Reviewerly/exHarmony huggingface dataset:
New proposed measures to evaluate contact tracing effectiveness

Abstract Issue/problem Contact tracing is an important and widely used method to control transmission of communicable diseases. The COVID-19 pandemic presented a new and big challenge with a high number of confirmed cases and contacts and subsequent high burden on contact tracing activities. Naturally, the effectiveness of such interventions needs to be assessed, and appropriate measures are still rather underdeveloped. Description of the problem We propose two new measures for assessing contact tracing effectiveness: “number needed to quarantine” (NNQ), which is the number of quarantined person-days needed to prevent one infectious person-day, and the proportion of infectious days prevented by the quarantined (PPID). We have applied these measures to assess COVID-19 contact tracing effectiveness in COVID-19 confirmed cases diagnosed between July and mid-September 2020 in a local Public Health Unity in the Northern region of Portugal (Espinho/Gaia). For robustness checks and accounting for the uncertainty of the infectiousness period, we used three different scenarios. Results Depending on the infectiousness period considered, we have found a NNQ between 19.8 and 41.8 and a PPID between 19.7% and 38.2%. Contact tracing effectiveness was higher for some specific groups such as cohabitants and symptomatic contacts. Effectiveness also decreased with the increasing time from diagnosis or symptom onset to contact isolation Lessons NNQ and PPID are straightforward and easy to use measures to evaluate contact tracing effectiveness in communicable diseases. Although this example focuses in the COVID-19 pandemic at a local setting, these measures can also be used for different communicable diseases and at different levels. This assessment step can be important for priority setting of transmission control activities but also on a health management perspective. Key messages • New measures to evaluate contact tracing effectiveness are proposed: “number needed to quarantine” and “proportion of prevented infectious days”. • These measures allow the identification of priority groups that must be quarantined, as well as time periods of intervention, for better transmission control.
Risk of SARS-CoV-2 infection in high and low-risk cohabitants, in Loures and Odivelas, Portugal

Abstract Background Effective contact tracing, vaccination, and isolation of cases of SARS-CoV-2 infection and their high-risk contacts constituted measures to contain the spread of COVID-19. In Portugal, in October 2021, low-risk cohabitants were lifted the obligation to isolate. The aim of this study was to estimate the relative risk of infection for close contacts, regarding the type of close contact and being cohabitants. Methods A descriptive longitudinal study, with an analytical component was performed. Sociodemographic and epidemiologic data from close contacts and confirmed cases in Loures and Odivelas, between October and November 2021, was collected from a regional database and from Trace COVID-19 platform. We performed a descriptive analysis and estimated the relative risk of SARS-CoV-2 positive test, stratified by type of contact and cohabitation, with 95% confidence level. Results We identified 200 confirmed cases and 428 people who were close contacts, corresponding to 502 different close contacts (59 people had contact with more than a case). From 502 close contacts, 268 were classified as low-risk and 230 as high-risk. Full time cohabitation was present in 310 of close contacts. Between contact tracing day and the next 4 weeks, 58 (10.9%) of close contacts tested positive. Risk of high-risk contacts testing positive was 2.7 [1.5-4.6], compared with low-risk contacts. Risk of cohabitants testing positive was 3.5 [1.6-7.7], compared with non-cohabitants. Risk of a high-risk cohabitant testing positive was 2.2 [1.1-4.4], compared with low-risk cohabitants. There was no higher risk of high-risk cohabitants testing positive compared with high-risk non-cohabitants. Same was true for low-risk cohabitants and non-cohabitants. Conclusions These results allow us to understand how to better stratify close contacts and apply isolation measures, according to the risk of testing positive. Further studies should be developed to assess the impact of other variables. Key messages • We identified an increased risk of testing positive in high-risk contacts, and in cohabitants. • Cohabitants could be stratified regarding being high or low-risk, with different measures being applied.
Training the Armed Forces personnel in COVID-19 contact tracing: a Portuguese case study

