Reviewerly/exHarmony huggingface dataset:
Analysis on Influencing Factors of Repair Order of Power Supply Station based on Grey Correlation Analysis

This paper introduces the research status of work order processing means in power supply substation, and points out that the current research focuses on using work order system to solve actual production problems, ignoring the analysis of the influencing factors of work order quantity. Therefore, this paper puts forward the analysis framework of the correlation degree between the work order of the power supply station and external factors, establishes the influencing factor analysis model based on the grey correlation method, and carries out the analysis from the medium and long-term time scale. The conclusions can be used for reference in practical production.
Generation of Netlist from a Hand drawn Circuit through Image Processing and Machine Learning

Circuit diagrams are used to depict electronic or electrical circuits graphically. It is simple for everyone to put their thoughts on paper. However, in order to conduct simulations in the different available tools, the circuit model needs be in digital form. This project presents several image processing and machine learning approaches for the conversion of hand-drawn circuits to netlists. Rather than training the dataset for all components, a technique based on the length ratios of a few of lines was employed to identify elements such as a voltage source, ground, and capacitor. Various image processing techniques are used to eliminate noise and prepare pictures for further processing. HOG feature extraction is utilized throughout the training and segmentation stages to detect resistor, diode, and inductor components. The final stage is to construct a netlist from the detected elements, wires, and their locations, as well as the identified nodes.
Dynamic Face Mask Detection Using Machine Learning

Omicron has impacted negatively on our everyday lives in a short amount of time. Nowadays wearing a face mask is mandatory to safeguard ourselves. With the use of the Face Mask Detection System, we can aid our society in preventing the spread of omicron. We used machine learning techniques such as TensorFlow, Keras, OpenCV, and Python to realise this concept in this paper. As a Monitor, it can keep track of multiple faces at once during a live stream. On different databases, it has a reliability of up to 90%. We can readily distinguish between persons wearing masks and those who are not, and the key benefit is that many faces may be recognised at the same time. It will be very useful in a crowd, and it can be improved by adding more features.
Human Detection in Aerial Images using Deep Learning Techniques

Activity recognition in drone-based surveillance is related to many computer vision problems such as pose estimation, object detection, image retrieval, face recognition, frame tagging in videos, and video action recognition. In a drone-based surveillance system, detection and recognition of human activities in a single frame is a challenging task as the clips are shot from an aerial view. Unlike activity recognition in static camera-captured videos where spatio-temporal features are utilized, they are not utilized in drone-captured images. This problem is addressed in this paper using HOG and Mask-RCNN. Experimental results show that the proposed method can be obtained more accurate results in many drone-based frames. This work produces high-quality segmentation through instance segmentation in addition to the histograms gradient-based method and also improves the accuracy of object detection in aerial images and gives the best classification results.
Legal Business Model Digitalization: The Post COVID-19 Legal Industry

The rapid expansion in globalization, changing economic and political conditions, health crisis, advancement in technologies, and customer demand shift have highlighted the business model concept’s growing importance in every business life walk. Despite the agreement on the business model’s role in innovation and firm success, this concept is unnoticed in the legal industry. The growing digitalization has disrupted the legal industry business model by enabling various cooperation, collaboration, value proposition, and value capture mechanism. Digitalization has innovated the legal business model and engaged them in the mundane adaptation in response to external environment changes. However, literature trends reveal that innovation in the legal business model is overlooked by academia. This study applied a case study method to highlight business model innovation in legal firms in the proposed framework of the business model navigator/magic triangle and business model canvas. We also took the market and industry trends and COVID-19 into consideration in the legal business landscape. This study proposed a digital business model and innovation operating model for the legal firms based on the identified fundamental pillars of digital business model components. This study expands the literature on digitalization and business model innovation by focusing on an ignored industry with substantial disruptive potential.
Lightweight Authentication Methods in IoT: Survey

In recent years, the Internet of Things has gained prominence as a potential communication paradigm with a wide range of applications, including smart cities, smart homes, and intelligent transportation systems. The information gathered by a variety of IoT smart devices may be safely stored on (cloud) servers. The communication between devices in networks has rendered people unable to communicate with one another due to security authentication challenges such as destruction or abuse of IoT devices. In the Internet of Things, security and privacy are important considerations. This paper conducts a review of existing research in the fields of identity management, lightweight authentication, and authorization that applies to the IoT environment. We sift through modern set of papers, classify them, and highlight current IoT security trends and its performance measures.
Local differential privacy-based frequent sequence mining

