Reviewerly/exHarmony huggingface dataset:
Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking

The use of mobiles phones when driving has been a major factor when it comes to road traffic i ncidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance hardware has paved the way for a more automated approach when it comes to video surveillance. In this work, we propose a custom-trained state-of-the-art object detector to work with roadside cameras to capture driver phone usage without the need for human intervention. The proposed approach also addresses the issues caused by windscreen glare and introduces the steps required to remedy this. Twelve pre-trained models are fine-tuned w ith o ur c ustom d ataset using four popular object detection methods: YOLO, SSD, Faster R-CNN, and CenterNet. Out of all the object detectors tested, YOLO yields the highest accuracy levels of up to ∼96% (AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> ) and frame rates of up to ∼30 FPS. DeepSORT object tracking algorithm is also integrated into the best-performing model in order to avoid logging duplicate violations.
Research on Gait Recognition Based on Lower Limb EMG Signal

To verify whether the EMG signals can recognize different gait under different road conditions, this paper selects 5 kinds of gait (walking on flat ground, uphill, downhill, upstep and downstep), collects the EMG signals of 4 muscles (Biceps femoris, lateral femoral, tibialis anterior, and gastrocnemius) of lower limbs during the movement, and analysis the differences of the peak values of EMG signals under different gait conditions. To compare the recognition effect, the thigh and calf EMG signals were used as input to recognize 5 kinds of gait. The results show that lower limb EMG signals can recognize different gait. The recognition rate for uphill and downhill gait by thigh EMG signal is low (75.37&#x0025; and 72.20&#x0025;), and that in calf EMG signal for uphill gait is low (73.94&#x0025;), but when the 4 muscles of thigh and calf are input together, the recognition rate of the five gait is improved, and the average recognition rate is 95.16&#x0025;.
Understanding Medical Conversations: Rich Transcription, Confidence Scores &amp;amp; Information Extraction

In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transformer-based Recurrent Neural Network Transducer (RNN-T) model tailored for long-form audio, which can produce rich transcriptions including speaker segmentation, speaker role labeling, punctuation and capitalization. On a representative test set, we compare performance of RNN-T models with different encoders, units and streaming constraints. Our transformer-based streaming model performs at about 20% WER on the ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on commas, 43% SER on question marks and 30% SER on capitalization. Our recognizer is paired with a confidence model that utilizes both acoustic and lexical features from the recognizer. The model performs at about 0.37 NCE. Finally, we describe a RNN-T based tagging model. The performance of the model depends on the ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75 for conditions, 0.76 for diagnosis, and 0.61 for treatments. While there is still room for improvement, our results suggest that these models are sufficiently accurate for practical applications.
Usability and Task Load of Applications in Augmented and Virtual Reality

The technology to support Augmented (AR) and Virtual Reality (VR) applications are considered increasingly mature for a broader, productive application in corporate environments. As yet, they have been underused, among other reasons, due to doubts about the acceptance and productive performance by employees. This paper presents a study that collected standardised measures for usability (SUS) and task load (NASA-RTLX) across nine different AR and VR use cases developed specifically for industrial and corporate settings. The resulting prototypes were tested with real potential users as well as with students. The results show that the participants had a positive opinion of the prototype’s usability and did not register an unusually high workload. According to the results, factors such as age and gender do not appear to affect usability perception, though they still affect the task-load perception. The study concludes that with human centred design and suitable support on implementation, AR and VR can be successfully used in corporate setting.
Evaluation of Potential Cryptocurrency Development Ability in Terrorist Financing

Abstract Terrorist financing is the economic basis of terrorist activities and the lifeline of terrorist organizations. In recent years, terrorist organizations have gradually come to use cryptocurrency to finance their activities based on traditional ways of raising funds. The anonymity of cryptocurrency is attractive to terrorist organizations, but its use remains at a low level. To explore the future development ability of cryptocurrency in terrorist financing, we study its internal characteristics and development status, as well as the supervisory systems of international organizations. This study hopes to enhance our understanding of the potential risks of cryptocurrency and serve as a reference for the fight against terrorist financing in the international community.
Estimation of daily diffuse solar radiation from clearness index, sunshine duration and meteorological parameters for different climatic conditions

This study aims to evaluate performance of 30 models (15 selected from literature and 15 newly developed) to estimate diffuse horizontal irradiance (Hd) using high-quality measurements for nine stations having different climatic conditions. The models (divided into three categories) are based on extraterrestrial global horizontal irradiance (Ho) and measured global horizontal irradiance (Hg) using sunshine duration ratio, clearness-index, temperature, relative humidity, and periodicity factor as input parameters. The performance of models was evaluated using statistical parameters; the performance of Hg based models (Category-II and Category-III) is better than Ho based models (Category-I). The performance of newly developed models in three categories is better than best models selected from the literature, due to higher number of input parameters and higher powers used. The best models in Category-I, Category-II and Category-III have rMBE (rRMSE) ranges from −0.3 to 0.6% (14 to 20%), −0.6 to 0.1% (10 to 17%) and −0.3 to 0.0% (10 to 17%) respectively. The performance of models varies in different zones, and different stations within same zone, due to variations of climatic conditions. The best models have rMBE within ±1% and rRMSE < 20%, and can be used for initial resource assessment of commercial solar PV projects for potential locations.
Gender Detection Based on Spatial Pyramid Matching

