Reviewerly/exHarmony huggingface dataset:
Smart Contract Security: A Practitioners' Perspective

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms have a statistically significant impact on practitioners' security perceptions and practices of smart contract development. Based on our findings, we highlight future research directions and provide recommendations for practitioners.
Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning

Environmental magnetism in combination with machine learning can be used to monitor heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of sediments from Chaohu Lake (China) were analyzed. The magnetic measurements, high- and low-temperature curves, and hysteresis loops showed the primary magnetic minerals were ferrimagnetic minerals in sediments. For most metals, their concentrations were highest during the wet season and lowest during the medium-water period. Cd, Hg, and Zn were moderately enriched and Cd and Hg posed a considerable ecological risk. A redundancy analysis indicated a relationship between physicochemical indexes and magnetic parameters and heavy metal concentrations. An artificial neural network (ANN) and support vector machine (SVM) were used to construct six models to predict the heavy metal concentrations and ecological risk index. The inclusion of both the physicochemical indexes and magnetic parameters as input factors in the models were significantly ameliorated the simulation accuracy for the majority of heavy metals. The training and test R, for Be, Fe, Pb, Zn, As, Cu, and Cr were > 0.8. The SVM showed better performance and hence it has potential for the efficient and economical long-term tracking and monitoring of heavy metal pollution in lake sediments.
Benchmark Test of Black-box Optimization Using D-Wave Quantum Annealer

In solving optimization problems, objective functions generally need to be minimized or maximized. However, objective functions cannot always be formulated explicitly in a mathematical form for complicated problem settings. Although several regression techniques infer the approximate forms of objective functions, they are at times expensive to evaluate. Optimal points of "black-box" objective functions are computed in such scenarios, while effectively using a small number of clues. Recently, an efficient method by use of inference by sparse prior for a black-box objective function with binary variables has been proposed. In this method, a surrogate model was proposed in the form of a quadratic unconstrained binary optimization (QUBO) problem, and was iteratively solved to obtain the optimal solution of the black-box objective function. In the present study, we employ the D-Wave 2000Q quantum annealer, which can solve QUBO by driving the binary variables by quantum fluctuations. The D-Wave 2000Q quantum annealer does not necessarily output the ground state at the end of the protocol due to freezing effect during the process. We investigate effects from the output of the D-Wave quantum annealer in performing black-box optimization. We demonstrate a benchmark test by employing the sparse Sherrington-Kirkpatrick (SK) model as the black-box objective function, by introducing a parameter controlling the sparseness of the interaction coefficients. Comparing the results of the D-Wave quantum annealer to those of the simulated annealing (SA) and semidefinite programming (SDP), our results by the D-Wave quantum annealer and SA exhibit superiority in black-box optimization with SDP. On the other hand, we did not find any advantage of the D-Wave quantum annealer over the simulated annealing. As far as in our case, any effects by quantum fluctuation are not found.
Player-AI Interaction: What Neural Network Games Reveal About AI as Play

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.
Optimal Importance Sampling for Federated Learning

Federated learning involves a mixture of centralized and decentralized processing tasks, where a server regularly selects a sample of the agents and these in turn sample their local data to compute stochastic gradients for their learning updates. The sampling of both agents and data is generally uniform; however, in this work we consider non-uniform sampling. We derive optimal importance sampling strategies for both agent and data selection and show that under convexity and Lipschitz assumptions, non-uniform sampling without replacement improves the performance of the original FedAvg algorithm. We run experiments on a regression and classification problem to illustrate the theoretical results.
Testing Machine Translation via Referential Transparency

