Reviewerly/exHarmony huggingface dataset:
Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.
Vehicle Detection and Tracking Using Thermal Cameras in Adverse Visibility Conditions

Level 5 autonomy, as defined by the Society of Automotive Engineers, requires the vehicle to function under all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions that include events such as sudden changes in lighting, smoke, fog, snow, and rain. No standalone sensor currently in the market can reliably perceive the environment in all conditions. While regular cameras, lidars, and radars will suffice for typical driving conditions, they may fail in some edge cases. The goal of this paper is to demonstrate that the addition of Long Wave Infrared (LWIR)/thermal cameras to the sensor stack on a self-driving vehicle can help fill this sensory gap during adverse visibility conditions. In this paper, we trained a machine learning-based image detector on thermal image data and used it for vehicle detection. For vehicle tracking, Joint Probabilistic Data association and Multiple Hypothesis Tracking approaches were explored where the thermal camera information was fused with a front-facing radar. The algorithms were implemented using FLIR thermal cameras on a 2017 Lincoln MKZ operating in College Station, TX, USA. The performance of the tracking algorithm has also been validated in simulations using Unreal Engine.
Underwater object detection using collaborative weakly supervision

Despite recent progress in deep learning, underwater object detection remains a challenge where noisy and imprecise images are provided as sources of supervision. This paper presents a novel underwater detection approach in the framework of weakly supervised learning. The idea is to train two deep learning detectors simultaneously, and let them teach each other based on the selection of the cleaner samples they see during the training. The backbone of each detector is Yolov5, which achieves balance between accuracy and speed. The method is tested on the URPC2021 benchmark dataset, and achieves state-of-the-art performance. Compared with the original Yolov5, the dual training mechanism improves the recognition accuracy by 10%.
Forward Secure Public-key Authenticated Encryption with Conjunctive Keyword Search

Abstract Public key encryption with keyword search is a promising primitive which enables search over encrypted data in secure data outsourcing services. In traditional construction, the associated keywords may be recovered from a given trapdoor by a malicious server through keyword guessing attacks. Therefore, the notion of public-key authenticated encryption with keyword search (PAEKS) was introduced, where a sender encrypts (and authenticates) the keywords using a receiver’s public key and its secret key. In this paper, we consider the forward security for PAEKS and introduce a new primitive: forward secure public-key authenticated encryption with keyword search (FS-PAEKS), which captures the information leakage risk from previously issued queries due to the updates on the outsourced data. Technically, we embed a non-interactively agreed key into the cipher-keyword generation algorithm, and bind the cipher-keyword and the trapdoor with a set converted from algorithm-generation time. Finally, we present an efficient FS-PAEKS scheme supporting conjunctive query, and prove its forward security against chosen keyword attacks and keyword guessing attacks. To illustrate practical performance, we implement our FS-PAEKS and related PAEKS schemes based on Enron dataset in real cloud environment.
Model Multiplicity: Opportunities, Concerns, and Solutions

Recent scholarship has brought attention to the fact that there often exist multiple models for a given prediction task with equal accuracy that differ in their individual-level predictions or aggregate properties. This phenomenon—which we call model multiplicity—can introduce a good deal of flexibility into the model selection process, creating a range of exciting opportunities. By demonstrating that there are many different ways of making equally accurate predictions, multiplicity gives model developers the freedom to prioritize other values in their model selection process without having to abandon their commitment to maximizing accuracy. However, multiplicity also brings to light a concerning truth: model selection on the basis of accuracy alone—the default procedure in many deployment scenarios—fails to consider what might be meaningful differences between equally accurate models with respect to other criteria such as fairness, robustness, and interpretability. Unless these criteria are taken into account explicitly, developers might end up making unnecessary trade-offs or could even mask intentional discrimination. Furthermore, the prospect that there might exist another model of equal accuracy that flips a prediction for a particular individual may lead to a crisis in justifiability: why should an individual be subject to an adverse model outcome if there exists an equally accurate model that treats them more favorably? In this work, we investigate how to take advantage of the flexibility afforded by model multiplicity while addressing the concerns with justifiability that it might raise?
Machine Learning, Convergence Digitalization, and the Concentration of Power: Enslavement by Design Using Techno-Biological Behaviors

