Reviewerly/exHarmony huggingface dataset:
Squeeze-and-excitation blocks embedded YOLO model for fast target detection under poor imaging conditions

How to detect targets under poor imaging conditions is receiving significant attention in recent years. The accuracy of object recognition position and recall rate may decrease for the classical YOLO model under poor imaging conditions because targets and their backgrounds are hard to discriminate. We proposed the improved YOLOv3 model whose basic structure of the detector is based on darknet-53, which is an accurate but efficient network for image feature extraction. Then Squeeze-and-Excitation (SE) structure is integrated after non-linearity of convolution to collect spatial and channel-wise information within local receptive fields. To accelerate inference speed, Nvidia TenorRT 6.0 is deployed into on Nvidia Jetson series low power platform. Experiments results show that the improved model may greatly achieve the inference speed without significantly reducing the detection accuracy comparing with the classic YOLOv3 model and some other up-to-date popular methods.
Rural Revitalization and Digital Financial Economic Development Model Based on Multimodal Sensor Fusion

Since the 19th National Congress of the Communist Party of China, the key to rural development has been to solve the problem of rural development. The “three rural issues” is a critical issue for the country’s economy. In addition, the implementation of the Rural Revitalization Strategy under the environment of data financial and economic development can be more convenient and efficient. However, the traditional sensor fusion technology still has the disadvantages of low efficiency and high cost in the application of Rural Revitalization and digital financial economic development model. In order to solve the shortcomings of this technology, this paper proposes a multimodal sensor fusion technology combined with BP neural network (BPNN, a commonly used feedback neural network) and Kalman filter (a linear filtering algorithm), and it constructs a rural economic development model combined with digital finance. This paper uses this technology to study the economic development model. Firstly, the research method is to visit and investigate the residents and basic economic situation of a village and then use the economic development model constructed in this paper to carry out the economic analysis of the BPNN model and Kalman filter model. It includes Mohr scatter diagram, economic income, industrial investment, product export, and household consumption. The results show that the Mohr scatter diagram of the village changes from the third quadrant (−0.7, −0.5) to the first quadrant (−0.7, 0.3); the difference between the economic income calculated by the Kalman filter algorithm and BPNN algorithm and the actual value shall not exceed 10%. The rural industrial investment index increased by 56.08, the product export index increased by 30.11, and the household consumption index increased by 13.10. It shows that the economic development model constructed in this paper has achieved good results.
Target Filter Tracking Algorithm Based on IABBSCA-IMM

Aiming at the problem that the probability transition matrix is set by prior information and the filtering parameters are fixed in the traditional interactive multimodel (IMM) algorithm, which leads to the model probability lag in the switching process and the low filtering and tracking accuracy, an interactive multimodel filter tracking (IABBSCA-IMM) algorithm with improved parameter adaptive and bare bone sine cosine optimization is proposed. First, the Markov probability transition matrix is dynamically adjusted and limited conditions are added through the parameter adaptation method; then, the filtering parameters Q and R are optimized by the bare bones sine cosine algorithm (BBSCA); finally, three motion models of CV (uniform velocity motion), CA (uniform acceleration motion), and CT (uniform velocity turning motion) are used to conduct filtering and tracking experiments on the target. The simulation results show that, compared with the IMM algorithm, the AMP-IMM algorithm, the IASCA-IMM algorithm, and the IABBSCA-IMM algorithm proposed in this study have the smallest position and velocity root mean square error (RMSE) in the X and Y directions, and the accuracy is better.
Point-of-Interest Recommendation Model Based on Graph Convolutional Neural Network

With the development of location-based social networks, the point-of-interest recommendation has become one of the research hotspots in the field of recommendation. However, traditional technologies like collaborative filtering are limited by the influence of data sparsity and cannot accurately capture the users’ preferences from the complex context. In order to address this problem, a recommendation model based on graph convolutional neural network is proposed, named RMGCN. RMGCN is composed of three parts: graph structure features extraction module, geographical factor evaluation module, and score calculation module. The graph structure feature extraction module is used to extract node features from the graph structure data composed of user check-in records. The geographical factor evaluation module is used to calculate the influence coefficient of geographical factors on user’s decision-making behaviors. The score calculation module is used to combine the output results of the above two modules and calculate the user’s preference scores of point-of-interests combined with temporal context and spatial context. Experimental results on two real-world datasets show that RMGCN has better recommendation performance than baselines.
Rushed to crack – On the perceived effectiveness of cybersecurity measures for secure behaviour under time pressure

