Reviewerly/exHarmony huggingface dataset:
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles

Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios. However, the poor photorealism and lack of diverse sensor modalities of existing simulation engines remain key hurdles towards realizing this potential. Here, we present VISTA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> Full code release for the VISTA data-driven simulation engine is available here: vista.csail.mit.edu., an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles. Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras, enabling the rapid generation of novel viewpoints in simulation and thereby enriching the data available for policy learning with corner cases that are difficult to capture in the physical world. Using VISTA, we demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle. The policies learned in VISTA exhibit sim-to-real transfer without modification and greater robustness than those trained exclusively on real-world data.
The <i>q</i>-Ary Antiprimitive BCH Codes

It is well-known that cyclic codes have efficient encoding and decoding algorithms. In recent years, antiprimitive BCH codes have attracted a lot of attention. The objective of this paper is to study BCH codes of this type over finite fields and analyse their parameters. Some lower bounds on the minimum distance of antiprimitive BCH codes are given. The BCH codes presented in this paper have good parameters in general, containing many optimal linear codes. In particular, two open problems about the minimum distance of BCH codes of this type are partially solved in this paper.
LSTM-ANN &amp; BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy

Machine learning is getting more and more advanced with the progression of state-of-the-art technologies. Since existing algorithms do not provide a palatable learning performance most often, it is necessary to carry on the trail of upgrading the current algorithms incessantly. The hybridization of two or more algorithms can potentially increase the performance of the blueprinted model. Although LSTM and BiLSTM are two excellent far and widely used algorithms in natural language processing, there still could be room for improvement in terms of accuracy via the hybridization method. Thus, the advantages of both RNN and ANN algorithms can be obtained simultaneously. This paper has illustrated the deep integration of BiLSTM-ANN (Fully Connected Neural Network) and LSTM-ANN and manifested how these integration methods are performing better than single BiLSTM, LSTM and ANN models. Undertaking Bangla content classification is challenging because of its equivocalness, intricacy, diversity, and shortage of relevant data, therefore, we have executed the whole integrated models on the Bangla content classification dataset from newspaper articles. The proposed hybrid BiLSTM-ANN model beats all the implemented models with the most noteworthy accuracy score of 93% for both validation & testing. Moreover, we have analyzed and compared the performance of the models based on the most relevant parameters.
Suspicious transaction detection in banking cyber–physical systems

Fraud risks and threats are the critical concerns in Banking Cyber–Physical System (BCPS). Alongside the other banking frauds (i.e., forgery, altered cheques, identity theft, and impersonation), suspicious transaction detection is crucial in fraud detection. It is generally unfeasible to perform early scammer detection. Alleviating BCPS threats requires a blend of risk infiltration, sensing, and detection. A secure and robust approach is considerably required for tracking the interacting agent’s provided credentials. The classical approaches provided in the literature are less competent, reliable, and accurate. Accordingly, in this paper, we provide the fastest, reliable, and efficient fraud detection approach. We use cognitive computing and quantum computing-based suspicious entity detection in the BCPS for the post-quantum era. The proposed approach achieves 97.04% of precision and 0.03% of error-rate against categorizing transactions as genuine or fraudulent. • Propose a reliable, intelligent scheme for suspicious transaction detection in a BCPS. • Performs cognitive computing and quantum computing-based suspicious transaction detection. • Proposed approach efficiently assists in proper and timely secure responding.
Client Selection for Federated Learning With Label Noise

Federated learning (FL) unleashes the full potential of training a global statistical model collaboratively from edge clients. In wireless FL, for the scarcity of spectrum, only a fraction of clients are capable to participate in the FL training in each round. On the other hand, the performance of FL suffers from the label noise, which naturally exists in the dataset of each client. To alleviate the label noise issue, prior works proposed several client selection algorithms, where the privacy of raw data might be compromised when extra information exchange was introduced. To avoid extra information exchange in FL, we propose to leverage an algorithm that measures the noise ratio of each client based on a clean validation dataset. We then propose an online client selection framework, supported by Copeland score and multi-arm bandits, which can select the client set with low noise ratios efficiently. The proposed algorithm is performed in the server side, thereby it can be implemented in the basic FL framework seamlessly. Simulation results demonstrate that our proposed algorithm spends less training time while guaranteeing the required accuracy compared to other baseline algorithms.
A Double Auction Mechanism for Resource Allocation in Coded Vehicular Edge Computing