The dawn of electronic business (e-business) changed the way that individuals interact not only with one another but also with the companies that supply them with goods and services, as well as with the government agencies on which they depend for welfare and security. We can speak of “digital business” as the (re)design or (re)definition of new or existing business models, and as the creation of increased flows and connectivity between customers and other entities, both internal and external to the business, among other defining features. In this editorial, we explore three interrelated levels of sociological and economic practice—micro, meso, and macro—as they pertain to advances in digital business <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , with the intention of revealing hidden dynamics and implications resulting from interactions between these levels. At the micro level, we consider the individual user. This can be interpreted as the “self” or the individual level (e.g., a person or person in singular interaction with another). At the meso level, we reflect on technological systems (e.g., information systems, biometrics, and data analysis through machine learning (ML), for the purposes of this article). This level is about groups and how they communicate in building knowledge, with a particular emphasis on what that knowledge means. The groups are made up of organizations, whether business or government agencies, or other collectives. And finally, at the macro level, we consider the societal context inclusive of communities (e.g., local/regional/national or international levels).
FP-GNN: Adaptive FPGA accelerator for Graph Neural Networks

Graph Neural Networks (GNNs) exhibit great success in graph data analysis and promote the evolution of artificial intelligence. The computing procedure of GNNs can be categorized into two phases: Aggregation and Combination, which have irregular and regular computing characteristics respectively. In this work, we present an adaptive FPGA accelerator, FP-GNN, to enable flexible and efficient GNN acceleration. First, this paper presents quantitative analysis of the impact on performance when changing the order of Aggregation and Combination. Then we propose an Adaptive GNN Accelerator framework (AGA) that adopts a unified processing module to support these two phases simultaneously, and an Adaptive Graph Partition strategy (AGP) to alleviate memory bottleneck and eliminate graph repartition overhead between GNN layers. In addition, we also propose multiple workflow optimizations for both Aggregation and Combination phases to achieve workload balance and feature sparsity elimination. Finally, we implement the FP-GNN on a Xilinx VCU128 FPGA, and conduct comprehensive experiments on various GNN configurations and platforms. Results show that FP-GNN achieves on average 665 × speedup with 3180 × energy efficiency and average 24.9 × speedup with 138 × energy efficiency compared to CPU and GPU, respectively, and also achieves state-of-the-art performance efficiency and energy efficiency compared to prior works. • Analyze the impact of execution order on performance and provide design guidelines. • Propose AGA framework that supports flexible execution order on a unified architecture. • Propose AGP strategy that eliminates graph repartition overhead between GNN layers. • Exploit feature and node level parallelism, workload balance and sparsity elimination. • Conduct comprehensive experiments to assess the performance and efficiency metrics.
Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility

The fast development of mobile edge computing (MEC) and service virtualization brings new opportunities to the deployment of interactive applications, e.g., VR education, stream gaming, autopilot assistance, at the network edge for better performance. Ensuring quality of experience (QoE) for such services often requires the satisfaction of multiple quality of service (QoS) factors, e.g., short delay, high throughput rate, low packet loss. Nevertheless, existing mobile edge networks often fail to meet these requirements due to the mobility of end users and the volatility of network conditions. In this paper, we propose a novel scheme that both reduces delay and adjusts data throughput rate for QoE enhancement. We design an online service placement and throughput rate adjustment (SPTA) algorithm which coordinately migrates virtual services while tuning their data throughput rates based on real-time bandwidth fluctuation. By implementing a small-scale prototype supporting stream gaming at the edge, we show the necessity and feasibility of our work. Based on data from the experiments, we conduct real-world trace driven simulations to further demonstrate the advantages of our scheme over existing baselines.
Hilab system, a new point-of-care hematology analyzer supported by the Internet of Things and Artificial Intelligence

