Reviewerly/exHarmony huggingface dataset:
Multi-Modality Sensing and Data Fusion for Multi-Vehicle Detection

With the recent surge in autonomous driving vehicles, the need for accurate vehicle detection and tracking is critical now more than ever. Detecting vehicles from visual sensors fails in non-line-of-sight (NLOS) settings. This can be compensated by the inclusion of other modalities in a multi-domain sensing environment. We propose several deep learning based frameworks for fusing different modalities (image, radar, acoustic, seismic) through the exploitation of complementary latent embeddings, incorporating multiple state-of-the-art fusion strategies. Our proposed fusion frameworks considerably outperform unimodal detection. Moreover, fusion between image and non-image modalities improves vehicle tracking and detection under NLOS conditions. We validate our models on the real-world multimodal ESCAPE dataset, showing 33.16% improvement in vehicle detection by fusion (over visual inference alone) over test scenarios with 30-42% NLOS conditions. To demonstrate how well our framework generalizes, we also validate our models on the multimodal NuScene dataset, showing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 22% improvement over competing methods.
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings

Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This article aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness , is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups. We propose a multi-task learning-based deep end-to-end model, FairSR, which consists of two parts. One is to learn and distill personalized sequential features from the given user and her item sequence for SR. The other is fairness-aware preference graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the knowledge of users’ and items’ attributes and their correlation into entity representations, and alleviating the unfair distributions of user attributes on items. Extensive experiments conducted on three datasets show FairSR can outperform state-of-the-art SR models in recommendation performance. In addition, the recommended items by FairSR also exhibit promising interaction fairness.
Distance course examination

The article deals with the theoretical foundations of carrying out distance course (DC) examination with the aim of creating the program for expert training. An expert is defined as a specialist who has experience in compiling distance courses, organization of distance learning as well as has and uses content tutor skills.The article also describes the basic skills that an expert should have as well as typical mistakes of the developers of distance courses are presented. On the basis of the research a DC ``Expert examination of distance course'' was compiled for experienced developers of distance courses and administrators of distance learning systems at different institutions. During the training, the learners analyzed the purpose of Bloom's taxonomy and its accessibility, the complexity of the text and its design, the tasks and learning activities, the quality of tests and the evaluation system, etc. The result of the course training is learner's report on the distance course examination and analysis of this work. 47 lecturers of Ukrainian educational institutions were trained and only 12 of them successfully completed the training.
CERN: Compact facial expression recognition net

Since the renaissance of deep learning (DL), facial expression recognition (FER) has received a lot of interest, with continual improvement in the performance. Hand-in-hand with performance, new challenges have come up. Modern FER systems deal with face images captured under uncontrolled conditions (also called in-the-wild scenario) including occlusions and pose variations. They successfully handle such conditions using deep networks that come with various components like transfer learning, attention mechanism and local-global context extractor. However, these deep networks are highly complex with large number of parameters, making them unfit to be deployed in real scenarios. Is it possible to build a light-weight network that can still show significantly good performance on FER under in-the-wild scenario? In this work, we methodically build such a network and call it as Compact Expression Recognition Net (CERN). We leverage on the aforementioned components of deep networks for FER, and analyse, and appropriately fit them to arrive at CERN. Our CERN is a low-calorie net with only 1.45M parameters, which is almost 50x less than that of a state-of-the-art (SOTA) architecture. It requires only 17MB of storage. Further, during inference, it can process at the real time rate of 40 frames per second (fps) in an intel-i7 cpu. Though it is low-calorie, it is still power-packed in its performance, overpowering other light-weight architectures, and even few high capacity architectures. Specifically, CERN reports 87.09%, 88.17% and 62.06% accuracies on in-the-wild datasets RAFDB, FERPlus and AffectNet respectively. It also exhibits superior robustness under occlusions and pose variations in comparison to other light-weight architectures from the literature. Codes are publicly available at https://github.com/1980x/CFERNet.
A French-to-English Machine Translation Model Using Transformer Network

Traditional machine translation based on RNN has two major defects: (1)it can only process word-by-word and result in slow training speed; (2)when the sentence is too long, gradient disappearance and explosion reduce the accuracy of translation. To solve this problem, this paper designs a Transformer based machine translation implemented by PyTorch. Compared with traditional machine translation, Transformer uses the attention mechanism to redesign the defects of RNN and effectively solves the problems of efficiency and forgetting. The French-English machine translation based on Transformer designed in this paper achieves a translation accuracy rate of 80% from French to English after training and practical application.
StaSiS-Net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3D laparoscopy