Abstract Background Local contact tracing teams needed to be reinforced in preparation for a peak in Covid-19 cases. The Portuguese Armed Forces showed availability and their members initiated a formal training facilitated by the Public Health Unit (PHU) of Amadora. Health systems must be prepared to respond to all threats, as the COVID-19 pandemic showed us the need for quick task shifting and the training of non-experts’ workers. Objectives The aim of the project was to develop contact tracing skills by non-health professionals, in the context of the COVID-19 pandemic. The training program was held online, with a total duration of 48 hours, distributed by the topics described: introduction to health and epidemiology concepts, the national guidelines, and the information systems (13h). We privileged demonstrative and participatory training methods, followed by continuous supervision of each contact tracing survey and constant feedback (35h). Learning support materials were sent out to assist the trainees, including written and video support. Results More than 200 personnel - sailors, soldiers and airmen - were trained. Each Lisbon and Tagus Valley area PHU was reinforced with a team of military professionals in order to support the contact tracing process, with an increase in the number of surveys completed. We highlight as positive aspects: increased number of contact tracing surveys carried out; growth of inter-institutional partnerships; freeing up of specialized PHU resources to other important tasks. As for negative aspects we focus on the complexity in health communication, the limited time for training, and the lack of specific health knowledge of the trainees. Conclusions This pandemic revealed the Portuguese need for a transdisciplinary approach in the provision of care, specially at a Public Health level. Training programs like these highlight the vital role of reshaping and reorganizing the healthcare workforce answering Public Health necessities. Key messages • Training programs for non-health workers must be prepared to reinforce health systems when necessary. The reinforcement of contact tracing teams by the Portuguese Armed Forces was a great example. • A transdisciplinary approach in the provision of care was essential during the COVID-19 pandemic. Specific training of non-health workers can be planned in time to respond to health threats.
The future of health digitalization: The case of Primary Health Care in Portugal

Abstract Background The sustainability of Healthcare Services will depend on the proper implementation of the digital transformation of Primary Health Care (PHC) Services. COVID-19 Pandemic have shown precisely this, where there was pressure from health Professionals and managers for the integration of teleconsultations within the health work processes. This study aims at developing scenarios for the digitalization of PHC in the Portuguese Health System. Methods This study follows the methodology of scenario development, similar to the future of community pharmacy services scenario study. A conceptual model of PHC services was developed, based on the literature and a set of interviews. The target group was the primary healthcare professionals: family doctors, nurses, and operational assistants. The time horizon was 2032 and, by selecting actors from different regions of Portugal, it was possible to achieve broad representativeness. Results Three focus groups were conducted. The first enabled to identify the two driving forces that may influence the digitalization of PHCs in the next 10 years: 1) service innovation and 2) Governance and Regulations. These two driving forces enabled to design three plausible scenarios: a) Innovate or Fade-away; b) Isolated PHC and c) Digital PHC. These scenarios were developed, and their impacts were reflected upon. It was found that the role of human Resources is critical. Conclusions The process of reflection and discussion for the identification of the different driving forces made the different actors discuss the different points of view and find a meeting point to reach a conclusion. Furthermore, this study allowed the different stakeholders to understand the measures and actions to be taken for PHC digitization to be implemented in the most effective way, allowing the sustainability of the National Health Service in Portugal, which until this point were only ideas discussed at the institutional and individual level.
Attitudes and experiences towards the IMMUNI App among Sapienza university students: a pilot study

Abstract Background IMMUNI is an app that was created to help fight epidemics, starting with COVID-19. The app has a contact tracing feature but its diffusion in Italy was low. In this pilot study, we investigated university students’ attitudes and experience towards the IMMUNI app. Methods This cross-sectional study was conducted at Sapienza University of Rome between 14 April and 19 April 2021. An online survey was administrated to university students of medical area. A multivariable logistic regression model was built to identify app download's predictors. Adjusted odds ratio (aOR) and 95% confidence interval (CI) were calculated. Results We collected 247 questionnaires (response rate: 78.2%). More than half of the students (65.0%) didn't download IMMUNI app mostly because of the belief that it was useless (30.0%). By contrast, the main reason for downloading was sense of duty (40.0%). Experience with the app was limited but the process was judged as lacking for the technical difficulties. As for hypothetical incentives, feedback on how the download could help against the pandemic was considered as the most effective (3.5 out of 5). In the multivariable analysis, higher likelihood of download was associated with higher fear of contagion for family and acquaintances (aOR:1.50, 95% CI: 1.01-2.23) and higher rating to the health management of the emergency (aOR: 1.33, 95% CI:1.00-1.76). The highest odds of download were found for participants who have been advised to download the app (aOR: 3.21, 95% CI:1.80-5.73). On the other hand, greater belief that the virus came from a laboratory was negatively associated with the download (aOR: 0.75, 95% CI: 0.60-0.93). Conclusions Strategies aimed at raising students’ awareness on the importance of health technologies, restoring confidence in health authorities, and limiting disinformation around SARS-Cov-2 should be devised. In addition, the app could be enriched with positive feedbacks for users, and some technical issues should be fixed. Key messages • The spread of digital technologies for public health purposes is fostered by trust in health institutions and improving health literacy and user engagement. • An efficient and user-friendly technology is recommended.
Systematic review on digitals tools used for contact tracing of COVID-19 patients: interim results

