Reviewerly/exHarmony huggingface dataset:
Efficient Pixel-Wise SVD Required for Image Processing Using the Color Line Feature

In color image processing based on mathematical optimization, a color-line image feature has been considered and many methods that give good results have been proposed. A color-line is a linear color distribution (correlation line) observed in a local image region, and is numerically represented by the sparsity of a data matrix generated from the neighboring pixel values. However, the calculation requires a lot of processing time because each data matrix is processed by inverse calculation or singular value decomposition (SVD) with some operations on the decomposed singular values. In this paper, in order to address this problem, we propose a method that can effectively compute SVD for each data matrix. Using the experimental knowledge that matrices obtained from neighboring regions (each centered at an adjacent pixel) are similar to each other, we intentionally design the SVD by using an iterative method (Arnoldi iteration), and propagate the converged singular vectors at a pixel to the next pixel as the initial vectors of the iteration. This propagation can drastically reduce the number of iterations required for convergence. Additionally, the singular values and vectors are obtained in descending order, which is advantageous when a matrix is reconstructed after reducing small singular values, so we can truncate the calculation when a singular value becomes lower than a threshold value. To show the effectiveness, we apply the proposed method to a denoising method, arranged structure-tensor total variation (ASTV), and show that the processing time is shortened by 95% compared to the naive method without losing numerical accuracy.
Analysis of Higher Education Policies in Spain for Covid-19: Adapted Presence

This article analyzes the educational policy proposals that the Spanish public universities have prepared for the new 2020-21 school year conditioned by the Covid-19 pandemic. The study is based on the recommendations offered by the Ministry of Universities of the Government of Spain, as well as the different resolutions or indications that the government teams of different Spanish universities have made. The study tries to answer three basic questions: What are the policies for the implementation of Is there consensus or dissonance in the interpretation of this concept? What strategic visions underlie the policies of Spanish universities on adapted presence? The article concludes by noting that there is a similar policy for the Spanish public university system derived from the recommendations of the Ministry of Universities on face-to-face attendance. It is also indicated that this concept does not exist in the academic bibliography, but that it responds to the modality of blended, hybrid or blended learning teaching. It ends by noting that these policies are short-term and that they respond to the health emergency, lacking a strategic vision on the digital transformation of higher education for the post-Covid-19 times.
A Recommender System to Help Refining Clinical Research Studies

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.
A Simple Assessment of Information Security Awareness in Hospital Staff Across Five Danish Regions

Information Security Awareness among employees in healthcare has become an essential part in safeguarding health information systems against cyber-attacks and data breaches. We present three simple security awareness questions that can be included in larger surveys gauging other aspects of information systems. The questions have been tested in a national Danish survey to evaluate correlations among medical profession, computer proficiency, experience, and place of employment. We find that dissatisfaction with system usability is strongly linked with reduced information security awareness, and that clinical professions have different responses to security concerns.
Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.
You can't put the genie back in the bottle : On the legal and conceptual understanding of genetic privacy in the era of personal data protection in Europe

This article sheds a light on how the data protection requirements enshrined in the General Data Protection Regulation (GDPR) relate to shaping genetic privacy in the context of a complex and integrated genetic testing enterprise. It suggests that the informational dimension of genetic privacy in the era of data protection could be described as a sphere of controlled access. Given that the GDPR does not prescribe quantitative or contextual limitations relating to access once the applicable requirements are met, one could argue that there are good preconditions for the field to head in the direction of genetic transparency. This puts on the agenda the questions of what challenges this could bring and whether adequate mechanisms exist to deal with them.
Validitas Booklet pada Materi Bakteri untuk Peserta Didik Kelas X SMA

Teaching and learning activities are the most basic activities in the entire educational process. The success of learning itself depends on the use of the selected learning resources. Based on the results of the observation questionnaire learning sources in the form of textbooks, student worksheets (LKPD), PPT slides and modules are available and used in schools, but additional learning resources are still needed because the learning resources used have not been able to attract interest and increase student learning motivation . Based on this, a product was developed in the form of a booklet on material for class X SMA students that was valid and practical. The average result of the booklet validity value is 86.45% with very valid criteria with a description of 84.25% in the content feasibility aspect, 88.33% in the linguistic aspect, 86.11% in the presentation aspect, and 87.50% in the the graphic aspect. The conclusion of the research that has been done is the development of booklets on bacterial material for class X SMA students fulfills the very valid category and is very suitable for use as a learning resource.
Content-Independent Online Handwriting Verification Based on Multi-Modal Fusion

User identity authentication is essencial for ensuring information security. With the widespread use of electronic devices, online handwriting verification becomes more important in identity authentication based on biometrics and widely used in financial, commercial, and forensic fields. In this paper, we propose a multi-path feature fusion network for multi-modal fusion of static and dynamic handwriting obtained by electronic devices to intensify the handwriting verification. Since traditional handwritten signature verification, of which the handwritten content just the writer’s name, is vulnerable to skilled forgery attacks, we propose a content-independent handwriting verification scheme to solve this problem. We also build a handwriting dataset with approximately 5400 samples of 30 individuals’ handwriting, which contributes to extracting content-independent handwriting style features. We test our method on widely used BiosecurID dataset and our dataset. The experimental results demonstrate the feasibility of the proposed method.
Comparison of Crossover in Genetic Algorithm for Discrete-Time System Identification

