Reviewerly/exHarmony huggingface dataset:
Ordinal sums, clockwise hackenbush, and domino shave

We present two rulesets, domino shave and clockwise hackenbush. The first is somehow natural and has, as special cases, stirling shave and Hetyei’s Bernoulli game. Clockwise hackenbush seems artificial, yet it is equivalent to domino shave. From the pictorial form of the game and a knowledge of hackenbush, the decomposition into ordinal sums is immediate. The values of clockwise bluered hackenbush are numbers, and we provide an explicit formula for the ordinal sum of numbers where the literal form of the base is {x | } or { | x}, and x is a number. That formula generalizes van Roode’s signed binary number method for blue-red hackenbush.
Second-Order NLP Adversarial Examples

Adversarial example generation methods in NLP rely on models like language models or sentence encoders to determine if potential adversarial examples are valid.In these methods, a valid adversarial example fools the model being attacked, and is determined to be semantically or syntactically valid by a second model.Research to date has counted all such examples as errors by the attacked model.We contend that these adversarial examples may not be flaws in the attacked model, but flaws in the model that determines validity.We term such invalid inputs second-order adversarial examples.We propose the constraint robustness curve, and associated metric ACCS, as tools for evaluating the robustness of a constraint to second-order adversarial examples.To generate this curve, we design an adversarial attack to run directly on the semantic similarity models.We test on two constraints, the Universal Sentence Encoder (USE) and BERTScore.Our findings indicate that such second-order examples exist, but are typically less common than first-order adversarial examples in stateof-the-art models.They also indicate that USE is effective as constraint on NLP adversarial examples, while BERTScore is nearly ineffectual.Code for running the experiments in this paper is available here.
A game of quantum advantage: linking verification and simulation

We present a formalism that captures the process of proving quantum superiority to skeptics as an interactive game between two agents, supervised by a referee. Bob, is sampling from a classical distribution on a quantum device that is supposed to demonstrate a quantum advantage. The other player, the skeptical Alice, is then allowed to propose mock distributions supposed to reproduce Bob's device's statistics. He then needs to provide witness functions to prove that Alice's proposed mock distributions cannot properly approximate his device. Within this framework, we establish three results. First, for random quantum circuits, Bob being able to efficiently distinguish his distribution from Alice's implies efficient approximate simulation of the distribution. Secondly, finding a polynomial time function to distinguish the output of random circuits from the uniform distribution can also spoof the heavy output generation problem in polynomial time. This pinpoints that exponential resources may be unavoidable for even the most basic verification tasks in the setting of random quantum circuits. Beyond this setting, by employing strong data processing inequalities, our framework allows us to analyse the effect of noise on classical simulability and verification of more general near-term quantum advantage proposals.
Virtual Environments for Therapeutic Rehabilitation

Virtual Environments can be deployed in support to occupational therapy sessions, aiding the recovery process from traumas that hinder everyday activities. Dexterity training, fine movement control and focus may be exploited through a VR simulation, aiming at creating an attractive environment. Such environments may capture the user attention, keeping him/her motivated. With those goals set, a virtual environment was designed which, through playful activities, aims at contributing with the rehabilitation process. In order to provide proprioception and neuroplasticity, the system uses a Phantom Omni (aka Geomagic Touch) and Oculus Rift, to provide immersion and touch feedback. The system uses that setup to enable patients to exercise limbs that were damaged, to acquire full rehabilitation. We perform a usability test, which allow a comparison of an immersive and non-immersive setup.
Word-Final Phoneme Segmentation Using Cross-Correlation

The goal of this paper is to present a word-final target phoneme automated segmentation method based on cross-correlation coefficients computed between a reference sound wave and a sample sound wave. Most existing Speech Sound Disorder (SSD) Screening solutions require human intervention to a greater or lesser extent and use segmentation methods based on hard-coded time frames. Moreover, existing solutions extract features from the frequency domain, which entails large amounts of computational power to the detriment of real-time feedback. The pre-processing algorithm proposed in this paper, implemented in a Python version 3.7 script, automatically generates 2 new .wav files corresponding to the phonemes found in word-final position in the initial sound waves. The newly-generated .wav files are meant to be used as valid and homogeneous input in a subsequent classification stage aimed at rigorously discriminating mispronunciations of the target phoneme and assist Speech-Language Pathologists (SLPs) with the SSD screening.
Research on sparsity of frequency modulated signal in fractional Fourier transform domain

