Reviewerly/exHarmony huggingface dataset:
iCCell: Uma Abordagem Distribuída na Internet das Coisas para Contagem de Fibroblastos

Cell count is a process usually performed in clinical analysis laboratories, very significant for the diagnosis of different diseases. With this motivation, this work aims to design an approach, called iCCell, which integrates an embedded platform, responsible for the acquisition of images captured from microscopes and a computational cloud infrastructure, responsible for both the counting of cells in these images, the storage, as well as the entire user interface. The iCCell was evaluated using images produced by the Clinical Neurosciences Laboratory of the Catholic University of Pelotas (UCPel). The results achieved so far are promising and point to the continuity of research.
Ryder Patzuk-Russell, The Development of Education in Medieval Iceland. (The Northern Medieval World.) Berlin/Boston, De Gruyter 2021

Article Ryder Patzuk-Russell, The Development of Education in Medieval Iceland. (The Northern Medieval World.) Berlin/Boston, De Gruyter 2021 was published on August 1, 2022 in the journal Historische Zeitschrift (volume 315, issue 1).
Scenario Generation Method in Chatbot in Cooperation with External System

The development of a chatbot requires the definition of a dialogue template for the chatbot, called a scenario. This paper proposes a method for generating the chatbot scenario using data from the external system, aiming to reduce the man-hours required for scenario definition and management. In the proposed method, the problem of the difference in format between the data of the external system and the chatbot scenario is solved by defining the mapping between the data. This mapping corresponds to two standard data structures in the external system: table format and object format. The scenario creator can create the mapping definition as a part of the scenario and can combine the scenario specific to the chatbot and the scenario generated from the external system. As a result of the trial, the proposed system reduces the required time for scenario creation by 50% compared with the existing method.
HUBUNGAN KEBIASAAN BERMAIN GAME ONLINE DENGAN PRESTASI BELAJAR PESERTA DIDIK KELAS TINGGI MIN 16 JAKARTA TIMUR

This study aims to determine the relationship between the habit of playing online games with the learning achievement of high grade students at MIN 16 Cipayung, East Jakarta. This research uses Associative Quantitative Research and uses correlation research methods. The tool used to collect data in this study was a questionnaire in the form of a statement in the form of a Likert scale for the habit of playing online games, and report cards for student achievement in the even semester of the 2021/2022 school year. In testing the hypothesis, the product moment correlation test was used to obtain a sig value. (2tailed) 0.03 or &lt; from 0.024, and the correlation value is 0.361. The conclusion of this study shows that there is a relationship between playing online games with the learning achievement of high grade students at MIN 16 East Jakarta.
Reversals Prior To Solution in Concept Identification *

In the typical two-category concept identification experiment, S is shown a series of complex patterns which vary in several, binary attributes. This chapter investigates whether S acquires partial knowledge about the solution to the problem. When S makes an error, he has not yet learned anything of relevance regarding the correct concept. The prediction from the theory is that the number of informed errors before learning for the Reversal Ss will be equal to the number of informed errors before learning made by the Control Ss. That is, no interference should result from the multiple reversals that occur during training. This prediction follows from the assumptions that learning occurs in one trial, that opportunities for giving up an irrelevant hypothesis, and hence learning, occur only after informed errors, and that the probability of learning following an informed error is not affected by the past S-R assignments for the relevant cues.
Solicitation Axiom

The Major Gifts ReportVolume 24, Issue 9 p. 5-5 Briefs Solicitation Axiom First published: 02 August 2022 https://doi.org/10.1002/mgr.31986Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume24, Issue9September 2022Pages 5-5 RelatedInformation
Identification of pharming in communication networks using ensemble learning

Pharming scams are carried out by exploiting the DNS as the main weapon while phishing attacks employ spoofed websites that appear to be legitimate to internet users. Phishing makes use of baits such as fake links but pharming leverages and negotiates on the DNS server to move and redirect internet users to a fake and simulated website.Having seen several challenges through pharming resulting into vulnerable websites, personal emails and accounts on social media, the usage and reliability on internet calls for caution. Against this backdrop, this work aims at enhancing pharming detection strategies by adopting machine learning classification algorithms. To further obtain the best classification results, an ensemble learning approach was adopted. The algorithms used include K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gaussian Naive Bayes, Logistic Regression, Support Vector Machine, Adaptive Boosting, Gradient Boosting, and Extra Trees Classifier. During the testing process, the classifiers were tested against four popular metrics: accuracy, recall, precision, F1 score, and Log loss. The results demonstrate the performance of all algorithms used, as well as their relationships. The ensemble model that included Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Gradient Boosting Classifier, AdaBoost Classifier, Extra Trees Classifier, and Random Forest produced the best results after evaluating them on the two datasets. Random Forest Classifiers showed a better performance of the classifiers, with mean accuracies of 0.932 and 0.939, respectively for each of the datasets when compared to 0.476 and 0.519 obtained for Naive Bayes.
Replication Robust Payoff Allocation in Submodular Cooperative Games

