Reviewerly/exHarmony huggingface dataset:
Validation of an Automated Quantitative Digital Pathology Approach for Scoring TMEM: A Prognostic Biomarker for Metastasis

Metastasis causes ~90% of breast cancer mortality. However, standard prognostic tests based mostly on proliferation genes do not measure metastatic potential. Tumor MicroEnvironment of Metastasis (TMEM), an immunohistochemical biomarker for doorways on blood vessels that support tumor cell dissemination is prognostic for metastatic outcome in breast cancer patients. Studies quantifying TMEM doorways have involved manual scoring by pathologists utilizing static digital microscopy: a labor-intensive process unsuitable for use in clinical practice. We report here a validation study evaluating a new quantitative digital pathology (QDP) tool (TMEM-DP) for identification and quantification of TMEM doorways that closely mimics pathologists' workflow and reduces pathologists' variability to levels suitable for use in a clinical setting. Blinded to outcome, QDP was applied to a nested case-control study consisting of 259 matched case-control pairs. Sixty subjects of these were manually scored by five pathologists, digitally recorded using whole slide imaging (WSI), and then used for algorithm development and optimization. Validation was performed on the remainder of the cohort. TMEM-DP shows excellent reproducibility and concordance and reduces pathologist time from ~60 min to ~5 min per case. Concordance between manual scoring and TMEM-DP was found to be >0.79. These results show that TMEM-DP is capable of accurately identifying and scoring TMEM doorways (also known as MetaSite score) equivalent to pathologists.
Scalable characterization of localizable entanglement in noisy topological quantum codes

Abstract Topological quantum error correcting codes have emerged as leading candidates towards the goal of achieving large-scale fault-tolerant quantum computers. However, quantifying entanglement in these systems of large size in the presence of noise is a challenging task. In this paper, we provide two different prescriptions to characterize noisy stabilizer states, including the surface and the color codes, in terms of localizable entanglement over a subset of qubits. In one approach, we exploit appropriately constructed entanglement witness operators to estimate a witness-based lower bound of localizable entanglement, which is directly accessible in experiments. In the other recipe, we use graph states that are local unitary equivalent to the stabilizer state to determine a computable measurement-based lower bound of localizable entanglement. If used experimentally, this translates to a lower bound of localizable entanglement obtained from single-qubit measurements in specific bases to be performed on the qubits outside the subsystem of interest. Towards computing these lower bounds, we discuss in detail the methodology of obtaining a local unitary equivalent graph state from a stabilizer state, which includes a new and scalable geometric recipe as well as an algebraic method that applies to general stabilizer states of arbitrary size. Moreover, as a crucial step of the latter recipe, we develop a scalable graph-transformation algorithm that creates a link between two specific nodes in a graph using a sequence of local complementation operations. We develop open-source Python packages for these transformations, and illustrate the methodology by applying it to a noisy topological color code, and study how the witness and measurement-based lower bounds of localizable entanglement varies with the distance between the chosen qubits.
Stress Recognition using Face Images and Facial Landmarks

In this paper, we propose a stress recognition algorithm using face images and face landmarks. In the case of stress recognition using a biological signal or thermal image, which is being studied a lot, a device for acquiring the corresponding information is required. In order to remedy this shortcoming, we proposed an algorithm that can recognize stress from images acquired with a general camera. We also designed a deep neural network that receives facial landmarks as input to take advantage of the fact that eye, mouth, and head movements are different from normal situations when a person is stressed. Experimental results show that the proposed algorithm recognizes stress more effectively.
Use of Nonlinear Dynamic Motivational Strategies to Manage L2 Academic Entitlement and Psychological Reactance

Expanding the dynamicity and nonlinearity of L2 motivation introduced by Bahari (2019a) based on the complex dynamics systems theory has served as the theoretical framework to introduce and contextualize nonlinear dynamic motivational strategies (NDMSs). The present study used the NDMSs to manage L2 academic entitlement and psychological reactance as two obstructive factors in the L2 learning-teaching process. For conceptualization purposes, a mixed methods approach was conducted among teachers as well as learners to examine the effectiveness of proposed strategies as a pedagogical tool to manage and minimize these obstructive factors in academic contexts. The observed effectiveness of the NDMSs at managing and minimizing the analyzed obstructive factors along with replacing teacher-centered and test-oriented L2 classrooms with a learner-friendly motivating L2 classroom has significant pedagogical and theoretical implications. The major finding of the study following a rigorous methodological triangulation of the data that was collected confirms the effectiveness of the NDMSs as an L2 teaching strategy to cater to the diversity of individual differences for the purpose of improving teacher-learner interactions. Drawing on the results, it can be safely concluded that the NDMSs as the independent variable of the study showed significant impact on managing and minimizing academic entitlement and psychological reactance.
The Squeeze On African Media Freedom

