Reviewerly/exHarmony huggingface dataset:
Pilot Implementation of a User-Driven, Web-Based Application Designed to Improve Sexual Health Knowledge and Communication Among Young Zambians: Mixed Methods Study

Digital health interventions show promise in improving the uptake of HIV services among adolescents and young people aged 15 to 24 years in sub-Saharan Africa.This study aimed to pilot-test a theory-based, empirically grounded web-based application designed to increase condom-related knowledge, sexual and reproductive health (SRH) communication, and healthier choices among young Zambians.We conducted a pre-post quasi-experimental evaluation of the user-driven Be in the Know Zambia (BITKZ) web application using web-based surveys and in-depth interviews (IDIs) on the phone. We enrolled participants using social media advertisements. Our final analysis set comprised 46.04% (749/1627) of participants in the intervention group (which received the BITKZ link) and 53.96% (878/1627) of participants in the comparison group (no intervention). We collected survey data at study enrollment (baseline) and 5 weeks after the first enrollment in each group. Approximately 85% (637/749) of BITKZ users completed a user survey, of whom 9.3% (59/637) participated in IDIs. We calculated the time interfacing with BITKZ using the application log files. We conducted descriptive analyses to describe baseline characteristics and the user experience. At the endline, we assessed association using a t test and adjusted logistic regression for binary outcomes and ordinal regression for ordered outcomes, conditioning on age, sex, marital status, and employment status. We used adjusted average treatment effects (aATE) to assess the effects of BITKZ intervention. We conducted rapid matrix analyses of IDI transcripts in Microsoft Excel, sorting the data by theme, gender, and experience rating.Users rated BITKZ highly (excellent: 352/609, 57.8%; good: 218/609, 35.8%). At the endline, the intervention group had a higher level of knowledge related to condoms (adjusted odds ratio [aOR]: 1.35, 95% CI 1.06-1.69) and on wearing condoms correctly (aOR: 1.23, 95% CI 1.02-1.49). Those who had full-time employment had increased odds of knowing how to wear condoms correctly (aOR: 1.67, 95% CI 1.06-2.63) compared with those who reported being unemployed, as did men when compared with women (aOR: 1.92, 95% CI 1.59-2.31). Those in the intervention group were more likely to score higher for intention to test for sexually transmitted infections (STIs; aATE 0.21; P=.01) and HIV (aATE 0.32; P=.05), as well as for resisting peer pressure (aATE 2.64; P=.02). IDIs corroborated increased knowledge on correct condom use among men and female condoms among women, awareness of STIs and testing, and resistance to peer pressure. Interviewees provided examples of more open SRH communication with partners and peers and of considering, adopting, and influencing others to adopt healthier behaviors.Despite the high baseline awareness of SRH among Zambian adolescents and young people with internet access, BITKZ provided modest gains in condom-related knowledge, resistance to peer pressure, and intention to test for STIs and HIV.
RACOD

RACOD is an algorithm/hardware co-design for mobile robot path planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm extension for runahead path exploration. CODAcc uses a novel MapReduce-style hardware computational model and massively parallelizes individual collision checks. RASExp predicts future path explorations and proactively computes its collision status ahead of time, thereby overlapping multiple collision detections. By affording multiple cheap CODAcc accelerators and overlapping collision detections using RASExp, RACOD significantly accelerates planning for mobile robots operating in arbitrary environments. Evaluations of popular benchmarks show up to 41.4× (self-driving cars) and 34.3× (pilotless drones) speedup with less than 0.3% area overhead.
Brain Drain Optimization: a Novel Approach for Task Scheduling in the Cloud Computing

These days, Cloud Computing is widely used in different applications such as Civis Analytics and AWS (Amazon Web Services). Through the virtualization of hardware and software, Cloud Computing delivers various services to users over the Internet. The main challenge of providing satisfactory quality of service to the users is workflow scheduling, an NP-hard problem that has been a topic of much research. In this paper, BRADO, a recently-developed swarm intelligence algorithm, has been introduced for allocating and scheduling tasks to appropriate resources. This proposed algorithm aims to minimize the total execution time of the whole workflow, known as makespan. The performance of this proposed algorithm has been evaluated using the CloudSim Plus toolkit. Moreover, the results have been compared with some well-known algorithms, including PSO and SA. Our findings show the superiority of the BRADO algorithm in allocating and scheduling tasks.
PEPFL: A framework for a practical and efficient privacy-preserving federated learning