Submodular functions have been a powerful mathematical model for a wide range of real-world applications. Recently, submodular functions are becoming increasingly important in machine learning (ML) for modelling notions such as information and redundancy among entities such as data and features. Among these applications, a key question is payoff allocation, i.e., how to evaluate the importance of each entity towards the collective objective? To this end, classic solution concepts from cooperative game theory offer principled approaches to payoff allocation. However, despite the extensive body of game-theoretic literature, payoff allocation in submodular games are relatively under-researched. In particular, an important notion that arises in the emerging submodular applications is redundancy, which may occur from various sources such as abundant data or malicious manipulations where a player replicates its resource and act under multiple identities. Though many game-theoretic solution concepts can be directly used in submodular games, naively applying them for payoff allocation in these settings may incur robustness issues against replication. In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication. Using this metric, we present conditions which theoretically characterise the robustness of semivalues, a wide family of solution concepts including the Shapley and Banzhaf value. Moreover, we empirically validate our theoretical results on an emerging submodular ML application, i.e., the ML data market.
General Discussion

The findings of this study can be explained by elasticity theory, frame theory, pragmeme theory and integrated pragmatics. For example, they show how language users carry out, adjust and adapt to the pragmeme of delivering online health information using EL in different and shifting contexts. This study supports the conceptualisations of these theories and more importantly it further develops elasticity theory through adopting enriched perspectives of pragmatics as well as metapragmatics.
Elastic Language Used in Chinese

There appears to be a combination of contributing factors that affect and shape the use of EL in the Chinese data: the nature of the medical condition (e.g. disease severity, sensitivity of mental conditions), the applications and restrictions of medicine (e.g. the groups suitable or unsuitable for vaccines), the society’s health policy (e.g. the groups eligible for free treatment), the written mode, the health professional’s stance towards the health information, and potentials and constraints of an elastic/vague term (e.g. keneng vs. sihu; hen vs. guodu). Social conventions in language use are also a significant factor, an issue that is further addressed in Chapter 6.
Quantum Circuit Theory

In this chapter, we develop the conditions to observe quantum fluctuations and quantum phenomena (entanglement, superpositions, etc.) in quantum circuits. Assuming the right conditions of temperature, we develop the quantum mechanical theory that models those fluctuations in a circuit built from nondissipative superconducting elements. We use this theory of circuit quantization to obtain the quantum Hamiltonians for microwave resonators and waveguides, for superconducting qubits of various types, and for other elements such as SQUIDs. The chapter closes with an illustration of how the same theory provides us with numerical methods to study the eigenstates, eigenenergies, and dynamics of said Hamiltonians.
On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?

The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning. A solution to a MRP is an explanation for the differences between the models of the human and the planning agent (robot). Most approaches to solving MRPs assume that the robot, who needs to provide explanations, knows the human model. This assumption is not always realistic in several situations (e.g., the human might decide to update her model and the robot is unaware of the updates). In this paper, we propose a dialog-based approach for computing explanations of MRPs under the assumptions that (i) the robot does not know the human model; (ii) the human and the robot share the set of predicates of the planning domain and their exchanges are about action descriptions and fluents' values; (iii) communication between the parties is perfect; and (iv) the parties are truthful. A solution of a MRP is computed through a dialog, defined as a sequence of rounds of exchanges, between the robot and the human. In each round, the robot sends a potential explanation, called proposal, to the human who replies with her evaluation of the proposal, called response. We develop algorithms for computing proposals by the robot and responses by the human and implement these algorithms in a system that combines imperative means with answer set programming using the multi-shot feature of clingo.
Planning and Scheduling in Digital Health with Answer Set Programming

