Reviewerly/exHarmony huggingface dataset:
Softwarized Attention-Based Context-Aware Group Recommendation Technology in Event-Based Industrial Cyber-Physical Systems

Industrial cyber-physical systems are smart systems, which amalgamate the physical processes with computational capabilities to seamlessly capture, monitor and control the entities and scenarios in industrial environments. Among them, event-based industrial cyber-physical systems (EICPSs), such as Meetup and Plancast, have gained rapid developments. EICPSs provide event recommendation service for groups, which alleviates the information overload problem. However, existing group recommendation models in EICPSs focus on how to aggregate the preferences of group members, failing to model the complex and deep influence of contexts on groups. In this article, we propose an attention-based context-aware group event recommendation model (ACGER) in EICPSs. ACGER models the deep, nonlinear influence of contexts on users, groups, and events through multilayer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we acquire the preference of a group from two perspectives: indirect preference and direct preference. To obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. To obtain the direct preference, we employ neural networks to learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EICPSs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on three real datasets from Meetup and Douban event show that our model ACGER significantly outperforms the state-of-the-art models.
Adaptive Real-Time Clustering Method for Dynamic Visual Tracking of Very Flexible Wings

Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, ) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system.
Data Compression Algorithm for Audio and Image using Feature Extraction

We aim to achieve the highest data compression ratio in a lossy scenario while still maintaining the original image or audio files characteristics and resolution/bitrate. For this we would run feature extraction on chunks of the data and store them in a database with a specific hash as a key. This hash will be stored in the file and the full data later reconstructed from the database. The database will be created by training on a vast range of data and storing only the most common chunks encountered by hash. The compression ratio achieved for image it is 0.01 over standard raw input data.
Keep the dark web close and your cyber security tighter

The dark web still offers a safe haven for criminal activity – especially those sites operating outside the reach of Western law enforcement. Much of the dark web is devoted to cybercrime, from sharing techniques and tools to selling stolen data and credentials. Having an ear to the ground in this shadowy underworld can give organisations an edge in identifying and defending against threats, from new trends to attacks specifically targeting them. But acquiring access to these secrets is no easy feat, explains Ziv Mador of Trustwave. “The golden age of the dark web marketplace is over”. So declared Europol in September 2020, after the triumphant announcement of operation ‘DisrupTOR’, a sting that led to the arrest of 179 criminals selling drugs and weapons. 1 The operation, which saw co-ordination between Europol and law agencies across the world, is the latest strike against the virtual marketplaces that are the beating heart of the criminal underworld today.
Decentralized Data Access with IPFS and Smart Contract Permission Management for Electronic Health Records

Through the adoption of smart contracts as a permission management database for electronic health records (EHRs), blockchain technology has introduced several advantages such as traceability, transparency and enhanced security. Despite that the permissions to a patient’s record are being managed in a more secure and decentralized manner, these records remain stored within the servers of local hospitals and health providers. As such, it remains perfectly possible to query an EHR from within the hospital infrastructure, without the query being stored or even be detected by the smart contract. Moreover, the introduction of smart contracts to facilitate the management of permissions results in a new challenge: how to verify an actor’s identity with public key cryptography and the handling of stolen or lost private keys. In this article, we propose a different design where we integrate the research performed by previous efforts to adopt blockchain technology for the management of permissions of EHRs together with the InterPlanetary File system (IPFS) as a peer-to-peer distributed network to provide decentralized storage for EHRs, and a governing body that performs the task of public key governance and identity verification.
Adaptive Hand Gesture Recognition System Using Machine Learning Approach

One of the most important problem faced by our society is that deaf and hearing-impaired people are finding it hard to come up with the advanced moving technology.The sign language plays a vital role for these people as only through the sign language they can communicate with each other.But for other people it is very challenging to understand the sign language.But as we know, it is not feasible for everyone to learn sign language, need of a smart gesture recognition system for sign language is evolved.Sign language basically includes the various movements of palms, hands and other body parts.Gesture depicts an image of a specific body part.Gesture recognition is an advanced technology which is used to recognize the posture of body parts.Objective of this type of software is to provide medium between the human body movements with computer system.In gesture recognition system particularly hand gesture recognition provides a much effective and natural way for non-verbal data exchange when working with HCI system.This paper presents a real time vision approach for recognizing hand gesture based on machine learning using MATALB.Information gathering is a first step in our proposed approach where we will develop dynamic hand gesture dataset of sign language.In next step we will represent our original proposed framework to depict hand gesture recognition with the help of hand shape and its various motions descriptors.This second step will recognize the sign once it has been captured and is much challenging trade, especially in a continuous stream which a main focus of our work.In this paper we have presented an adaptive hand gesture recognition system using machine learning approach which is adaptable to multiple applications, thus making the gesture recognition systems to be application adaptive.
Neural networks for quantum inverse problems

Abstract Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.
Low-shot Learning in Natural Language Processing

