Reviewerly/exHarmony huggingface dataset:
Handwritten Chinese Characters Recognition Using Two-Stage Hierarchical Convolutional Neural Network

Convolutional Neural Networks (CNNs) are widely used for handwritten character recognition tasks. The task of handwritten Chinese character recognition is especially outlined by a large number of target classes. The total number of Chinese characters exceeds 40,000, the average Chinese uses several thousand characters in his speech. In this article, we will work with about 4,000 Chinese characters. Dealing with a large number of classes might be problematic for a single-network approach in terms of both speed and accuracy, especially when running on a CPU. In this paper, we suggest an approach to meet this challenge to do separate handwritten Chinese characters recognition. We propose that a hierarchy of multiple neural networks should be used wherein a first-level network chooses a second-level network that performs the final recognition. Further, as every second-level network is only trained for a subset of classes, it can be simpler and more accurate than the whole classification network. In our approach, we combine several classes of Chinese characters with similar external features in one group (cluster) and work with these clusters further. Experimental results on CASIA 200-class and 3755-class handwritten datasets are presented, which compare the proposed hierarchical approach with the classical single-network approach.
Modified Least Significant Bit Technique for Securing Medical Images

The confidentiality and safety of patient records is a significant concern for medical professionals. So protections must be placed to guarantee that illegal individuals do not have access to medical images (Patient’s description). Hence, the objective of this study is to secure digital medical images being transmitted over the internet from being accessed by an intruder. The study, therefore, proposed a modified Least Significant Bit (LSB) algorithm implemented on a MATLAB 2018a programming environment, and the proposed system was compared with the existing system using three performance metrics which are PSNR. MSE and SSIM. The result showed that the proposed approach outperformed the current standard methods by producing a more robust, high capacity, and highly imperceptible stego image. The comparative analysis conducted also showed that the PSNR valve is higher, and MSE value is lower when compared with existing systems. It was concluded that the projected technique accomplishes excellently in making the medical image transmitted to be more secured, robust, and invisible, thereby making the communication to be unnoticeable by an intruder or attacker.
Clause-Level Tense, Mood, Voice and Modality Tagging for German

We present a language-independent clausizer (clause splitter) based on Universal Dependencies (Nivre et al., 2016), and a clause-level tagger for grammatical tense, mood, voice and modality in German.The paper recapitulates verbal inflection in German-always juxtaposed with its close relative English-and transforms the linguistic theory into a rule-based algorithm.We achieve state-of-the-art accuracies of 92.6% for tense, 79.0% for mood, 93.8% for voice and 79.8% for modality in the literary domain.Our implementation is available at https://gitlab.gwdg.de/tillmann.doenicke/tense-tagger.
Finding the Best k for the Dimension of the Latent Space in Autoencoders

In machine learning, one of the most efficient feature extraction methods is autoencoder which transforms the data from its original space to a latent space. The transformed data is then used for machine learning downstream tasks rather than the original data. However, there is little research about choosing the best number of latent space dimensions (k) for autoencoders that can affect the result of these tasks. In this paper, we focus on the impact of k on the accuracy of a downstream task. Concretely, we survey recently developed autoencoders and their characteristics, and conduct experiments using different autoencoders and k for extracting information from different datasets. We then present the accuracy of a classifier on the extracted datasets and the reconstruction error of the autoencoders according to k. From the empirical results, we recommend the best k of the latent space dimension for each dataset and each autoencoder.
Three-party quantum private computation of cardinalities of set intersection and union based on GHZ states

Abstract Private Set Intersection Cardinality (PSI-CA) and Private Set Union Cardinality (PSU-CA) are two cryptographic primitives whereby two or more parties are able to obtain the cardinalities of the intersection and the union of their respective private sets, and the privacy of their sets is preserved. In this paper, we propose a three-party protocol to finish these tasks by using quantum resources, where every two, as well as three, parties can obtain the cardinalities of the intersection and the union of their private sets with the help of a semi-honest third party (TP). In our protocol, GHZ states play a role in encoding private information that will be used by TP to compute the cardinalities. We show that the presented protocol is secure against well-known quantum attacks. In addition, we analyze the influence of six typical kinds of Markovian noise on our protocol.
Distance learning in modern conditions: overview of opportunities and limitations

