Reviewerly/exHarmony huggingface dataset:
Implementing Digital Pathology into Veterinary Academics and Research

The advantages of digital pathology (DP) have been recognized as early as 1963, but only within the last decade or so have the advancements of slide scanners and viewing software made the use and implementation of DP feasible in the classroom and in research. Several factors must be considered prior to undertaking the project of implementing the DP workflow in any setting, but particularly in an academic environment. Sustained and open dialogue with information technology (IT) is critical to the success of this enterprise. In addition to IT, there is a multitude of criteria to consider when determining the best hardware and software to purchase to support the project. The goals and limitations of the laboratory and the requirements of its users (students, instructors, and researchers) will ultimately direct these decisions. The objectives of this article are to provide an overview of the opportunities and challenges associated with the integration of DP in education and research, to highlight some important IT considerations, and to discuss some of the requirements and functionalities of some hardware and software options.
A Block Image Encryption Algorithm Based on 2D Chaotic System

Aiming at the problem that some encryption algorithms based on 1D chaotic systems cannot ensure sufficient security when encrypting color images and grayscale images, this paper proposed a block image encryption algorithm based on 2D chaotic systems. The algorithm has four modules. The first module is to transpose the image into a 1D array and then perform block encryption. The second module is to use 2D Logistic chaotic system to generate pseudo-random sequence, 2D Standard chaotic system to generate S-boxes and P-boxes. The third module uses the CFB mode in the block cipher and perform two-way encryption. The fourth module uses the key and ciphertext to determine the number of bits to shift. Therefore, the algorithm has good performance and has certain application prospectst.
Space Efficient Two-Dimensional Orthogonal Colored Range Counting

In the two-dimensional orthogonal colored range counting problem, we preprocess a set, P, of n colored points on the plane, such that given an orthogonal query rectangle, the number of distinct colors of the points contained in this rectangle can be computed efficiently. For this problem, we design three new solutions, and the bounds of each can be expressed in some form of time-space tradeoff. By setting appropriate parameter values for these solutions, we can achieve new specific results with (the space costs are in words and e is an arbitrary constant in (0,1)): - O(nlg³ n) space and O(√nlg^{5/2} n lg lg n) query time; - O(nlg² n) space and O(√nlg^{4+e} n) query time; - O(n (lg² n)/(lg lg n)) space and O(√nlg^{5+e} n) query time; - O(nlg n) space and O(n^{1/2+e}) query time. A known conditional lower bound to this problem based on Boolean matrix multiplication gives some evidence on the difficulty of achieving near-linear space solutions with query time better than √n by more than a polylogarithmic factor using purely combinatorial approaches. Thus the time and space bounds in all these results are efficient. Previously, among solutions with similar query times, the most space-efficient solution uses O(nlg⁴ n) space to answer queries in O(√nlg⁸ n) time (SIAM. J. Comp. 2008). Thus the new results listed above all achieve improvements in space efficiency, while all but the last result achieve speed-up in query time as well.
Design and Simulation of Indonesian Language Application Exam with Mobile-based End User Development Method

This paper is a descriptive study with a qualitative approach. The method of data collection is the documentation study. The results of the study show that as follows, examinations using computer-based systems have technical obstacles including the occurrence of power outages or power outages, computer specifications used on computers must be in accordance with the criteria of application requirements, examinations using computers can save the budget in financing the means of examinations , tests using a computer can be categorized as environmentally friendly because they no longer use paper, where paper-making materials come from wood. Some of the remedial actions that can be taken are as follows, solving problems regarding power outages can be overcome by having a backup power supply coming from the UPS, procuring and repairing server computer hardware that is not in accordance with the requirements criteria..Keywords: Simulation, Mobile, Exam, End User Development.
Comparing reproduced cyber experimentation studies across different emulation testbeds

Cyber testbeds provide an important mechanism for experimentally evaluating cyber security performance. However, as an experimental discipline, reproducible cyber experimentation is essential to assure valid, unbiased results. Even minor differences in setup, configuration, and testbed components can have an impact on the experiments, and thus, reproducibility of results. This paper documents a case study in reproducing an earlier emulation study, with the reproduced emulation experiment conducted by a different research group on a different testbed. We describe lessons learned as a result of this process, both in terms of the reproducibility of the original study and in terms of the different testbed technologies used by both groups. This paper also addresses the question of how to compare results between two groups' experiments, identifying candidate metrics for comparison and quantifying the results in this reproduction study.
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization

We introduce Europarl-ASR, a large speech and text corpus of parliamentary debates including 1 300 hours of transcribed speeches and 70 million tokens of text in English extracted from European Parliament sessions.The training set is labelled with the Parliament's non-fully-verbatim official transcripts, timealigned.As verbatimness is critical for acoustic model training, we also provide automatically noise-filtered and automatically verbatimized transcripts of all speeches based on speech data filtering and verbatimization techniques.Additionally, 18 hours of transcribed speeches were manually verbatimized to build reliable speaker-dependent and speaker-independent development/test sets for streaming ASR benchmarking.The availability of manual non-verbatim and verbatim transcripts for dev/test speeches makes this corpus useful for the assessment of automatic filtering and verbatimization techniques.This paper describes the corpus and its creation, and provides off-line and streaming ASR baselines for both the speaker-dependent and speaker-independent tasks using the three training transcription sets.The corpus is publicly released under an open licence.
AN ANALYTICAL STUDY ON CLOUD COMPUTING

This paper is about the An Analytical Study on Cloud Computing. Cloud computing is the development of parallel computing, grid computing, distributed computing, and virtualization technologies which define the shape of a new area. In contrast to a variety of benefits related with cloud computing, there are certain challenges as well. These challenges include security, privacy and reliability of data, high costs of data transfers, and regularity in the availability of services, and bugs in large-scale distributed systems. Cloud computing is a method of computing in a place that provides users with the ability of information technology as a service and allows them to have access to these services on the Internet without having limited information. Cloud computing can help businesses transform their closing server infrastructures into dynamic environments, expanding and reducing server capacity depending on their demand. Cloud is a image to describe web as a space where computing has been pre-installed and close as a service; data, operating systems, applications, storage and processing power close on the web ready to be shared. In cloud computing data owners expand their complex data management systems from community sites to the public cloud for great pliability and cost-effective. The searching of this study that highlight there are five main issues related with cloud computing execution which are Mobility and Cloud Government Application security issues, Cloud Security data, cloud security platform, Cloud Security Services and Application, cloud network security issues and infrastructure issues.
Serverless Computing (Dagstuhl Seminar 21201)

In the backbone of our digital society, cloud computing enables an efficient, utility-like ecosystem of developing, composing, and providing software services. Responding to a trend to make cloud computing services more accessible, fine-grained, and affordable, serverless computing has gained rapid adoption in practice, and garnered much interest from both industry and academia. However successful, serverless computing manifests today the opportunities and challenges of emerging technology: a rapidly growing field but scattered vision, plenty of new technologies but no coherent approach to design solutions from them, many simple applications but no impressive advanced solution, the emergence of a cloud continuum (resources from datacenters to the edge) but no clear path to leverage it efficiently, and overall much need but also much technical complexity.Several related but disjoint fields, notably software and systems engineering, parallel and distributed systems, and system and performance analysis and modeling, aim to address these opportunities and challenges. Excellent collaboration between these fields in the next decade will be critical in establishing serverless computing as a viable technology.We organized this Dagstuhl seminar to bring together researchers, developers, and practitioners across disciplines in serverless computing, to develop a vision and detailed answers to the timely and relevant, open challenges related to the following topics: - Topic 1: design decisions for serverless systems, platforms, and ecosystems, - Topic 2: software engineering of serverless applications, but also systems, platforms, and ecosystems - Topic 3: applications and domain requirements for serverless computing, - Topic 4: evaluation of serverless solutions, and beyond (privacy, cyber-physical systems, etc.).In this document, we report on the outcomes of Dagstuhl Seminar 21201 Serverless Computing by integrating diverse views and synthesizing a shared vision for the next decade of serverless computing.
WATS-SMS: A T5-Based French Wikipedia Abstractive Text Summarizer for SMS

