Reviewerly/exHarmony huggingface dataset:
Digital ecosystem as an instrument of digital transformation of the university

Digital transformation is inevitable, regardless of the personal attitude of people to this issue. Researchers distinguish between organizational transformation using digital technologies (identity enhancement) and digital transformation (creating a new identity). In other words, digital technologies can become a tool for non-systemic transformations of existing structures, but thanks to understanding their nature, one can find ourselves in a situation of evolution of the structures themselves and, accordingly, the daily life of a person and social practices. The article proposes the concept of the digital ecosystem of the university as an instrument of digital transformation, which contributes to the development of the digital transformation methodology and can be used as the basis for the digital maturity index of the university and the development of a calculation formula. The research findings may be of interest to policymakers in charge of digital transformation policy at universities. The article reveals that in scientific periodicals the term "ecosystem" is interpreted in three semantic clusters: business ecosystem; innovative ecosystems; digital ecosystems. In this study, the concept of an ecosystem is interpreted in the context of digital transformation in relation to the digital environment of the university. The digital ecosystem is an instrumental concept, i.e. a means of digital transformation that reconstructs the environment and identity of the university. The digital ecosystem as a means of digital transformation, formulated by the author of the article, structurally contains the following elements: digital environment, which includes information and technical infrastructure; interaction participants (stakeholders); system of connections between participants; involvement of participants; the functions of the university reflected in the figure; idea and values, including regarding digital technologies; digital productivity and adequacy (implementation of digital solutions used by participants to maintain the ecosystem). A digital university is not only about having a network architecture or a digitally packaged educational product. It is also about the participants, their engagement, values and digital productivity. It should be borne in mind that if digital data is a raw material for the digital economy, then digital productivity becomes an important element of human digital activity.
The zero trust security platform for data trusteeship

Cloud storage is a low-cost and convenient storage method, but the nature of cloud storage determines the existence of security risks for data uploaded by users. In order to ensure the security of users' data in third-party cloud platforms, a zero trust security platform for data trusteeship is proposed. The platform introduces the concept of zero trust, which meets the needs of users to upload sensitive data to untrusted third-party cloud platforms by implementing multiple functional modules such as sensitivity analysis service, cipher index service, attribute encryption service.
TAKE IT YA 3AZIZI: CODE-SWITCHING ON FACEBOOK BY JORDANIAN BILINGUALS

This study scrutinizes the structural aspects of Jordanian Romanized Arabic-English code-switching (CS) on Facebook. All posts examined are written in Romanized Arabic, a new trend of writing on computer-mediated communication (CMC). Three types of CS are examined, i.e., inter-sentential, intra-sentential, and tags, though the greatest focus is on the intra-sentential instances because of their syntactic complexity. Different syntactic categories are examined and critically scrutinized. Finally, the validity of two controversial syntactic constraints, i.e., the Equivalence Constraint (EC) and the Free Morpheme Constraint (FMC) by Poplack (1980) are tested. The findings show that nouns and noun phrases are the most frequently used type of switches among the other syntactic categories, i.e., verbs and verb phrases, adjectives, adverbs, prepositions and prepositional phrases, and pronouns. The results also obviously reveal that there is a violation to the two constraints, namely The Free Morpheme Constraint and The Equivalence Constraint.
Observer control for bearings-only tracking using possibility functions

Bearings-only tracking using passive sensors is important for covert surveillance of moving targets. This paper adopts a mathematical formulation of bearings-only tracking in the framework of possibility theory, where uncertainties are represented using possibility functions, rather than usual probability distributions. Possibility functions have the capacity to deal robustly with partial (incomplete) specification of mathematical models and have been found particularly useful in model mismatch situations. The paper explores the design of reward functions which provide information gain in the context of observer motion control, in the framework of possibilistic recursive filter for bearings-only tracking. Numerical results demonstrate that in the presence of a model mismatch, the proposed framework performs better than the Bayesian probabilistic framework for stochastic filtering and control.
Designing Musical Games for Electroacoustic Improvisation