Machine translation software has seen rapid progress in recent years due to the advancement of deep Neural Networks. People routinely use machine translation software in their daily lives for tasks such as ordering food in a foreign restaurant, receiving medical diagnosis and treatment from foreign doctors, and reading international political news online. However, due to the complexity and intractability of the underlying Neural Networks, modern machine translation software is still far from robust and can produce poor or incorrect translations; this can lead to misunderstanding, financial loss, threats to personal safety and health, and political conflicts. To address this problem, we introduce referentially transparent inputs (RTIs), a simple, widely applicable methodology for validating machine translation software. A referentially transparent input is a piece of text that should have similar translations when used in different contexts. Our practical implementation, Purity, detects when this property is broken by a translation. To evaluate RTI, we use Purity to test Google Translate and Bing Microsoft Translator with 200 unlabeled sentences, which detected 123 and 142 erroneous translations with high precision (79.3% and 78.3%). The translation errors are diverse, including examples of under-translation, over-translation, word/phrase mistranslation, incorrect modification, and unclear logic.
AdolescentBot: Understanding Opportunities for Chatbots in Combating Adolescent Sexual and Reproductive Health Problems in Bangladesh

Traditional face-to-face health consultation-based systems largely failed to attract teenagers to get reproductive and sexual health supports from doctors and practitioners in Bangladesh as ‘sex’ or ‘adolescent’ related issues are considered social taboos and are rarely discussed openly with anyone. This has damaging implications for the physiological and mental well-being of a large group of people. In this paper, we study chatbot’s effectiveness to assist adolescents in seeking reproductive and sexual health supports by analyzing the responses from 256 participants, including adolescents and medical personnel from six different regions of Bangladesh. We prototype an interactive chatbot, namely AdolescentBot, and analyzed users’ communication patterns, feelings, and contexts of use as the first point of support for getting adolescence related health advice. Our analysis finds that a chatbot can satisfy most of the users’ queries, and the majority of the queries are associated with wrong-beliefs. Finally, we discuss ethical and societal issues with chatbot usage and recommend a set of design propositions for the AdolescentBot.
Infrared Image Super-Resolution via Transfer Learning and PSRGAN

Recent advances in single image super-resolution (SISR) demonstrate the power of deep learning for achieving better performance. Because it is costly to recollect the training data and retrain the model for infrared (IR) image super-resolution, the availability of only a few samples for restoring IR images presents an important challenge in the field of SISR. To solve this problem, we first propose the progressive super-resolution generative adversarial network (PSRGAN) that includes the main path and branch path. The depthwise residual block (DWRB) is used to represent the features of the IR image in the main path. Then, the novel shallow lightweight distillation residual block (SLDRB) is used to extract the features of the readily available visible image in the other path. Furthermore, inspired by transfer learning, we propose the multistage transfer learning strategy for bridging the gap between different high-dimensional feature spaces that can improve the PSGAN performance. Finally, quantitative and qualitative evaluations of two public datasets show that PSRGAN can achieve better results compared to the SR methods.
High Resolution Face Age Editing

Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to encode a face image to age-invariant features, and learn a modulation vector corresponding to a target age. We then combine these two elements to produce a realistic image of the person with the desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for fine-grained age editing on high resolution images in a single unified model. Source codes are available at https://github.com/InterDigitalInc/HRFAE.
Considerations for the Design and Implementation of COVID-19 Contact Tracing Apps: Scoping Review

Given the magnitude and speed of SARS-CoV-2 transmission, achieving timely and effective manual contact tracing has been a challenging task. Early in the pandemic, contact tracing apps generated substantial enthusiasm due to their potential for automating tracing and reducing transmission rates while enabling targeted confinement strategies. However, although surveys demonstrate public interest in using such apps, their actual uptake remains limited. Their social acceptability is challenged by issues around privacy, fairness, and effectiveness, among other concerns.This study aims to examine the extent to which design and implementation considerations for contact tracing apps are detailed in the available literature, focusing on aspects related to participatory and responsible eHealth innovation, and synthesize recommendations that support the development of successful COVID-19 contact tracing apps and related eHealth technologies.Searches were performed on five databases, and articles were selected based on eligibility criteria. Papers pertaining to the design, implementation, or acceptability of contact tracing apps were included. Articles published since 2019, written in English or French, and for which the full articles were available were considered eligible for analysis. To assess the scope of the knowledge found in the current literature, we used three complementary frameworks: (1) the Holistic Framework to Improve the Uptake and Impact of eHealth Technologies, (2) the Montreal model, and (3) the Responsible Innovation in Health Assessment Tool.A total of 63 articles qualified for the final analysis. Less than half of the selected articles cited the need for a participatory process (n=25, 40%), which nonetheless was the most frequently referenced item of the Framework to Improve the Uptake and Impact of eHealth Technologies. Regarding the Montreal model, stakeholder consultation was the most frequently described level of engagement in the development of contact tracing apps (n=24, 38%), while collaboration and partnership were cited the least (n=2, 3%). As for the Responsible Innovation in Health framework, all the articles (n=63, 100%) addressed population health, whereas only 2% (n=1) covered environmental considerations.Most studies lacked fundamental aspects of eHealth development and implementation. Our results demonstrate that stakeholders of COVID-19 contact tracing apps lack important information to be able to critically appraise this eHealth innovation. This may have contributed to the modest uptake of contact tracing apps worldwide. We make evidence-informed recommendations regarding data management, communication, stakeholder engagement, user experience, and implementation strategies for the successful and responsible development of contact tracing apps.
Pinch, Click, or Dwell: Comparing Different Selection Techniques for Eye-Gaze-Based Pointing in Virtual Reality

