasive Ductal Carcinoma

Image processing techniques have improved dramatically in recent years to help pathologists identify cancer cells. Like convolutional neural networks (CNNs), deep learning methods are increasingly used for imaging processes and analysis in histopathology images. This research aims to demonstrate the detection of histopathological pictures linked with the prediction of invasive ductal carcinomas (IDC) and non-IDC in the breasts. A complex problem is the detection of IDC in histopathological images, as cancer includes minor entities with a variety of shapes that can readily be confused with other objects or facts in the picture. As a result, the suggested research recommends three distinct CNN architectures for identifying IDC using histopathological images, referred to as 10-layer, 19-layer, and 20-layer convolutional neural networks, respectively. Excellent values of sensitivity, precision, and low classification error rate have been achieved to detect IDC in histopathology images using deep layer-convolutional neural networks. Using 19 layer-convolutional neural networks, the efficiency obtained was 87%, revealing improved results for deep layer convolutional neural network architecture.
Covid, Courts, Communists and Common Sense

Covid-19 is a serial killer. In less than two years it has taken the lives of over five million people. It preys on the vulnerable and the elderly. Seniors in long term care facilities are a favorite target.&#x0D; Governments have reacted differently to the threat. Some, including China and Australia, have adopted an aggressive, no- nonsense approach, locking down major cities for months at a time. Others, including Sweden and Brazil were, at least initially, more restrained and laissez faire, allowing their citizens to move about freely and letting the virus run its natural course. Countries also differed in the extent to which the general public was engaged in deciding which approach to adopt. In some the public were very active; in others not at all. In China, public debate and criticism were prohibited. Decisions were made by senior members of the Communist Party from behind closed doors and policies were presented as a fait accompli. In Europe and the United States, members of the general public were much more vocal and outspoken. Citizens who disagreed with their governments organized large protests and demonstrations and, when they were not listened to, took their political masters to court.
Making Access: Increasing Inclusiveness in Making

In this one-day workshop we are going to make access. We aim to counteract the phenomenon that access to making (e.g., in makerspaces, fablabs, etc.) is not equally distributed, with certain groups of people being underrepresented (e.g., women*1). After brief introductions from participants and a set of three impulse keynotes, we will envision and “make” interventions together, such as speculative or provocative objects and actions. The workshop takes a constructive stance with the goal to not rest on empirical and theoretical findings or individual experiences, but to translate those into viable interventions. These serve as exemplars of findings with the clear goal of being deployed soon after.
The State of the (CHI)Art

We are all researchers, practitioners, and educators – but many of us are also artists, makers, curators. Our arts practice is part of what makes up our sense of self, but also influences our interests and directions in digital and technological enquiry. There exist spaces where the traditional lives alongside the computational, or where the two are blended, no less valid in purpose or value. We seek to investigate this liminal environment, and explore the current state of art in HCI, computer science and other related fields, shifting boundaries as to what "art" is in these spaces. By bringing together like-minded and creative individuals, this workshop aims to both inspire and legitimise our diverse practices, present viewpoints, create meaningful outputs, host discussions, and work toward the future of this plurality.
Application of Digital Information Technology to the Quality Management System of Power Grid Engineering

The construction quality of the power grid determines whether it can operate safely and reliably. Therefore, in order to improve the construction quality of power grid projects, three technologies, GIS technology, BIM technology, and Internet of Things technology, are introduced in the article. At the beginning of this article, the author started research on the application of the three technologies in the process of power grid construction, and concluded that the application of the three technologies can greatly reduce the input of manpower and material resources, save costs, improve the efficiency of power grid construction, and can also improve the management and control efficiency of power grid construction quality. Finally, the author looks forward to the development of the three technologies by analyzing the future development trend of informatization.
STIFT: A Spatio-Temporal Integrated Folding Tree for Efficient Reductions in Flexible DNN Accelerators

Increasing deployment of Deep Neural Networks (DNNs) recently fueled interest in the development of specific accelerator architectures capable of meeting their stringent performance and energy consumption requirements. DNN accelerators can be organized around three separate NoCs, namely distribution, multiplier, and reduction networks (or DN, MN, and RN, respectively) between the global buffer(s) and the compute units (multipliers/adders). Among them, the RN, used to generate and reduce the partial sums produced during DNN processing, is a first-order driver of the area and energy efficiency of the accelerator. RNs can be orchestrated to exploit a Temporal, Spatial or Spatio-Temporal reduction dataflow. Among these, Spatio-Temporal reduction is the one that has shown superior performance. However, as we demonstrate in this work, a state-of-the-art implementation of the Spatio-Temporal reduction dataflow, based on the addition of Accumulators (Ac) to the RN (i.e., RN+Ac strategy), can result into significant area and energy expenses. To cope with this important issue, we propose STIFT (that stands for Spatio-Temporal Integrated Folding Tree ) that implements the Spatio-Temporal reduction dataflow entirely on the RN hardware substrate (i.e., without the need for the extra accumulators). STIFT results into significant area and power savings regarding the more complex RN+Ac strategy, at the same time its performance advantage is preserved.
Passtrans: An Improved Password Reuse Model Based on Transformer

