Reviewerly/exHarmony huggingface dataset:
Linking Souls to Humans with ZKBID: Accountable Anonymous Blockchain Accounts for Web 3.0 Decentralized Identity

A decentralized identity system that can provide users with self-sovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0. The accounting system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity: users can create their accounts without registering with a central agent. Such an identity system is endowed with anonymity property: nobody knows the account's owner because the relationship between an account and the owner is invisible. Thus, user privacy is well protected even though the account's data is public. However, a disadvantage of such complete anonymity is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain. In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities. Mistrust can be a major hurdle to the large-scale deployment of Web 3.0. This work proposes ZKBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems. ZKBID links souls (blockchain accounts) to humans (users) in a one-to-one manner to truly reflect the societal relationships and interactions between humans on the blockchain. With ZKBID, the users are accountable for their accounts anonymously, preserving privacy. ZKBID authenticates users using face match and then maps authenticated users to accounts. Zero-knowledge proofs encode the face match results, and user-account mappings employ linkable ring signatures to preserve anonymity. We implemented ZKBID and built a blockchain test network for evaluation purposes. Our tests demonstrate the effectiveness of ZKBID and suggest proper ways to configure ZKBID system parameters.
Sharia Compliance of Cryptocurrencies: Data-Driven Sharia Compliance Assessment

In recent years, a significant percentage of the population has engaged in the trade and use of cryptocurrencies, demonstrating the widespread interest in the field. The usefulness of cryptocurrencies as an option to fiat currency is one of the most pressing issues that arise. The fiat money system is primarily reliant on commercial banks, which require bank accounts to process individual payments. The legality of cryptocurrencies has been susceptible to subjective interpretation, and this research presents a new objective AI methodology for determining whether currencies comply with Sharia. The framework consists of an unsupervised BIRCH clustering method that allows for the grouping of volatilities and logarithmic returns based on a variety of periodic data. The approach gave a solid rationale for deciding automatically which cryptocurrency may not comply with Sharia law. The results indicate that, over longer time intervals, the volatility of various cryptocurrencies vary substantially. This permits the proper distinction between cryptocurrencies that comply with Sharia and those that do not. The methodology proposes an automated and data-driven method to objectively establish the Compliance with sharia of cryptocurrencies, enabling users to readily determine whether it is permitted to use such cryptocurrencies.
PENYELENGGARAAN PROGRAM BELAJAR BARENG (BELBAR) UNTUK SISWA SD DI DESA KABAR UTARA UNTUK MENGISI AKTIVITAS SISWA PADA MASA PANDEMI

This community service aims to provide additional lessons for elementary school children. This Studying together collaborates with the youth of Syubban Syu'ada, MI NW library, and SDN 1 Kabar. The methods used in this program are speech, learning while playing, group work methods, etc. The target to be achieved in this activity is that they fill the time at home during online learning with useful things so that children do not be left behind in understanding the material presented online. The results of this program are students understand the material as well as know some easy and fun method of learning that was not given by the school.
Defining Logical Systems via Algebraic Constraints on Proofs

We present a comprehensive programme analysing the decomposition of proof systems for non-classical logics into proof systems for other logics, especially classical logic, using an algebra of constraints. That is, one recovers a proof system for a target logic by enriching a proof system for another, typically simpler, logic with an algebra of constraints that act as correctness conditions on the latter to capture the former; for example, one may use Boolean algebra to give constraints in a sequent calculus for classical propositional logic to produce a sequent calculus for intuitionistic propositional logic. The idea behind such forms of reduction is to obtain a tool for uniform and modular treatment of proof theory and provide a bridge between semantics logics and their proof theory. The article discusses the theoretical background of the project and provides several illustrations of its work in the field of intuitionistic and modal logics. The results include the following: a uniform treatment of modular and cut-free proof systems for a large class of propositional logics; a general criterion for a novel approach to soundness and completeness of a logic with respect to a model-theoretic semantics; and, a case study deriving a model-theoretic semantics from a proof-theoretic specification of a logic.
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

Randomized smoothing is one of the most promising frameworks for certifying the adversarial robustness of machine learning models, including Graph Neural Networks (GNNs). Yet, existing randomized smoothing certificates for GNNs are overly pessimistic since they treat the model as a black box, ignoring the underlying architecture. To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes. Compared to existing certificates, we certify robustness to much stronger adversaries that control entire nodes in the graph and can arbitrarily manipulate node features. Our certificates provide stronger guarantees for attacks at larger distances, as messages from farther-away nodes are more likely to get intercepted. We demonstrate the effectiveness of our method on various models and datasets. Since our gray-box certificates consider the underlying graph structure, we can significantly improve certifiable robustness by applying graph sparsification.
Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)

We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the ''cluster merging'' version of the coref-hoi model, which brings up to 10.33% improvement 1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of corefhoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.
Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-waves