This paper study the low-shot learning paradigm in Natural Language Processing (NLP), which aims to provide the ability that can adapt to new tasks or new domains with limited annotation data, like zero or few labeled examples. Specifically, Low-shot learning unifies the zero-shot and few-shot learning paradigm. Diverse low-shot learning approaches, including capsule-based networks, data-augmentation methods, and memory networks, are discussed for different NLP tasks, for example, intent detection and named entity typing. We also provide potential future directions for low-shot learning in NLP.
Application of Vector Autoregressive with Exogenous Variable: Case Study of Closing Stock Price of PT INDF.Tbk and PT ICBP.Tbk

Abstract Multivariate time series are widely used in various fields such as finance, economics, and the stock market. One analysis model that is widely used for multivariate time series data is the VAR model. Vector autoregressive (VAR) is a model used to describe the relationship between several variables. The VAR model provides an alternative approach that is very suitable for forecasting purposes and is very suitable for solving economic data problems. The variables used in this study consisted of endogenous variables with closing prices of ICBP and INDF shares and exogenous variables with exchange rates collected from January 2017 to July 2020. In this study, the best model, VARX (1,0), was obtained. also the relationship between variables through the impulse response function and granger causality. Furthermore, forecasting is also carried out for the next 30 days using the best model, VARX (1,0).
Troll Tracking: Examining Rhetorical Circulation of Anti-Intellectual Ideologies in Right-Wing Media Attacks

Abstract This case study proposes a method of troll tracking to analyze how right-wing media attacks circulate and contribute to increasing anti-intellectualism in the United States. Given the increase in right-wing political “news” sites as originators of trolling activity and their propensity to espouse anti-intellectualism, we are interested in understanding more deeply how particular instances of this targeted outrage become rhetorical. Our analysis reveals how metaknowledge circulates misinformation through commentary that carries anti-intellectual ideologies rapidly across time and space. This process of detachment reveals important implications about the (re)production of anti-intellectual ideologies, the evocation of collective outrage that manifests as sexism and transphobia, and the gatekeeping of academic knowledge production.
PST: A More Practical Adversarial Learning-Based Defense Against Website Fingerprinting

To prevent serious privacy leakage from website fingerprinting (WF) attacks, many traditional or adversarial WF defenses have been released. However, traditional WF defenses such as Walkie-Talkie (W-T) still generate patterns that might be captured by the deep learning (DL) based WF attacks, which are not effective. Adversarial perturbation based WF defenses better confuse WF attacks, but their requirements for the entire original traffic trace and perturbating any points including historical packets or cells of the network traffic are not practical. To deal with the effectiveness and practicality issues of existing defenses, we proposed a novel WF defense in this paper, called PST. Given a few past bursts of a trace as input, PST Predicts subsequent fuzzy bursts with a neural network, then Searches small but effective adversarial perturbation directions based on observed and predicted bursts, and finally Transfers the perturbation directions to the remaining bursts. Our experimental results over a public closed-world dataset demonstrate that PST can successfully break the network traffic pattern and achieve a high evasion rate of 87.6%, beating W-T by more than 31.59% at the same bandwidth overhead, with only observing 10 transferred bursts. Moreover, our defense adapts to WF attacks dynamically, which could be retrained or updated.
Glove detection system based on VGG-16 network

Electric power enterprises play an important role in construction and development of country. Generally, the work environments of Power Grid are in high-altitude and high-voltage. In order to ensure reliable operation of national power grid and the safety of workers, workers must wear appropriate equipment to ensure safety. At present, there are few studies on workers wearing gloves. To solve this problem, we propose glove detection system based on VGG-16 network. Cameras obtain images monitored workers in real time and transmit images to the glove detection system for detecting. If it is found that workers are not wearing gloves safely, the warning is given. Because there is no dataset about gloves, we created our own dataset. Through experiments, our system achieves 84.75% accuracy, 88.67% precision and 27% false alarm rate on our dataset.
Thermal Face Recognition Based on Multi-scale Image Synthesis

We present a transformation-based method to achieve thermal face recognition. Given a thermal face, the proposed model transforms the input to a synthesized visible face, which is then used as a probe to compare with visible faces in the database. This transformation model is built on the basis of a generative adversarial network, mainly with the ideas of multi-scale discrimination and various loss functions like feature embedding, identity preservation, and facial landmark-guided texture synthesis. The evaluation results show that the proposed method outperforms the state of the art.
A High-Speed Elliptic Curve Cryptography Processor for Teleoperated Systems Security

Teleoperated robotic systems are those in which human operators control remote robots through a communication network. The deployment and integration of teleoperated robot’s systems in the medical operation have been hampered by many issues, such as safety concerns. Elliptic curve cryptography (ECC), an asymmetric cryptographic algorithm, is widely applied to practical applications because its far significantly reduced key length has the same level of security as RSA. The efficiency of ECC on GF (p) is dictated by two critical factors, namely, modular multiplication (MM) and point multiplication (PM) scheduling. In this paper, the high-performance ECC architecture of SM2 is presented. MM is composed of multiplication and modular reduction (MR) in the prime field. A two-stage modular reduction (TSMR) algorithm in the SCA-256 prime field is introduced to achieve low latency, which avoids more iterative subtraction operations than traditional algorithms. To cut down the run time, a schedule is put forward when exploiting the parallelism of multiplication and MR inside PM. Synthesized with a 0.13 um CMOS standard cell library, the proposed processor consumes 341.98k gate areas, and each PM takes 0.092 ms.
An empirical study into finding optima in stochastic optimization of neural networks