While a pinch action is gaining popularity for selection of virtual objects in eye-gaze-based systems, it is still unknown how well this method performs compared to other popular alternatives, e.g., a button click or a dwell action. To determine pinch’s performance in terms of execution time, error rate, and throughput, we implemented a Fitts’ law task in Virtual Reality (VR) where the subjects pointed with their (eye-)gaze and selected / activated the targets by pinch, clicking a button, or dwell. Results revealed that although pinch was slower, made more errors, and had less throughput compared to button clicks, none of these differences were significant. Dwell exhibited the least errors but was significantly slower and achieved less throughput compared to the other conditions. Based on these findings, we conclude that the pinch gesture is a reasonable alternative to button clicks for eye-gaze-based VR systems.
A systematic map of research characteristics in studies on augmented reality and cognitive load

In this paper, we present results from a systematic review of research on Augmented Reality (AR) with a special focus on cognitive load (CL). A total of 64 studies from the years 2007 to 2019 were analyzed. The number of publications on AR and CL is steadily increasing. While studies are often conducted by multidisciplinary teams, most are from the US and Taiwan. From a methodological perspective, quantitative research methods with experimental designs dominate. Usually, studies are conducted as media comparison studies measuring effects of AR on declarative or procedural knowledge compared to one or more control groups. The examination of AR focuses on different components, with assistance systems and instructional materials being the most common. Mostly, studies are about see-through, marker-based, spatial, and location-based AR. Markerless or web AR applications are not yet in this sample. The influence of AR glasses on the cognitive load is most often investigated, followed by mobile devices such as smartphones or tablets. Among the survey instruments, the Nasa Task Load Index (Nasa TLX) is used most frequently; only three studies use dual task methods to measure the cognitive load. Implications for future research projects are presented and should contribute to the advancement of research on AR and cognitive load. More research is definitely needed.
Improved Brain Age Estimation With Slice-Based Set Networks

Deep Learning for neuroimaging data is a promising but challenging direction. The high dimensionality of 3D MRI scans makes this endeavor compute and data-intensive. Most conventional 3D neuroimaging methods use 3D-CNN-based architectures with a large number of parameters and require more time and data to train. Recently, 2D-slice-based models have received increasing attention as they have fewer parameters and may require fewer samples to achieve comparable performance. In this paper, we propose a new architecture for BrainAGE prediction. The proposed architecture works by encoding each 2D slice in an MRI with a deep 2D-CNN model. Next, it combines the information from these 2D-slice encodings using set networks or permutation invariant layers. Experiments on the BrainAGE prediction problem, using the UK Biobank dataset, showed that the model with the permutation invariant layers trains faster and provides better predictions compared to other state-of-the-art approaches.
Redactable Blockchain Supporting Supervision and Self-Management