Time pressure, a common phenomenon in everyday workplace environments, is an important driver for non-secure cybersecurity behaviour in organisations. Under time pressure, users are more likely to rely upon fast, affect-driven decision making, increasing their susceptibility to make mistakes and justify non-secure workarounds. This contributes to the role of human error in cybersecurity and counteracts cybersecurity measures (CSMs) designed to protect organisations from threats and vulnerabilities. In this study, we report results from an online survey (N = 207), investigating how users perceive the effectiveness of CSMs for facilitating secure behaviour under time pressure. Understanding how users perceive the effectiveness of CSMs is important to inform the design and implementation of such measures in practice. We find that perceived CSM effectiveness differs greatly across measures. Thereby, users’ appreciation of incident severity and the general level of time pressure in their daily lives emerge as important antecedents. We discuss theoretical and practical implications for the design and implementation of CSMs.
Post-Quantum Secure Identity-Based Encryption Scheme using Random Integer Lattices for IoT-enabled AI Applications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, where the IoT smart devices send securely the sensing data to their nearby gateway nodes(s) with the help of IBE and the gateway node(s) secure aggregate the data from the smart devices by decrypting the messages using the proposed IBE decryption. Later, the gateway nodes will securely send the aggregated data to the cloud server(s) and the Big data analytics is performed on the authenticated data using the Artificial Intelligence (AI)/Machine Learning (ML) algorithms for accurate and better predictions.
A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research

Road safety research is a data‐rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by confounding and, when conditioning on data to make inferences net of this, observed effects that are subject to uncertainty due to diminishing sample sizes. We demonstrate how visual data analysis approaches can inject rigor into exploratory analysis of such datasets. A framework is presented whereby graphics are used to expose, model and evaluate spatial patterns in observational data, as well as protect against false discovery. Evidence for the framework is presented through an applied data analysis of national crash patterns recorded in STATS19, the main source of road crash information in Great Britain. Our framework moves beyond typical depictions of exploratory data analysis and transfers to complex data analysis decision spaces characteristic of modern geographical analysis.
#Segments: A Dominant Factor of Password Security to Resist against Data-driven Guessing

Understanding which factors dominate password security is vital for users to create their secure passwords. Prior works generally consider the password length and the number of character classes as the dominant factors. However, creating secure passwords based on the above two factors becomes much more challenging than before due to the emergence of powerful data-driven guessing methods, e.g., the Probabilistic Context-free Grammars (PCFG) and its variations, Markov-based methods, and neural-network-based methods. In this paper, inspired by the segments used in PCFG, where a segment is a continuous string whose characters have a strong correlation, we conduct a comprehensive empirical analysis and find that the number of segments (# Segments for short) is a dominant factor of password security to resist against data-driven guessing. That is, the increase of # Segments generally leads to a significant improvement of password security. The observation helps us explore an optimised identification method for segments, referred to as re-segment, which reduces # Segments as much as possible to obtain accurate # Segments by leveraging five popular patterns (i.e., keyboard, abbreviation, leet, mixture, and component), to evaluate password security more accurately from an adversary’s viewpoint. Then we propose an efficient data-driven guessing method, referred to as ReSeg-PCFG, by leveraging re-segment based on the latest version of PCFG. Our study shows that ReSeg-PCFG outperforms the state-of-the-art data-driven guessing methods in almost all scenarios; e.g., it outperforms the latest version of PCFG by up to 79.34% at 1014 guesses, a commonly used threshold of off-line attacks.
Direct generation of high brightness path entangled N00N states using structured crystals and shaped pump beams

Optical N00N states are N-photon path entangled states with important applications in quantum metrology. However, their use was limited till now owing to the difficulties of generating them in an efficient and robust manner. Here we propose and experimentally demonstrate two new simple, compact and robust schemes to generate path entangled N00N states with N = 2 that emerge directly from the nonlinear interaction. The first scheme is based on shaping the pump beam, and the second scheme is based on modulating the nonlinear coefficient of the crystal. These new methods exhibit high coincidence count rates for the detection of a N00N state, reaching record value of 2 × 105 coincidences per second. We observe super-resolution by measuring the second order correlation on the generated N = 2 state in an interferometric setup, showing the distinct fringe periodicity at half of the optical wavelength. Our findings may pave the way towards scalable and efficient sources for super-resolved quantum metrology applications and for the generation of bright squeezed vacuum states.
A Method of Hepatocytes Segmentation in Microscopic Images of Trypan Blue Stained Cellular Suspension

