Reviewerly/exHarmony huggingface dataset:
Turkish sign language digits classification with CNN using different optimizers

Sign language is a way for hearing-impaired people to communicate among themselves and with people without hearing impairment. Communication with the sign language is difficult because few people know this language and the language does not have universal patterns. Sign language interpretation is the translation of visible signs into speech or writing. The sign language interpretation process has reached a practical solution with the help of computer vision technology. One of the models widely used for computer vision technology that mimics the work of the human eye in a computer environment is deep learning. Convolutional neural networks (CNN), which are included in deep learning technology, give successful results in sign language recognition as well as other image recognition applications. In this study, the dataset containing 2062 images consisting of Turkish sign language digits was classified with the developed CNN model. One of the important parameters used to minimize network error of the CNN model during the training is the learning rate. The learning rate is a coefficient used to update other parameters in the network depending on the network error. The optimization of the learning rate is important to achieve rapid progress without getting stuck in local minimums while reducing network error. There are several optimization techniques used for this purpose. In this study, the success of four different training and test processes performed with SGD, RMSprop, Adam and Adamax optimizers were compared. Adam optimizer, which is widely used today with its high performance, was found to be the most successful technique in this study with 98.42% training and 98.55% test accuracy.
Determining the Number of Neurons in Artificial Neural Networks for Approximation, Trained with Algorithms Using the Jacobi Matrix

How can we determine the optimal number of neurons when constructing an artificial neural network? This is one of the most frequently asked questions when working with this type of artificial intelligence. Experience has brought the understanding that it takes an individual approach for each task to specify the number of neurons. Our method is based on the requirement of algorithms looking for a minimum of functions of type 𝑺􁈺𝒛􁈻 􀵌 Σ 􁈾𝝋𝒊 𝒎 􁈺𝒛 􁈻􁈿𝟐 𝒊􀭀𝟏 that satisfy the inequality 𝒑 􀵑 𝒎, where p is the dimensionality of the argument z, and m is the number of functions. Formulas for an upper limit of the required neurons are proposed for networks with one hidden layer and for networks with r hidden layers with an equal number of neurons.
Fast Synthetic Dataset for Kitchen Object Segmentation in Deep Learning

Object recognition has been widely investigated in computer vision for many years.Currently, this process is carried out through neural networks, but there are very few public datasets available with mask and class labels of the objects for the training process in usual applications.In this paper, we address the problem of fast generation of synthetic datasets to train neural models because creating a handcraft labeled dataset with object segmentation is a very tedious and time-consuming task.We propose an efficient method to generate a synthetic labeled dataset that adequately combines background images with foreground segmented objects.The synthetic images can be created automatically with random positioning of the objects or, alternatively, the method can produce realistic images by keeping the realism in the scales and positions of the objects.Then, we employ Mask-RCNN deep learning model, to detect and segment classes of kitchen objects using images.In the experimental evaluation, we study both synthetic datasets, automatic or realistic, and we compare the results.We analyze the performance with the most widely used indexes and check that the realistic synthetic dataset, quickly created through our method, can provide competitive results and accurately classify the different objects.
Features and application of the cluster method of its organizations in lessons in secondary school and higher pedagogical institutions

The article considers pedagogical practice as one of the necessary components of the process of becoming a future teacher. The essence of the concept of “pedagogical practice” as a way of studying the educational process in the school based on the direct participation of trainees in it is revealed. All types of pedagogical practice are highlighted and characterized in the article, the organization, goals and tasks of each type of pedagogical practice are described in detail.
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field.In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP.While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task.In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF).Furthermore, we apply each model to a multitude of distinct domains.We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score.Furthermore, we find that the choice of domain significantly influenced the performance regardless of the respective data size or the model chosen.
Extreme Learning Machine Based on Maximum Weighted Mean Discrepancy for Unsupervised Domain Adaptation

Extreme Learning Machine (ELM) has shown fast learning speed and good generalization property in single-domain problems, such as classification and regression.However, the assumption that the training and testing data are subject to identical distribution often leads to significant performance degradation of ELM in cross-domain problems.To cope with unsupervised domain adaptation problems by ELM, we propose a novel method called Extreme Learning Machine based on Maximum Weighted Mean Discrepancy (ELM-MWMD) in this paper, which learns an adaptive ELM classifier with both labeled source data and unlabeled target data.Firstly, the cross-domain weight coefficients are specifically designed and assigned for each sample in source and target domains, fully considering the effects of individual information.Then the source classifier is adapted to the target domain by minimizing the distribution discrepancy between the two domains, both the marginal distribution and conditional distribution are simultaneously reduced to obtain a more accurate target classifier.Moreover, the predicted results for target data are utilized as pseudo labels to further improve the classification accuracy in multiple iterations.Extensive experiments on public image datasets demonstrate that ELM-MWMD performs better than several existing state-of-the-art domain adaptation methods by computation efficiency and classification accuracy.
Bridging Resolution: A Survey of the State of the Art

Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution.Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community.Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols.This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions.
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation.