The complete blood count (CBC) is one of the most requested tests by physicians. CBC tests, most realized in conventional hematological analyzers, are restricted to centralized laboratories due to frequent maintenance, large devices, and expensive costs required. On the other hand, most handheld CBC devices commercially available show high prices and are not liable to calibration or control procedures, which results in poor quality compared to standard hematology instruments. The Hilab system is a small-handed hematological platform that uses microscopy and chromatography techniques for blood cells and hematimetric parameters analysis through artificial intelligence, machine learning, and deep learning techniques. For clinical evaluation of the handheld CBC device, 450 blood samples were analyzed. The samples encompassed normal (82%) and pathological conditions (18%), such as thalassemias (2.2%), anemias (6.6%), and infections (9.2%). For all analytes, accuracy, precision, method comparison, and flagging capabilities of the Hilab System, were compared with the Sysmex XE-2100 (Sysmex, Japan) results. The sample source (venous and capillary) influences were also evaluated. Pearson correlation, Student t test, bias, and the Bland-Altman plot of each blood count analyte were calculated and shown. The significance level was set at p ≤ 0.05. For clinical evaluation, Hilab System and the Sysmex XE-2100 showed a strong correlation (r ≥ 0.9) for most evaluated parameters. In the precision study, analytes showed CV inside the limits established according to European Federation of Clinical Chemistry and Laboratory Medicine guidelines. The flagging capabilities of the Hilab system, compared to the manual microscopy technique, presented high sensibility, specificity, and accuracy. Venous and capillary samples (p > 0.05) do not differ statistically. Considering the need for point-of-care CBCs, the study indicated that the Hilab system provides fast, accurate, low cost, and robust analysis for reliable clinical use.
Laser triangulation measurement system with Scheimpflug calibration based on the Monte Carlo optimization strategy

We propose a linear laser triangulation measurement system using Scheimpflug calibration based on the Monte Carlo optimization strategy. A Scheimpflug inclination camera calibration model is introduced in the measurement system for improving the image definition in small-range measurements with a large depth-of-field. To address the nonlinear optimization problem between the instrument resolution and measurement range, the Monte Carlo method is adopted to determine the optimal optical parameters (scattering angle, Scheimpflug angle, and focus length) in a practical measurement system. Furthermore, we experimentally constructed the measurement system to demonstrate the measurement precision by measuring a standard step block (measurement range 15 mm). The performance parameters of the maximum measurement error, maximum standard deviation, and linearity are obtained as ±7 μm, 0.225 μm, and 0.046%, respectively. Finally, the proposed measurement system based on the Monte Carlo optimization strategy is promising for high-precision measurements in industrial applications and provides guidance for optimizing the design parameters of ranging measurement sensors.
An efficiency curve for evaluating imbalanced classifiers considering intrinsic data characteristics: Experimental analysis

Balancing the accuracy rates of the majority and minority classes is challenging in imbalanced classification. Furthermore, data characteristics have a significant impact on the performance of imbalanced classifiers, which are generally neglected by existing evaluation methods. The objective of this study is to introduce a new criterion to comprehensively evaluate imbalanced classifiers. Specifically, we introduce an efficiency curve that is established using data envelopment analysis without explicit inputs (DEA-WEI), to determine the trade-off between the benefits of improved minority class accuracy and the cost of reduced majority class accuracy. In sequence, we analyze the impact of the imbalanced ratio and typical imbalanced data characteristics on the efficiency of the classifiers. Empirical analyses using 68 imbalanced data reveal that traditional classifiers such as C4.5 and the k-nearest neighbor are more effective on disjunct data, whereas ensemble and undersampling techniques are more effective for overlapping and noisy data. The efficiency of cost-sensitive classifiers decreases dramatically when the imbalanced ratio increases. Finally, we investigate the reasons for the different efficiencies of classifiers on imbalanced data and recommend steps to select appropriate classifiers for imbalanced data based on data characteristics.
Distributed learning of fully connected neural networks using independent subnet training

Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning partitions models and data over many machines, allowing model and dataset sizes beyond the available compute power and memory of a single machine. In practice though, distributed ML is challenging when distribution is mandatory, rather than chosen by the practitioner. In such scenarios, data could unavoidably be separated among workers due to limited memory capacity per worker or even because of data privacy issues. There, existing distributed methods will utterly fail due to dominant transfer costs across workers, or do not even apply. We propose a new approach to distributed fully connected neural network learning, called independent subnet training (IST), to handle these cases. In IST, the original network is decomposed into a set of narrow subnetworks with the same depth. These subnetworks are then trained locally before parameters are exchanged to produce new subnets and the training cycle repeats. Such a naturally "model parallel" approach limits memory usage by storing only a portion of network parameters on each device. Additionally, no requirements exist for sharing data between workers (i.e., subnet training is local and independent) and communication volume and frequency are reduced by decomposing the original network into independent subnets. These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution. We show experimentally that IST results in training times that are much lower than common distributed learning approaches.
Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The results obtained are usually summarized through average statistics, either in numeric tables or simple plots. Such approaches fail to reveal interesting subtleties about algorithmic performance, including which observations an algorithm may find easy or hard to classify, and also which observations within a dataset may present unique challenges. Recently, a methodology known as Instance Space Analysis was proposed for visualizing algorithm performance across different datasets. This methodology relates predictive performance to estimated instance hardness measures extracted from the datasets. However, the analysis considered an instance as being an entire classification dataset and the algorithm performance was reported for each dataset as an average error across all observations in the dataset. In this paper, we developed a more fine-grained analysis by adapting the ISA methodology. The adapted version of ISA allows the analysis of an individual classification dataset by a 2-D hardness embedding, which provides a visualization of the data according to the difficulty level of its individual observations. This allows deeper analyses of the relationships between instance hardness and predictive performance of classifiers. We also provide an open-access Python package named PyHard, which encapsulates the adapted ISA and provides an interactive visualization interface. We illustrate through case studies how our tool can provide insights about data quality and algorithm performance in the presence of challenges such as noisy and biased data.
Masked Transformer for Image Anomaly Localization

Image anomaly detection consists in detecting images or image portions that are visually different from the majority of the samples in a dataset. The task is of practical importance for various real-life applications like biomedical image analysis, visual inspection in industrial production, banking, traffic management, etc. Most of the current deep learning approaches rely on image reconstruction: the input image is projected in some latent space and then reconstructed, assuming that the network (mostly trained on normal data) will not be able to reconstruct the anomalous portions. However, this assumption does not always hold. We thus propose a new model based on the Vision Transformer architecture with patch masking: the input image is split in several patches, and each patch is reconstructed only from the surrounding data, thus ignoring the potentially anomalous information contained in the patch itself. We then show that multi-resolution patches and their collective embeddings provide a large improvement in the model's performance compared to the exclusive use of the traditional square patches. The proposed model has been tested on popular anomaly detection datasets such as MVTec and head CT and achieved good results when compared to other state-of-the-art approaches.
Building separable approximations for quantum states via neural networks

Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable. To tackle this task, we parametrize separable states with a neural network and train it to minimize the distance to a given target state with respect to a differentiable distance, such as the trace distance or Hilbert--Schmidt distance. By examining the output of the algorithm, we obtain an upper bound on the entanglement of the target state, and construct an approximation for its closest separable state. We benchmark the method on a variety of well-known classes of bipartite states and find excellent agreement, even up to local dimension of $d=10$, while providing conjectures and analytic insight for isotropic and Werner states. Moreover, we show our method to be efficient in the multipartite case, considering different notions of separability. Examining three- and four-party GHZ and W states, we recover known bounds and obtain additional ones, for instance, for triseparability.