As an emerging joint learning model, federated learning is a promising way to combine model parameters of different users for training and inference without collecting users' original data. However, a practical and efficient solution has not been established in previous work due to the absence of efficient matrix computation and cryptography schemes in the privacy-preserving federated learning model, especially in partially homomorphic cryptosystems. In this paper, we propose a Practical and Efficient Privacy-preserving Federated Learning (PEPFL) framework. First, we present a lifted distributed ElGamal cryptosystem for federated learning, which can solve the multi-key problem in federated learning. Secondly, we develop a Practical Partially Single Instruction Multiple Data (PSIMD) parallelism scheme that can encode a plaintext matrix into single plaintext for encryption, improving the encryption efficiency and reducing the communication cost in partially homomorphic cryptosystem. In addition, based on the Convolutional Neural Network (CNN) and the designed cryptosystem, a novel privacy-preserving federated learning framework is designed by using Momentum Gradient Descent (MGD). Finally, we evaluate the security and performance of PEPFL. The experiment results demonstrate that the scheme is practicable, effective, and secure with low communication and computation costs.
Light-Weight Face Shape Classifier for Real-Time Applications

Deep neural networks (DNNs) are memory and computationally intensive; hence they are difficult to apply to real-time systems with limited resources. Therefore, the DNN models need to be carefully optimized. The solution was a model based on a convolutional neural network (CNN) called MobileNet that decreases the computational and space complexities with classification precision loss by utilizing depthwise separable convolutions. This study uses MobileNet vl architecture to improve image classification complexities to reach an acceptable complexity that can be used in real-time applications that require a hasty response from the model. In this study, the MobileNet model was trained on a dataset consisting of 5000 images to be classified into the 5 human face shapes oval, square, heart, oblong, and round. The model has an F1-score of 0.781, recall of 0.782, precision of 0.78, and achieved an accuracy of 98.8%. With this level of accuracy, a real-time application that is result-driven would benefit significantly from this model.
A Phygital Toolkit for Rapidly Designing Smart Things at School

Designing smart things, so as to ideate and program them, is a complex process and an empowerment opportunity. Toolkits can help non-experts engage in their design, that is, in their ideation and programming. This paper presents the IoTgo toolkit, made of card-based material and digital components. Playing with IoTgo can help understand the design context, ideate smart things for it, make them communicate and program them with patterns. In recent years, IoTgo was used and evolved at a distance with few participants. This paper presents a study with it, for the first time in presence, in a high-school. Results are discussed and suggest what role IoTgo or similar toolkits can play in the rapid design of smart things with non-seasoned designers or programmers.
Selectivity Functions of Range Queries are Learnable

This paper explores the use of machine learning for estimating the selectivity of range queries in database systems. Using classic learning theory for real-valued functions based on shattering dimension, we show that the selectivity function of a range space with bounded VC-dimension is learnable. As many popular classes of queries (e.g., orthogonal range search, inequalities involving linear combination of attributes, distance-based search, etc.) represent range spaces with finite VC-dimension, our result immediately implies that their selectivity functions are also learnable. To the best of our knowledge, this is the first attempt at formally explaining the role of machine learning techniques in selectivity estimation, and complements the growing literature in empirical studies in this direction. Supplementing these theoretical results, our experimental results demonstrate that, empirically, even a basic learning algorithm with generic models is able to produce accurate predictions across settings, matching state-of-art methods designed for specific queries, and using training sample sizes commensurate with our theory.
Teachers’ roles in online learning communities: a case study from the digitally underdeveloped country

In order to prevent the interruption in the education process during the first wave of the Covid-19 pandemic, which would inevitably lead to serious socio-economic consequences, the universities from low-income countries, including those from Bosnia and Herzegovina, shifted to “emergency remote teaching,” a type of teaching that is different from high-quality online education by its name and implementation method. A case study has been designed to research the role of teachers in creating an efficient learning community in the cases of emergency remote teaching. The aim of this study was to assess the perception of students with regard to the teachers’ role in “emergency remote teaching.” The process of collecting data from students started two weeks before the end of the term. The research results suggest that the crucial role of teachers in building learning communities in postwar and low-income countries is to advocate and implement the changes in their education systems, as well as the changes in understanding the concept of social responsibility that should lead to the transformation of “emergency remote teaching” model into pre-planned online learning in near future.
Using PSO metaheuristic to solve photometric 3D reconstruction