System identification is a process where a mathematical model is derived in order to explain dynamical behaviour of a system. One of its step is model structure selection and it is crucial that, in this step, an adequate model i.e. a model with a good balance between parsimony and accuracy of the model is selected in approximating the system. Genetic algorithm (GA), a method known for optimisation, is used for selecting a model structure. GA is known to be able to reduce much computational burden. This paper investigates the effect of different types of crossover, namely, single-point, double-point, multiple-point and uniform crossover, within GA in producing an optimum model structure for system identification. This was carried out using a computational software on a number of simulated data. As a conclusion, using Akaike Information Criterion as objective function, single point crossover produces the model with the best balance in most of the tests.
The Reasonable Balancing of Rights and Interests at the Time of Pandemic

This essay starts with the consideration that the precautionary measures adopted by the Italian legislator and the public administration to deal with the pandemic emergency, while highly restrictive of constitutional freedoms, were commonly deemed reasonable and proportionate. It then continues with some recent rulings of the Italian Constitutional Court. The analysis aims to show that the balance of rights implemented by the legislator and judged by the Court is affected by the state of emergency: the state of emergency makes reasonable what, in ordinary conditions, would have been absolutely disproportionate. Proportionality, in fact – which is necessarily inherent to precautionary approach – finds a different balance point according to the context in which the precautionary measures are adopted. In 2006, Italy adopted its pandemic plan, which was never updated as was required in accordance with the WHO guidelines formulated in 2017 and 2018. Nevertheless, the essay points out that the precautionary principle must not only shape the answer to the state of emergency, but also affect its planning and prevention. Moreover, the findings show there was no lack of emergency forecasts or of measures suitable for the pandemic purpose, and that Italy had failed to implement these measures. Therefore, in the long term, the measures adopted by the Italian legislature and the public administration have turned out to be completely unreasonable.
Identifying Distributional Perspectives from Colingual Groups

Perspective differences exist among different cultures or languages.A lack of mutual understanding among different groups about their perspectives on specific values or events may lead to uninformed decisions or biased opinions.Automatically understanding the group perspectives can provide essential background for many downstream applications of natural language processing techniques.In this paper, we study colingual groups 1 and use language corpora as a proxy to identify their distributional perspectives.We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages.On a held out set of diverse topics including marriage, corruption, democracy, our model achieves high correlation with human judgements regarding intra-group values and inter-group differences.
Development of CNN-based Virtual Mouse

The technique of building a process of interaction between human and computer is evolving since the invention of technology. The mouse is a superb invention in HCI (Human-Computer Interaction) technology. Though wireless mouse technology is invented still, that technology isn't completely device free. A Bluetooth mouse has the need of battery power and connecting dongle. The proposed mouse system is beyond this limitation. This paper proposes a virtual mouse system supported HCI using computer vision and hand gestures. Gestures captured with a built-in camera or webcam and processed by a Convolutional Neural Network Model for classification among the desired mouse operations. The users are going to be allowed to regulate a number of the pc cursor functions with their hand gestures. Primarily, a user can perform left clicks, right clicks, and double clicks, scrolling up or down using their hand in several gestures. This technique captures frames employing a webcam or built-in cam and processes the frames to make them track-able and then recognizes different gestures made by users and perform the mouse functions. Therefore the proposed mouse system eliminates device dependency so as to use a mouse.
Modeling and Validating Security and Confidentiality in System Architectures

The importance of security in computer and information systems is increasing as network-connected computer systems become more ubiquitous. The objective of security is to verify that the computing platform is secured and that data and information are properly accessed and handled by users and applications, ensuring data confidentiality and integrity. To develop a framework for modeling and verifying security as a data quality attribute, designers need to identify parameters and variables with the expressive power to capture and represent security models and determine the type of analysis to enable. This report presents an approach for modeling and validating confidentiality based on the Bell–LaPadula security model using the Architecture Analysis and Design Language (AADL). The report describes the Bell–LaPadula security model and elaborates how security and Bell–LaPadula attributes are mapped to concepts and represented in AADL. It then describes modeling and validating security in AADL models, considering conditions that need to be enforced for a system to ensure conformance to the Bell–LaPadula security policy. It also presents the analysis capabilities provided by AADL and examples modeled in AADL.
MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization

This paper details a Consumer Health Question (CHQ) summarization model submitted to MEDIQA 2021 for shared task 1: Question Summarization.Many CHQs are composed of multiple sentences with typos or unnecessary information, which can interfere with automated question answering systems.Question summarization mitigates this issue by removing this unnecessary information, aiding automated systems in generating a more accurate summary.Our summarization approach focuses on applying multiple pre-processing techniques, including question focus identification on the input and the development of an ensemble method to combine question focus with an abstractive summarization method.We use the state-of-art abstractive summarization model, PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization), to generate abstractive summaries.Our experiments show that using our ensemble method, which combines abstractive summarization with question focus identification, improves performance over using summarization alone.Our model shows a ROUGE-2 F-measure of 11.14% against the official test dataset.
Guaranteeing Information Integrity Through Blockchains for Smart Cities