Nearly thirty years after governments loosened control over broadcasters and publishers, Africa's media face increasing threats. New laws are resulting in the imprisonment of journalists and closure of media houses, while internet shutdowns and "social-media taxes" are increasingly common strategies to limit the mobilizing and informational potentials of digital technologies. These challenges are occurring in the midst of eroding public support for free media, as the latest Afrobarometer data show increased backing for government restrictions across the continent. Africans' confidence in their media seems to be declining, potentially due to concerns over bias, hate speech, and disinformation.
Exploring the Disruptiveness of Cryptocurrencies: A Causal Layered Analysis-Based Approach

The purpose of this study is to explore whether the diffusion of cryptocurrencies represents a disruptive change and what is the potential magnitude of this change. To this end, we take disruptive innovation theory as our point of departure to scrutinize cryptocurrencies as an instance of socio-technical change. We employ Causal Layered Analysis to develop our four-layer analytical framework to conceptually examine the changes pertained by the diffusion of cryptocurrencies. We provide examples of changes company-level, industry-level as well as societal changes where cryptocurrencies have played a central role. On a company level, cryptocurrencies provide a cost-efficient means for cross-border money transfer and thus pose a significant threat to the existing intermediary-based business models. On an industry level, many central banks are experimenting with crypto- or digital currencies. On a societal level, cryptocurrencies play an important role in particularly when the traditional institutions and societal structures collapse. For example, in Venezuela's recent political and economic crisis, cryptocurrencies contributed to the development of a parallel financial system when the bolivar practically lost its value. Our study provides an analytical framework to systematically evaluate the potential disruptive nature of cryptocurrencies as well as other blockchain-based technologies.
GPU-Oriented Architecture for an End-to-End Image/Video Codec Based on JPEG2000

Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system.Current standards often need to be implemented in hardware or using expensive solutions to meet the real-time requirements of some environments.Contrarily to this trend, this paper proposes an end-to-end codec architecture running on inexpensive Graphics Processing Units (GPUs) that is based on, though not compatible with, the JPEG2000 international standard for image and video compression.When executed in a commodity Nvidia GPU, it achieves real time processing of 12K video.The proposed S/W architecture utilizes four CUDA kernels that minimize memory transfers, use registers instead of shared memory, and employ a double-buffer strategy to optimize the streaming of data.The analysis of throughput indicates that the proposed codec yields results at least 10× superior on average to those achieved with JPEG2000 implementations devised for CPUs, and approximately 4× superior to those achieved with hardwired solutions of the HEVC/H.265video compression standard.
MultiPAD: A Multivariant Partition-Based Method for Audio Adversarial Examples Detection

Adversarial examples have been highlighted as a serious threat to various deep neural networks. The defense against adversarial examples is extremely urgent. This paper proposes an efficient multivariant partition based method to detect audio adversarial examples. Various partition strategies are exploited to obtain sufficient features that can help us to distinguish audio adversarial examples from clean samples. Using these features, a classification model is trained to detect audio adversarial examples. These features are also combined and compared to analyze their detection performance. The performance is evaluated on the Mozilla Common Voice dataset and the LibriSpeech dataset. Experimental results based on Mozilla Common Voice dataset show that the detection accuracy and AUC value of the model achieve 94.8% and 0.97 respectively, which are 13.5% and 0.08 higher than using the features of the existing work. Experimental results based on LibriSpeech dataset show that the detection accuracy and AUC value of the model achieve 100% and 1.00 respectively, which are 10% and 0.10 higher than the existing work.
Be FAIR to your data

Wouldn't it be great, if experimental data were findable wherever they were? If experimental data were accessible' regardless of the storage place and format? If experimental data were interoperable independent of the author or its origin? If experimental data were reusable for further analysis without experimental repetition? The current state of the art of data acquisition in the laboratory is very diverse. A lot of different devices are used, analogue as well as digital ones. Usually all experimental setups and observations are summarized in a handwritten lab notebook, independently from digital or analogue sources. To change the actual and common way of laboratory data acquisition into a digital and modern one, electronic lab notebooks can be used. A challenge of science is to facilitate knowledge discovery by assisting humans and machines in their discovery of scientific data and their associated algorithms and workflows. FAIR describes a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable.
A Robust Tracking-by-Detection Algorithm Using Adaptive Accumulated Frame Differencing and Corner Features