Automatic detection and count of cells in microscopic images of cellular suspension could provide a useful tool for scientists, that have to perform this task by hand. This paper presents an algorithm that can count and outline hepatocytes using a set of filters and texture analysis. The set of filters and algorithms that were used prior to morphological segmentation were in order: Enhancing local contrast using CLAHE algorithm, Morphological gradient, Anisotropic Diffusion, Sobel edge detector. After this set of filters, morphological segmentation is carried out which consists of: Morphological gradient, Imposing minima, Watershed flooding. Further improving and developing of the algorithm is planned. Clustered cells need to be separated. When this goal is accomplished cells can be counted and an algorithm that is capable of classification of cells as dead or alive will be developed.
Lightweight ECC Coprocessor With Resistance Against Power Analysis Attacks Over NIST Prime Fields

This brief proposes a compact and fast elliptic curve cryptography coprocessor using affine coordinates. Although using affine coordinates requires compute-intensive modular inversion (ModInv), the proposed coprocessor can be accelerated using the improved ModInv. In addition, the study proposes a novel point multiplication method that is effective in preventing simple power analysis when affine coordinates are used. Using point randomization, the proposed coprocessor is also resistant to statistical power analysis (PA) such as differential PA (DPA) and correlation PA (CPA), and it requires 60 k gate counts and can perform one elliptic curve point multiplication within 1 ms at the maximum clock frequency.
Automatic Speech-Based Smoking Status Identification

Identifying the smoking status of a speaker from speech has a range of applications including smoking status validation, smoking cessation tracking, and speaker profiling. Previous research on smoking status identification mainly focuses on employing the speaker's low-level acoustic features such as fundamental frequency (F0), jitter, and shimmer. However, the use of high-level acoustic features, such as Mel Frequency Cepstral Coefficients (MFCC) and filter bank (Fbank) for smoking status identification, has rarely been explored. In this study, we utilise both high-level acoustic features (i.e., MFCC, Fbank) and low-level acoustic features (i.e., F0, jitter, shimmer) for smoking status identification. Furthermore, we propose a deep neural network approach for smoking status identification by employing ResNet along with these acoustic features. We also explore a data augmentation technique for smoking status identification to further improve the performance. Finally, we present a comparison of identification accuracy results for each feature settings, and obtain the best accuracy of 82.3%, a relative improvement of 12.7% and 29.8% on the initial audio classification approach and rule-based approach, respectively.
Acoustic Anomaly Detection of Mechanical Failure: Time-Distributed CNN-RNN Deep Learning Models

This paper aims to perform anomaly detection on machine sound using various combinations of CNN-RNN deep learning models. The Convolutional Neural Network (CNN) was combined with three popular Recurrent Neural Network (RNN), which are Long short-term memory cell (LSTM), Gated recurrent unit (GRU), and Recurrent neural network (RNN) were used in this study. Besides, Time-distributed function was proposed in the CNN layer to perform local feature extraction from the time-series data represented by MFCC coefficients. The developed models were then evaluated with MIMII dataset. The evaluation includes confusion matrix, accuracy, precision, sensitivity, F1 score, AUC, and training speed. Results show that CNN-LSTM model outperforms the other and achieve more than 99% in the assessment parameters. CNN-GRU model however has the fastest training speed, which is 45% faster than the CNN-LSTM model.
Hierarchical Contrastive Learning for Unsupervised 3d Action

Unsupervised contrastive 3D action representation learning has made great progress recently. How-ever, most works rely on only the direct instance-level comparison with unreasonable positive/negative constraint, which degrades the learning performance. In this paper, we propose a Hierarchical Con-trastive Scheme (HCS) for unsupervised skeleton 3D action representation learning, which takes ad-vantage of multi-level contrast. Specifically, we keep the instance-level contrast to draw the di˙erent augmentations of the same instance close, targets to learn intra-instance consistency. Then we extend the contrastive objective from individual instances to clusters by enforcing consistency between clus-ter assignment from di˙erent instance of same category, aims at learning inter-instance consistency. Compared with previous methods, HCS enables intra/inter-instance consistency pursuit via multi-level contrast, without inflexible positive/negative constraint, which leads to a more discriminative feature space. Experimental results validate that the proposed framework outperforms the previous state-of-the-art methods on the challenging NTU RGB+D and PKU-MMD datasets.
Leveraging Zero Trust Security Strategy to Facilitate Compliance to Data Protection Regulations