Text summarization remains a challenging task in the natural language processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.
Facial Beauty Prediction From Facial Parts Using Multi-Task and Multi-Stream Convolutional Neural Networks

Automatic analysis of facial beauty has become an emerging computer vision problem in recent years. Facial beauty prediction (FBP) aims at developing a human-like model that automatically makes facial attractiveness predictions. In this study, we present and evaluate a face attractiveness prediction approach using facial parts as well as a multi-task learning scheme. First, a deep convolutional neural network (CNN) pre-trained on massive face datasets is utilized for face attractiveness prediction, which is capable of automatic learning of high-level face representations. Next, we extend our deep model to other facial attribute recognition tasks. Hence, a multi-task learning scheme is leveraged by our deep model to learn optimal shared features for three correlated tasks (i.e. facial beauty assessment, gender recognition as well as ethnicity identification). To further enhance the attractiveness computation accuracy, specific regions of face images (i.e. left eye, nose and mouth) as well as the whole face are fed into multi-stream CNNs (i.e. three two-stream networks). Each two-stream network adopts a facial part as well as the full face as input. Extensive experiments are conducted on the SCUT-FBP5500 benchmark dataset, where our approach indicates significant improvement in accuracy over the other state-of-the-art methods.
Comparison of classification algorithms using feature selection

In this paper we tackle a classification problem using 4 algorithms: C4.5, JRip, k-NN, SVM Linear. Experiments were conducted using the Divorce dataset, consisting of 170 instances, 54 features, and 2 classes. We performed feature selection using the CFS filter method, obtaining just 4 relevant features. We ran the algorithms 30 times over the train set to obtain the best model, both for the dataset using all attributes and the subset with relevant features. With the best model we ran 30 times over the test set of both datasets and we used the balanced accuracy metric to perform a Wilcoxon test. Results show that, with a significant diference, it is better to use only the relevant features rather than the whole dataset.
El profesorado de Educación Física ante la situación de educación a distancia por Covid-19

This study aims to analyse the perception of Physical Education (PE) teachers on different aspects of on-line education in Spain due to the Covid-19 pandemic.An on-line questionnaire designed "ad hoc" has been applied to 526 PE teachers.The results show significant differences between a first period (March 13th-April 4th) and a second period (April 15th-end of course).Educational centres have adapted to the new circumstances in a positive way and teachers have felt less and less insecure.Most of the teachers have used a formative assessment.When grading, most of the teachers have approached what the educational legislation indicates (Order EFP / 365/2020), without fulfilling it exactly, because they have not used the third evaluation in a positive way for students who have been connected.Finally, it considers that the PE should have a more important role during confinement than the one they perceive they have had.It seems necessary to carry out more research about how PE teachers have approached this strange period, as well as to generate dynamics of permanent training to improve teaching work during the 2020-21 academic year.
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference

Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning. We propose solving the general Stochastic Shortest-Path Markov Decision Process (SSP MDP) as probabilistic inference. Furthermore, we discuss online and offline methods for planning under uncertainty. In an SSP MDP, the horizon is indefinite and unknown a priori. SSP MDPs generalize finite and infinite horizon MDPs and are widely used in the artificial intelligence community. Additionally, we highlight some of the differences between solving an MDP using dynamic programming approaches widely used in the artificial intelligence community and approaches used in the active inference community. F
Design of an Integrated III–V on silicon semiconductor laser for spiking neural networks

We present the simulation of an individual spiking laser to be used as a photonic neuron for a spiking neural network scale-up. The neuron is a semiconductor laser optimized for pulsed operation through modification of the CEA Leti III-V on Si integrated continuous wave laser’s heterostructure. Its full integration into a complete silicon photonics platform makes it ideal for large scale photonic SNN circuits.
An Effective Bicubic Convolution Interpolation-Based Iterative Luma Optimization for Enhancing Quality in Chroma Subsampling