Mini-batch sub-sampling (MBSS) in neural network training is unavoidable due to growing data demands, memory-limited computational resources such as graphical processing units, and the dynamics of on-line learning. This study distinguishes between static MBSS and dynamic MBSS. In static MBSS, mini-batches are intermittently fixed during training, resulting in smooth but biased loss functions. During dynamic MBSS, mini-batches are resampled at every loss evaluation, resulting in sampling induced discontinuities by trading sampling bias for sampling variance. This renders classical minimization strategies ineffective in dynamic MBSS losses, as these may locate spurious sampling induced minima, while critical points may not exist. This paper re-evaluates the information used to define optima in stochastic loss functions of neural networks by defining the solution to a stochastic optimization problem as the stochastic non-negative associated gradient projection point (SNN-GPP). We demonstrate that SNN-GPPs offer a more robust description of full-batch optima than minimizers and critical points. An empirical investigation compares local minima to SNN-GPPs for the potential training of a simple neural network training problem with different activation functions. Since SNN-GPPs better approximate the location of true optima, we conclude that line searches locating SNN-GPPs can contribute significantly to automating neural network training.
Automatic Attendance System Using Deep Learning

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Diagnostic captioning: a survey

Abstract Diagnostic captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination. DC can assist inexperienced physicians, reducing clinical errors. It can also help experienced physicians produce diagnostic reports faster. Following the advances of deep learning, especially in generic image captioning, DC has recently attracted more attention, leading to several systems and datasets. This article is an extensive overview of DC. It presents relevant datasets, evaluation measures, and up-to-date systems. It also highlights shortcomings that hinder DC’s progress and proposes future directions.
A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports

Breast cancer diagnosis is based on radiology reports describing observations made from medical imagery, such as X-rays obtained during mammography. The reports are written by radiologists and contain a conclusion summarizing the observations. Manually summarizing the reports is time-consuming and leads to high text variability. This paper investigates the automated summarization of Dutch radiology reports. We propose a hybrid model consisting of a language model (encoder-decoder with attention) and a separate BI-RADS score classifier. The summarization model achieved a ROUGE-L F1 score of 51.5% on the Dutch reports, which is comparable to results in other languages and other domains. For the BI-RADS classification, the language model (accuracy 79.1 %) was outperformed by a separate classifier (accuracy 83.3 %), leading us to propose a hybrid approach for radiology report summarization. Our qualitative evaluation with experts found the generated conclusions to be comprehensible and to cover mostly relevant content, and the main focus for improvement should be their factual correctness. While the current model is not accurate enough to be employed in clinical practice, our results indicate that hybrid models might be a worthwhile direction for future research.
An intention multiple-representation model with expanded information

Short text is the main carrier for people to express their ideas and opinions. And it is very important as well a big challenge to understand the meaning of short text or recognize the semantic patterns of different short texts. Most of existing methods use word embedding and short text interaction to learn short text pairs semantic patterns. However, some of these methods are complicated and cannot fully capture the relations of words and the interaction of short text pairs. To solve this problem, a self-attention based model, that is, Knowledge Learning for Matching Question (KLMQ) is proposed. Using part-of-speech to mine the relations of words, and the model obtains the relations of short texts from the grammar, syntax and morphology. Meanwhile, it adopts information fusion strategy to enhance the interaction between short text pairs, which ensures the model works well with the expanded information, such as the order of words, the correlation of words and the relations of short text pairs. To verify the correctness of the proposed model and the efficiency of the expanded information, extensive experiments were carried out on public datasets. Experimental results show that the model performs better than that of traditional neural network models and the expanded information can much improve the performances of intention multiple-representation recognition.
Skeleton Guided Conflict-Free Hand Gesture Recognition for Robot Control

The skeleton analysis introduced by Kinect has been an efficient way for interaction. Skeleton analysis interaction is more intuitive and aligns better with human natural behaviors compared with traditional approaches. However, skeleton analysis often has the problem of producing conflict gesture identifications if two interaction movements are similar. Additionally, it always mistakenly recognizes some unconscious or intentional body movements as positive gestures. To this end, we proposed a new interaction method enhanced by both vision algorithms and deep learning. An improved residual neural network is employed to recognize gestures which are then used for distinguishing similar body movements. A combined human-computer interaction scheme is proposed which includes three main components: (a) a hand shape segmentation approach enhanced by skin color detection and skeleton joint tracking, (b) the deep learning augmented detection for changes of gestures and (c) a deep learning-based gesture command recognition for robot control. Experiments are conducted using the proposed method for robot interaction. The results demonstrate that unconscious body movements can be accurately identified. Similar body movements can also be distinguished robustly. The proposed method can run in real-time with competitive performance.