Reviewerly/exHarmony huggingface dataset:
Impacto de la COVID-19 en el aprendizaje de estudiantes con discapacidad (Impact of COVID-19 on Learning of Students with Disabilities)

Spanish Abstract: El objetivo de este articulo fue describir el impacto de la pandemia COVID-19 en el aprendizaje de estudiantes con discapacidad, en el contexto internacional. El estudio se sustenta en la literatura revisada, emitida fundamentalmente por la Organizacion de las Naciones Unidas para la Educacion, la Ciencia y la Cultura (UNESCO) y por el Fondo de las Naciones Unidas para la Infancia (UNICEF). Se desarrollo una investigacion de tipo descriptiva, correspondiente al nivel perceptual. Se describio el impacto de la COVID-19 en estudiantes con discapacidad, concluyendose con una matriz FODA, aportando una herramienta de diagnostico para la etapa post-pandemia.English Abstract: The objective of this article was to describe the impact of the COVID-19 pandemic on the learning of students with disabilities, in the international context. The study is based on the reviewed literature, issued mainly by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and by the United Nations Children's Fund (UNICEF). A descriptive research was carried out, corresponding to the perceptual level. The impact of COVID-19 on students with disabilities was described, concluding with a SWOT matrix, providing a diagnostic tool for the post-pandemic stage.
Answering multi-dimensional range queries under local differential privacy

In this paper, we tackle the problem of answering multi-dimensional range queries under local differential privacy. There are three key technical challenges: capturing the correlations among attrib...
Same as the parameter of an equation construction

Rett (2013) proposes that the difference between scalar equatives (Mary is as tall as Sue is) and similatives (Mary danced as Sue did) in terms of whether a parameter marker (i.e. the underlined as) is needed, can be attributed to their difference in whether lexicalized arguments are equated (degrees vs. manners). This paper shows that the same-sentence, which is a kind of equation construction, poses a challenge to Rett (2013) at first sight and to maintain her proposal, I argue that a null parameter marker must co-occur with the parameter same. Moreover, the existence of such a null parameter marker is not simply postulated to fit same into Rett (2013) but rather reveals something deep about this word, as it can straightforwardly account for its extraordinary scoping pattern (Dowty, 1985; Barker, 2007; Brasoveanu, 2011).
Adapting random forests to cope with heavily censored datasets in survival analysis

We address a survival analysis task where the goal is to predict the time passed until a subject is diagnosed with an age-relateddisease. The main challenge is that subjects’ data are very often censored, i.e., their time to diagnosis is only partly known. We propose a new Random Forest variant to cope with censored data, and evaluate it in experiments predicting the time to diagnosis of 8 age-related diseases, for data from the English Longitudinal Study of Ageing (ELSA) database. In these experiments, the proposed Random Forest variant, in general, outperformed a well-known Random Forest variant for censored data.
Open Architecture Design of Universal Terminal for Multiple Measurement and Control Scenarios of Distribution Network

Abstract Introduce the IoT platform technology based on SDN (Software Defined Network), cloud-based, flexible management and configuration of smart terminals, and support access to millions of terminal devices and smart operation and maintenance management. The traditional monitoring system is integrated with the Internet of Things platform, and on the basis of realizing the general SCADA system monitoring function, through the side-cloud collaboration, the lean management of the distribution station area is realized.
Understanding and Supporting Quality Requirements Engineering in Software-intensive Product Development

[Background] Quality requirements deal with how well a product should perform the intended functionality. Failure to meet essential quality requirements can result in customer dissatisfaction, unus ...
Forensic Analysis of Failing Software Projects: Issues and Challenges

Software project failure has directly and indirectly cost trillions of dollars over the past fifty years.The reasons for failure are many.Recently, we began investigating methods for software project forensic analysis in order to develop principles and practices that would enable better understanding why, when, where, how, and what causes software project failure.This paper examines some of the issues and challenges in performing forensic analysis of failing or failed software projects.We propose an initial model based on this first analysis to assist in critically evaluating potential causes and assessing the potential for failure.
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING

Gradient quantization is widely adopted to mitigate communication costs in distributed learning systems. Existing gradient quantization algorithms often rely on design heuristics and/or empirical evidence to tune the quantization strategy for different learning problems. To the best of our knowledge, there is no theoretical framework characterizing the trade-off between communication cost and model accuracy under dynamic gradient quantization strategies. This paper addresses this issue by proposing a novel dynamic quantized SGD (DQSGD) framework, which enables us to optimize the quantization strategy for each gradient descent step by exploring the trade-off between communication cost and modeling error. In particular, we derive an upper bound, tight in some cases, of the modeling error for arbitrary dynamic quantization strategy. By minimizing this upper bound, we obtain an enhanced quantization algorithm with significantly improved modeling error under given communication overhead constraints. Besides, we show that our quantization scheme achieves a strengthened communication cost and model accuracy trade-off in a wide range of optimization models. Finally, through extensive experiments on large-scale computer vision and natural language processing tasks on CIFAR-10, CIFAR-100, and AG-News datasets, respectively. we demonstrate that our quantization scheme significantly outperforms the state-of-the-art gradient quantization methods in terms of communication costs.
While the Chatbot's Away, the Mice Will Play

OPINION article Front. Digit. Health, 02 February 2021 | https://doi.org/10.3389/fdgth.2021.617013
Simplifying teaching continuous integration and continuous deployment with hands-on application in a web development course

Teaching web programming can lend itself as a course where students can learn to develop with version control, containers, continuous integration (CI), and continuous deployment or delivery (CD). I...
Syllabification in Indonesian Language

Dictionary cannot be separated with syllabification because it is one of the crucial parts of the reference book. It has been widely known that each language has its own linguistic system which creates differences between one language to another; and one of those differences is the system of syllabification. Syllabification, in Indonesian language system owns different rules compared to other languages. The practice of syllabification for Indonesian words considers the structure of the vowels and the consonants, as well as the syllabic itself, and the other rules of syllabification.
MementoMap: A Web Archive Profiling Framework for Efficient Memento Routing

With the proliferation of public web archives, it is becoming more important to better profile their contents, both to understand their immense holdings as well as to support routing of requests in Memento aggregators. A memento is a past version of a web page and a Memento aggregator is a tool or service that aggregates mementos from many different web archives. To save resources, the Memento aggregator should only poll the archives that are likely to have a copy of the requested Uniform Resource Identifier (URI). Using the Crawler Index (CDX), we generate profiles of the archives that summarize their holdings and use them to inform routing of the Memento aggregator’s URI requests. Additionally, we use full text search (when available) or sample URI lookups to build an understanding of an archive’s holdings. Previous work in profiling ranged from using full URIs (no false positives, but with large profiles) to using only top-level domains (TLDs) (smaller profiles, but with many false positives). This work explores strategies in between these two extremes. For evaluation we used CDX files from Archive-It, UK Web Archive, Stanford Web Archive Portal, and Arquivo.pt. Moreover, we used web server access log files from the Internet Archive’s Wayback Machine, UK Web Archive, Arquivo.pt, LANL’s Memento Proxy, and ODU’s MemGator Server. In addition, we utilized historical dataset of URIs from DMOZ. In early experiments with various URI-based static profiling policies we successfully identified about 78% of the URIs that were not present in the archive with less than 1% relative cost as compared to the complete knowledge profile and 94% URIs with less than 10% relative cost without any false negatives. In another experiment we found that we can correctly route 80% of the requests while maintaining about 0.9 recall by discovering only 10% of the archive holdings and generating a profile that costs less than 1% of the complete knowledge profile. We created MementoMap, a framework that allows web archives and third parties to express holdings and/or voids of an archive of any size with varying levels of details to fulfil various application needs. Our archive profiling framework enables tools and services to predict and rank archives where mementos of a requested URI are likely to be present. In static profiling policies we predefined the maximum depth of host and path segments of URIs for each policy that are used as URI keys. This gave us a good baseline for evaluation, but was not suitable for merging profiles with different policies. Later, we introduced a more flexible means to represent URI keys that uses wildcard characters to indicate whether a URI key was truncated. Moreover, we developed an algorithm to rollup URI keys dynamically at arbitrary depths when sufficient archiving activity is detected under certain URI prefixes. In an experiment with dynamic profiling of archival holdings we found that a MementoMap of less than 1.5% relative cost can correctly identify the…
Trade Secrets and Artificial Intelligence: Opportunities &amp;amp; Challenges

Nowadays, Artificial Intelligence technology is attracting increasing attention at the global level. From the legal point of view, legislators and researchers' most prominent concern revolves around how to provide adequate protection for the components of Artificial Intelligence to stimulate further innovation. In this context, Trade secrets are emerging as a useful legal tool to protect Artificial Intelligence innovations, especially as they are adopted by a significant number of companies to protect the components of Artificial Intelligence. The article reviews the features that make Trade Secrets well-suited protect Artificial Intelligence technology, as well as the challenges that limit Trade Secrets' ability to protect it. The article also deals with Trade Secrets management and its role in enhancing legal protection for companies' Trade Secrets and better investing them. In conclusion, and after showing the positive impact of Trade secrets on the development of Artificial Intelligence technology, the article calls on policymakers to shape Trade Secrets laws at the national, regional, and international levels to keep pace with technological developments in the field of artificial intelligence.
Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes

Diabetes is a group of metabolic diseases which is indicated by the occurrence of hyperglycemia caused by abnormalities in insulin secretion in the body. Many deaths are caused by diabetes, if this disease is not treated immediately, diabetes can cause damage to other organs such as blindness, stores, heart problem and even kidney problem. A best method is needed in classifying diabetes in order to detect diabetes early. Research related to the classification of diabetes using several calcification methods has been done before. In this study, two classification methods were compared, namely decision tree and naïve Bayes. Measurement methods were carried out through cross validation. The results obtained from this study are the best algorithms among the two algorithms to determine diabetes sufferers
The application of panoramic segmentation network to medical image segmentation

In recent years, the image segmentation method based on deep learning has achieved outstanding performance in the field of medical image, but there are still some issues which need to be solved. In semantic segmentation, corresponding category is classified for each pixel in the image, while the instance segmentation conducts detection and segmentation of target in area-of-interest. With the continuous development of deep learning, these two tasks are increasingly integrated to realize panoramic image segmentation. This paper proposes a panoramic image segmentation network. Firstly, the bisenet network architecture is integrated to the image segmentation branch; secondly, in the image detection branch, the mask-rcnn network architecture is employed for this network; thirdly, these two branches use the same backbone network for simultaneous training and mutual improvement. Finally, we apply the proposed method in both the street view database and LiTS medical database. The results of massive experiments show that our algorithm has great performance.
Row-by-Column, Plexiglass &amp; Zoom, Oh My! A K-12 COVID-19 Storm / A Pilot

We are 21 years into the 21st century, and educational practices across North America were woefully unprepared to &amp;lsquo;flip the switch&amp;rsquo; to online learning; at times no education occurred at all, not online or onsite. The COVID-19 pandemic disruptor storm peeled off the layers of blindfolds time accrued in an instant. Issues included three areas. Area one&amp;mdash;unpreparedness: digital illiteracy relative to online learning and corresponding teaching models, equity issues pertaining to internet access and computer access, platforms that varied and were unreliable. Area two&amp;mdash;inconsistent: (if any) guidelines on how to teach onsite, or those from a disease control group dictating a six-foot distancing, masks, plexiglass, and row-by-column with eyes facing forward (back to a 19th century teaching didactic model), and smaller class sizes. Area three-time/space continuum: the combining of online and onsite, teaching loads, and maintenance. This &amp;lsquo;alpha&amp;rsquo; research study tried to capture a historic moment in time. A Human-centered Research Design (HcRD) protocol with three techniques to mitigate bias was used: (1) online survey, (2) focused interviews, and (3) crowd-sourced photographic content across two countries&amp;mdash;USA and Canada as a convenience sample. The findings will reveal a &amp;lsquo;just-in-time&amp;rsquo; snap shot of the tactics used pre- and current-, as well as ideas for post-pandemic&amp;mdash;this research&amp;rsquo;s differentiator. The storm of COVID-19 played unprecedented havoc on schools across North America, but there are important learnings and these, along with some insights will be shared.
DeepComp: Which Competing Event Will Hit the Patient First?

When taking care of complex patients with multiple morbidities, accurately predicting the occurrence of each cause-specific event is critical for designing optimal treatment plans. However, standard survival analysis cannot deal with the multiple (usually competing) adverse events and views those competing events as censored. This will result in biased estimation of the incidence rate. In this paper, we propose a deep learning based survival analysis algorithm called DeepComp to jointly predict the progress of the competing events, which can thus inform the doctors which event is more likely to hit the patient first. DeepComp constructs a multi-task recurrent neural network (RNN) and views the conditional probability of each competing event at each time point as the output of each RNN cell. Then the probability chain rule is utilized to combine them together. In this way, the survival probability and the risk for each competing event over the time space are obtained. The multitask structure not only prevents the model from unreasonable censoring but also aids the model in capturing the complex hidden association among the competing events. A novel penalty is added to the loss function to better discriminate the competing risks for each particular patient, which could benefit treatment decision-making. We conduct comprehensive experiments on two real-world clinical data sets and one synthetic data set. The proposed DeepComp method achieves significant performance improvement compared to the state-of-the-art baseline methods.
An invisible DWT watermarking algorithm using noise removal with application to dermoscopic images

