Reviewerly/exHarmony huggingface dataset:
SenseCollect

On-body sensor-based human activity recognition (HAR) lags behind other fields because it lacks large-scale, labeled datasets; this shortfall impedes progress in developing robust and generalized predictive models. To facilitate researchers in collecting more extensive datasets quickly and efficiently we developed SenseCollect. We did a survey and interviewed student researchers in this area to identify what barriers are making it difficult to collect on-body sensor-based HAR data from human subjects. Every interviewee identified data collection as the hardest part of their research, stating it was laborious, consuming and error-prone. To improve HAR data resources we need to address that barrier, but we need a better understanding of the complicating factors to overcome it. To that end we conducted a series of control variable experiments that tested several protocols to ascertain their impact on data collection. SenseCollect studied 240+ human subjects in total and presented the findings to develop a data collection guideline. We also implemented a system to collect data, created the two largest on-body sensor-based human activity datasets, and made them publicly available.
Handwritten English word recognition using a deep learning based object detection architecture

Handwriting is used to distribute information among people. To access this information for further analysis the page needs to be optically scanned and converted to machine recognizable form. Due to unconstrained writing styles along with connected and overlapping characters, handwriting recognition remains a challenging task. Most of the methods in the literature use lexicon-based approaches and train their models on large datasets having near 50 K word samples to achieve good results. This results in high computational requirements. While these models use around 50 K words in their dictionary when recognizing handwritten English text, the actual number of words in the dictionary is much higher than this. To this end, we propose a handwriting recognition technique to recognize handwritten English text based on a YOLOv3 object recognition model that is lexicon-free and that performs sequential character detection and identification with a low number of training samples (only 1200 word images). This model works well without any dependency on writers’ style of writing. This is tested on the IAM dataset and it is able to achieve 29.21% Word Error Rate and 9.53% Character Error Rate without a predefined vocabulary, which is on par with the state-of-the-art lexicon-based word recognition models.
Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets

Over the last decade, the amount of Arabic content created on websites and social media has grown significantly.Opinions are shared openly and freely on social media, a process that provides a rich source for trend analyses.These analyses could be accomplished artificially by natural language processing tasks, such as sentiment analysis.Those tasks are implemented initially using machine learning.Due to its accuracy in studying unstructured data, deep learning has been increasingly used as well.The gated recurrent unit (GRU) is a promising approach in textual analysis and exhibits large morphological variations.We propose two neural models, i.e., the stacked gated recurrent unit (SGRU) and stacked bidirectional gated recurrent unit (SBi-GRU), with word embedding to mine Arabic opinions.We also propose a new way of discarding stop words using automatic sentiment refinement (ASR) instead of using manually collected stop words or using low quality available Arabic stop words' lists.The performance of our proposed models is compared with that of long short-term memory (LSTM), the support vector machine (SVM), and the most recent pretrained Arabic bidirectional encoder representations from transformers (AraBERT).In addition, we compare our models' performance to that of an ensemble architecture of the abovementioned models to find the best model architecture for Arabic natural language processing (NLP).To the best of our knowledge, no previous studies have applied either the unidirectional or bidirectional SGRU for Arabic sentiment classification.Furthermore, no ensemble models have been implemented from these architectures for the Arabic language.The results show that the six-layer SGRU stacking and five-layer SBi-GRU stacking achieve the highest accuracy and that the ensemble method outperforms all other models, with an accuracy exceeding 90%.
Bias Issues and Solutions in Recommender System