Frequent sequence mining (FSM) is a fundamental component for analyzing sequential data in the big data era. However, collecting and analyzing sequence data incurs serious privacy issues for users. Local differential privacy (LDP) is a prevalent privacy paradigm for data statistics and analysis. Due to the high-dimensionality of sequence data, directly applying LDP on FSM faces severe challenges such as heavy perturbation, waste of privacy budget, poor accuracy, and high computational costs. This paper proposed PrivFSM and PrivFSM∗ which are two LDP-compliant FSM mechanisms with high data utility and low computational cost. Specifically, PrivFSM iteratively constructs sequences into a prefix tree under LDP to mine frequent sequences from huge domain. Besides, PrivFSM leverages a threshold-based pruning strategy to prune fake nodes, which reduces perturbations and computation costs. As an augmented version of PrivFSM, PrivFSM∗ no longer splits privacy budget, but partitions user set to avoid heavy noise. PrivFSM∗ maximizes the utilization of users' data by applying the whole data on every iteration with a complete privacy budget. We conduct theoretical analysis on error-bound and extensive experiments on both real-world and synthetic datasets. The experimental results demonstrate the high data utility of our proposed algorithms when compared with other state-of-the-art mechanisms.
Human Activity Recognition with Privacy Preserving using Deep Learning Algorithms

Human activity recognition is an extensively researched topic in the field of computer vision. Recognizing human activities without revealing a person’s identity is one such use case. To solve this, we propose a practical method for human activity recognition (HAR) while maintaining anonymity. It captures and distributes data from a variety of sources while respecting the privacy of the individuals concerned. At the core of our approach is (DBN-RGMAA) based on deep neural networks, which are not only more accurate but can also be deployed in real-time video surveillance systems. Hence, this work presents a deep learning-based scheme for privacy-preserving human activities. Initially, for extracting the features from raw video data, a Deep Belief Network (DBN) is used. To increase the HAR identification rate, Hybrid Deep Fuzzy Hashing Algorithm (HDFHA) is employed to capture dependencies between two actions. Finally, the privacy model enhances the privacy of humans while permitting a highly accurate approach towards action recognition by the Recursive Genetic Micro-Aggregation Approach (RGMAA). The implementation is executed and the performances are evaluated by Accuracy, Precision, Recall, and F1 Score. A dataset named HMDB51 is used for empirical study. Our experiments using the Python data science platform reveal that the OPA-PPAR outperforms existing methods.
Fast Assessment Method of Transmission Lines Switching Overvoltage Based on BP Artificial Neural Network

In the response of grid emergency and the design of electric transmission and transformation equipment, fast assessment method is involved to check the no-load switching overvoltage of transmission lines. This paper presents fast assessment method of no-load switching overvoltage of risk transmission lines based on error back-propagation (hereinafter referred to as BP) artificial neural algorithm. According to the theoretical equations of no-load switching overvoltage of transmission lines, the system intensity, the line parameters and the line length are selected as the input variables. While the switching overvoltage risk levels, which are converted from simulation results to reduce the statistical property of the transient overvoltage, are selected as the output variables. Then, the BP neural network algorithm could construct the mapping relationship be-tween the input and output variables. Finally, several 500kV transmission lines in the actual grid are gathered to verify the trained BP neural network. In this case, the error caused by the discreteness of circuit breaker switching time, the statistic properties of transient overvoltage and the uncertainty of artificial intelligence algorithm can be reduced by multiple estimations to obtain the most probable value.
Multi-Task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing interest due to its advantages of joint optimization and low latency when compared to traditionally cascaded pipelines. Existing E2E SLU models usually follow a two-stage configuration where an Automatic Speech Recognition (ASR) network first predicts a transcript which is then passed to a Natural Language Understanding (NLU) module through an interface to infer semantic labels, such as intent and slot tags. This design, however, does not consider the NLU posterior while making transcript predictions, nor correct the NLU prediction error immediately by considering the previously predicted word-pieces. In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system. In this work, we propose a streamable multi-task semantic transducer model to address these considerations. Our proposed architecture predicts ASR and NLU labels auto-regressively and uses a semantic decoder to ingest both previously predicted word-pieces and slot tags while aggregating them through a fusion network. Using an industry scale SLU and a public FSC dataset, we show the proposed model outperforms the two-stage E2E SLU model for both ASR and NLU metrics.
Exploration strategies for articulatory synthesis of complex syllable onsets