Abstract A new approach for the digital watermarking process is proposed to be part of the pre-processing stage of a computer-aided diagnosis system. We propose to embed a denoised image acting as the watermark image in the original host image with the final goal of improving the quality of demoscopic images for further image processing operation related to CAD. The proposed algorithm uses Discrete Wavelet Transform (DWT) corroborated with some basic properties of Human Visual System such as Contrast Sensitive Function (CSF) and Noise Visibility Function (NVF) with the goal of correlating the texture properties and noise. This approach hides the watermark (i.e. denoised version of the host image) in high-pass subbands that are focused on image features. The main concern is to evaluate the distortion produced to the host image by watermarking and an objective quality measure function, i.e. Weighted Peak Signal-to-Noise Ratio (WPSNR), is used to evaluate the existing differences between the original and watermarked images. The proposed approach is tested using the available skin lesion images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen. The experiment results show the improved performance of the proposed scheme against a 3 × 3 median filtering attack in comparison with the a 5 × 5 median filtering attack.
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization

Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels. However, existing methods rely on the classification task, which could result in a response map only attending on discriminative object regions as the network does not need to see the entire object for optimizing the classification loss. To tackle this issue, we propose a principled and end-to-end train-able framework to allow the network to pay attention to other parts of the object, while producing a more complete and uniform response map. Specifically, we introduce the mixup data augmentation scheme into the classification network and design two uncertainty regularization terms to better interact with the mixup strategy. In experiments, we conduct extensive analysis to demonstrate the proposed method and show favorable performance against state-of-the-art approaches.
VHDL based Neuro-Fuzzy Lithium-Ion Hybrid Super Capacitors management: (Advantages of the high-level descriptions of neural fuzzy logic based systems)

VHDL language imposes some limitations, compared with the flexibility and expressiveness of other fuzzy logic oriented languages. In order to achieve behavioural modelling, a VHDL description style can be used where the system structure description (fuzzy sets, rule base etc.) and the operator description (connectives and fuzzy operations) are defined separately. It allows describing both the fuzzy system structure and the processing algorithm independently. The description format allows the use of linguistic hedges in order to compact the rules defining the system's behaviour. The paper analyses the potential advantages for the use of bespoke high-level descriptions and the feasibility of the translation into a VHDL code with a GUI interface of a controller described by a fuzzy logic oriented language. This study is encased in a particular application: Lithium-Ion Hybrid Super Capacitors management. The Lithium-Ion Hybrid Super Capacitor is a novel technology breaking new ground in the technology sector. The Lithium-Ion Hybrid Super Capacitor (LIHC) is fast evolving as the missing link between the Electric Double Layer Capacitor (EDLC) and the Lithium-Ion Battery (LIB), is a distinct hybrid of the two technologies. To be effective, it requires a management controller, which the paper targets to produce in a VHDL hardware description language.
Security in Quantum Computing

Quantum key distribution will bring more confidentiality and privacy of communication in the future ICT world and will solve the eavesdropping issue. Domains regarded are e-government, e-commerce, e-health, transmission of biometric data, intelligent transport systems and more. So far, quantum researches focus on using properties of the qubit to bring improvements in technologies from our days. The purpose of this paper is to describe the quantum encryption methods. These methods can bring more efficiency of security in existing communications. In this matter, many encryption architectures have been proposed. As an example, the QKD architecture is presented in this paper.
Service Oriented Cloud Computing- The State of the Art

Cloud computing has emerged as a powerful new technology. The processing and computation power embedded in the cloud technology is not only flexible but also infinitely scalable and cost effective. Service oriented architecture (SOA) is a perfect stage for cloud computing. SOA has allowed customers and organizations to achieve cloud computing and reap its benefits that would not have been possible through any other architecture. This paper discusses the concept and importance of service oriented cloud computing by highlighting possible architectures, their benefits and critical success factors.
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks

Relational inductive biases have a key role in building learning agents that can generalize and reason in a compositional manner. While relational learning algorithms such as graph neural networks (GNNs) show promise, we do not understand their effectiveness to adapt to new tasks. In this work, we study the logical generalization capabilities of GNNs by designing a benchmark suite grounded in first-order logic. Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics, represented as knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct procedurally generated logical worlds. We use GraphLog to evaluate GNNs in three different setups: single-task supervised learning, multi-task (with pretraining), and continual learning. Unlike previous benchmarks, GraphLog enables us to precisely control the logical relationship between the different worlds by controlling the underlying first-order logic rules. We find that models' ability to generalize and adapt strongly correlates to the availability of diverse sets of logical rules during multi-task training. We also find the severe catastrophic forgetting effect in continual learning scenarios, and GraphLog provides a precise mechanism to control the distribution shift. Overall, our results highlight new challenges for the design of GNN models, opening up an exciting area of research in generalization using graph-structured data.