Neuromorphic computing using spin waves is promising for high-speed nanoscale devices, but the realization of high performance has not yet been achieved. Here we show, using micromagnetic simulations and simplified theory with response functions, that spin-wave physical reservoir computing can achieve miniaturization down to nanoscales keeping high computational power comparable with other state-of-art systems. We also show the scaling of system sizes with the propagation speed of spin waves plays a key role to achieve high performance at nanoscales.
Builtin Types viewed as Inductive Families

State of the art optimisation passes for dependently typed languages can help erase the redundant information typical of invariant-rich data structures and programs. These automated processes do not dramatically change the structure of the data, even though more efficient representations could be available. Using Quantitative Type Theory, we demonstrate how to define an invariant-rich, typechecking time data structure packing an efficient runtime representation together with runtime irrelevant invariants. The compiler can then aggressively erase all such invariants during compilation. Unlike other approaches, the complexity of the resulting representation is entirely predictable, we do not require both representations to have the same structure, and yet we are able to seamlessly program as if we were using the high-level structure.
Controlling the bursting size in the two-dimensional Rulkov model

We propose to control the orbits of the two-dimensional Rulkov model affected by bounded noise. For the correct parameter choice the phase space presents two chaotic regions separated by a transient chaotic region in between. One of the chaotic regions is the responsible to give birth to the neuronal bursting regime. Normally, an orbit in this chaotic region cannot pass through the transient chaotic one and reach the other chaotic region. As a consequence the burstings are short in time. Here, we propose a control technique to connect both chaotic regions and allow the neuron to exhibit very long burstings. This control method defines a region Q covering the transient chaotic region where it is possible to find an advantageous set $S \in Q$ through which the orbits can be driven with a minimal control. In addition we show how the set S changes depending on the noise intensity affecting the map, and how the set S can be used in different scenarios of control.
On the Approximation Accuracy of Gaussian Variational Inference

The main computational challenge in Bayesian inference is to compute integrals against a high-dimensional posterior distribution. In the past decades, variational inference (VI) has emerged as a tractable approximation to these integrals, and a viable alternative to the more established paradigm of Markov Chain Monte Carlo. However, little is known about the approximation accuracy of VI. In this work, we bound the TV error and the mean and covariance approximation error of Gaussian VI in terms of dimension and sample size. Our error analysis relies on a Hermite series expansion of the log posterior whose first terms are precisely cancelled out by the first order optimality conditions associated to the Gaussian VI optimization problem.
OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems

Deep learning methods can be used to produce control policies, but certifying their safety is challenging. The resulting networks are nonlinear and often very large. In response to this challenge, we present OVERT: a sound algorithm for safety verification of nonlinear discrete-time closed loop dynamical systems with neural network control policies. The novelty of OVERT lies in combining ideas from the classical formal methods literature with ideas from the newer neural network verification literature. The central concept of OVERT is to abstract nonlinear functions with a set of optimally tight piecewise linear bounds. Such piecewise linear bounds are designed for seamless integration into ReLU neural network verification tools. OVERT can be used to prove bounded-time safety properties by either computing reachable sets or solving feasibility queries directly. We demonstrate various examples of safety verification for several classical benchmark examples. OVERT compares favorably to existing methods both in computation time and in tightness of the reachable set.
Post-Quantum Key Agreement Protocol based on Non-Square Integer Matrices

We present in this paper an algorithm for exchanging session keys, coupled with a hashing encryption module. We show schemes designed for their potential invulnerability to classical and quantum attacks. In turn, if the parameters included were appropriate, brute-force attacks exceed the (five) security levels used in the NIST competition of new post-quantum standards. The original idea consists of products of rectangular matrices in Zp as public values and whose factorization is proved to be an NP-complete problem. We present running times as a function of the explored parameters and their link with operational safety. To our knowledge there are no classical and quantum attacks of polynomial complexity available at hand, remaining only the systematic exploration of the private-key space.
English Teaching and Mastering for Learners with Intellectual Disabilities

The purpose of this research is to find out the challenges and obstacles that arise in teaching through analysis of review papers over the last 10 years with a traditional literature review approach. This study aims at investigating English Teaching and Mastering for Learners with Intellectual Disabilities according to a literature review that the researchers found in a reputable journal. The researcher used traditional literature review. Based on the ten articles analysis, teaching and mastering English for learners with intellectual disabilities have some challenges, obstacles and methods depending on the grade level. Several challenges and obstacles in teaching and mastering have been solved. This study aims to reveal English Teaching and Mastering for Learners with Intellectual Disabilities. Knowing the teaching and mastering process in teaching English for learners with Intellectual Disability is important in order to know what to change from our educational system. Problems inevitably happen in teaching English, especially when teaching learners with disabilities.
An Automatic Method for Generating Symbolic Expressions of Zernike Circular Polynomials

