Reviewerly/exHarmony huggingface dataset:
Improving Readability for Automatic Speech Recognition Transcription

Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to grammatical errors, disfluency, and other noises common in spoken communication. These readable issues introduced by speakers and ASR systems will impair the performance of downstream tasks and the understanding of human readers. In this work, we present a task called ASR post-processing for readability (APR) and formulate it as a sequence-to-sequence text generation problem. The APR task aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of speakers. We further study the APR task from the benchmark dataset, evaluation metrics, and baseline models: First, to address the lack of task-specific data, we propose a method to construct a dataset for the APR task by using the data collected for grammatical error correction. Second, we utilize metrics adapted or borrowed from similar tasks to evaluate model performance on the APR task. Lastly, we use several typical or adapted pre-trained models as the baseline models for the APR task. Furthermore, we fine-tune the baseline models on the constructed dataset and compare their performance with a traditional pipeline method in terms of proposed evaluation metrics. Experimental results show that all the fine-tuned baseline models perform better than the traditional pipeline method, and our adapted RoBERTa model outperforms the pipeline method by 4.95 and 6.63 BLEU points on two test sets, respectively. The human evaluation and case study further reveal the ability of the proposed model to improve the readability of ASR transcripts.
Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network

This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network.Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field.1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992-2000), the development period (2001-2015), and the rapid growth period (2016-2021). There were two technology frontiers in the budding period (1992-2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001-2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016-2021), namely digital pathology (DP), deep learning (DL) algorithms-convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system.Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.
<i>Blang</i>: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models, networks, and domain specific problems such as sequence alignments and reconstructions, pedigrees, and phylogenies. In principle, Bayesian inference should be particularly wellsuited in such scenarios, as the Bayesian paradigm provides a principled way to obtain confidence assessment for random variables of any type. However, much of the recent work on making Bayesian analysis more accessible and computationally efficient has focused on inference in Euclidean spaces. In this paper, we introduce Blang, a domain specific language and library aimed at bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types while using a declarative syntax similar to the popular family of probabilistic programming languages, BUGS. Blang is augmented with intuitive language additions to create data types of the user's choosing. To perform inference at scale on such arbitrary state spaces, Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov chain Monte Carlo methods.
Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning

Fall detection in humans is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. Therefore, automatic fall detection is one of the most relevant problems in geriatrics. Bioradiolocation-based methods have already shown their efficiency in contactless fall detection. However, there is still a wide range of areas to improve the precision of fall recognition based on view-independent concepts. In particular, in this paper, we propose an approach based on a more complex multi-channel system (three or four bioradars) in combination with the wavelet transform and transfer learning. In the experiments, we have used several radar configurations for recording different movement types. Then, for the binary classification task, a pre-trained convolutional neural network AlexNet has been fine-tuned using scalograms. The proposed systems have shown a noticeable improvement in the fall recognition performance in comparison with the previously used two-bioradar system. The accuracy and Cohen's kappa of the two-bioradar system are 0.92 and 0.86 respectively, whereas the accuracy and Cohen's kappa of the four-bioradar system are 0.99 and 0.99 respectively. The three-bioradar system's performance turned out to be in between two of the aforementioned systems and its calculated accuracy and Cohen's kappa are 0.98 and 0.97 respectively. These results may be potentially used in the design of a contactless multi-bioradar fall detection system.
Multiple Kernel Subspace Learning for Clustering and Classification

In the face of high-dimensional and complex data, effective subspace can preserve specific statistical properties and provide an appropriate representation of data, which generally facilitates the underlying tasks such as clustering or classification. Meanwhile, multiple kernel learning is a technique to combine multiple kernels from different feature spaces effectively. Thus, by incorporating multiple kernels into the process of subspace learning, different feature spaces can be projected into a unified subspace. This paper proposes the Multiple Kernel Subspace Learning (MKSL) for embedding the original space into a unified subspace. Multiple kernels of different feature spaces are combined by MKSL in the process of learning, which can extend the suitability for various applications. Moreover, to generate the optimal combination kernel of subspace learning, we propose a two-step iteration strategy to learn the appropriate kernel weights and transformation matrix of projecting simultaneously. Furthermore, our proposed formulation of MKSL can introduce different prior knowledge such as class information and neighborhood relationships. Thus it is competent to the unsupervised learning, semi-supervised learning, and supervised learning. Extensive experiments are conducted on diverse datasets, and the performances are comprehensively evaluated on different tasks. The experimental results indicate that the proposed algorithm is outstanding in unsupervised clustering task and effective in supervised and semi-supervised classification tasks.
The end of the active work break? Remote work, sedentariness and the role of technology in creating active break-taking norms