The photometric stereo is a basic domain in computer vision. It consists to reconstruct a 3D object using several intensity images taken by the same camera position and obtained under different lighting conditions. The photometric stereo allows to resolve the ambiguity of the Shape From Shading which reconstruct the 3D object using only one gray scale image and based on the same concepts of the image formation field. The Shape from Shading is considered as an ill-posed problem due to lack of information.In the literature we can find several works to solve the Photometry Stereo (PS). In this work we are interested to improve the 3D reconstruction by the mathematical solution "SVD decomposition" which is widely used and very known. We start our paper with an introduction, then we show the proposed method. we propose a new approach with several configurations to solving the Photometric Stereo (PS) using the Particle Swarm Optimization (PSO) meta-heuristic. We also propose an improvement using the integrability constraint on the 3D surface. Finally we show the obtained results and a comparison with a Singular Value Decomposition (SVD) method.
Like, dislike, or just do it? How developers approach software development tasks

Software developers work on various tasks and activities that contribute towards creating and maintaining software applications, frameworks, or other software components. These include technical (e.g., writing code and fixing bugs) and non-technical activities (e.g., communicating within or outside teams to understand, clarify, and resolve issues) as part of their day-to-day responsibilities. Interestingly, there is an aspect of desirability associated with these tasks and activities.
Using a test battery to compare three remote, video-based eye-trackers

A multitude of eye-trackers are available differing in cost and usability. Accuracy and precision provide a benchmark for performance, however, parameters given by manufacturers are often recorded under perfect conditions and may not apply to lab conditions with human subjects. This preregistered study compared three remote eye-trackers under realistic lab conditions: The high-cost EyeLink 1000+, the medium-cost Tobii Pro X3-120, and the low-cost Gazepoint GP3 HD Desktop. Participants (N = 25) completed an extensive test battery twice with each device. Parameters were compared within-subjects using robust linear mixed models. The EyeLink performed better than the Tobii Pro X3-120 and GP3 HD in most categories (accuracy EyeLink: 0.436°; Tobii Pro X3-120: 1.22°; GP3 HD: 1.03°), however, with suboptimal head positions, the Tobii Pro X3-120’s and GP3 HD's accuracies equaled or exceeded the EyeLink's. Tobii Pro X3-120 and GP3 HD performed comparably. Benchmark information, obtained under various realistic recording conditions, is provided for each device.
QMLP: An Error-Tolerant Nonlinear Quantum MLP Architecture using Parameterized Two-Qubit Gates

Despite potential quantum supremacy, state-of-the-art quantum neural networks (QNNs) suffer from low inference accuracy. First, the current Noisy Intermediate-Scale Quantum (NISQ) devices with high error rates of 10− 3 to 10− 2 significantly degrade the accuracy of a QNN. Second, although recently proposed Re-Uploading Units (RUUs) introduce some non-linearity into the QNN circuits, the theory behind it is not fully understood. Furthermore, previous RUUs that repeatedly upload original data can only provide marginal accuracy improvements. Third, current QNN circuit ansatz uses fixed two-qubit gates to enforce maximum entanglement capability, making task-specific entanglement tuning impossible, resulting in poor overall performance. In this paper, we propose a Quantum Multilayer Perceptron (QMLP) architecture featured by error-tolerant input embedding, rich nonlinearity, and enhanced variational circuit ansatz with parameterized two-qubit entangling gates. Compared to prior arts, QMLP increases the inference accuracy on the 10-class MNIST dataset by 10% with 2 × fewer quantum gates and 3 × reduced parameters. Our source code is available and can be found in https://github.com/chuchengc/QMLP/.
Coded DNN Watermark: Robustness against Pruning Models Using Constant Weight Code

Deep Neural Network (DNN) watermarking techniques are increasingly being used to protect the intellectual property of DNN models. Basically, DNN watermarking is a technique to insert side information into the DNN model without significantly degrading the performance of its original task. A pruning attack is a threat to DNN watermarking, wherein the less important neurons in the model are pruned to make it faster and more compact. As a result, removing the watermark from the DNN model is possible. This study investigates a channel coding approach to protect DNN watermarking against pruning attacks. The channel model differs completely from conventional models involving digital images. Determining the suitable encoding methods for DNN watermarking remains an open problem. Herein, we presented a novel encoding approach using constant weight codes to protect the DNN watermarking against pruning attacks. The experimental results confirmed that the robustness against pruning attacks could be controlled by carefully setting two thresholds for binary symbols in the codeword.
Design advancements for an integrated mission management system for small air transport vehicles in the COAST project