High-quality articulatory speech synthesis has many potential applications in speech science and technology. However, developing appropriate mappings from linguistic specification to articulatory gestures is difficult and time consuming. In this paper we construct an optimisation-based framework as a first step towards learning these mappings without manual intervention. We demonstrate the production of syllables with complex onsets and discuss the quality of the articulatory gestures with reference to coarticulation.
A survey of methods for revealing and overcoming weaknesses of data-driven Natural Language Understanding

Abstract Recent years have seen a growing number of publications that analyse Natural Language Understanding (NLU) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data. This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language. We summarise and discuss the findings and conclude with a set of recommendations for possible future research directions. We hope that it will be a useful resource for researchers who propose new datasets to assess the suitability and quality of their data to evaluate various phenomena of interest, as well as those who propose novel NLU approaches, to further understand the implications of their improvements with respect to their model’s acquired capabilities.
Recognizing micro actions in videos by learning multi-layer local features

Recognizing micro actions such as slight head shaking or hand clapping from videos can be challenging since they only involve small movements of local body parts. In this paper, we propose to fuse features from both higher-level and lower-level layers of convolutional neural networks for improving the accuracy of micro-action recognition. Deep features in higher-level layers have been shown to be effective in recognizing general actions, such as biking and jumping, that involve relatively large movements. Different from features in higher-level layers, features in lower-level layers are usually of higher resolution and can help capture small motions in micro actions. In this paper, we employ class-discriminative information as a guidance in lower-level layers to learn local features that are highly associated with micro-action regions. In the experiments, we evaluate the proposed method on two micro-action video datasets and achieve new state-of-the-art performance. We also test the proposed method on two general-action video datasets with promising performance.
Dynamic attenuation scheme in measurement-device-independent quantum key distribution over turbulent channels

Measurement-device-independent quantum key distribution (MDI QKD) offers great security in practice because it removes all detector side channels. However, conducting MDI QKD over free-space channels is challenging. One of the largest culprits is the mismatched transmittance of the two independent turbulent channels causing a reduced Hong-Ou-Mandel visibility and thus a lower secret key rate. Here we introduce a dynamic attenuation scheme, where the transmittance of each of the two channels is monitored in real time by transmitting bright light pulses from each users to the measurement device. Based on the measured channel transmittance, a suitable amount of attenuation is introduced to the low-loss channel at the measurement device. Our simulation results show a significant improvement of QKD performance, especially when using short raw keys.
A Byzantine-Resilient Dual Subgradient Method for Vertical Federated Learning

Federated learning (FL) raises new challenges on security risks, especially when the FL system involves Byzantine clients that send corrupted or adversarial messages to the central server for deteriorating the training paradigm. While there is an extensive research on robust algorithms for horizontal or data-partitioned FL problems, the exploration in Byzantine-resilient vertical or feature-partitioned FL is quite limited. In this paper, we provide a problem formulation of vertical FL in the presence of Byzantine attacks, and propose a Byzantine-resilient dual subgradient method. Convergence analysis is established, and the influence of the Byzantine clients is also clarified. Numerical experiments show the proposed algorithm is robust to various Byzantine attacks on vertical FL.
Controllable Speech Representation Learning Via Voice Conversion and AIC Loss

Speech representation learning transforms speech into features that are suitable for downstream tasks, e.g. speech recognition, phoneme classification, or speaker identification. For such recognition tasks, a representation can be lossy (non-invertible), which is typical of BERT-like self-supervised models. However, when used for synthesis tasks, we find these lossy representations prove to be insufficient to plausibly reconstruct the input signal. This paper introduces a method for invertible and controllable speech representation learning based on disentanglement. The representation can be decoded into a signal perceptually identical to the original. Moreover, its disentangled components (content, pitch, speaker identity, and energy) can be controlled independently to alter the synthesis result. Our model builds upon a zero-shot voice conversion model AutoVC-F0, in which we introduce alteration invariant content loss (AIC loss) and adversarial training (GAN). Through objective measures and subjective tests, we show that our formulation offers significant improvement in voice conversion sound quality as well as more precise control over the disentangled features.
AdderIC: Towards Low Computation Cost Image Compression