Given the threats that the smart city faces especially tampering the integrity of information, it has become necessary to integrate more robust and decentralized technologies that ensure transparency and sustainability of the system. Blockchain is a technology initially directed to limit the manipulation in financial transactions. One of the most popular currencies adopting this technology is Bitcoin, the latter has been very successful due to the high protection it provides. In this work, we integrate blockchain technology to become the mainstay for protecting all types of information that is collected by smart devices within a smart city. We give the blockchain structure and its internal components, and accordingly we propose an architecture for the IoT system that is compatible with the constrained devices. We explain the steps of communications between nodes, highlight the limitations, and propose solutions to them. Then, we conclude the work with experiments to show the effectiveness and the validity of the proposed architecture. The obtained results were more satisfactory, which encourage us to apply it in reality.
Interpreting Text Classifiers by Learning Context-sensitive Influence of Words

Many existing approaches for interpreting text classification models focus on providing importance scores for parts of the input text, such as words, but without a way to test or improve the interpretation method itself.This has the effect of compounding the problem of understanding or building trust in the model, with the interpretation method itself adding to the opacity of the model.Further, importance scores on individual examples are usually not enough to provide a sufficient picture of model behavior.To address these concerns, we propose MOXIE (MOdeling conteXt-sensitive In-fluencE of words) with an aim to enable a richer interface for a user to interact with the model being interpreted and to produce testable predictions.In particular, we aim to make predictions for importance scores, counterfactuals and learned biases with MOXIE.In addition, with a global learning objective, MOXIE provides a clear path for testing and improving itself.We evaluate the reliability and efficiency of MOXIE on the task of sentiment analysis.* Work done during an internship at Amazon AWS AI, USA.1 No offense is intended towards any particular community in this or in subsequent sections.Rather, we are interested in probing for unexpected biases.
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

This paper provides a statistical analysis of high-dimensional batch Reinforcement Learning (RL) using sparse linear function approximation. When there is a large number of candidate features, our result sheds light on the fact that sparsity-aware methods can make batch RL more sample efficient. We first consider the off-policy policy evaluation problem. To evaluate a new target policy, we analyze a Lasso fitted Q-evaluation method and establish a finite-sample error bound that has no polynomial dependence on the ambient dimension. To reduce the Lasso bias, we further propose a post model-selection estimator that applies fitted Q-evaluation to the features selected via group Lasso. Under an additional signal strength assumption, we derive a sharper instance-dependent error bound that depends on a divergence function measuring the distribution mismatch between the data distribution and occupancy measure of the target policy. Further, we study the Lasso fitted Q-iteration for batch policy optimization and establish a finite-sample error bound depending on the ratio between the number of relevant features and restricted minimal eigenvalue of the data's covariance. In the end, we complement the results with minimax lower bounds for batch-data policy evaluation/optimization that nearly match our upper bounds. The results suggest that having well-conditioned data is crucial for sparse batch policy learning.
Learning a Universal Template for Few-shot Dataset Generalization

Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples. To this end, we propose to utilize the diverse training set to construct a universal template: a partial model that can define a wide array of dataset-specialized models, by plugging in appropriate components. For each new few-shot classification problem, our approach therefore only requires inferring a small number of parameters to insert into the universal template. We design a separate network that produces an initialization of those parameters for each given task, and we then fine-tune its proposed initialization via a few steps of gradient descent. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.
Optimizing Internal Overlaps by Self-Adjusting Resource Allocation in Multi-Stage Computing Systems

With the rise of big data, more and more users will launch computing systems to process a large volume of data in various applications. A Scheduling algorithm is crucial to the performance of the processing platforms, especially when they are concurrently executing a batch of jobs. Such jobs usually represent multiple stages. Each stage produces the intermediate data which will be piped to the next stage for further processing. However, the scheduling problem in a big data computing system is different from the traditional multi-stage job scheduling problem as for any two consecutive stages, the later stage usually starts before the former stage is finished to “shuffle” the intermediate data. In this paper, we consider MapReduce/Hadoop as a representative computing system and develop a new strategy named OMO, Optimize MapReduce Overlap with a Good Start (Reduce) and a Good Finish (Map). A MapReduce job contains two consecutive phases: map and reduce. Our general target is to optimize the internal overlap between these two phases. There are two new techniques included in our solution, Lazy start of reduce tasks and Batch finish of map tasks, which aim to approach an effective alignment of the two phases based on the characteristics of the MapReduce process. OMO has been implemented on the Hadoop system with extensive experiments for performance evaluation. The results show that OMO's performance is superior in terms of total completion time (i.e., makespan) of a batch of jobs.