We propose a tracking-by-detection algorithm to track the movements of meeting participants from an overhead camera. An advantage of using overhead cameras is that all objects can typically be seen clearly, with little occlusion; however, detecting people from a wide-angle overhead view also poses challenges such as people’s appearance significantly changing due to their position in the wide-angle image, and generally from a lack of strong image features. Our experimental datasets do not include empty meeting rooms, and this means that standard motion based detection techniques (e.g., background subtraction or consecutive frame differencing) struggle since there is no prior knowledge for a background model. Additionally, standard techniques may perform poorly when there is a wide range of movement behaviours (e.g. periods of no movement and periods of fast movement), as is often the case in meetings. Our algorithm uses a novel coarse-to-fine detection and tracking approach, combining motion detection using adaptive accumulated frame differencing (AAFD) with Shi-Tomasi corner detection. We present quantitative and qualitative evaluation which demonstrates the robustness of our method to track people in environments where object features are not clear and have similar colour to the background. We show that our approach achieves excellent performance in terms of the multiple object tracking accuracy (MOTA) metrics, and that it is particularly robust to initialisation differences when compared with baseline and state of the art trackers. Using the Online Tracking Benchmark (OTB) videos we also demonstrate that our tracker is very strong in the presence of background clutter, deformation and illumination variation.
COVID-19 Spread Pattern Using Support Vector Regression

Pandemics are rare and happen in about 100 years period. Current pandemic, COVID-19, occurs in the industrial 4.0 era where there is a rapid development computation. Yet, the scientists in every country face difficulty in predicting the growth simulation of this pandemic. The paper tries to use a soft computing algorithm to predict the pattern of the COVID-19 pandemic in Indonesia. Support Vector Regression was used in Google Interactive Notebook with some kernels for comparison, i.e. radial basis function, linear and polynomial. The testing results showed that radial basis function outperformed other kernels as a regressor with some parameters should follows the real condition, i.e. gamma, c, and epsilon.
Applying Expert Heuristic as an a Priori Knowledge for FRIQ-Learning

Many Reinforcement Learning methods start the learning phase from an empty, or randomly filled knowledge-base.Having some a priori knowledge about the way as the studied system could be controlled, e.g. in the form of some state-action control rules, the convergence speed of the learning process can be significantly improved.In this case, the learning stage could start from a sketch, from a knowledge-base formed based upon the already existing knowledge.In this paper.the a priori (expert) knowledge is considered to be given in the form state-action fuzzy control rules of a Fuzzy Rule Interpolation (FRI) reasoning model and the studied reinforcement learning method is restricted to be a Fuzzy Rule Interpolation-based Q-Learning (FRIQ-Learning) method.The main goal of this paper is the introduction of a methodology, which is suitable for merging the a priori stateaction fuzzy control rule-base to the initial state-action-value function (Q-function) representation.For demonstrating the benefits of the suggested methodology, the a priori knowledge-base accelerated FRIQ-Learning solution of the "mountain car" benchmark is also discussed briefly in the paper.
Development of future foreign language teachers’ information literacy and digital skills in Ukrainian context

The aims of this paper are to share the results of the action research of the future foreign language teachers’ information literacy and digital skills development and to demonstrate the opportunities for their development while pre-service teacher training process in the course of Methodology of foreign language teaching and practical course of foreign language. The article discusses the skills of information and communication technologies use in learning and teaching as an important component of future teachers’ training and a contribution to the sustainable development of the country. The projects which influenced the action research elaborating are described. A short overview of relevant experience within the studied issue is presented. The procedure of action research is described, the examples from author-tailored course are given.
Analog neuron hierarchy