Frequency modulated (FM) signal is extensively applied in sonar, radar, laser and emerging optical cross-research, its sparsity is a common basic issue for the sampling, denoising and compression of FM signal. This paper mainly studies the sparsity of FM signal in the fractional Fourier transform (FRFT) domain, and a maximum singular value method (MSVM) is proposed to estimate the compact FRFT domain of FM signal. This method uses the maximum singular value of amplitude spectrum of FM signal to measure the compact domain, and WOA is used to search the compact domain, which effectively improves the shortcomings of the existing methods. Compared with MNM and MACF, this method gives a sparser representation of FM signal in the FRFT domain, which has less number of significant amplitudes. Finally, the primary application of this method in the FM signal filtering is given.
Context Aware Contrastive Opinion Summarization

Model-based approaches for context-sensitive contrastive summarization depend on hand-crafted features for producing a summary. Deriving these hand-crafted features using machine learning algorithms is computationally expensive. This paper presents a deep learning approach to provide an end-to-end solution for context-sensitive contrastive summarization. A hierarchical attention model referred to as Contextual Sentiment LSTM (CSLSTM) is proposed to automatically learn the representations of context, feature and opinion words present in review documents of each entity. The resultant document context vector is a high-level representation of the document. It is used as a feature for context-sensitive classification and summarization. Given a set of summaries from positive class and a negative class of two entities, the summaries which have high contrastive score are identified and presented as context-sensitive contrastive summaries. Experimental results on restaurant dataset show that the proposed model achieves better performance than the baseline models.
Netnography to Uncover Cryptomarkets

This chapter illustrates the application of netnographic practices within cryptomarkets on the Dark Web. The five studies examined demonstrate a continuity of learnings and present thick methodological description and ethical considerations raised by doing research in cryptomarkets. Cryptomarkets are online drug markets where goods and services are exchanged between parties, somewhat like eBay or Amazon, and utilise encryption technologies to evade centralised regulation and conceal user identities (Ferguson, 2017; Martin, 2014). The initial studies by Van Hout and Bingham (2013a, 2013b, 2014) provide early characterisations of drug consumerism in Silk Road. My subsequent study with Barratt and colleagues (Barratt, Lenton, Maddox, & Allen, 2016) explored the impacts of cryptomarkets on drug use trajectories. This study marks a technique pivot point around the seizure of the Silk Road marketplace towards multi-sited approaches. The subsequent studies considered examine Silk Road’s successors and focus on the performance of pseudonymous identity in high-risk transactions (Ferguson, 2017), the embedded values around drug distribution and consumption (Masson & Bancroft, 2018), and social acceptance of the drug cryptomarket environment (Kowalski, Hooker, & Barratt, 2019). The chapter demonstrates the versatility and dynamism of netnographic practices, while at the same time illustrating their transformation and subversion by the context.
Silicon quantum photonic device for multidimensional controlled unitaries

We present a fully reconfigurable silicon quantum photonic device capable of performing controlled four-dimensional unitary operations with 0 . 84 ± 0 . 02 fidelity. We report its characterisation by process tomography and deploy it to successfully perform a quantum model learning protocol.
Semantic Segmentation for Object Detection and Grasping with Humanoid Robots

Humanoid robots are expected to support to human at environments such as the house hospital, and hotel. For this aim, the humanoid robots should detect and recognize the objects. In this article, semantic segmentation algorithms are proposed to carry out for this aim. Different networks from the literature are used. The simulation results are compared in that accuracy, segmentation performance coefficients and parameter number. The obtained results show that the semantic segmentation is good at this task for the humanoid robots
The Utilization Of Google Classroom And Zoom Meeting As Distance Learning Media In COVID-19 Pandemic

The Covid-19 Pandemic outbreak changed and disrupted various aspects of life, including in the world of education. This changes the learning system from conventional to online. Online learning systems are the right obligation and choice in the current situation. Various forms of patterns and systems are offered, in this article the author conducts research on the effectiveness of online learning systems by using Google Classroom and Zoom Meeting as learning media in the midst of the Covid-19 epidemic at Jembatan Budaya Middle School. This learning system uses a meeting system and material explanations by means of video conferencing through the Zoom Meeting application and for daily assignments given through Google Classroom and for daily attendance and assessment systems, it is given through the Google Form contained in Google Classroom. The results of the author's research found that the learning system using Google Clasroom and Zoom meetings on the subjects of Hindu and Budhi Pekerti Religious Education at Jembatan Budaya Middle School became more effective and efficient in the midst of the current Covid-19 pandemic situation. This aims to ensure that the teaching and learning process continues to run well, as well as to provide good assurance for schools, teachers, students and parents to stay safe from transmission of the Covid-19 virus.
Innovative methods, forms and technologies in the field of education

The article is concerned with a search of new methods, forms and technologies in a sphere of education to improve the efficiency of teachers’ work at the present time. The authors mark the priority directions of the education system development: (1) creation of a high quality, technological infrastructure of the education system; (2) the use of innovative methods and educational technologies; (3) development of new technologies for educational purposes. The article deals with ICT, e-learning, blockchain, VR-technologies, augmented reality technologies, etc.
The Innovative Research on the Vocational Skill Appraisal of Higher Vocational Colleges under the New Situation