Passwords have been widely used in online authentication, and they form the front line that protects our data security and privacy. But the security of password may be easily harmed by insecure password generator. Massive reports state that users are always keen to generate new passwords by reusing or fine-tuning old secrets. Once an old password is leaked, the users may suffer from credential tweaking attacks. We propose a password reuse model PassTrans and simulate credential tweaking attacks. We evaluate the performance in leaked password datasets, and the results show that 67.51% of accounts is breakable under 1,000 guesses, indicating our model is accurate in capturing password reuse behavior.
WordMarkov: A New Password Probability Model of Semantics

To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.
A geometric perspective: experimental evaluation of the quantum Cramer-Rao bound

The power of quantum sensing rests on its ultimate precision limit, quantified by the quantum Cram\'er-Rao bound (QCRB), which can surpass classical bounds. In multi-parameter estimation, the QCRB is not always saturated as the quantum nature of associated observables may lead to their incompatibility. Here we explore the precision limits of multi-parameter estimation through the lens of quantum geometry, enabling us to experimentally evaluate the QCRB via quantum geometry measurements. Focusing on two- and three-parameter estimation, we elucidate how fundamental quantum uncertainty principles prevent the saturation of the bound. By linking a metric of "quantumness" to the system geometric properties, we investigate and experimentally extract the attainable QCRB for three-parameter estimations.
Trustworthy AI Solutions for Cyberbiosecurity Challenges in Water Supply Systems

The recent increased adoption of AI into cyber-physical systems requires that AI models perform as intended and without security blunders. However, for such critical AI deployments, serious adversaries and data poisoning issues are still persistent. The digital (data-driven) infrastructure of the bioeconomy is progressively being utilized by state and private sector entities; especially through leveraging models and data sets for real-time decision support. Models however, can be easily targeted by adversaries or be prone to unintentional quality issues. Accordingly, our research is dedicated to defining methods that mitigate different types of attacks and their undesired consequences. We are building an AI framework that can detect anomalies and malicious acts (in water supply systems) as well as analyze cause-effect measures in multiple scenarios. The proposed defense strategies include assessing the potential vulnerabilities that can be exploited in the convergence of AI and physical systems. Such outliers can cause extensive civilian harm, produce damaging bio-security incidents, and threaten agricultural and food production. The new framework will be applied to multiple physical systems with the aim of creating meta-learning outcomes for supporting the wide-scale adoption of trustworthy AI.
Query-Based Keyphrase Extraction from Long Documents

Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant keyphrases should be extracted. The developed system employs a pre-trained BERT model and adapts it to estimate the probability that a given text span forms a keyphrase. We experimented using various context sizes on two popular datasets, Inspec and SemEval, and a large novel dataset. The presented results show that a shorter context with a query overcomes a longer one without the query on long documents.
Complex amplitude field reconstruction in atmospheric turbulence based on deep learning

In this paper, we use deep neural networks (DNNs) to simultaneously reconstruct the amplitude and phase information of the complex light field transmitted in atmospheric turbulence based on deep learning. The results of amplitude and phase reconstruction by four different training methods are compared comprehensively. The obtained results indicate that the training method that can more accurately reconstruct the complex amplitude field is to input the amplitude and phase pattern pairs into the neural network as two channels to train the model.
System segmentation of Lungs in images chest x-ray using the generative adversarial network

One of the most common medical imaging methods is a chest x-ray, as it contributes to the early detection of lung cancer compared to other methods. this work presents the use of a generative adversarial network to perform lung chest x-ray image segmentation. The network is two frameworks neural (generator and discriminator). In our work the generator is trained to generate a mask for the input of a given original image, the discriminator distinguishes between the original mask and the generated mask, the final objective is to generate masks for the input. The model is trained and evaluated, well generalized experimental results of the JSRT dataset reveal that the proposed model can a dice score of 0.9778, which is better than other reported state-of-the-art results.
Communicational and Computational Efficient Federated Domain Adaptation

The emerging paradigm of Federated Learning enables mobile users to collaboratively train a model without disclosing their privacy-sensitive data. Nevertheless, data collected from different mobile users may not be independent and identically distributed. Thus directly applying the trained model to a new mobile user usually leads to performance degradation due to the so-called domain shift. Unsupervised Domain Adaptation is an effective technique to mitigate domain shift and transfer knowledge from labeled source domains to the unlabeled target domain. In this article, we design a Federated Domain Adaptation framework that extends Domain Adaptation with the constraints of Federated Learning to train a model for the target domain and preserve the data privacy of all the source and target domains. As mobile devices usually have limited computation and communication capabilities, we design a set of optimization methods that significantly enhance our framework’s computation and communication efficiency, making it more friendly to resource-constrained edge devices. Evaluation results on three datasets show that our framework has comparable performance with the standard centralized training approach, and the optimization methods can reduce the computation and communication overheads by up to two orders of magnitude.
On power analysis attacks against hardware stream ciphers

Power analysis attacks are a type of attack which measures and analyses the power consumption of electronic circuits to extract secret information, particularly the secret encryption key.These attacks have become a huge threat for embedded systems, in which the security depends on the secret encryption key for the ciphers.Hence, many researchers try to find vulnerabilities of cryptosystems against power analysis attacks, so that they can develop countermeasures to ensure the security of such systems.In this paper, we review some of the recent power analysis attacks on modern hardware stream ciphers such as Trivium, Grain and MICKEY.