Abstract Background Contact tracing is a public health intervention implemented in synergy with other measures, such as testing, physical distancing, and vaccination, to curb the COVID-19 pandemic. Digital solutions have been developed worldwide to enhance the contact tracing process. The aim of the study was to evaluate the effectiveness and impact of tracking COVID-19 patients using digital tools. Methods A systematic literature review was performed on eight online databases to identify observational studies on digital contact tracing, published 2020-2021, in English. Studies identified through the ‘Population Health Information Research Infrastructure’ project were also included. An ad hoc form has been deployed for data extraction of relevant information. Quality assessment of the included studies will be performed using validated tools. Results Over 8000 records were identified, of which 27 met the inclusion criteria: 16 modelling and 11 population-based studies. A study was based on GPS technology, four were Bluetooth-based, and others used digital technologies and manual tracing. The uptake rate of the tools ranged 19-100% across the studies. Most studies compared digital contact tracing with other strategies (e.g., no intervention, lockdown). Digital contact tracing was associated with improved identification of contact persons (9 studies), reduction of the effective reproduction number or covid-19 infections (8 studies), and increased effectiveness in combination with other containment measures (9 studies). Security and privacy issues were considered in 8 studies. Conclusions Digital contact tracing contributes in reducing further transmission, especially with sufficient population uptake of the applications and in combination with other public health measures. However, its deployment has been limited by security and privacy issues. Further studies are required to investigate the combined impact of digital and conventional contact tracing and enhance privacy and security.
Censorship of the Internet

Computer technology is changing faster than the laws governing it, raising the question of whether laws formuluted to address conflicts arising in the "real world" are applicable to conflicts in "cyberspace." This question is particularly relevant to the debate over sexually explicit material on the Internet. This article argues that because the Supreme Court's current approach to the obscenity is not transferable to cyberspace, a different legal framework is necessary for dealing with" cybersmut. "
A graph-based deep learning approach for illegal transaction detection in Bitcoin.

Abstract In recent years, cryptocurrencies have been used as a new way to conduct transactions and transfer money among individuals, so the volume of daily transactions in their networks has reached to several billion dollars. The anonymity of users alongside the high security and privacy properties has led many criminals to the cryptocurrency networks to carry out their illegal transactions. However, public access to the blockchain of Bitcoin and many other cryptocurrencies, allows individuals and financial institutions to obtain information about some of these activities. Several approaches, such as investigating financial flow in the blockchain, statistical analysis, and machine learning methods, have been introduced to detect illegal transactions. This paper uses a deep learning model based on a graph convolutional network and multi-layer perceptron to classify Bitcoin transactions based on their applications. We extract several features from the transaction graph, then by doing some preprocessing on our data, we train a model, which is able to predict illicit transactions with an f1-score of 97.09% which outperforms previous approaches to this problem.
Contrast independent biologically inspired translational optic flow estimation

The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950's, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems.
Efficient and Stable Fully Dynamic Facility Location

We consider the classic facility location problem in fully dynamic data streams, where elements can be both inserted and deleted. In this problem, one is interested in maintaining a stable and high quality solution throughout the data stream while using only little time per update (insertion or deletion). We study the problem and provide the first algorithm that at the same time maintains a constant approximation and incurs polylogarithmic amortized recourse per update. We complement our theoretical results with an experimental analysis showing the practical efficiency of our method.
Extracting Temporal Event Relation with Syntax-guided Graph Transformer

Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temporal relationship between them. This paper thus proposes a new Syntax-guided Graph Transformer network (SGT) to mitigate this issue, by (1) explicitly exploiting the connection between two events based on their dependency parsing trees, and (2) automatically locating temporal cues between two events via a novel syntax-guided attention mechanism. Experiments on two benchmark datasets, MATRES and TB-Dense, show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classification; This improvement also proves to be robust on the contrast set of MATRES. The code is publicly available at https://github.com/VT-NLP/Syntax-Guided-Graph-Transformer.
Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks

With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs are "black-boxes", and cannot be interpreted by humans, which is a substantial concern in safety-critical systems. To mitigate this issue, researchers have begun working on explainable AI (XAI) methods, which can identify a subset of input features that are the cause of a DNN's decision for a given input. Most existing techniques are heuristic, and cannot guarantee the correctness of the explanation provided. In contrast, recent and exciting attempts have shown that formal methods can be used to generate provably correct explanations. Although these methods are sound, the computational complexity of the underlying verification problem limits their scalability; and the explanations they produce might sometimes be overly complex. Here, we propose a novel approach to tackle these limitations. We (1) suggest an efficient, verification-based method for finding minimal explanations, which constitute a provable approximation of the global, minimum explanation; (2) show how DNN verification can assist in calculating lower and upper bounds on the optimal explanation; (3) propose heuristics that significantly improve the scalability of the verification process; and (4) suggest the use of bundles, which allows us to arrive at more succinct and interpretable explanations. Our evaluation shows that our approach significantly outperforms state-of-the-art techniques, and produces explanations that are more useful to humans. We thus regard this work as a step toward leveraging verification technology in producing DNNs that are more reliable and comprehensible.
PELATIHAN LAYANAN BIMBINGAN KELOMPOK DENGAN MENGGUNAKAN TEKNIK TOKEN ECONOMY UNTUK MENINGKATKAN MINAT BELAJAR SISWA KELAS X DI MAN KOTA PALANGKA RAYA

nterest is an impulse from within a person to do something with pleasure or pleasure without coercion from other parties. However, many students still have low interest in learning. This study was conducted to determine whether the application of group guidance services using the token economy technique can increase student interest in learning. This research is an experimental research, with a one group pre-test and post-test design. The sample was taken using a non-probability sampling technique so that there were eight students of class X MIPA-4 MAN Palangka Raya City were assigned as samples in this study. Data collection techniques in this study used by researchers are questionnaires, observation, and documentation. Then the researcher analyzed the data using parametric statistics from the Paired Samples Test (SPSS version 22 program) because the researcher's data were normally distributed. The author finally draws conclusions based on statistical results. The results showed that the calculation of the value of the paired t-test obtained was sig.2 tailed of 0.000 &lt;0.05 with a t count of -7.072 which means the null hypothesis is rejected and the alternative hypothesis is accepted. The alternative hypothesis in this study is that the application of group guidance services using the token economy technique can increase student interest in learning for class X MIPA-4 MAN Palangka Raya City. Thus, it can be concluded that the application of group guidance services using the token economy technique can increase student interest in learning for class X MIPA-4 MAN Palangka Raya City for the 2020/2021 academic year.
FUN ENGLISH TO INCREASE VOCABULARY AND MOTIVATION FOR KINDERGARTEN CHILDREN OF SANTA RITA CASSIA, KEFAMENANU-TTU

Community service was done as a concrete implementation of NTT Provincial Governor Regulation No. 56 2018. Following the adoption of the regulation, the team considered that it was necessary to promote the importance of English for the NTT community, especially for young children. As a Lingua Franca, English plays an important role; yet English language teaching is currently limited to lessons in class only without the assistance of its usage outside the classroom. The team believes it was a need to carry out a community service movement such as teaching that was more focused on the use of English language skills as early as possible. Introducing English to children from an early age was expected to improve the quality of the younger generation, especially in the language field. The method of implementing this community service was a teaching method by using children's songs and flashcards. The teaching was done face-to-face. The community service activities were run well and smoothly.