The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The analyses show that the double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance. From the simulation, the utility of auctioneer increases when the number of vehicles and edge servers increases.
Context-Aware Online Client Selection for Hierarchical Federated Learning

Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Servers (ESs) as intermediaries to perform model aggregation in proximity can reduce the transmission overhead, and it enables great potential in low-latency FL, where the hierarchical architecture of FL (HFL) has been attracted more attention. Designing a proper client selection policy can significantly improve training performance, and it has been widely investigated in conventional FL studies. However, to the best of our knowledge, systematic client selection policies have not yet been fully studied for HFL. In addition, client selection for HFL faces more challenges than conventional FL (e.g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO)). In this article, we investigate a client selection problem for HFL, where the NO learns the number of successful participating clients to improve training performance (i.e., select as many clients in each round) as well as under the limited budget on each ES. An online policy, called Context-aware Online Client Selection (COCS), is developed based on Contextual Combinatorial Multi-Armed Bandit (CC-MAB). COCS observes the side-information (context) of local computing and transmission of client-ES pairs and makes client selection decisions to maximize NO's utility given a limited budget. Theoretically, COCS achieves a sublinear regret compared to an Oracle policy on both strongly convex and non-convex HFL. Simulation results also support the efficiency of the proposed COCS policy on real-world datasets.
Human Behavior Recognition Model Based on Feature and Classifier Selection

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.
Learning multi-level structural information for small organ segmentation

Deep neural networks have achieved great success in medical image segmentation problems such as liver, kidney, the accuracy of which already exceeds the human level. However, small organ segmentation (e.g., pancreas) is still a challenging task. To tackle such problems, extracting and aggregating multi-scale robust features become essentially important. In this paper, we develop a multi-level structural loss by integrating the region, boundary, and pixel-wise information to supervise feature fusion and precise segmentation. The novel pixel-wise term can provide information complementary to the region and boundary loss, which helps to discover more local information from the image. We further develop a multi-branch network with a saliency guidance module to better aggregate the three levels of features. The coarse-to-fine segmentation architecture is adopted to use the prediction on the coarse stage to obtain the bounding box for the fine stage. Comprehensive evaluations are performed on three benchmark datasets, i.e., the NIH pancreas, ISICDM pancreas, and MSD spleen dataset, showing that our models can achieve significant increases in segmentation accuracy compared to several state-of-the-art pancreas and spleen segmentation methods. Furthermore, the ablation study demonstrates the multi-level structural features help both the training stability and the convergence of the coarse-to-fine approach.
Localize, Group, and Select: Boosting Text-VQA by Scene Text Modeling

As an important task in multimodal context understanding, Text-VQA (Visual Question Answering) aims at question answering through reading text information in images. It differentiates from the original VQA task as Text-VQA requires large amounts of scene-text relationship understanding, in addition to the cross-modal grounding capability. In this paper, we propose Localize, Group, and Select (LOGOS), a novel model which attempts to tackle this problem from multiple aspects. LOGOS leverages two grounding tasks to better localize the key information of the image, utilizes scene text clustering to group individual OCR tokens, and learns to select the best answer from different sources of OCR (Optical Character Recognition) texts. Experiments show that LOGOS outperforms previous state-of-the-art methods on two Text-VQA benchmarks without using additional OCR annotation data. Ablation studies and analysis demonstrate the capability of LOGOS to bridge different modalities and better understand scene text.
A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Since the emergence of coronavirus disease-2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring).These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions.While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse.Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data.In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic.We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic.We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability.We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few.Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions.With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard.
ML-Decoder: Scalable and Versatile Classification Head