Excessive sedentariness can impair workers' health and productivity. The move to working from home as a result of the Covid-19 pandemic eliminated many workday opportunities for physical activity. This, coupled with a blurring of boundaries between work and non-work periods, put many at risk of overwork and musculoskeletal issues. We examined how the sudden transition to working from home influenced people's ability to take physically active work breaks. We found that the absence of social norms associated with the presence of colleagues in the work environment left workers uncertain about whether and when it is appropriate to take breaks. The pressure to demonstrate productivity while working asynchronously led to increased sedentariness and decreased break-taking. We propose that online tools that promote flexible social norms around break-taking could empower remote workers to incorporate regular physical activity into their days, without compromising the beneficial aspects of asynchronous working.
Towards geospatial blockchain: A review of research on blockchain technology applied to geospatial data

Abstract. In recent years, geospatial big data has been generated at a very high speed, and the data volume is becoming increasingly massive. In order to realize the full potential of geospatial big data, there has been a strong requirement and push to embrace the value of open science. However, it is still challenging to preserve the privacy and integrity of geospatial data in data sharing and management. Blockchain as d distributed ledger technology has a series of good characteristics, such as decentralization, trust-free, transparency, tamper-free, consensus and security, etc. These characteristics of blockchain are beneficial for facilitating geospatial data sharing and management, and hence promoting the development of open GIS. In this paper, we provide a comprehensive review on the literature that involves how blockchain technology is applied to geospatial data, especially in geospatial data privacy and integrity preservation. First, the background knowledge on geospatial data privacy and blockchain technology are introduced. Then, we reviewed how blockchain technology is applied to geospatial data, followed by the conclusion of the topic. This review is beneficial for understanding how blockchain technology can be applied to geospatial domain by integrating geospatial technologies like GIS and remote sensing.
Lotus species classification using transfer learning based on VGG16, ResNet152V2, and MobileNetV2

Technology has played an increasingly important role in daily life. Especially, technology in object classification that comes in to make human life more comfortable as well as to help people of all ages learning unlimited in anywhere, anytime. Lotus Museum located in Rajamangala University of Technology Thanyaburi (RMUTT) that is open to the general public to learn as well as to cultivate awareness for propagation and result in future preservation. In this paper, we proposed lotus species classification with three pre-trained weights in the ImageNet dataset: visual geometry group (VGG16), residual neural network (ResNet152V2), and MobileNetV2. Fine-turning is used in the last layer after retrained with the custom data we provided. The experimental result shows the accuracy of VGG16, ResNet152V2, and MobileNetV2 are 98.5%, 98.0%, and 99.5% respectively. Therefore, MobileNetV2 not only gives the best accuracy than others but also uses the lowest parameters which are effective in computation time and proper to mobile devices. The proposed research paper on lotus classification base on transfer learning is an effective way to encourage and support people to learn without limitations.
Optimal Linear Fusion of Dimension-Reduced Estimates Using Eigenvalue Optimization

Data fusion in a communication constrained sensor network is considered. The problem is to reduce the dimensionality of the joint state estimate without significantly decreasing the estimation performance. A method based on scalar subspace projections is derived for this purpose. We consider the cases where the estimates to be fused are: (i) uncorrelated, and (ii) correlated. It is shown how the subspaces can be derived using eigenvalue optimization. In the uncorrelated case guarantees on mean square error optimality are provided. In the correlated case an iterative algorithm based on alternating minimization is proposed. The methods are analyzed using parametrized examples. A simulation evaluation shows that the proposed method performs well both for uncorrelated and correlated estimates.
Issues and Challenges of Internet of Things: A Survey

The Internet of things (IoT) paradigm has impacted the current high-tech lifestyle greatly. To name a few: smart cities, smart transportation, smart industries, pollution and energy control all owe their existence to IoT. Expansive research and investigations have been conducted for the implementation of IoT and this implementation without a doubt comes along with a lot of challenges. Such challenges and issues must be overcome in order to attain the full potential of IoT. This research paper aims to discuss the various issues and challenges faced in the field of IoT, especially the ones pertaining to security and privacy. Aside from the challenges, the paper dives into the probable solutions for dealing with all the key issues of IoT along with the architecture and important application domains of IoT.
Generating ad creatives using deep learning for search advertising

We generated advertisement creatives programmatically using deep neural networks.A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads.We treated the ad generation task as a text summarization problem and built a sequence to sequence model.In order to assess the validity of our approach, we conducted experiments on four datasets.Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters.Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up.The choice of word embedding used in the representation of the input altered the model's performance.When the source and the target shared common sequences during training, the model produced the best results.
Distribution of Multiplexed Continuous‐Variable Entanglement for Quantum Networks