In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered.The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account.Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios.We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available.Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g.those based on Answer Set programming will be limited.Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.
Towards Realizing an “All-Photonic” Quantum Repeater based on a Spin-Photon Quantum Interface

I will discuss our recent proposal on deterministic generation of photonic repeater graph states using only a single quantum emitter, our plans for its experimental implementation, and its applications in quantum repeaters and networks.
Chapter 12: Causality

We quote from [187]: “A statistical model is an amazing association engine. It makes it possible to detect associations between causes and their effects. But a statistical model is never sufficient for inferring cause, because the statistical model makes no distinction between the wind causing the branches to sway and the branches causing the wind to blow. Facts outside the data are needed to decide which explanation is correct.”
The Relationship Between System Quality, Self-efficacy and Satisfaction in Humanities Class using K-MOOC: Focusing on the Moderated Mediating Effect of Perceived Usefulness

Recently, the interest in the Humanities is increasing through convergence with other fields using online by pursuing understanding and desirability of life.&#x0D; Accordingly, the purpose of this study is to investigate the moderating mediation impact on whether the mediating effect of self-efficacy in the influence of system quality on satisfaction is moderated by perceived usefulness in Humanities class using K-MOOC. The results of this study are as follows: (1) there was a moderating effect of perceived usefulness in the relationship between system quality and self-efficacy, (2) there was a mediating effect of the self-efficacy in the relationship between system quality and satisfaction, and (3) the mediating effect of the self-efficacy was moderated by the perceived usefulness in the system quality affecting the satisfaction. In conclusion, this study presented significant implications on what factors faculty and a higher education institution should consider in Humanities class using an online through the research results.
A Refinement-Based Approach to Spectre Invulnerability Verification

Several mitigations to thwart Spectre attacks have been proposed. However, design errors or trojans can be exploited to circumvent these mitigations. We have developed a highly-automated formal verification methodology that can detect if modern microprocessor designs are vulnerable to Spectre and its variants or prove otherwise. The methodology comprises of a refinement-based formal Spectre invulnerability property, and refinement maps, abstractions and invariants required to efficiently check the property. The methodology was evaluated on 29 benchmarks based on the RSD, which is an open source RISC-V based out-of-order superscalar processor. The benchmarks incorporated two Spectre defenses including software-controlled speculation and data track. Also incorporated were various implementation errors and hardware Trojans such as internally-triggered, rare-circuit-state and time-bombs that induce Spectre. What was evaluated was if the methodology could efficiently prove that an implementation is not vulnerable to Spectre and provide a counterexample otherwise. The methodology provided accurate classification for all benchmarks. The verification times were very efficient, ranging from 30 seconds to 1,500 seconds.
EDAML 2022 Invited Speaker 2: AI Algorithm and Accelerator Co-design for Computing on the Edge

In a conventional top-down design flow, deep-learning algorithms are first designed concentrating on the model accuracy, and then accelerated through hardware accelerators trying to meet various system design targets on power, energy, speed, and cost. However, this approach often does not work well because it ignores the physical constraints that the hardware architectures themselves would have towards the deep neural network (DNN) algorithm design and deployment, especially for the DNNs that will be deployed unto edge devices. Thus, an ideal scenario is that algorithms and their hardware accelerators are developed simultaneously. In this talk, we will present our DNN/Accelerator co-design and co-search methods. Our results have shown great promises for delivering high-performance hardware-tailored DNNs and DNNtailored accelerators naturally and elegantly. One of the DNN models coming out of this co-design method, called SkyNet, won a double championship in the competitive DAC System Design Contest for both the GPU and the FPGA tracks for low-power object detection.
Significance of Distance Measures for Speaker Anonymization

Privacy preservation methods for voice data are evolving day by day. A recent state-of-the-art voice privacy algorithm uses an x-vector and neural source-filter (NSF)- based anonymization approach that converts the original input voice into a pseudo speaker’s voice. The method uses an affinity propagation clustering (APC) algorithm to choose a pseudo speaker’s x-vector. Finding a set of distance measures for this clustering technique is important to get optimal anonymization. To that effect, in this paper, an attempt has been made to investigate the effect of six distance measures, namely, Euclidean, cosine, probabilistic linear discriminant analysis (PLDA), correlation, Manhattan, and Mahalanobis for voice privacy preservation using an x-vector-based anonymization system. This approach gave a 4.75% relative improvement in Equal Error Rate(EER) for original enrolls and anonymized trials. In addition, 11.49% relative improvement in EER is observed for anonymized enrolls and trials. Experimental results show that Mahalanobis and Pearson correlation coefficient-based distance are better choices for anonymization tasks. It provides better speaker de-identification and good speech intelligibility without increasing system complexity.
Recognize What Most Contributes To Your Magazine's Success