The similarity and homogeneous visual appearance of male and female handwriting make gender detection from off-line handwritten document images a challenging research problem. In this paper, an effective method based on spatial pyramid matching is proposed for gender detection from handwritten document images. In the proposed method, the input handwritten document image is progressively divided into several sub-regions from coarse to fine levels. The weighted histograms of the sub-regions are then calculated. This process is resulting in a spatial pyramid feature set which is an extension of the orderless bag-of-features image representation. Classical classifiers, such as Support Vector Machines and ensemble classifiers, are considered for determining the gender (male and female) of individuals from their handwriting. Experiments were conducted on two benchmarks, QUWI and MSHD datasets, and the proposed method provided a promising improvement in gender detection accuracies, especially in script-dependent scenarios, compared with the results reported in the literature.
Improved SAT Models for NFA Learning

Grammatical inference is concerned with the study of algorithms for learning automata and grammars from words. We focus on learning Nondeterministic Finite Automaton of size k from samples of words. To this end, we formulate the problem as a SAT model. The generated SAT instances being enormous, we propose some model improvements, both in terms of the number of variables, the number of clauses, and clauses size. These improvements significantly reduce the instances, but at the cost of longer generation time. We thus try to balance instance size vs. generation and solving time. We also achieved some experimental comparisons and we analyzed our various model improvements.
On the Linear Capacity of Conditional Disclosure of Secrets

Conditional disclosure of secrets (CDS) is the problem of disclosing as efficiently as possible, one secret from Alice and Bob to Carol if and only if the inputs at Alice and Bob satisfy some function f. The information theoretic capacity of CDS is the maximum number of bits of the secret that can be securely disclosed per bit of total communication. All CDS instances, where the capacity is the highest and is equal to 1/2, are recently characterized through a noise and signal alignment approach and are described using a graph representation of the function f, G<inf>f</inf>. In this work, we go beyond the best case scenarios and further develop the alignment approach to characterize the linear capacity of a class of CDS instances to be (p- 1)/(2p), where <tex>$p$</tex> is a covering parameter of G<inf>f</inf>.
Machine Learning based Software-Defined Networking Traffic Classification System

The paper aims to use machine learning models in SDN network traffic classification and compare their performance. In this sense, a simple SDN architecture was created in which traffic is generated using the ”D-ITG” utility. Traffic data is saved using a processing script and then used for training and testing the machine learning models used. In order to be able to label the traffic based on the groups generated by the unsupervised model we used the supervised model with the best performance. To achieve the highest possible classification performance in the case of the unsupervised classification model, we previously chose the centroids of the groups and made a comparison between the results thus obtained and those resulting from the use of randomly generated centroids which is a new approach and resulted in an accuracy of 83.5 percent, which is better than in the literature, for such a small number of groups.
Variational-autoencoder signal detection for MIMO-OFDM-IM

This paper proposes a deep learning (DL) based method for signal detection in multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) systems using variational autoencoder (VAE). A framework of variational optimization is constructed and the learning deep neural network structure with fully connected layers is also designed. The network is trained offline by exploiting the dataset generated by simulations, and then the well-trained model is applied for signal detection in MIMO-OFDM-IM system. The derivation on the loss function for network training is presented from the perspective of variational inference. Also, a regularization parameter is introduced in order to achieve better performance. Numerical results are provided and the performance demonstrates the merits of the proposed method by comparison with the traditional detection algorithms. Finally, complexity measured by the runtime of the program utilizing existing detectors and the proposed method are also presented. The bit error rate (BER) performance which is close to the existing hand-crafted detectors can be obtained with lower runtime using the proposed DL network.
Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability

Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.
Interpretability-Guided Defense Against Backdoor Attacks to Deep Neural Networks

As an emerging threat to deep neural networks (DNNs), backdoor attacks have received increasing attentions due to the challenges posed by the lack of transparency inherent in DNNs. In this article, we develop an efficient algorithm from the interpretability of DNNs to defend against backdoor attacks to DNN models. To extract critical neurons, we deploy sets of control gates following neurons in layers, and the function of a DNN model can be interpreted as semantic sensitivities of neurons to input samples. A backdoor identification approach, derived from the activation frequency distribution on critical neurons, is proposed to reveal anomalies of particular neurons produced by backdoor attacks. Subsequently, a feasible and fine-grained pruning strategy is introduced to eliminate backdoors hidden in DNN models, without the need of retraining. Extensive experiments demonstrate that the proposed algorithm can identify and eliminate malicious backdoors efficiently in both single-target and multitarget scenarios with the performance of a DNN model retained to a large extent.
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization

Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The \uniqname method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications. The implementation of the paper is available at https://github.com/Lancer555/NICE
Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection

Massive regression and forecasting tasks are generally cost-sensitive regression learning problems with asymmetric costs between over-prediction and under-prediction. However, existing classic methods, such as clustering and feature selection, are subject to difficulties in dealing with small datasets. As one of the key challenges, it is difficult to statistically validate the importance of features using traditional algorithms (e.g. the Boruta algorithm) owing to insufficient available data. By leveraging the feature information intra-cluster (item group with similar attributes), we propose an intra-cluster product favoured (ICPF) feature selection algorithm to select the information based on the traditional filtering method (specifically the Boruta algorithm in our study). The experimental results show that the ICPF algorithm significantly reduces the number of dimensions of the selected feature set and improves the performance of cost-sensitive regression learning. The misprediction cost decreased by 33.5% (linear-linear cost function) and 32.4% (quadratic-quadratic cost function) after adopting the ICPF algorithm. In addition, the advantage of the ICPF algorithm is robust to other regression models, such as random forest and XGboost.
Constrained top-k nearest fuzzy keyword queries on encrypted graph in road network

With the gradual growth of road networks, the graphs used to represent road networks have been becoming larger and larger. It is a preferable choice for the user to outsource large-scale graphs for road networks to cloud and allow the cloud to store and query the information from graphs for him. It improves query efficiency greatly and reduces the user's burden remarkably. To protect the privacy of graph data, the user has to encrypt the graph before uploading it to the cloud. It makes performing query on the encrypted graph become a challenge. In this paper, we propose the first scheme for performing a constrained top-k nearest keyword query on an encrypted graph, even if a typo is made by the user. In the proposed scheme, we construct a new index structure based on the 2-hop cover label index to calculate the constrained shortest distance. In order to support fuzzy keyword query, we generate the fuzzy keyword set to build the keyword index in the graph. The security analysis shows the proposed scheme satisfies CQA-security and the experiments demonstrate the proposed scheme is efficient.
Low Latency Cross-Shard Transactions in Coded Blockchain

Although blockchain, the supporting technology of Bitcoin and various cryptocurrencies, has offered a potentially effective framework for numerous applications, it still suffers from the adverse affects of the impossibility triangle. Performance, security, and decentralization of blockchains normally do not scale simultaneously with the number of participants in the network. The recent introduction of error correcting codes in sharded blockchain by Li et al. partially settles this trilemma, boosting throughput without compromising security and decentralization. In this paper, we improve the coded sharding scheme in three ways. First, we propose a novel 2-Dimensional Sharding strategy, which inherently supports cross-shard transactions, alleviating the need for complicated inter-shard communication protocols. Second, we employ distributed storage techniques in the propagation of blocks, improving latency under restricted bandwidth. Finally, we incorporate polynomial cryptographic primitives of low degree, which brings coded blockchain techniques into the realm of feasible real-world parameters.
FedCo: A Federated Learning Controller for Content Management in Multi-party Edge Systems

Managing cache content at the edge is one of the many use cases of 5G-and-beyond networks. However, increasing the density of Edge Data Centers (EDCs) to service requests is a crucial problem. To overcome this problem, recent research has advanced mobile device architecture paradigms and the content caching in a Mobile Device Cloud (MDC). These two service locations (EDCs and MDC) are registered with the Mobile Network Operator (MNO), enabling the MNO to control the content placement for profit maximization. As the user demands for content items are directly related to the QoS perceived by the user, it is important to understand the future popularity of the content items and to place them appropriately. Additionally, privacy issues have increased over time because of sensitive user information being divulged at the MDC. To preserve privacy, a branch of machine learning called Federated Learning (FL) can train machine learning models leaving the data in the end user devices. The paper contributions are as follows: (1) We introduce an FL algorithm called FedCo, that trains a deep-neural network (DNN) to predict the user demand of a specific content, so as to manage the content files placement at EDC and MDC sites. (2) We then conduct a theoretical evaluation of user demand behavior via prospect theory to justify revenue maximization for an MNO. (3) We show numerically via a multimedia content delivery use-case how the proposed model compares favorably with two state-of-the-art designs.
Hybrid attention mechanism for few‐shot relational learning of knowledge graphs

Few-shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few-shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self-attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one-hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL-One and Wiki-One datasets.
Kinship Verification Based on Cross-Generation Feature Interaction Learning

Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification. Particularly, an effective collaborative weighting strategy is constructed to explore the characteristics of cross-generation relations by corporately extracting features of both parents and children image pairs. Specifically, we take parents and children as a whole to extract the expressive local and non-local features. Different from the traditional works measuring similarity by distance, we interpolate the similarity calculations as the interior auxiliary weights into the deep CNN architecture to learn the whole and natural features. These similarity weights not only involve corresponding single points but also excavate the multiple relationships cross points, where local and non-local features are calculated by using these two kinds of distance measurements. Importantly, instead of separately conducting similarity computation and feature extraction, we integrate similarity learning and feature extraction into one unified learning process. The integrated representations deduced from local and non-local features can comprehensively express the informative semantics embedded in images and preserve abundant correlation knowledge from image pairs. Extensive experiments demonstrate the efficiency and superiority of the proposed model compared to some state-of-the-art kinship verification methods.