Zernike circular polynomials (ZCP) play a significant role in optics engineering. The symbolic expressions for ZCP are valuable for theoretic analysis and engineering designs. However, there are still two problems which remain open: firstly, there is a lack of sufficient mathematical formulas of the ZCP for optics designers; secondly the formulas for inter-conversion of Noll's single index and Born-Wolf's double indices of ZCP are neither uniquely determinate nor satisfactory. An automatic method for generating symbolic expressions for ZCP is proposed based on five essential factors: the new theorems for converting the single/double indices of the ZCP, the robust and effective numeric algorithms for computing key parameters of ZCP, the symbolic algorithms for generating mathematical expressions of ZCP, and meta-programming \&amp; \LaTeX{} programming for generating the table of ZCP. The theorems, method, algorithms and system architecture proposed are beneficial to both optics design process, optics software, computer-output typesetting in publishing industry as well as STEM education.
Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS

Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data distribution. These can lead to spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with known causal relations. The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS. We provide a comprehensive set of comparative analyses of biasing the model using different types of knowledge. We found that (i) knowledge that corrects the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected. We also demonstrate the behavior of the model and the effectiveness of domain knowledge on a real-world dataset.
MessageNet: Message Classification using Natural Language Processing and Meta-data

In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks, combined with a novel technique for infusing the meta-data input that is typically available in messages such as the sender information, timestamps, attached image, audio, affiliations, and more. As we demonstrate throughout the paper, going beyond the mere text by leveraging all available channels in the message, could yield an improved representation and higher classification accuracy. To achieve message representation, each type of input is processed in a dedicated block in the neural network architecture that is suitable for the data type. Such an implementation enables training all blocks together simultaneously, and forming cross channels features in the network. We show in the Experiments Section that in some cases, message's meta-data holds an additional information that cannot be extracted just from the text, and when using this information we achieve better performance. Furthermore, we demonstrate that our multi-modality block approach outperforms other approaches for injecting the meta data to the the text classifier.
Applying Quantum Error-correcting Codes for Fault-tolerant Blind Quantum Cloud Computation

Blind Quantum Computation (BQC) is a delegation computing protocol that allows a client to utilize a remote quantum server to implement desired quantum computations while keeping her inputs, outputs, and algorithms private. However, qubit errors during the quantum computation are important issues that need to be addressed. In this paper, we propose a fault-tolerant blind quantum computation protocol with quantum error-correcting codes to avoid the accumulation and propagation of qubit errors during the computational process. We further propose to apply the concatenated codes in the protocol to improve the error correction performance. More importantly, we quantify the resource consumption of photon pulses by our optimal level concatenation codes. Extensive simulation results show that our scheme not only can improve the preparation efficiency but also reduce quantum resource consumption, which shows a significant improvement to realize a fault-tolerant BQC.
Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning

In this paper, we focus on addressing the open-set face identification problem on a few-shot gallery by fine-tuning. The problem assumes a realistic scenario for face identification, where only a small number of face images is given for enrollment and any unknown identity must be rejected during identification. We observe that face recognition models pretrained on a large dataset and naively fine-tuned models perform poorly for this task. Motivated by this issue, we propose an effective fine-tuning scheme with classifier weight imprinting and exclusive BatchNorm layer tuning. For further improvement of rejection accuracy on unknown identities, we propose a novel matcher called Neighborhood Aware Cosine (NAC) that computes similarity based on neighborhood information. We validate the effectiveness of the proposed schemes thoroughly on large-scale face benchmarks across different convolutional neural network architectures. The source code for this project is available at: https://github.com/1ho0jin1/OSFI-by-FineTuning
RePAD: Real-time Proactive Anomaly Detection for Time Series

During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.
INTERVENTION PROGRAM FOR FL TEXT COMPREHENSION

Purpose. The article is devoted to the study of an innovative method that can be used by teachers to develop reading skills in foreign language lessons, in particular, with the help of an intervention program for reading comprehension in English, which can be effectively used in the process of teaching/ learning a foreign language, as well as generalization of research results taking into account the approbation of the proposed method.&#x0D; Methodology. The research is based on the theoretical study and synthesis of scientific and methodological literature, as well as Internet resources related to the research topic; general scientific methods – analysis and generalization are used.&#x0D; Results. The results of the study showed that the intervention program for foreign language (FL) text comprehension can be used in reading classes at different stages of work on a foreign language text, which contributes to the activation of students’ learning activities, increasing their language competence and motivation. As a result of the analysis of the scientific material and its approbation, it was suggested that the integration of the intervention program into the process of teaching a foreign language, in particular the English language, will allow teachers to improve their teaching methods, and students to develop their processes of understanding and perception of the text more actively, increase their motivation to read in a foreign language. This will create a positive social atmosphere in the classroom, which will contribute to the subsequent achievement of the required educational results.&#x0D; Practical implications. The results of the study showed that it is possible to optimize the methodology of teaching a foreign language by stimulating cognitive and metacognitive processes and supporting affective and social determinants when working with foreign language texts, which contributes to a deeper understanding of what is read.