Recommender systems (RS) have demonstrated great success in information seeking. Recent years have witnessed a large number of work on inventing recommendation models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, etc. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and develop debiasing strategies when necessary. Therefore, bias issues and solutions in recommender systems have drawn great attention from both academic and industry.
Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. With the rapid growth of cloud users and the emergence of fog computing, the demand for cloud/fog resources has increased dramatically. Also, the incremental use of the cloud/fog resources and their applications has increased energy consumption and carbon emitted, which caused significant environmental challenges. Optimizing the placement of requested applications (e.g., in the form of virtual machines) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architecture and consequently their carbon emissions. However, due to the geographic distribution of cloud and fog data centers, there is a variety of carbon emission levels to consider, which makes optimizing the placement of applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize carbon emissions (CO2) by optimizing the usage of cloud/fog resources. These resources are managed by virtual machines (VMs), which can be optimally and virtually used. In our model, we used the British Telecom (BT) as a telecom network example with an aim to minimize the CO2 emission, considering several scenarios of traffic demand during different times of the day and year (seasons). The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO2 emission reduced by shortening network journey, and CO2 increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO2 emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.
Unsupervised descriptor selection based meta-learning networks for few-shot classification

Meta-learning aims to train a classifier on collections of tasks, such that it can recognize new classes given few samples from each. However, current approaches encounter overfitting and poor generalization since the internal representation learning is obstructed by backgrounds and noises in limited samples. To alleviate those issues, we propose the Unsupervised Descriptor Selection (UDS) to tackle few-shot learning tasks. Specifically, a descriptor selection module is proposed to localize and select semantic meaningful regions in feature maps without supervision. The selected features are then mapped into novel vectors by a task-related aggregation module to enhance internal representations. With a simple network structure, UDS makes adaptation between tasks more efficient, and improves the performance in few-shot learning. Extensive experiments with various backbones are conducted on Caltech-UCSD Bird and miniImageNet, indicate that UDS achieves the comparable performance to state-of-the-art methods, and improves the performance of prior meta-learning methods.
Feature Boosting, Suppression, and Diversification for Fine-Grained Visual Classification

Learning feature representation from discriminative local regions plays a key role in fine-grained visual classification. Employing attention mechanisms to extract part features has become a trend. However, there are two major limitations in these methods: First, they often focus on the most salient part while neglecting other inconspicuous but distinguishable parts. Second, they treat different part features in isolation while neglecting their relationships. To handle these limitations, we propose to locate multiple different distinguishable parts and explore their relationships in an explicit way. In this pursuit, we introduce two lightweight modules that can be easily plugged into existing convolutional neural networks. On one hand, we introduce a feature boosting and suppression module that boosts the most salient part of feature maps to obtain a part-specific representation and suppresses it to force the following network to mine other potential parts. On the other hand, we introduce a feature diversification module that learns semantically complementary information from the correlated part-specific representations. Our method does not need bounding boxes/part annotations and can be trained end-to-end. Extensive experimental results show that our method achieves state-of-the-art performances on several benchmark fine-grained datasets. Source code is available at https://github.com/chaomaer/FBSD.
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human skeleton. This may reduce adaptation capacity of GCN to tackle complex spatio-temporal pose variations in videos. To alleviate this problem, we propose a novel Dynamical Graph Network (DG-Net), which can dynamically identify human-joint affinity, and estimate 3D pose by adaptively learning spatial/temporal joint relations from videos. Different from traditional graph convolution, we introduce Dynamical Spatial/Temporal Graph convolution (DSG/DTG) to discover spatial/temporal human-joint affinity for each video exemplar, depending on spatial distance/temporal movement similarity between human joints in this video. Hence, they can effectively understand which joints are spatially closer and/or have consistent motion, for reducing depth ambiguity and/or motion uncertainty when lifting 2D pose to 3D pose. We conduct extensive experiments on three popular benchmarks, e.g., Human3.6M, HumanEva-I, and MPI-INF-3DHP, where DG-Net outperforms a number of recent SOTA approaches with fewer input frames and model size.
Occlusion-Aware UAV Path Planning for Reconnaissance and Surveillance

Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable, especially in environments dangerous to humans, for example, in radiation or pollution-exposed areas. Two path-planning algorithms for reconnaissance and surveillance are proposed in this paper, which ensures every point on the target ground area can be seen at least once in a complete surveillance circle. Moreover, the geometrically complex environments with occlusions are considered in our research. Compared with many existing methods, we decompose this problem into a waypoint-determination problem and an instance of the traveling-salesman problem.
Vehicle detection from road image sequences for intelligent traffic scheduling