Nonprofit Communications ReportVolume 20, Issue 9 p. 5-5 Primary Publications Recognize What Most Contributes To Your Magazine's Success First published: 04 August 2022 https://doi.org/10.1002/npcr.32015Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume20, Issue9September 2022Pages 5-5 RelatedInformation
A history of unquestioning faith

Vocational awe is an emerging concept within LIS used to highlight how narratives andmetaphors involving professional self-reverence and sacredness serve to absolve librarianship ofaccountability for the complex and problematic legacies of institutional oppression andexploitation. This panel explores the vocational awe narrative and its implications for LISresearch in two ways. First, by sharing panelists’ experiences and struggles with vocational aweand second, by engaging the LIS research community in appropriate responses to vocationalawe, such as advocating for library workers and responsible allyship with marginalized groups.
Lessons learned from earthquakes

The lessons learned based on the reconnaissance visits in Iran and other countries in South Asia are summarized. Field observations performed after many earthquakes indicate that there has been a major difference between the observed and the expected ground motions and intensities. A typical ground fissure was observed at a distance about 9 km southeast of Kathmandu toward Bhaktapur as an aftermath of the 2015 Gorkha, Nepal, earthquake. The 2005 Balakot, Pakistan, earthquake occurrence accompanied by landslide. Huge falling stones destroyed the roads in different places and blocked the traffic, leading to extreme congestion. The low-budget collective apartments showed very vulnerable to the strong motion during earthquake.
Lang-n-Send Extended: Sending Regular Expressions to Monitors

In prior work, Cimini has presented LANG-N-SEND, a π-calculus with language definitions.In this paper, we present an extension of this calculus called LANG-N-SEND +m .First, we revise LANG-N-SEND to work with transition system specifications rather than its language specifications.This revision allows the use of negative premises in deduction rules.Next, we extend LANG-N-SEND with monitors and with the ability of sending and receiving regular expressions, which then can be used in the context of larger regular expressions to monitor the execution of programs.We present a reduction semantics for LANG-N-SEND +m , and we offer examples that demonstrate the scenarios that our calculus captures.
Photonic Spiking Neural Network with Resonant Tunnelling Diode Optoelectronic Neurons

We report high-speed, energy-efficient artificial optoelectronic spiking neurons based upon resonant tunnelling diodes (RTD). Using these, we demonstrate a photonic spiking neural network (perceptron) numerical model for classification of spatiotemporal pulse patterns with 94% accuracy.
Blockchain networks' energy consumption and environmental impact: Economic, political, and social discourses and key argumentations

Blockchain networks&#x0027; energy consumption has been a hot and controversial issue. This presentation analyzes the discourses surrounding blockchains&#x0027; energy consumption and takes a look at the actors and actions involved. It also provides a critical evaluation of a number of considerations and factors that affect blockchain networks&#x0027; energy consumption and resulting environmental impacts.
"Is It My Turn?"

Immersive analytics has the potential to promote collaboration in machine learning (ML). This is desired due to the specific characteristics of ML modeling in practice, namely the complexity of ML, the interdisciplinary approach in industry, and the need for ML interpretability. In this work, we introduce an augmented reality-based system for collaborative immersive analytics that is designed to support ML modeling in interdisciplinary teams. We conduct a user study to examine how collaboration unfolds when users with different professional backgrounds and levels of ML knowledge interact in solving different ML tasks. Specifically, we use the pair analytics methodology and performance assessments to assess collaboration and explore their interactions with each other and the system. Based on this, we provide qualitative and quantitative results on both teamwork and taskwork during collaboration. Our results show how our system elicits sustained collaboration as measured along six distinct dimensions. We finally make recommendations how immersive systems should be designed to elicit sustained collaboration in ML modeling.
Doing away with cash bail

In the US, people charged with a crime usually need to post a large cash bond to be released before trial &#8212; a system used barely anywhere else in the world. This doesn&#8217;t enhance public safety and causes great hardship to defendants and families. There&#8217;s a better way, researchers say.