In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.
A Parallel Packed Memory Array to Store Dynamic Graphs

The ideal data structure for storing dynamic graphs would support fast updates as well as fast range queries which underlie graph traversals such as breadth-first search. The Packed Memory Array (PMA) seems like a good candidate for this setting because it supports fast updates as well as cache-efficient range queries. Concurrently updating a PMA raises challenges, however, because an update may require rewriting the entire structure.This paper introduces a parallel PMA with intra- and inter-operation parallelism and deadlock-free polylogarithmicspan operations. Our main observation is that the PMA is well-suited to concurrent updates despite occasionally requiring a rewrite of the entire structure because 1) most of the updates only write to a small part of the structure and 2) the worst case is highly parallel and cache-efficient.To evaluate our data structure, we implemented Parallel Packed Compressed Sparse Row (PPCSR), a dynamic-graph processing framework that extends the Ligra interface with graph updates. We show that PPCSR is on average about 1.6x faster on graph kernels than Aspen, a state-of-the-art graph-streaming system. PPCSR achieves up to 80 million updates per second and is 2 – 5x faster than Aspen on most batch sizes. Finally, PPCSR is competitive with Ligra and Ligra+, two state-of-the-art static graph-processing frameworks.
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs

Interest has been rising lately towards modeling time-evolving knowledge graphs (KGs). Recently, graph representation learning approaches have become the dominant paradigm for link prediction on temporal KGs. However, the embedding-based approaches largely operate in a black-box fashion, lacking the ability to judge the results' reliability. This paper provides a future link forecasting framework that reasons over query-relevant subgraphs of temporal KGs and jointly models the graph structures and the temporal context information. Especially, we propose a temporal relational attention mechanism and a novel reverse representation update scheme to guide the extraction of an enclosing subgraph around the query. The subgraph is expanded by an iterative sampling of temporal neighbors and attention propagation. As a result, our approach provides human-understandable arguments for the prediction. We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task. While being more explainable, our model also obtains a relative improvement of up to 17.7 % on MRR compared to the previous best KG forecasting methods. We also conduct a survey with 53 respondents, and the results show that the reasoning arguments extracted by the model for knowledge forecasting are aligned with human understanding.
A new Paradigm and Meta-Model for Cyber-Physical-Social Systems

The notion of Cyber-Physical-Social System (CPSS) and similar concepts using different acronyms emerged as a major paradigm shift to facilitate the interaction between human and Cyber-Physical System (CPS). However, human interaction behaviour is the result of multiple social dimensions governed by complex environmental, cultural and contextual factors which are not yet fully understood. Additionally, works in this direction still lack a well established systemic foundation. Thus, handling properly the social factor in Human-CPS interaction remains an open challenge. In this paper, we present a new perspective and a formalisation for the CPSS paradigm, which is grounded on the theory of systems. The aim of this is to provide a general framework to handle social dimensions in Human-CPS interactions. We propose a meta-model, which provides a conceptual ground to design CPSS spaces where CPS are enhanced with social capabilities.
High-Fidelity Control of Superconducting Qubits Using Direct Microwave Synthesis in Higher Nyquist Zones

Control electronics for superconducting quantum processors have strict requirements for accurate command of the sensitive quantum states of their qubits.Hinging on the purity of ultra-phase-stable oscillators to upconvert very-low-noise baseband pulses, conventional control systems can become prohibitively complex and expensive when scaling to larger quantum devices, especially as high sampling rates become desirable for fine-grained pulse shaping.Few-gigahertz radio-frequency (RF) digital-to-analog converters (DACs) present a more economical avenue for high-fidelity control while simultaneously providing greater command over the spectrum of the synthesized signal.Modern RF DACs with extra-wide bandwidths are able to directly synthesize tones above their sampling rates, thereby keeping the system clock rate at a level compatible with modern digital logic systems while still being able to generate high-frequency pulses with arbitrary profiles.We have incorporated custom superconducting qubit control logic into off-the-shelf hardware capable of low-noise pulse synthesis up to 7.5 GHz using an RF DAC clocked at 5 GHz.Our approach enables highly linear and stable microwave synthesis over a wide bandwidth, giving rise to high-resolution control and a reduced number of required signal sources per qubit.We characterize the performance of the hardware using a five-transmon superconducting device and demonstrate consistently reduced two-qubit gate error (as low as 1.8%), which we show results from superior control chain linearity compared to traditional configurations.The exceptional flexibility and stability further establish a foundation for scalable quantum control beyond intermediate-scale devices.
Learning to Reach Goals via Iterated Supervised Learning

Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study RL algorithms that use imitation learning to acquire goal reaching policies from scratch, without the need for expert demonstrations or a value function. In lieu of demonstrations, we leverage the property that any trajectory is a successful demonstration for reaching the final state in that same trajectory. We propose a simple algorithm in which an agent continually relabels and imitates the trajectories it generates to progressively learn goal-reaching behaviors from scratch. Each iteration, the agent collects new trajectories using the latest policy, and maximizes the likelihood of the actions along these trajectories under the goal that was actually reached, so as to improve the policy. We formally show that this iterated supervised learning procedure optimizes a bound on the RL objective, derive performance bounds of the learned policy, and empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms in several benchmark tasks.
The Carbon Footprint of Bitcoin