The article analyzes the scientific psychological and pedagogical literature on the implementation of distance education (DE) in modern conditions. Every year, the number of information and communication technologies that are integrated into the field of education increases. In 2020, in order to prevent the spread of diseases caused by the new coronavirus infection COVID-19, DE proved its relevance. Definitions are given concerning the essence of up; advantages and prospects of UP are highlighted, as well as disadvantages and limitations. A separate review of restrictions on the implementation of DE in the context of a pandemic on the territory of the Russian Federation is given. It is noted that it is necessary to take into account the relationship between modern education and the sphere of professional work in order to train competitive specialists. The analysis of the implementation of up DE 2020 is confirmed by the assessments of experts and specialists in the field of education, who note that those students who in modern conditions spend the second academic year on a permanent DE have a high chance of entering the labor market without a sufficient stock of formed knowledge, skills, competencies and without a sufficiently strong motivation and readiness for further self-study and continuous professional education. In the context of the development of the digital educational environment in pre-school education, it is necessary to consider IT not as a separate "traditional" training, but as a new stage for full-time and part-time forms, where various information technologies can be used.
A radix‐8 modulo 2 <sup> <i>n</i> </sup> multiplier using area and power‐optimized hard multiple generator

The moduli 2n multiplier plays a vital role in the design of a residue number system processor. When the radix-8 booth-encoded technique is adopted to design this kind of multipliers, the hard multiple generator is crucial in terms of area, power, and delay. This paper presents an area and power optimization technique for this kind of generators and its implementation in modulo 2n multiplier to improve the performance. The proposed hard multiplier generator (HMG) uses only ⌈log2n⌉-2 prefix levels and total prefix operators. The synthesis of the proposed architectures is done using the Cadence tool at Generic Process design Kit-45 nm technology. The post-synthesis result of HMG shows 20.27%–36.57%, 2.43%–18.41% saving in area and power, respectively, while the post-layout result of HMG shows 20.01%–35.26% and 1.33%–29.44% saving in area and power, respectively. The post-layout result of modulo 2nmultiplier using optimized HMG shows 7.88%–10.04%, 7.87%–12.50%, 3.09%–11.29%, and 3.11%–8.79% saving in area, power, switching energy and Area delay product, respectively.
Using WebQual 4.0 For Measuring Quality of E-learning Services During COVID-19 Pandemic

The purpose of this study is to measure the quality of website services in the form of STMIK Dipanegara Makassar e-learning in during the COVID-19 pandemic using WebQual 4.0, lecturers and students were asked to do the learning process from home, where STMIK Dipanegara Makassar provided e-learning that could be used by lecturers and students.This study recognizes differences in actual perceptions and ideal expectations of website users in the form of e-learning.The method used is WebQual 4.0, WebQual 4.0 is used to measure usability, quality of information, and quality of service interactions on web pages in the form of e-learning.The results of the study based on the recapitulation of all indicators from 115 respondents consisting of lecturers and students obtained 69.1% agree and 12.8% strongly agree.Combined, the results are 81.9%, and 16.6% disagree and 1.5% strongly disagree.This shows that based on the results of the questionnaire at the time of the e-learning of the COVID-19 pandemic, STMIK Dipanegara Makassar already has a good quality website service, but there are still certain parts that need to be addressed.
Distributional Reinforcement Learning via Moment Matching

We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return. We formulate a method that learns a finite set of statistics from each return distribution via neural networks, as in the distributional RL literature. Existing distributional RL methods however constrain the learned statistics to predefined functional forms of the return distribution which is both restrictive in representation and difficult in maintaining the predefined statistics. Instead, we learn unrestricted statistics, i.e., deterministic (pseudo-)samples, of the return distribution by leveraging a technique from hypothesis testing known as maximum mean discrepancy (MMD), which leads to a simpler objective amenable to backpropagation. Our method can be interpreted as implicitly matching all orders of moments between a return distribution and its Bellman target. We establish sufficient conditions for the contraction of the distributional Bellman operator and provide finite-sample analysis for the deterministic samples in distribution approximation. Experiments on the suite of Atari games show that our method outperforms the standard distributional RL baselines and sets a new record in the Atari games for non-distributed agents.
Energy-Efficient Scheduling of Deadline-Sensitive and Budget-Constrained Workflows in the Cloud