In order to refine the analysis of the computational power of discrete-time recurrent neural networks (NNs) between the binary-state NNs which are equivalent to finite automata (level 3 in the Chomsky hierarchy), and the analog-state NNs with rational weights which are Turing-complete (Chomsky level 0), we study an intermediate model αANN of a binary-state NN that is extended with α≥0 extra analog-state neurons. For rational weights, we establish an analog neuron hierarchy 0ANNs ⊂ 1ANNs ⊂ 2ANNs ⊆ 3ANNs and separate its first two levels. In particular, 0ANNs coincide with the binary-state NNs (Chomsky level 3) being a proper subset of 1ANNs which accept at most context-sensitive languages (Chomsky level 1) including some non-context-free ones (above Chomsky level 2). We prove that the deterministic (context-free) language L#={0n1n∣n≥1} cannot be recognized by any 1ANN even with real weights. In contrast, we show that deterministic pushdown automata accepting deterministic languages can be simulated by 2ANNs with rational weights, which thus constitute a proper superset of 1ANNs. Finally, we prove that the analog neuron hierarchy collapses to 3ANNs by showing that any Turing machine can be simulated by a 3ANN having rational weights, with linear-time overhead.
Virtual Reality Technology for Maintenance Training

This paper is part of a bigger research project that aims at establishing an integrated VR environment for preparing, evaluating, and executing maintenance training in virtual environments. In order to apply the virtual reality technology to maintenance task training, this paper presents a Virtual Reality for Training in Maintenance Task (VR-TMT) platform using the simulation of the process of a gearbox disassembly and assembly as an example. The system setup and configuration are presented in detail, as well as the analysis of the experience, perception level and side effects of using VR devices. The results show that the user experience and perception levels on provided by the VR-TMT platform are on a good level. In addition to that, the VR-TMT system is easy to use, understand and flexible for transport. Users can therefore profit from the VR-TMT platform to train anywhere and anytime according to their needs. The majority of the experimental users experienced only few side effects, in particular both physical and visual tiredness. However, the experiment confirmed that the immersive experience of VR-supported maintenance task training outweighs the reported side effects.
EACO: An Enhanced Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud Computing is emerging as an influential architecture to perform complex and large scale computing.It provides on-demand access to services on the "Pay-as-you-go" method.In cloud computing environment Task Scheduling is an essential technique that is required for allocating tasks to appropriate resources for proper resource utilization and optimizing overall system performance.Task scheduling is an NP-hard problem.Many researchers have proposed various algorithms like ACO, PSO, GA, and Bat etc to get optimal results.In this paper, the enhanced Ant Colony Optimization (EACO) algorithm has been proposed that serves improved task scheduling with minimum makespan while maintaining cost.This algorithm mainly contributes in minimizing total completion time for scheduling tasks on resources.This is attained by splitting the ordered submitted tasks into bunches -the sub list of tasks.The main goal of EACO is to minimize total execution time.The proposed algorithm EACO is simulated using the CloudSim toolkit and compared with the existing nature-inspired algorithm.The experimental results show that the presented algorithm improves results in terms of makespan.
Perfectly Secure Shannon Cipher Construction Based on the Matrix Power Function

A Shannon cipher can be used as a building block for the block cipher construction if it is considered as one data block cipher. It has been proved that a Shannon cipher based on a matrix power function (MPF) is perfectly secure. This property was obtained by the special selection of algebraic structures to define the MPF. In an earlier paper we demonstrated, that certain MPF can be treated as a conjectured one-way function. This property is important since finding the inverse of a one-way function is related to an N P -complete problem. The obtained results of perfect security on a theoretical level coincide with the N P -completeness notion due to the well known Yao theorem. The proposed cipher does not need multiple rounds for the encryption of one data block and hence can be effectively parallelized since operations with matrices allow this effective parallelization.
A Joint Back-Translation and Transfer Learning Method for Low-Resource Neural Machine Translation

Neural machine translation (NMT) for low-resource languages has drawn great attention in recent years. In this paper, we propose a joint back-translation and transfer learning method for low-resource languages. It is widely recognized that data augmentation methods and transfer learning methods are both straight forward and effective ways for low-resource problems. However, existing methods, which utilize one of these methods alone, limit the capacity of NMT models for low-resource problems. In order to make full use of the advantages of existing methods and further improve the translation performance of low-resource languages, we propose a new method to perfectly integrate the back-translation method with mainstream transfer learning architectures, which can not only initialize the NMT model by transferring parameters of the pretrained models, but also generate synthetic parallel data by translating large-scale monolingual data of the target side to boost the fluency of translations. We conduct experiments to explore the effectiveness of the joint method by incorporating back-translation into the parent-child and the hierarchical transfer learning architecture. In addition, different preprocessing and training methods are explored to get better performance. Experimental results on Uygur-Chinese and Turkish-English translation demonstrate the superiority of the proposed method over the baselines that use single methods.