Developing accurate and real-time algorithms for a non-invasive three-dimensional representation and reconstruction of internal patient structures is one of the main research fields in computer-assisted surgery and endoscopy. Mono and stereo endoscopic images of soft tissues are converted into a three-dimensional representation by the estimation of depth maps. However, automatic, detailed, accurate and robust depth map estimation is a challenging problem that, in the stereo setting, is strictly dependent on a robust estimate of the disparity map. Many traditional algorithms are often inefficient or not accurate. In this work, novel self-supervised stacked and Siamese encoder/decoder neural networks are proposed to compute accurate disparity maps for 3D laparoscopy depth estimation. These networks run in real-time on standard GPU-equipped desktop computers and the outputs may be used for depth map estimation using the a known camera calibration. We compare performance on three different public datasets and on a new challenging simulated dataset and our solutions outperform state-of-the-art mono and stereo depth estimation methods. Extensive robustness and sensitivity analyses on more than 30000 frames has been performed. This work leads to important improvements in mono and stereo real-time depth map estimation of soft tissues and organs with a very low average mean absolute disparity reconstruction error with respect to ground truth.
A robust clustering algorithm based on the identification of core points and KNN kernel density estimation

Density peaks clustering (DPC) has been proved to be an effective clustering method and applied to many scientific fields. It can detect the density peak within each cluster and assign the rest of the points in a single step. However, the original method suffers from several drawbacks. First of all, the authors provided two density metrics for the density estimation of large and small datasets. However, the arbitrary choice of the density metric will influence the effectiveness of DPC. Besides, the identification of cluster centers requires assigning thresholds manually for two extra quantities, which lowers the robustness of DPC. Moreover, the stepwise procedure of DPC in assigning the remaining points after cluster centers have been found may cause the propagation of errors. To eliminate these drawbacks, we proposed a new KNN based clustering method named ICKDC. The new framework aims at representing a cluster by a set of core points instead of a single object for the center and classifying the rest of the objects effectively based on the distribution information. Generally, the process of KNN based local density estimation, core points identification and integration, and the classification of the non-core objects allow ICKDC to reveal the structure of clusters. The experiments on various synthetic and real-world datasets demonstrate the superiority of ICKDC over several widely used clustering algorithms and state-of-the-art DPC-variants.
Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable's microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios.
Forecasting blockchain adoption in supply chains based on machine learning: evidence from Palestinian food SMEs

Purpose This paper seeks to discover whether the technical, organisational and technology acceptance model (TAM) factors will significantly affect the adoption of blockchain technology (ABT) amongst SMEs. Design/methodology/approach The research employs structural equation modelling (SEM) and a machine learning approach to identify factors influencing the ABT behaviour that leaders can use to predict the prospect of the ABT in their enterprises. Information was collected from 255 respondents representing 166 SMEs in the food industry, Palestine. Findings The analyses reveal that the ABT is positively and significantly shaped by TAM factors: (1) perceived benefits and (2) perceived ease of using blockchain. Simultaneously, the former is significantly influenced by compatibility and upper management support, while the latter is affected by complexity. Finally, education and training affect both factors. Originality/value This paper is amongst the first attempts to examine the ABT behaviour in the food industry using the integration of SEM and machine learning approach.
CubeSat Security Attack Tree Analysis

Once a novelty, small satellites, often referred to as CubeSats, have become important tools for a variety of space activities ranging from exploration to defense. Their relative affordability and short development timeline have made them attractive options to complement larger space vehicles, conduct reconnaissance and other finite tasks. The specialized nature of many CubeSat missions do not make their security any less important as their missions could easily be matters of national security. This paper demonstrates the use of attack tree analysis to assess vulnerabilities of a CubeSat. First, we abstract and build an architectural model of an operational CubeSat. We then create a series of attack trees for the abstracted architecture to illustrate a series of potential attack vectors for small satellites. We conclude by discussing some strategies that could be employed to improve CubeSat resilience.
A quantitative argumentation-based Automated eXplainable Decision System for fake news detection on social media

Social media is flooded with rumors, which make fake news detection a pressing problem. Many black-box approaches have been proposed to automatically predict the veracity of claims. These methods are lack of interpretability. Thus, we propose a Quantitative Argumentation-based Automated eXplainable Decision-making System (QA-AXDS) to tackle this problem and provide users with explanations about the results. The system is fully data-driven in its processes, which allows our models to make greater use of data and be more automatic and scalable than other quantitative framework models. In terms of interpretability, the system can automatically acquire human-level knowledge, and interact with users in the form of dialog trees through explanatory models, thus helping them understand the internal reasoning process of the system. The experimental results show that our system has better transparency and interpretability than other approaches based on the pure machine learning methods, and performs competitively in accuracy among others. In addition, the explanation model provides a way to improve the algorithms when some problems are identified by checking the explanations.
Privacy Preserving with Modified Grey Wolf Optimization Over Big Data Using Optimal K Anonymization Approach

An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method’s practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform.
Breaking away from labels: The promise of self-supervised machine learning in intelligent health