Due to the rapid advancement of Cloud computing, more and more users are running their scientific and business workflow applications in the Cloud. The energy consumption of these workflows is high, which negatively affects the environment and also increases the operational costs of the Cloud providers. Moreover, most of the workflows are associated with budget constraints and deadlines prescribed by Cloud users. Thus, one of the main challenges of workflow scheduling is to make it energy-efficient for Cloud providers. At the same time, it should prevent budget and deadline violations for Cloud users. To address these issues, we consider a heterogeneous Cloud environment and propose an energy-efficient scheduling algorithm for deadline-sensitive workflows with budget constraints. Our algorithm ensures that the workflow is scheduled within the budget while reducing energy consumption and deadline violation. It utilizes Dynamic Voltage and Frequency Scaling (DVFS) to adjust the voltage and frequency of the virtual machines (VMs) executing tasks of the workflow. These adjustments help to achieve significant energy savings. Extensive simulation using real-world workflows and comparison with some state-of-art approaches validate the effectiveness of our proposed algorithm.
Goal-Directed Empowerment: Combining Intrinsic Motivation and Task-Oriented Behavior

Empowerment is an information-theoretic measure representing the capacity of an agent to affect its environment. It quantifies its ability to inject information in the environment via its actions and to recapture this information through its sensors. In a nutshell, it measures the number of future options available and perceivable by the agent. Originally, the definition of empowerment does not depend on any particular extrinsic goal and it is determined only by the interaction of the agent with the world and the structure of its action-perception cycle. In this article, we introduce a new formalism that combines empowerment maximization with externally specifiable goal-directed behavior. This has two main implications: on the one hand, the study of the relationship between empowerment optimization and goal-directedness, to investigate to which extent these two desirable behaviors can co-exist; on the other hand, from a more operational point of view, the derivation of a method to generate a behavior (i.e., a policy of a Markov decision process) that is both empowered and goal-directed, in order to design agents capable of being as “empowered” as possible when facing any extrinsic task. Finally, we study how this hybrid policy is able to handle problems of uncertain or changing goals and delayed goal commitment.
A simple, lightweight framework for testing RESTful services with TTCN-3

Micro-service architecture has become a standard software architecture style, with loosely coupled, specified, and implemented services, owned by small teams and independently deployable. TTCN-3, as test specification and implementation language, allows an easy and efficient description of complex distributed test behavior and seems to be a natural fit to test micro-services. TTCN-3 is independent of the underlying communication and data technology, which is strength and weakness at the same time. While tools and frameworks are supporting micro-service developers to abstract from the underlying data, implementation, and communication technology, this support has to be modeled in a TTCN-3 based test system, manually. This paper discusses the concepts of a TTCN-3 framework on the four different levels of the Richardson-Maturity Model, introducing support for testing hypermedia controls, HATEOAS, proposes a TTCN-3 framework and open-source implementation to realize them and demonstrates its application by a concrete example.
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games

In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle.An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly.However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time.This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic "zeroshot" scenarios where out-of-domain object categories are involved.To overcome this issue, we introduce a novel "imagination" module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time.Our imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat?! zero-shot scenario (Suglia et al., 2020), and it improves the Oracle and Guesser accuracy by 2.08% and 12.86% in the GuessWhat?! benchmark, when no gold categories are available at inference time.The imagination module also boosts reasoning about object properties and attributes.
Garbage Detection Using YOLOv3 in Nakanoshima Challenge

Object detectors using deep learning are currently used in various situations, including robot demonstration experiments, owing to their high accuracy. However, there are some problems in the creation of training data, such as the fact that a lot of labor is required for human annotations, and the method of providing training data needs to be carefully considered because the recognition accuracy decreases due to environmental changes such as lighting. In the Nakanoshima Challenge, an autonomous mobile robot competition, it is challenging to detect three types of garbage with red labels. In this study, we developed a garbage detector by semi-automating the annotation process through detection of labels using colors and by preparing training data by changing the lighting conditions in three ways depending on the brightness. We evaluated the recognition accuracy on the university campus and addressed the challenge of using the discriminator in the competition. In this paper, we report these results.
SoftSort: A Continuous Relaxation for the argsort Operator