This paper describes the background and motivations behind the author’s electroacoustic game-pieces Pathfinder (2016) and ICARUS (2019), designed specifically for his performance practice with an augmented drum kit. The use of game structures in music is outlined, while musical expression in the context of commercial musical games using conventional game controllers is discussed. Notions such as agility, agency and authorship in music composition and improvisation are in parallel with game design and play, where players are asked to develop skills through affordances within a digital game-space. It is argued that the recent democratisation of game engines opens a wide range of expressive opportunities for real-time game-based improvisation and performance. Some of the design decisions and performance strategies for the two instrument-controlled games are presented to illustrate the discussion; this is done in terms of game design, physical control through the augmented instrument, live electronics and overall artistic goals of the pieces. Finally, future directions for instrument-controlled electroacoustic game-pieces are suggested.
Application and Thinking of Cloud Accounting in Accounting Informatization

Abstract The history of accounting basically comes into being with all the economic activities of human beings. In the past, our ancestors carved numbers and words on the rock wall. The most common thing was keeping accounts, such as what assets were available at that time, how to distribute them and how much was left, which is now called asset recording and asset distribution. With the development of the society and the refinement of the accounting work, the construction of accounting information has gradually become a hot spot in the industry, and is also the focus and development trend of the current accounting reform.
Pushing the Boundaries of Asymptotic Optimality in Integrated Task and Motion Planning

Integrated task and motion planning problems describe a multi-modal state space, which is often abstracted as a set of smooth manifolds that are connected via sets of transitions states. One approach to solving such problems is to sample reachable states in each of the manifolds, while simultaneously sampling transition states. Prior work has shown that in order to achieve asymptotically optimal (AO) solutions for such piecewise-smooth task planning problems, it is sufficient to double the connection radius required for AO sampling-based motion planning. This was shown under the assumption that the transition sets themselves are smooth. The current work builds upon this result and demonstrates that it is sufficient to use the same connection radius as for standard AO motion planning. Furthermore, the current work studies the case that the transition sets are non-smooth boundary points of the valid state space, which is frequently the case in practice, such as when a gripper grasps an object. This paper generalizes the notion of clearance that is typically assumed in motion and task planning to include such individual, potentially non-smooth transition states. It is shown that asymptotic optimality is retained under this generalized regime.
Artificial intelligence using for medical diagnosis via implementation of expert systems

Modern biomedical technologies development affords to provide the doctor with colossal amount of information about patients organism condition. However, the opportunity of using this data for medical diagnosis fully now is a distantive perspective only. The reason is a humans limited ability in assessment and interpretation this data arrays. The solution seems in artificial intelligence and expert systems wide introduction to medicine. Currently, almost all authors consider various options for constructing artificial neural networks as a way to implement artificial intelligence. This approach, which goes back to the fundamental theorem of A.N. Kolmogorov, the works of V.I. Arnold and Hecht-Nielsen [3], demonstrates excellent capabilities in a number of pattern recognition problems, which are reduced to revealing hidden details against the background of input noises. Much less often is mentioned such a method of modeling formal thinking as expert systems, which arose in the 1960s and then went into the shadows. Since the inception of cybernetics, computer programmers have tried to reproduce the mechanism of human thinking, that is, the task was to teach the computer to "think". The first known results in the field of creating and using intelligent systems were laid by the work of Norbert Wiener and G.S. Altshuller. At the same time, the creation of intelligent systems was reduced to the development of programs that solve problems using a variety of heuristic methods based on the property of human thinking to generalize.
CDEC: a constrained deep embedded clustering

Purpose The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence. Design/methodology/approach In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts. Findings First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed. Originality/value Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.
Emotion Speech Synthesis Method Based on Multi-Channel Time–Frequency Domain Generative Adversarial Networks (MC-TFD GANs) and Mixup