Traditionally, prior to compressing an RGB full-color image, for each converted 2×2 CbCr block BCbCr, chroma subsampling only downsamples BCbCr, but without changing the luma block BY at all. In the current research, a special linear interpolation-based, namely the COPY-based, chroma subsampling-first luma modification (CSFLM) study has attempted to change the luma block for enhancing the quality of the reconstructed RGB full-color image. In this paper, a fast and effective nonlinear interpolation, namely the bicubic convolution interpolation (BCI), based iterative luma modification method for CSFLM is proposed. In our iterative method, a BCI-based distortion function and its convex property proof are first provided. Next, based on the proposed convex distortion function, a pseudoinverse technique is applied to obtain the initial luma modification solution, and then an iterative method is proposed to improve the initial luma modification solution. Based on five testing image datasets, namely the IMAX, Kodak, SCI (screen content images), CI (classical images), and Video datasets, the thorough experimental results have demonstrated that on the newly released Versatile Video Coding (VVC) platform VTM-12.0, our iterative luma modification method achieves substantial quality, execution-time, and quality-bitrate tradeoff improvements when compared with the existing state-of-the-art methods.
Application of Reinforcement Learning in Open Space Planner for Apollo Auto

Local planner makes a trajectory physically executable for an agent. Open Space Planner of Apollo framework based on nonlinear optimization methods smooths the trajectory received from a global planner. Such dependency on a global planner forces an agent to relaunch both planners when local changes occur (e.g., when an environment has dynamic obstacles), what can waste too much time. In this article, we consider a different approach which is based on reinforcement learning. This method allows agent generate a trajectory using information about environment (the current and goal state, lidar sensors, etc.). Experiments conducted on the simplified environment show that such algorithm can be implemented as the local planner in Apollo infrastructure.
Constructivism and contextual based learning in improving indonesian language learning outcomes in elementary school using online learning techniques in the middle of the covid 19 pandemic

This study aims to determine the success of implementing constructivism-based and contextual learning systems in Indonesian language learning, in particular, the five-grade first semester at SD Negeri 44 OKU with on-line learning techniques in the midst of the Covid-19 pandemic. This study uses qualitative research methods. The data collection techniques used were participant observation, structured interviews, and documentation. The results showed several recommendations for distance learning designs by viewing teaching as a process of interaction between learning actors. Interactions that are mediated by this technology need to be carefully designed and implemented based on the needs of students. Apart from what has been shared here, the spaces for innovation are still very wide open in the design and implementation of distance learning, with learning spaces to continuously improve teaching. The vast space for innovation and learning is what frees educators to continue to observe, ask, try, find, and reflect on what forms of distance learning are most appropriate to the context of their students.
Interactivity in Linear Diagrams

Linear diagrams have been shown to be an effective method for representing set-based data. Moreover, design principles have been empirically developed that, when followed, improve the efficacy of linear diagrams. These principles are task-independent. However, linear diagrams may be produced to aid with a variety of tasks, for which different representations may be more effective. In this paper, we introduce simple interactivity into linear diagrams. Namely, we gave users: the ability to move sets; the ability to move overlaps (set-intersections); and the ability to focus the diagram on a particular group of sets. Whether these interactions improved cognition was investigated via two empirical studies. In the first, we observed that interactivity improved participants’ accuracy, confidence and speed. In the second, we observed that these improvements were based on the diagrams participants produced in the first study, rather than being an artefact of interactivity itself. We conclude that adding simple interactivity is useful in the case of linear diagrams.
Special Issue on “Augmented Reality, Virtual Reality &amp; Semantic 3D Reconstruction”

Augmented Reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs [...]
Induced Local Attention for Transformer Models in Speech Recognition

The transformer models and their variations currently are considered the prime model architectures in speech recognition since they yield state-of-the-art results on several datasets. Their main strength lies in the self-attention mechanism, where the models receive the ability to calculate a score over the whole input sequence and focus on essential aspects of the sequence. However, the attention score has some flaws. It is heavily global-dependent since it takes the whole sequence into account and normalizes along the sequence length. Our work presents a novel approach for a dynamic fusion between the global and a local attention score based on a Gaussian mask. The small networks for learning the fusion process and the Gaussian masks require only few additional parameters and are simple to add to current transformer architectures. With our exhaustive evaluation, we determine the effect of localness in the encoder layers and examine the most effective fusion approach. The results on the dataset TEDLIUMv2 demonstrate a steady improvement on the dev and the test set for the base transformer model equipped with our proposed fusion procedure for local attention.