The immutability of blockchain is crucial to the security of many blockchain applications, while it is still desired or even legally obliged to allow for redacting the contents of blockchain for some scenarios. In this work, we revisit the conflict between the immutability and redaction of blockchain, and put forward a new fine-grained redactable blockchain with a semi-trusted regulator, who follows our protocol but has a tendency to abuse his power. To the best of our knowledge, it is the first blockchain that not only supports the supervision of blockchain content, but also allows users themselves to manage their own data. To this end, we introduce a new variant of chameleon-hash function, named stateful Chameleon Hash with Revocable Subkey, which is important for building our redactable blockchain and may be of independent interest. We also propose a black-box construction from standard chameleon-hash functions, and prove its security properties under our proposed security notions. At last, we provide a proof-of-concept implementation. The evaluation results demonstrate that our redactable blockchain is practical and can be adopted with small additional overhead compared to the immutable blockchain.
Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition

One of the most significant challenges in Human Activity Recognition using wearable devices is inter-class similarities and subject heterogeneity. These problems lead to the difficulties in constructing robust feature representations that might negatively affect the quality of recognition. This study, for the first time, applies deep triplet networks with various triplet loss functions and mining methods to the Human Activity Recognition task. Moreover, we introduce a novel method for constructing hard triplets by exploiting similarities between subjects performing the same activities using the concept of Hierarchical Triplet Loss. Our deep triplet models are based on the recent state-of-the-art LSTM networks with two attention mechanisms. The extensive experiments conducted in this paper identify important hyperparameters and settings for training deep metric learning models on widely-used open-source Human Activity Recognition datasets. The comparison of the proposed models against the recent benchmark models shows that deep metric learning approach has the potential to improve the quality of recognition. Specifically, at least one of the implemented triplet networks shows the state-of-the-art results for each dataset used in this study, namely PAMAP2, USC-HAD and MHEALTH. Another positive effect of applying deep triplet networks and especially the proposed sampling algorithm is that feature representations are less affected by inter-class similarities and subject heterogeneity issues.
IoT-based crowd monitoring system: Using SSD with transfer learning

The constantly developing urbanization and the emergence of smart cities require better security surveillance and crowd monitoring systems. The growing availability of the Internet of Things (IoT) devices in public and private organizations also provide intelligent and secure surveillance solutions for real-time monitoring in public spaces. This article introduces an IoT-based crowd surveillance system that uses a deep learning model to detect and count people using an overhead view perspective. The Single Shot Multibox Detector (SSD) model with Mobilenetv2 as the basic network is used for the detection of people. The detection model’s accuracy is enhanced with a transfer learning approach. Two virtual lines are defined to count how many people are leaving and entering the scene. In order to assess performance, experiments are performed using different video clips. Results indicate that transfer learning increases the overall detection performance of the system with an accuracy of 95%.
Invariant Extended Kalman Filtering for Underwater Navigation

Recent advances in the utilization of Lie Groups for robotic localization have led to dramatic increases in the accuracy of estimation and uncertainty characterization. One of the novel methods, the Invariant Extended Kalman Filter (InEKF) extends the Extended Kalman Filter (EKF) by leveraging the fact that some error dynamics defined on matrix Lie Groups satisfy a log-linear differential equation. Utilization of these observations result in linearization with minimal approximation error, no dependence on current state estimates, and excellent convergence and accuracy properties. In this letter we show that the primary sensors used for underwater localization, inertial measurement units (IMUs) and doppler velocity logs (DVLs) meet the requirements of the InEKF. Furthermore, we show that singleton measurements, such as depth, can also be used in the InEKF update with minor modifications, thus expanding the set of measurements usable in an InEKF. We compare convergence, accuracy and timing results of the InEKF to a quaternion-based EKF using a Monte Carlo simulation and show notable improvements in long-term localization and much faster convergence with negligible difference in computation time.
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion

We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way. The backbone of our approach is a real-time capable, lightweight encoder-decoder that relies on cost volumes computed from pairs of images. We extend it by placing a ConvLSTM cell at the bottleneck layer, which compresses an arbitrary amount of past information in its states. The novelty lies in propagating the hidden state of the cell by accounting for the viewpoint changes between time steps. At a given time step, we warp the previous hidden state into the current camera plane using the previous depth prediction. Our extension brings only a small overhead of computation time and memory consumption, while improving the depth predictions significantly. As a result, we outperform the existing state-of-the-art multi-view stereo methods on most of the evaluated metrics in hundreds of indoor scenes while maintaining a real-time performance. Code available: https://github.com/ardaduz/deep-video-mvs