Medicine is undergoing an unprecedented digital transformation, as massive amounts of health data are being produced, gathered, and curated, ranging from in-hospital (e.g., intensive care unit [ICU]) to person-generated data (wearables). Annotating all these data for training purposes in order to feed to deep learning models for pattern recognition is impractical. Here, we discuss some exciting recent results of self-supervised learning (SSL) applications to high-resolution health signals. These examples leverage unlabeled data to learn meaningful representations that can generalize to situations where the ground truth is inadequate or simply infeasible to collect due to the high burden or associated costs. The most prominent bottleneck of deep learning today is access to labeled, carefully curated datasets, and self-supervision on health signals opens up new possibilities to eliminate data silos through general-purpose models that can transfer to low-resource environments and tasks.
Semantic Context-Aware Image Style Transfer

To provide semantic image style transfer results which are consistent with human perception, transferring styles of semantic regions of the style image to their corresponding semantic regions of the content image is necessary. However, when the object categories between the content and style images are not the same, it is difficult to match semantic regions between two images for semantic image style transfer. To solve the semantic matching problem and guide the semantic image style transfer based on matched regions, we propose a novel semantic context-aware image style transfer method by performing semantic context matching followed by a hierarchical local-to-global network architecture. The semantic context matching aims to obtain the corresponding regions between the content and style images by using context correlations of different object categories. Based on the matching results, we retrieve semantic context pairs where each pair is composed of two semantically matched regions from the content and style images. To achieve semantic context-aware style transfer, a hierarchical local-to-global network architecture, which contains two sub-networks including the local context network and the global context network, is proposed. The former focuses on style transfer for each semantic context pair from the style image to the content image, and generates a local style transfer image storing the detailed style feature representations for corresponding semantic regions. The latter aims to derive the stylized image by considering the content, the style, and the intermediate local style transfer images, so that inconsistency between different corresponding semantic regions can be addressed and solved. The experimental results show that the stylized results using our method are more consistent with human perception compared with the state-of-the-art methods.
FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization

With the trend to deploy Deep Neural Network (DNN) inference models on edge devices with limited resources, quantization techniques have been widely used to reduce on-chip storage and improve computation throughput. However, existing DNN quantization work deploying quantization below 8-bit may be either suffering from evident accuracy loss or facing a big gap between the theoretical improvement of computation throughput and the practical inference speedup. In this work, we propose a general framework, called FILM-QNN, to quantize and accelerate multiple DNN models across different embedded FPGA devices. First, we propose the novel intra-layer, mixed-precision quantization algorithm that assigns different precisions onto the filters of each layer. The candidate precision levels and assignment granularity are determined from our empirical study with the capability of preserving accuracy and improving hardware parallelism. Second, we apply multiple optimization techniques for the FPGA accelerator architecture in support of quantized computations, including DSP packing, weight reordering, and data packing, to enhance the overall throughput with the available resources. Moreover, a comprehensive resource model is developed to balance the allocation of FPGA computation resources (LUTs and DSPs) as well as data transfer and on-chip storage resources (BRAMs) to accelerate the computations in mixed precisions within each layer. Finally, to improve the portability of FILM-QNN, we implement it using Vivado High-Level Synthesis (HLS) on Xilinx PYNQ-Z2 and ZCU102 FPGA boards. Our experimental results of ResNet-18, ResNet-50, and MobileNet-V2 demonstrate that the implementations with intra-layer, mixed-precision (95% of 4-bit weights and 5% of 8-bit weights, and all 5-bit activations) can achieve comparable accuracy (70.47%, 77.25%, and 65.67% for the three models) as the 8-bit (and 32-bit) versions and comparable throughput (214.8 FPS, 109.1 FPS, and 537.9 FPS on ZCU102) as the 4-bit designs.
Evaluation Methods and Measures for Causal Learning Algorithms

The convenient access to copious multifaceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have, therefore, witnessed great effort in developing causal learning algorithms aiming to help artificial intelligence (AI) achieve human-level intelligence. Due to the lack of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with Big Data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We, then, examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of Big Data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning.
Body Maps: A Generative Tool for Soma-based Design

Body maps are visual documents, where somatic experiences can be drawn onto a graphical representation of an outline of the human body. They hold the ability to capture complex and non-explicit emotions and somatic felt sensations, elaborating narratives that cannot be simply spoken. We present an illustrative example of “how-to” complete a body map, together with four case studies that provide examples of using body maps in design research. We identify five uses of body maps as generative tools for soma-based design, ranging from sampling bodily experience, heightening bodily self-awareness, understanding changing bodily experience over time, identifying patterns of bodily experience, and transferring somatic experiential qualities into physical designs. The different requirements for scaffolding the use of body maps in user-centred design versus first-person autobiographical design research are discussed. We provide this Pictorial as a resource for designers and researchers who wish to integrate body maps into their practice.