&lt;p&gt;With the continuous expansion of student sources in higher vocational colleges, the number of graduates is increasing day by day, along with the increasing employment pressure. Besides, there are many shortcomings in the development of talent training and achievement recognition. In response to this problem, this article analyzes the necessity of vocational skill appraisal in higher vocational colleges under the new situation. On this basis, it refers to the shortcomings in the emergence stage, and then combines the author's work experience to explore reform strategies and implementation plans, aiming to provide reference for related workers.&lt;/p&gt;
Cellular Structure for a Digital Fiat Currency

This chapter collects the slides from Prof. Andrew Rose (U C Berkeley and Dean NUS Business School since 2019) who commented on Prof. Engels’ “The Implications of Digital Currencies for Monetary Policy and the International Monetary System” (see Chapter 7).Prof. Andrew Rose agreed with the main conclusion that private sector digital currencies would have little immediate effect on monetary policy or the international monetary system. However, the potential of digital currency to increase capital mobility over time may make the choice between fixed exchange rate and monetary sovereignty sharper. Digital currencies may facilitate the loosening of capital controls. Prof. Rose commented that private digital currencies, even in aggregate, are fairly small: the total stock of them is less than 2% of worldwide currency and less than 4% of daily foreign exchange transactions. Because of their inherent volatility, private digital currencies do not meet the requirements of being money: a medium of exchange, unit of account and store of value. Private digital currencies cannot discipline the central bank’s monopoly on money supply. More fundamentally, it is not clear if private (decentralized) digital currencies can be algorithmically designed to stabilize prices, counter business cycles and serve as a lender of last resort, all of which are currently functions residing with the monetary authority.Prof. Rose argued that a central bank digital currency can indeed give a monetary authority the flexibility to offer negative interest rates. A central bank, however, needs to safeguard against hacking and create its own ‘know your client’ (KYC) protocols and risks substituting practices for commercial banks’ deposits. He further pointed out that with or without a central bank digital currency, the monetary authority’s policy assignment remains: a society gives its central bank power and independence in return for price and financial stability.
Free Choice Permission in Defeasible Deontic Logic

Free Choice Permission is one of the challenges for the formalisation of norms. In this paper, we follow a novel approach that accepts Free Choice Permission in a restricted form. The intuition behind the guarded form is strongly aligned with the idea of defeasibility. Accordingly, we investigate how to model the guarded form in Defeasible Deontic Logic extended with disjunctive permissions.
Efficient information-theoretic-statistical equation for image similarity: Comparison with Statistical Similarity and Information-Theoretic Similarity.

Several methods of image similarity were introduced to handle different forms of distortions of the image. Most of these techniques are based on statistics, other techniques are information-theoretical that are more modern and more effective than mathematical. in this paper, we present a different than its Common technique, a hybrid face image similarity measure by merging statistic and information theory, which is called in this paper as (ITSM), The proposed method (ITSM) Joins three mathematically balanced equations. The first we have newly created in this paper, entropic equation (EE) based on standard information-theoretic measures like (Shannon entropy, joint entropy). The second also newly created, histogram equation (HE) based on (image histogram and joint histogram). The third one is the standard statistic (SSIM). The (ITSM) was tested in Gaussian noise versus (SSIM) and Histogram-Entropic Scale (ITSSIM), where good results were obtained even under a large range of PSNRs. Simulation tests using the MATLAB images, (AT&T and FEI Brazilian face image) databases indicate that the Suggested hybrid technique outperforms the (SSIM) and (ITSSIM) techniques by being able to detect resemblance in faces under very low PSNR.
Research on Signal Modulation Recognition Method Based on Deep Belief Network

In order to solve the problem that high-order signal features need to be extracted manually and the recognition accuracy is not high in the process of signal modulation recognition, this paper applies the depth confidence network to the modulation recognition and studies the method of signal modulation recognition based on the Deep Belief Networks (DBN). In this paper, Restricted Boltzmann Machine (RBM) is used to build the network model of DBN, and then the simulation module of the data set needed by DBN is introduced. The DBN is trained by generating signal data, and the recognition of signal is realized. Simulation results show that the recognition accuracy of the method is higher than that of other machine learning algorithm.
Development of Ukrainian and Global Online Education

The article enlightens the activities of the main public online educational platforms in Ukraine, their role in the modernization of education and their socio-political influence on changes in the state. Interdependence is observed between the specificity, inherent in different stages of distance education progress. Problematic issues in the field of innovative educational technologies are highlighted: socialization of students, difficulties in control of their knowledge, metacognitive monitoring of mastering the material by them etc. The degree of organization in operation of electronic cabinets is examined and other measures supposed to introduce the telecommunicative teaching means in Ukrainian higher education.
Competências digitais de professores na modalidade de aulas online: estudo de caso no contexto da crise sanitária