Abstract As one of the most challenging and promising topics in speech field, emotion speech synthesis is a hot topic in current research. At present, the emotion expression ability, synthesis speed and robustness of synthetic speech need to be improved. Cycle-consistent Adversarial Networks (CycleGAN) provides a two-way breakthrough in the transformation of emotional corpus information. But there is still a gap between the real target and the synthesis speech. In order to narrow this gap, we propose an emotion speech synthesis method combining multi-channel Time–frequency Domain Generative Adversarial Networks (MC-TFD GANs) and Mixup. It includes three stages: multichannel Time–frequency Domain GANs (MC-TFD GANs), loss estimation based on Mixup and effective emotion region stacking based on Mixup. Among them, the gating unit GTLU (gated tanh linear units) and the image expression method of speech saliency region are designed. It combines the Time–frequency Domain MaskCycleGAN based on improved GTLU and the time-domain CycleGAN based on saliency region to form the multi-channel GAN in the first stage. Based on Mixup method, the calculation method of loss and the aggravation degree of emotion region are designed. Compared with several popular speech synthesis methods, the comparative experiments were carried out on the interactive emotional dynamic motion capture (IEMOCAP) corpus. The bi-directional three-layer long short-term memory (LSTM) model was used as the verification model. The experimental results showed that the mean opinion score (MOS) and the unweighted accuracy (UA) of the speech generated by the synthesis method were improved, and the improvements were 4% and 2.7%, respectively. The current model was superior to the existing GANs model in subjective evaluation and objective experiments, ensure that the speech generated by this model had higher reliability, better fluency and emotional expression ability.
Proposing Blockchain Technology Based Zakat Management Model to Enhance Muzakki’s Trust in Zakat Agencies: A Conceptual Study

Objective – This study proposes a conceptual framework of Zakat based management model using blockchain technology with its transparent, secure, auditable, and efficient system in order to enhance the trust in zakat agencies. Design/methodology – This study is based on literature and theoretical reviews. This study took BAZNAS as the model of the Indonesian zakat agency to use Blockchain-based zakat payment on which zakat payers can track the fund allocated to the beneficiaries directly. Results – This research demonstrates the importance of Blockchain and smart contract technologies in zakat management by developing a model that combines zakat management agencies with the components of Blockchain and smart contract technologies. Research limitations/implications – The proposed model can contribute to the sustainability and efficiency of zakat agencies and is in line with the poverty eradication effort in Indonesia.
Hybrid Encryption Model Based on Advanced Encryption Standard and Elliptic Curve Pseudo Random

Securing multimedia applications becomes a major challenge with the violation of the information increasing currently. In this paper, a novel method for color image encryption is proposed. The procedure of encryption is performed using cooperation between Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES) with CTR (Counter) mode. In the cryptographic system, we have proposed to take advantage of the Elliptic Curve Random Generator to generate a sequence of arbitrary numbers based on the curve. The random generation step is founded on the public key sharing and a changing point G. Then, the AES-CTR is performed to these sequences using arbitrary keys for image encryption. The use of the AES alongside greatly distributed random results an interesting encryption method. Security analysis is successfully performed and our experiments prove that the suggested technique provides the basis of cryptography with more simplicity and correctness.
PyEI: A Python package for ecological inference

An important question in some voting rights and redistricting litigation in the U.S. is whether and to what degree voting is racially polarized.In the setting of voting rights cases, there is a family of methods called "ecological inference" (see especially King, 1997) that uses observed data, pairing voting outcomes with demographic information for each precinct in a given polity, to infer voting patterns for each demographic group.
A Conﬂict Tolerant Logic of Explicit Evidence