Recently, learned image compression methods have shown their outstanding rate-distortion performance when compared to traditional frameworks. Although numerous progress has been made in learned image compression, the computation cost is still at a high level. To address this problem, we propose AdderIC, which utilizes adder neural networks (AdderNet) to construct an image compression framework. According to the characteristics of image compression, we introduce several strategies to improve the performance of AdderNet in this field. Specifically, Haar Wavelet Transform is adopted to make AdderIC learn high-frequency information efficiently. In addition, implicit deconvolution with the kernel size of 1 is applied after each adder layer to reduce spatial redundancies. Moreover, we develop a novel Adder-ID-PixelShuffle cascade upsampling structure to remove checkerboard artifacts. Experiments demonstrate that our AdderIC model can largely outperform conventional AdderNet when applied in image compression and achieve comparable rate-distortion performance to that of its CNN baseline with about 80% multiplication FLOPs and 30% energy consumption reduction.
Fine-Grained Dynamic Loss for Accurate Single-Image Super-Resolution

With a wide range of applications, single-image super-resolution (SISR) is a very important computer vision task. There are many SISR strategies which are based on the CNNs improvement, such as residual connections, deeper networks, and attention mechanisms. Although such kind of strategies often improve the SISR performance, they increase CNNs’ computational complexity and complex image texture areas still remain hard to be reconstructed. In particular, all the existing loss functions are minimized in the whole dynamic range, e.g. 0-255 in case of 8 bit image, which causes difficulty of CNNs’ learning in texture image regions above all. Developing new loss function provides a promising SISR solution, i.e. one should be beyond the existing regression loss functions, which encounter problem in reconstructing the image texture details. For such goal, this paper proposes a dynamic fine-grained loss function. It consists of both an existing regression loss function and a new pixel classification loss together with a dynamic regression range loss. Extensive experiments conducted on the benchmark SISR show that the proposed method can achieve better results and without extra computational cost.
Accurate Instance Segmentation Via Collaborative Learning

We propose an instance segmentation model, named CoMask, that effectively alleviates the scale variation issue and addresses the precise localization. Specifically, we develop a multi-scale feature extraction module (MSFEM) to exploit multi-scale spatial cues. Besides, the channel attention mechanism is also adopted to further enhance the discriminating ability. Equipped with MSFEMs, multi-scale and multi-level features can be extracted to better characterize objects of various sizes and provide affluent high-level semantic information. For precise localization, we propose a collaborative learning framework to compute coarse masks and regresses position-sensitive dense offsets. The foreground confidence of each position is then assigned as the weight of the corresponding bounding box to calculate a weighted average. Thus, we can mitigate interference of background regions. After obtaining the final regressed bounding boxes, finer foreground masks can be calculated. We conduct experiments on MS COCO dataset. Experimental results validate that CoMask is competitive compared with state-of-the-art models.
Few-Shot Gaze Estimation with Model Offset Predictors

Due to the variance of optical properties across different people, the performance of a person-agnostic gaze estimation model may not generalize well on a specific person. Though one may achieve better performance by training a person-specific model, it typically requires a large number of samples which is not available in real-life scenarios. Hence, few-shot gaze estimation method is preferred for the small number of samples from a target person. However, the key question is how to close the performance gap between a "few-shot" model and the "many-shot" model. In this paper, we propose to learn a person-specific offset predictor which outputs the difference between the person-agnostic model and the many-shot person-specific model with as few as one training sample. We adapt the knowledge to a new person by using the average of meta-learned offset predictors parameters as the initialization of the new offset predictor. Experiments show that the proposed few-shot person-specific model is not only closer to the corresponding many-shot person-specific model but also has better accuracy than the SOTA few-shot gaze estimation methods in multiple gaze datasets.
Multi-Modal Learning with Text Merging for TEXTVQA

Text visual question answer (TextVQA) is an important task of visual text understanding, which requires to understand the text generated by text recognition module and provide correct answers to specific questions. Recent works of TextVQA have tried to combine text recognition and multi-modal learning. However, due to the lack of effective preprocessing of text recognition output, existing approaches suffer from serious contextual information missing, which leads to unsatisfactory performance. In this work, we propose a Multi-Modal Learning framework with Text Merging (MML&TM in short) for TextVQA, where we develop a text merging (TM) algorithm, which can effectively merge the word-level text obtained from the text recognition module to construct line-level and paragraph-level texts for enhancing semantic context, which is crucial to visual text understanding. The TM module can be easily incorporated into the multi-modal learning framework to generate more comprehensive answers for TextVQA. We evaluate our method on a public dataset ST-VQA. Experimental results show that our TM algorithm can obtain complete semantic information, which subsequently helps MML&TM generate better answers for TextVQA.