With the rapid development of unmanned aerial vehicle (UAV) technology, the UAV surveillance system has attracted extensive attention in the intelligent transportation community. In this paper, an object detection model with global context cross (YOLO-GCC) is proposed for identifying small sized traffic elements in UAV image sequences. The concept of the asymmetric convolution is introduced to increase the robustness of the object detection model. Moreover, a global context attention module is added to extract more efficient features to ensure the real-time performance while improving the detection accuracy of small objects. The evaluation and comparison results on multiple UAV datasets demonstrate the effectiveness of the proposed model. Furthermore, an intelligent traffic signal scheduling algorithm named Traffic Deep Q-Network(Traffic-DQN) using deep reinforcement learning is introduced, which utilizes the traffic flow data obtained from YOLO-GCC as the benchmark for traffic scheduling. The experimental results demonstrate that the proposed algorithm can effectively alleviate traffic congestion compared with other methods.
Edge Intelligence for Object Detection in Blockchain-Based Internet of Vehicles: Convergence of Symbolic and Connectionist AI

Reliable detection of objects is a crucial requirement to improve the safety of the visual Internet of Vehicles (V-IoVs). The resemblance of objects to each other or the background makes the detection of objects difficult. The traditional connectionist artificial intelligence (All-based object detection model is a potential approach to increase detection performance, but it has poor interpretability and high computational complexity. Edge intelligence has demonstrated a considerably good balance between efficiency and computation complexity, integrating multi-access edge computing and AI. In this article, an edge AI framework is designed to perform the object detection task, and a novel abductive learning algorithm is proposed to realize the interpretability and robustness of AI in the V-IoV system. Based on the you only look once (YOLO) classifier, the abductive model is used to learn from the data, which combines symbolic and connectionist AI. Moreover, blockchain is developed for model sharing. Compared to the state of the art, simulation results show the high accuracy of the proposed algorithm. Moreover, the proposed approach has interpretability and strong robustness.
Local quantum uncertainty versus negativity through Gisin states

We investigate the pairwise quantum correlations in standard Gisin states and in Gisin states based on bipartite spin-coherent states by employing quantum negativity and quantum local uncertainty as bona fide quantum correlations measures. Gisin states are defined as mixtures of separable mixed states and some pure entangled ones. We compare the behavior of the two quantifiers of Gisin states and we find that both measures exhibit a sudden change in terms of the mixing parameter. Furthermore, we show that entangled Gisin states contain nonclassical correlations that are captured by the local quantum uncertainty and cannot be revealed by the negativity quantifier.
Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to operate in real time so that can efficiently support an autonomous driving scenario. To this end, the presented overview puts a particular emphasis on the presentation of all those approaches which permit inference time reduction, while an analysis of the existing methods is addressed by taking into account their end-to-end functionality, as well as a comparative study that relies upon a consistent evaluation framework. Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep learning for autonomous driving.
Freedom within a cage: how patriarchal gender norms limit women’s use of mobile phones in rural central India

Introduction India has one of the highest gender gaps in mobile phone access in the world. As employment opportunities, health messaging (mHealth), access to government entitlements, banking, civic participation and social engagement increasingly take place in the digital sphere, this gender gap risks further exacerbating women’s disadvantage in Indian society. This study identifies the factors driving women’s unequal use of phones in rural Madhya Pradesh, India. Methods We interviewed mothers of 1-year-old children (n=29) who reported that they had at least some access to a mobile phone. Whenever possible, we also spoke to their husbands (n=23) and extended family members (n=34) through interviews or family group discussions about the use of phones in their households, as well as their perspectives on gender and phone use more broadly. Our analysis involved comparing wife–husband pairs to assess differences in phone access and use, and thematic coding on the determinants of women’s phone use using an iteratively developed conceptual framework. Results While respondents reported that women could use the phone without needing permission, this apparent ‘freedom’ existed in a context that severely constrained women’s actual use, most directly through: (1) narrow expectations and desires around how women would use phones, (2) women’s dependence on men for phone ownership and lower proximity to phones, (3) the poorer functionality of women’s phones; (4) women’s limited digital skills, and (5) time allocation constraints, wherein women had less leisure time and were subject to social norms that discouraged using a phone for leisure. Conclusion Our framework, presenting the distal and proximate determinants of women’s phone use, enables more nuanced understanding of India’s digital divide. Addressing these determinants is vital to shift from re-entrenching unequal gender relations to transforming them through digital technology.
Spatial-Temporal-Cost Combination Based Taxi Driving Fraud Detection for Collaborative Internet of Vehicles