Purpose This paper aims to describe the activities that are ongoing, in the Cost Optimized Avionics SysTem (COAST) project, to design an integrated mission management system (IIMS) to be used as support to the pilot and/or to act as a backup in case of pilot incapacitation onboard on small air transport (SAT) vehicles, under single-pilot operations. Design/methodology/approach The COAST project, funded by Clean Sky 2 programme, is developing enabling technologies for single-pilot operations in the European Aviation Safety Agency CS-23 category vehicles. Such technologies include specific tools that are designed as individual enablers for single-pilot operations and specifically address: the real-time support to pilot’s decision making in maintaining the vehicle self-separation (this technology is the tactical separation system [TSS]); the real-time support to pilot’s situational awareness about observed and forecasted weather conditions (this technology is the advanced weather awareness system [AWAS]); and the real-time management of emergency conditions due to pilot’s incapacitation under single-pilot operations (this technology is the flight reconfiguration system [FRS]). Based on the outcomes of the design activities of such individual tools, in the COAST project emerged the opportunity to proceed with the design of a further system, leveraging the individual tools and benefitting from their integration. Findings The IMMS design started in the year 2020 and the activities carried out up to mid-2021 allowed to define the concept of operations of the system, its high-level requirements (functional, interface and operational requirements) and the preliminary system architecture. Originality/value The IMMS contributes enabling the implementation of single-pilot operations in CS-23 category vehicles, thanks to the possibility to support, in normal operational conditions, the pilot’s decision-making and, in emergency conditions due to pilot’s incapacitation, the automatic flight management up to the safe destination.
Approximate distillation of quantum coherence

Coherence distillation is a basic information-theoretic task in the resource theory of coherence. In this paper, we develop the framework of the approximate coherence distillation under strictly incoherent operations. This protocol considers the situation that we cannot transform an initial state $\rho$ into a target state $\psi$ with certainty; instead, we aim to deterministically transform the initial state $\rho$ into an intermediate state $\phi$ that is most approximate to the target state $\psi$ in terms of fidelity. We also present the explicit conversion strategy of the approximate coherence distillation.
A novel approach of developing all-optical frequency encoded dibit-based Peres gate using reflective semiconductor optical amplifier

In recent trends, digital systems in the light of power dissipation are a crucial issue. In computing, the computational process of reversible logic is bijective and can decrease the rising issue of power dissipation. In reversible circuit design, Peres gate considered as one of the fundamental reversible gate. Therefore, a Peres gate using Add/Drop Multiplexer (ADM) and Reflective Semiconductor Optical Amplifier (RSOA) is proposed in this paper. Frequency encoding scheme and dibit-based logic are incorporated here. In long range transmission, frequency encoding bears huge benefits in respect with the other encoding techniques. This encoding technique may decrease the probability of bit error. Due to the high gain and low noise property of RSOA, the proposed design can perform operations like computation, data processing, etc. at ultra-high speed with low noise. MATLAB Simulink (R2018a) software has been used to verify the operation of the proposed design.
A Dual Image Watermarking Scheme Based on WPT And Chaotic Encryption for Medical Data Protection

Medical data security has always been a major concern for researchers due to its importance. In this context, digital watermarking is a security technique widely used to protect the integrity and authenticity of the medical image. We propose in this paper an image watermarking algorithm based on Wavelet Packet, in which we were able to insert a watermark image and the electronic patient record EPR inside the medical image. Then, to increase the scheme’s security and confidentiality, the watermarked image was encrypted using a chaotic logistic map. Medical images from various modalities, as well as normal and pathological images, were used to test our method. The Peak Signal-to-Noise Ratio, Normalized Correlation, And Bit Error Rate were also designed to evaluate the robustness and imperceptibility. According to the results, our approach outperforms other watermarking systems for both watermarked images and retrieved information when compared to other approaches and under a variety of common attacks.
Linking task structure and neural network dynamics

The solutions found by neural networks to solve a task are often inscrutable. We have little insight into why a particular structure emerges in a network. By reverse engineering neural networks from dynamical principles, Dubreuil, Valente et al. show how neural population structure enables computational flexibility.
QDS-COVID: A visual analytics system for interactive exploration of millions of COVID-19 healthcare records in Brazil

COVID-19 is responsible for the deaths of millions of people around the world. The scientific community has devoted its knowledge to finding ways that reduce the impact and understand the pandemic. In this work, the focus is on analyzing electronic health records for one of the largest public healthcare systems globally, the Brazilian public healthcare system called Sistema Único de Saúde (SUS). SUS collected more than 42 million flu records in a year of the pandemic and made this data publicly available. It is crucial, in this context, to apply analysis techniques that can lead to the optimization of the health care resources in SUS. We propose QDS-COVID, a visual analytics prototype for creating insights over SUS records. The prototype relies on a state-of-the-art datacube structure that supports slicing and dicing exploration of charts and Choropleth maps for all states and municipalities in Brazil. A set of analysis questions drives the development of the prototype and the construction of case studies that demonstrate the potential of the approach. The results include comparisons against other studies and feedback from a medical expert.