This article presents the main results of research carried out in an institution of higher education located at Monterrey, Mexico in the current COVID-19’s pandemic in the first academic semester of 2020. Its main objective is to understand the full scope of the online academic continuance process and its relationship with the teacher’s digital competences improvement. A qualitative and interpretive research approach was followed based on case study. Semi-structured interviews, nonparticipant observations and focus groups were the main research techniques and methods used for data collection. Research findings demonstrated that adapting the unanticipated change in the online classes favored the development of teachers’ digital competences through an emerging process of self-training which contributed to the fulfillment of the criteria for ensuring educational quality in the health crisis context.
A study on interpersonal meaning negotiated teacher-students classroom interaction in vocational high school

Interaction and teaching learning process are two things that cannot be separated. Interaction is a main point or component in communication. Communication skill has to be mastered by students including Vocational students because they need this skill as preparation for their professional work in the future. A good communication is when it has meaning in interaction. Halliday &amp; Matthiessen, (2014) stated that there are three potential meanings, one of them are interpersonal meaning. This study was aimed to analyse interpersonal meaning between teacher and students in the eleventh grade of SMKN 2 Purwokerto and how it construct their role through various kinds of interaction. There were two activities in gathering the data of this study: observation and interview. In analyzing the data, the researcher used Mood system proposed by Halliday &amp; Matthiessen (2004). Theory from Harmer (2001) and Johnson and Paulston (1976, cited in Richard and Rogers 1986) also used to analyze the role relationship between teacher and students. The findings showed that teacher and students produced three types of speech functions (Mood clause) besides Non-typical Mood Clause and Minor clause. In the class, both teacher and students applied different types of classroom interaction in which they took many roles. It can be concluded that teacher and students delivered various types of clause, classroom interaction, and roles during the teaching learning process.
Análisis de apreciaciones referidas a la educación personalizada correspondiente al planteamiento pedagógico de Víctor García Hoz

It is presented appreciations and analysis on personalized&nbsp;education taking into account the responses of educators&nbsp;with extensive experience in this pedagogical proposal.&nbsp;The scenarios or questions correspond to nine aspects: The&nbsp;challenge of the complexity of learning teaching, opinion&nbsp;on this approach, innovation, tradition, teacher attitude,&nbsp;dimension of person, teacher, student, and parent. The&nbsp;answers are contrasted according to the approach of a&nbsp;pedagogy of the person proposed by Víctor García Hoz. The&nbsp;objectives were to see the dynamics of the interdependence&nbsp;of some aspects of personalized education based on nine&nbsp;criteria of analysis.&nbsp;Ten participants were interviewed using a questionnaire&nbsp;with nine questions and the answers were recorded on&nbsp;&nbsp;video and then transcribed the text to enable the respective&nbsp;analysis. The results are presented using figures that illustrate&nbsp;in a summary and representative manner, as far as possible,&nbsp;the considerations derived from the responses. The analysis&nbsp;emphasizes the complexity of the topic and suggests some&nbsp;questions to continue research on this topic.&nbsp
Symbolic Explanation Module for Fuzzy Cognitive Map-Based Reasoning Models

In recent years, pattern classification has started to move from computing models with outstanding prediction rates to models able to reach a suitable trade-off between accuracy and interpretability. Fuzzy Cognitive Maps (FCMs) and their extensions are recurrent neural networks that have been partially exploited towards fulfilling such a goal. However, the interpretability of these neural systems has been confined to the fact that both neural concepts and weights have a well-defined meaning for the problem being modeled. This rather naive assumption oversimplifies the complexity behind an FCM-based classifier. In this paper, we propose a symbolic explanation module that allows extracting useful insights and patterns from a trained FCM-based classifier. The proposed explanation module is implemented in Prolog and can be seen as a reverse symbolic reasoning rule that infers the inputs to be provided to the model to obtain the desired output.
Core Cloud Concepts: Compute

The breadth of cloud computing is evident from the legion of services that CSPs (cloud service providers) make available, the massive infrastructure deployed globally to support it, and the billions of dollars getting invested into the field. As more enterprises adopt the cloud and start investing in it, architects and development teams face a tough job. They need to make many choices toward the setup of an enterprise’s cloud footprint. Many of these are infrequent and lasting decisions; they seldom change after the initial setup. For instance, the choice of connectivity from an enterprise’s existing data center to a CSP is a one-time setup. However, for rolling out the microservices architecture on the cloud, some of the platform choices will need more variations. We walked through the various facets of cloud computing—compute, storage, and connectivity.