In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the decoder architecture, and using a novel group-decoding scheme, ML-Decoder is highly efficient, and can scale well to thousands of classes. Compared to using a larger backbone, ML-Decoder consistently provides a better speed-accuracy trade-off. ML-Decoder is also versatile - it can be used as a drop-in replacement for various classification heads, and generalize to unseen classes when operated with word queries. Novel query augmentations further improve its generalization ability. Using ML-Decoder, we achieve state-of-the-art results on several classification tasks: on MS-COCO multi-label, we reach 91.1% mAP; on NUS-WIDE zero-shot, we reach 31.1% ZSL mAP; and on ImageNet single-label, we reach with vanilla ResNet50 backbone a new top score of 80.7%, without extra data or distillation. Public code will be available.
Software Architecture Challenges for ML Systems

Developing machine learning (ML) systems, just like any other system, requires architecture thinking. However, there are characteristics of ML components that create challenges and unique quality attribute (QA) concerns for software architecture and design activities, such as data-dependent behavior, detecting and responding to drift over time, and timely capture of ground truth to inform retraining. This paper presents four categories of software architecture challenges that need to be addressed to support ML system development, maintenance and evolution: software architecture practices for ML systems, architecture patterns and tactics for ML-important QAs, monitorability as a driving QA, and co-architecting and co-versioning. These challenges were collected from targeted workshops, practitioner interviews, and industry engagements. The goal of our work is to encourage further research in these areas and use the information presented in this paper to guide the development of empirically-validated practices for architecting ML systems.
Efficient Revocable Storage Attribute-based Encryption With Arithmetic Span Programs in Cloud-Assisted Internet of Things

Revocable storage and efficient description of the access policy are necessary to enhance the practicality of the attribute-based encryption (ABE) in real-life scenarios, such as cloud-assisted Internet of Things (IoT). Nevertheless, existing ABE works fail to balance the two vital factors. In this paper, we construct an efficient revocable storage ciphertext-policy attribute-based encryption with arithmetic span programs (RS-CPABE-ASP). The arithmetic span program (ASP) is elegantly utilized as the access structure to reduce the unnecessary cost for defining access policy. Combining the indirect revocation and the ciphertext update mechanism, our work prevents the revoked user unable to access the newly generated data and the old data that can be accessed before. As shown in the outsourced version of RS-CPABE-ASP, the costly part for users to decrypt the data can be outsourced to powerful cloud servers. In this way, users in our RS-CPABE-ASP are able to access their data in a more efficient way by merely one exponential operation. Finally, we carry out detailed theoretical analysis and experimental simulations to evaluate the performance of our work. The results fairly show that our proposed work is efficient and feasible in cloud-assisted IoT.
Reversible Data Hiding By Using CNN Prediction and Adaptive Embedding

In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works.
A multi-modal personality prediction system

The behavior, mental-health, emotion, life choices, social nature, and thought patterns of an individual are revealed by personality. Cyber forensics, personalized services, recommender systems are some of the examples of automatic personality prediction. A deep learning based personality prediction system has been developed in this work. Facial and ambient features are extracted from the visual modality using Multi-task Cascaded Convolutional Networks (MTCNN) and ResNet, respectively; the audio features are extracted using the VGGish Convolutional Neural Networks (VGGish CNN), and the text features are extracted using n-gram Convolutional Neural Networks (CNN). The extracted features are then passed to a fully connected layer followed by sigmoid for the final output prediction. Finally, the text, visual and audio modalities are combined in different ways: (i) concatenation of features in multi-modal setting, and (ii) application of different attention mechanisms for fusing features. The dataset released in Chalearn-17 is used for evaluating the performance of the system. From the obtained results, it can be concluded that, the concatenation of features extracted from different modalities attains comparable results with the averaging method (late fusion). It is also shown that a hand full of images are enough for attaining comparable performance.
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets

Many existing works have made great strides towards reducing racial bias in face recognition. However, most of these methods attempt to rectify bias that manifests in models during training instead of directly addressing a major source of the bias, the dataset itself. Exceptions to this are BUPT-Balancedface/RFW [34] and Fairface [14], but these works assume that primarily training on a single race or not racially balancing the dataset are inherently disadvantageous. We demonstrate that these assumptions are not necessarily valid. In our experiments, training on only African faces induced less bias than training on a balanced distribution of faces and distributions skewed to include more African faces produced more equitable models. We additionally notice that adding more images of existing identities to a dataset in place of adding new identities can lead to accuracy boosts across racial categories. Our code is available at https://github.com/j-alex-hanson/rethinking-race-face-datasets.