Participation in the Bitcoin blockchain validation process requires specialized hardware and vast amounts of electricity, which translate into a significant carbon footprint. Here we demonstrate a methodology for estimating the power consumption associated with Bitcoin’s blockchain based on IPO filings of major hardware manufacturers, insights on mining facility operations, and mining pool compositions. We then translate our power consumption estimate into carbon emissions, using the localization of IP-addresses. We determine the annual electricity consumption of Bitcoin, as of November 2018, to be 48.2 TWh, and estimate that annual carbon emissions range from 23.6 to 28.8 MtCO2. This means that the level of emissions produced by Bitcoin sits between the levels produced by the nations of Jordan and Mongolia. With this article, we aim to gauge the external costs of Bitcoin, and inform the broader debate on the costs and benefits of cryptocurrencies. The externalities we discuss here may help policy-makers in setting the right rules as the adoption journey of blockchain has just started.
Uses and Resources of Technologies by Mathematics Students Prior to COVID-19

Digital competence in education includes, amongst other things, the ability to create and manage content and information, mastery of communication tools, and solving technological problems. The access to information and communication technologies (ICT) for educational purposes could have affected the academic performance of mathematics students prior to COVID-19. The objectives were to (1) analyze family and economic factors that could influence the use of ICT for educational purposes, and (2) determine what attributes of Melilla’s mathematics students could explain an additional variation in the use of ICT for academic purposes before COVID-19. A total of 2018 students at secondary schools in Melilla were included in this cross-sectional study. A validated questionnaire with 14 questions regarding ICT and its uses and resources in mathematics learning was administered to students. Statistical analysis revealed that 63.81% of students used ICT to study mathematics and 36.19% did not. Of this percentage, 30.22% failed mathematics and 68.43% reported that ICT does not help them improve their grades. The use of ICT to study mathematics was influenced by students’ perceptions of its usefulness for their academic performance and learning. We also found an association with educational level and time spent on the Internet.
Secure Opto-Audio Cryptosystem Using XORing Mask and Hartley Transform

This research paper proposes a secure opto-audio cryptosystem using XORing mask and Hartley transform (HT).The digital one dimensional (1D) plainaudio data is converted into two dimensional plainaudio map (2D PAM) and after that, the 2D PAM is divided into 2D plainaudio blocks (2D PABs).The basic idea of the proposed opto-audio encryption system depends on XORing each block of the 2D PABs with a single image selected from a personal image database that can be viewed as a secret key in the proposed opto-audio cryptosystem.Each block of the mixed 2D PABs is then transposed using the chaotic logistic adjusted sine map (LASM) and optically encrypted with HT.The XOR and LASM are implemented digitally while HT implemented optically.An additional XORing mask step helps to remove residual intelligibility from the 2D PABs, fill in speechless gaps in spoken conversations, and destroy both the pitch and format details.The utilization of chaotic LASM allows efficient noise immunity.A comparative study is held between the proposed opto-audio encryption system and other related audio encryption systems in terms the standard well known encryption metrics.The results have confirmed the efficiency of the introduced opto-audio encryption system.The proposed opto-audio encryption system security is explored from an accurate encryption point of view, and tests confirmed the superiority of the proposed opto-audio encryption system from the encryption point of view.
From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.
EOCD: An ensemble optimization approach for concept drift applications

Data streams applications generate a continuous stream of data in a high rate that it is not possible to store all data in available memory. Hence, it is important to apply techniques that are capable of learning concepts according to data presentation, taking into consideration available time, processing and memory resources. This paper presents a new ensemble-based approach to detect concept drift in the data stream context. This approach uses an explicit mechanism to adapt to concept drifts using a genetic algorithm in order to define the best ensemble configuration for the current scenario (detected drifts). The main aim of this approach is to provide an efficient structure to detect and to adapt to concept drifts and, as a consequence, to improve the performance of this system. Our findings show that the proposed method delivers outstanding results, outperforming the results delivered by the most efficient methods presented in the literature.
Online Adversarial Purification based on Self-supervised Learning

Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. In this paper, we combine canonical supervised learning with self-supervised representation learning, and present Self-supervised Online Adversarial Purification (SOAP), a novel defense strategy that uses a self-supervised loss to purify adversarial examples at test-time. Our approach leverages the label-independent nature of self-supervised signals and counters the adversarial perturbation with respect to the self-supervised tasks. SOAP yields competitive robust accuracy against state-of-the-art adversarial training and purification methods, with considerably less training complexity. In addition, our approach is robust even when adversaries are given the knowledge of the purification defense strategy. To the best of our knowledge, our paper is the first that generalizes the idea of using self-supervised signals to perform online test-time purification.