While sorting is an important procedure in computer science, the argsort operator - which takes as input a vector and returns its sorting permutation - has a discrete image and thus zero gradients almost everywhere. This prohibits end-to-end, gradient-based learning of models that rely on the argsort operator. A natural way to overcome this problem is to replace the argsort operator with a continuous relaxation. Recent work has shown a number of ways to do this, but the relaxations proposed so far are computationally complex. In this work we propose a simple continuous relaxation for the argsort operator which has the following qualities: it can be implemented in three lines of code, achieves state-of-the-art performance, is easy to reason about mathematically - substantially simplifying proofs - and is faster than competing approaches. We open source the code to reproduce all of the experiments and results.
Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics

Abstract Purpose of Review Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images. Recent Findings Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond. Summary Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.
Categorical and algebraic aspects of the intuitionistic modal logic IEL― and its predicate extensions

Abstract The system of intuitionistic modal logic $\textbf{IEL}^{-}$ was proposed by S. Artemov and T. Protopopescu as the intuitionistic version of belief logic (S. Artemov and T. Protopopescu. Intuitionistic epistemic logic. The Review of Symbolic Logic, 9, 266–298, 2016). We construct the modal lambda calculus, which is Curry–Howard isomorphic to $\textbf{IEL}^{-}$ as the type-theoretical representation of applicative computation widely known in functional programming.We also provide a categorical interpretation of this modal lambda calculus considering coalgebras associated with a monoidal functor on a Cartesian closed category. Finally, we study Heyting algebras and locales with corresponding operators. Such operators are used in point-free topology as well. We study complete Kripke–Joyal-style semantics for predicate extensions of $\textbf{IEL}^{-}$ and related logics using Dedekind–MacNeille completions and modal cover systems introduced by Goldblatt (R. Goldblatt. Cover semantics for quantified lax logic. Journal of Logic and Computation, 21, 1035–1063, 2011). The paper extends the conference paper published in the LFCS’20 volume (D. Rogozin. Modal type theory based on the intuitionistic modal logic IEL. In International Symposium on Logical Foundations of Computer Science, pp. 236–248. Springer, 2020).
Performance Evaluation of Python Based Data Analytics Frameworks in Summit: Early Experiences

The explosion in the volumes of data generated from ever-larger simulation campaigns and experiments or observations necessitates competent tools for data wrangling and analysis). While the Oak Ridge Leadership Computing Facility (OLCF) provides a variety of tools to perform data wrangling and data analysis tasks, Python based tools often lack scalability, or the ability to fully exploit the computational capability of OLCF’s Summit supercomputer. NVIDIA RAPIDS and Dask offer a promising solution to accelerate and distribute data analytics workloads from personal computers to heterogeneous supercomputing systems. We discuss early performance evaluation results of RAPIDS and Dask on Summit to understand their capabilities, scalability, and limitations. Our evaluation includes a subset of RAPIDS libraries, i.e., cuDF, cuML, and cuGraph, and Chainer’s CuPy, and their multi-GPU variants when available.We also draw on the observed trends from the performance evaluation results to discuss best practices for maximizing performance.
Memory consumption reduction for identity document classification with local and global features combination

In this paper we explore possibilities of memory cost reduction without significant loss of classification accuracy in connection with the problem of the ID document type recognition on mobile devices. The studied classic approach is based on representing images using constellation of feature points and descriptors. The distortion parameters are estimated by applying RANSAC. Experimental data details the approach limitations (memory, speed and accuracy) in dependence of the descriptor type. In order to maintain accuracy when using low dimensional descriptors we suggest to modify the basic approach using additional features characteristic of the document such as straight lines and quadrangles. In addition, an early filtration of the samples and the hypotheses used in RANSAC. It was shown that the proposed modifications have a positive contribution for all types of descriptors considered. The suggested algorithm was tested using the open dataset MIDV-500. The modified approach allows to achieve an accuracy improvement and significant speed up of distortion parameters estimation in RANSAC. It was shown that using compact descriptors in conjunction with the presented method allows reduce required memory cost by more than 7 times with near-zero (0.2%) loss of accuracy, and more than 14 times with the loss of accuracy is about 18%.