Standard epistemic modal logic is unable to adequately deal with the FrauchigerRenner paradox in quantum physics. We introduce a novel justiﬁcation logic CTJ, in which the paradox can be formalized without leading to an inconsistency. Still CTJ is strong enough to model traditional epistemic reasoning. Our logic tolerates two diﬀerent pieces of evidence such that one piece justiﬁes a proposition and the other piece justiﬁes the negation of that proposition. However, our logic disallows one piece of evidence to justify both a proposition and its negation. We present syntax and semantics for CTJ and discuss its basic properties. Then we give an example of epistemic reasoning in CTJ that illustrates how the diﬀerent principles of CTJ interact. We continue with the formalization of the Frauchiger–Renner thought experiment and discuss it in detail. Further, we add a trust axiom to CTJ and again discuss epistemic reasoning and the paradox in this extended setting.
Reliability testing, noise and error correction of real quantum computing devices

From Pharmacology to Cryptography and from Geology to Astronomy are some of the scientific fields in which Quantum Computing potentially will take off and fly high. Big Quantum Computing vendors invest a large amount of money in improving the hardware and they claim that soon enough a quantum program will be hundreds of thousands of times faster than a typical one we know nowadays. But still the reliability of such systems is the main obstacle. In this work, the reliability of real quantum devices is tested and techniques of noise and error correction are presented while measurement error mitigation is explored. In addition, a well-known string matching algorithm (Bernstein-Vazirani) was applied to the real quantum computing device in order to measure its accuracy and reliability. Simulated environments were also used in order to evaluate the results. The results obtained, even if these were not 100% accurate, are very promising which proves that even these days a quantum computer working side by side with a typical one is reliable and especially when error mitigation techniques are applied.
Exploring Simple Detection Techniques for DNS-over-HTTPS Tunnels

While DNS tunneling has shown promise as a censorship circumvention technique, it is limited by the plaintext nature of the DNS protocol, which renders it easily detectable to censors. DNS-over-HTTPS (DoH) [16] resolves this detectability obstacle, by encrypting the entire DNS protocol inside HTTPS. DoH tunneling shows promise as a medium for circumvention as its adoption increases in everyday usage, but it may still be vulnerable to flow-based attacks. This paper explores the design space of threshold-based attacks and defences on encrypted DNS tunnels. We identify thresholds separating tunnel traffic from browser-generated DoH traffic using packet size, packet rate, and throughput. We further propose modifications for encrypted DNS tunnels to evade flow-based detection and measure the reduction in usability. Notably, throughput is decreased by at least 27x and page load time is increased by at least 23x. However, despite the cutback in usability, we outline the potential for DNS tunnels to work in conjunction with, and obfuscate the registration traffic of, other anti-censorship tools.
Optimization and assessment of a sequential extraction procedure for calcium carbonate rocks

Abstract Sequential extraction analyses are widely used for the determination of element speciation in sediments and soils. Typical sequential extraction protocols were developed to extract from low-carbonate samples and therefore are not necessarily suitable for high-carbonate samples. In this study, we tested increased reagent to sample ratios to adjust an existing sequential extraction procedure to analyze high-CaCO 3 samples with concentrations ranging from 70 to above 90 %. Complete dissolution of the CaCO 3 phase, and a higher extraction efficiency of manganese associated with the carbonate phase, was achieved when using four times the original reagent to sample ratio in the 2nd extraction step. This increase of reagent did not compromise the extraction of subsequent phases as shown by unaffected Fe concentrations in a low-carbonate sample. Hence, an essential outcome was that increasing the solution to sample ratio did not lead to the dissolution of other sedimentary phases, such as hydrous and crystalline iron oxides or sulfides. Thus, compared to other extraction protocols that use a lower reagent to sample ratio in the carbonate dissolution step, the new protocol allowed the complete extraction of oxide and sulfide phases in the following extraction steps. Furthermore, the study demonstrated the benefit of replacing Na-acetate with NH 4 -acetate to extract exchangeable ions and carbonates. We observed increased intensities for several analytes, i.e., trace metals such as Mo and As, due to less suppression of the analyte signal by NH 4 -acetate than by Na-acetate during analysis by inductively coupled plasma optical emission spectrometry (ICP-OES).
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.