Vehicle-to-vehicle interaction and collaboration can provide us with a large number of mobile traffic trajectories that can be used to analyze driving behavior. In this article, we propose a spatio-temporal cost combination based framework for taxi driving fraud detection. First, the point of interest where taxis interact and collaborate with collaborative Internet of Vehicles participants is identified, and a baseline trajectory model is built to determine the typical trajectory distribution. Second, a statistical model is used to calculate the travel distribution, travel time, and travel cost. At the same time, the taxi trajectory points are converted into evolving graphs to detect the abnormality of the local road segment. Then, we can analyze the causes of outlier trajectories combined with the perception of abnormal road environments. Finally, the trajectories of real taxis were used to evaluate outliers, which proves the effectiveness and efficiency of the method.
A great disturbance in the crypto: Understanding cryptocurrency returns under attacks

A non-traditional type of financial asset, cryptocurrencies based on public blockchains are still little understood in their real-world behavior. Exogenous events such as cyber-attacks and their media coverage can strongly affect their supply and demand, adoption and usage, efficiency, and infrastructural development, thus influencing their price stability and market valuation. Given the great technical complexity of blockchains, we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of cryptocurrencies. On these grounds, we survey the most common types of attacks for Proof-of-Work (PoW) cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are available. Due to data availability, our event study analysis focuses on instances of 51% attacks, hard forks, and wallet attacks. The main goal of our analysis is to understand the relationship between technical events (cyber-attacks and coordinated user/miner behavior) and the economic impacts surrounding them. We aim to develop a deeper understanding of these systems that are objects of great research interest in separate disciplines, supporting policymakers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.
Self-Adjusting Multitask Particle Swarm Optimization

Particle swarm optimization algorithm has become a promising approach in solving multitask optimization (MTO) problems since it can transfer knowledge with easy implementation and high searching efficiency. However, in the process of knowledge transfer, negative transfer is common because it is difficult to evaluate whether knowledge is effective for population evolution. Therefore, how to obtain and transfer the effective knowledge to curb the negative transfer is a challenging problem in MTO. To deal with this problem, a self-adjusting multitask particle swarm optimization (SA-MTPSO) algorithm is designed to improve the convergence performance in this article. First, a knowledge estimation metric, combining the decision space knowledge and the target space knowledge for each task, is designed to describe the effectiveness of knowledge. Then, the effective knowledge is obtained to promote the knowledge transfer process. Second, a self-adjusting knowledge transfer mechanism, based on the effective knowledge and the self-adjusting transfer method, is developed to achieve effective knowledge transfer. Then, the ineffective knowledge is removed to solve the negative transfer problem. Third, the convergence analysis is given to guarantee the effectiveness of the SA-MTPSO algorithm theoretically. Finally, the proposed algorithm is compared with some existing MTO algorithms. The results show that the performance of the proposed algorithm is superior to most algorithms on negative transfer suppression and convergence.
Formal Analysis of Composable DeFi Protocols

Decentralized finance (DeFi) has become one of the most successful applications of blockchain and smart contracts. The DeFi ecosystem enables a wide range of crypto-financial activities, while the underlying smart contracts often contain bugs, with many vulnerabilities arising from the unforeseen consequences of composing DeFi protocols together. In this paper, we propose a formal process-algebraic technique that models DeFi protocols in a compositional manner to allow for efficient property verification. We also conduct a case study to demonstrate the proposed approach in analyzing the composition of two interacting DeFi protocols, namely, Curve and Compound. Finally, we discuss how the proposed modeling and verification approach can be used to analyze financial and security properties of interest.