Implementing privacy requirements into technology is cumbersome. On the one hand, we see the speed at which technology develops; on the other hand, we observe ambiguity during the implementation of privacy regulations into the operation of organisations. It is like replacing one engine of a plane during the flight. You cannot freeze the environment and implement, test and release. Zero Trust Security is a strategic approach to information security, defining critical segments that house crown jewels and implement security measures according to a structured process that also encompasses data privacy requirements. A Zero Trust Segment can be a High Valuable Asset that processes Personally Identifiable Information (PII) that needs protection. The aim of this paper is to describe the GDPR implementation problems at hand and elaborate on the empirical examination with Chief Information Security Officers (CISO's) and Data Protection Officers (DPO's) to complement the ON2IT Zero Trust Framework with additional data protection requirements.
An Advanced Algorithm for Email Classification by Using SMTP Code

Email is an essential communication tool for modern people and offers a variety of functions. After the outbreak of COVID-19, the importance of emails enhanced further as non-face-to-face work increased. However, with the spread and dissemination of emails, cybercrime that abused emails has also increased. The number of cases of stealing or damaging email users by impersonating public institutions such as the National Police Agency, the Prosecutor’s Office, or the WHO. This study proposes an advanced algorithm of email classification using an SMTP response code to strengthen the level of email security. The proposed system is located on the side of the recipient’s email server and operates upon receipt of the email. When an email is received, it automatically verifies whether the domain of the email sender is normally registered in DNS. Thereafter, MX, SPF, and PTR records are extracted and combined to determine the state of the sending server. When additional verification is required, a proposed algorithm automatically connects the communication session to the sender to request the SMTP response code. The proposed algorithm was applied to two organizations and succeeded in classifying received emails into various categories. This study contributes to the literature on email classification by presenting new ideas in the process of sender verification.
Improved Unsupervised Statistical Machine Translation <i>via</i> Unsupervised Word Sense Disambiguation for a Low-Resource and Indic Languages

Besides word order, word choice is a key stumbling block for machine translation (MT) in morphologically rich languages due to homonyms and polysemous difficulties. On the other hand, un-translated/improperly translated words are a severe issue for Statistical Machine Translation (SMT) models. The quantity of parallel training corpus has limited unsupervised SMT (USMT) systems. Still, current research lines have successfully trained SMT systems in an unsupervised manner using monolingual data alone. However, there is still a need to enhance the translation quality of the MT output due to unaligned and improperly sensed words. This problem is addressed by incorporating unsupervised Word Sense Disambiguation (WSD) into the decoding phase of USMT. The work provided a compendium of SMT systems for five translation tasks, i.e. En→Indic languages for the WMT test dataset and evaluated on BLEU and METEOR evaluation metrics. The studies were performed on En→Hi, En→Kn, En→Ta, En→Te, and En→Be tasks and showed an improvement in BLEU points by 2.3, 2.68, 0.78, 2.32, and 1.79, respectively, and METEOR points by 1.07, 1.34, 0.72, 0.693, and 1.191, respectively, over the baseline model.
A Large Volume Natural Tamil Character Dataset

This paper focuses on recognizing the individual Tamil characters in images of natural scenes. Even though natural character dataset are available publicly for different languages, no specific standardized dataset is available for natural Tamil characters. In this work, we present a publicly available Tamil character dataset “Mepco-TamNatChar” for natural Tamil character recognition. This research work presents the preliminary results on natural Tamil character recognition using spatial and machine learnt features. We report an accuracy of 87.50 ± 1.42% for a train test ratio of 50:50 using machine learnt features.
Application of Deep Learning Techniques for Prostate Cancer Grading Using Histopathological Images

Prostate cancer is one of the most dangerous cancers that affect men around the world. Pathologists use a variety of approaches to grade prostate cancer. Among them, microscopic examination of biopsy tissue images is the most efficient method. A timely and accurate diagnosis plays a critical role in preventing cancer from progressing. The recent achievement of deep learning (DL), notably in the convolution neural networks (CNN), is exceptional in medicine. In this study, we have investigated and compared the performance of the state-of-the-art CNN models, namely MobileNet-V2, ResNet50, DenseNet121, DenseNet169, VGG16, VGG19, Xception, InceptionV3, InceptionResNet-V2, and EfficientNet-B7 for prostate cancer grading using histopathological images. The performance of pre-trained CNNs has been evaluated on the publicly available Prostate cancer grade assessment (PANDA) dataset. On this multi-class classification problem, the EfficientNet-B7 model has achieved the highest classification accuracy of 90.90%. With such a high rate of success, the EfficientNet-B7 model may be a useful method for pathologists in determining the stage of prostate cancer.