Abstract Quantum networks, the backbone of the future quantum internet, require flexible and precise control to faithfully share entanglement with different users. These networks will allow multiple applications including distributed quantum computation and information theoretic secure communication. In this scenario, orbital angular momentum (OAM) can be employed to enhance the capacity of quantum information processing and to distribute quantum entanglement in an efficient and scalable way with multiple users. Here, a method for realizing a multiplexed continuous variable (CV) quantum entanglement distribution based on a four‐wave mixing process combined with an active routing technique is experimentally demonstrated. By exploiting Sagnac‐based OAM sorters, the OAM entanglement can be distributed and measured with the different users in a quasi‐complete star network configuration. The generations of multiple sets of OAM multiplexed bipartite CV entanglement with four users and six users are proposed and demonstrated here. In addition, the propagation of one mode through a 1 km of single‐mode fiber shows the applicability of this approach. These results propose and demonstrate a new method for distributing CV entanglement in a quantum network enlarging the range of applicability of CV quantum information protocols.
Hackers Cybercrime - Computer Security: Ethical Hacking

Today cybercrime is at a record high, costing businesses and individuals billions of dollars every year. What is even more frightening is that this figure represents just the last five years with no hope of it ever ending. The evolution of technology and the increasing accessibility of smart technologies means that there are many access points to users' homes to exploit. Cybercrime is on the rise in the world of technology today. Criminals using the technologies of the World Wide Web exploit the personal information of Internet users to their advantage. They happily use the dark web to buy and sell illegal products and services. They even manage to gain access to classified government information. While law enforcement tries to address the growing problem, the number of criminals continues to grow, taking advantage of the anonymity of the Internet.
A Security Situation Awareness Approach for IoT Software Chain Based on Markov Game Model

Since Internet of Things (IoT) has been widely used in our daily life nowadays, it is regarded as a promising and popular application of the Internet, and has attracted more and more attention.However, IoT is also suffered by some security problems which seriously affect the implementation of IoT system.Similar to traditional software, IoT software is always threated by many vulnerabilities, thus how to evaluate the security situation of IoT software chain becomes a basic requirement.In this paper, A framework of security situation awareness for IoT software chain is proposed, which mainly includes two processes: IoT security situation classification based on support vector machine and security situation awareness based on Markov game model.The proposed method firstly constructs a classification model using support vector machine (IoT) to automatically evaluates the security situation of IoT software chain.Based on the situation classification, we further proposed to adopt Markov model to simulate and predict the next behaviors of participants that involved in IoT system.Additionally, we have designed and developed a security situation awareness system for IoT software chain, the developed system supports the detection of typical IoT vulnerabilities and inherits more than 20 vulnerability detection methods, which shows great potential in IoT system protection.
Probabilistic analysis of context caching in Internet of Things applications

Context caching plays an increasingly important role in delivering near real-time responses for context-aware distributed Internet of Things (IoT) applications, services and systems. A context management platform (CMP), a middleware which acts as an aggregator and redirector of contextual information to support smart IoT applications, requires adaptive context caching to process and manage enormous amounts of context stemming from IoT. In this work, we propose a novel approach to estimating the context information’s demand probability, which helps improve the context retrieval performance of a CMP under near real-time constraints. The proposed approach uses context query logs and applies machine learning algorithms to estimate the context caching probability for context caching. We further use an evolutionary technique for optimising the context caching probability to improve the context retrieval performance of the CMP. We conduct an experimental evaluation using a research prototype CMP, Context-as-a-Service (CoaaS) and show that the proposed technique can significantly improve the context retrieval performance. Analysis of the experimental results showed with context caching probability optimized by evolutionary technique there is an average percentage decrease of 43.68% in the response time of CoaaS.
ARMISTICE: Microarchitectural Leakage Modeling for Masked Software Formal Verification

Side-channel attacks are powerful attacks for retrieving secret data by exploiting physical measurements, such as power consumption or electromagnetic emissions. Masking is a popular countermeasure as it can be proven secure against an attacker model. In practice, software-masked implementations suffer from a security reduction due to a mismatch between the considered leakage sources in the security proof and the real ones, which depend on the microarchitecture. We propose ARMISTICE, a framework for formally verifying the absence of leakage in first-order masked implementations taking into account modeled microarchitectural sources of leakage. As a proof of concept, we present the modeling of an Arm Cortex-M3 core from its RTL description and leakage test vectors, as well as the modeling of the memory of an STM32F1 board, exclusively using leakage test vectors. We show that, with these models, ARMISTICE pinpoints vulnerable instructions in real-world masked implementations and helps the design of masked software implementations which are practically secure.
Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA

Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs is the learning process. This process is about finding optimal values in the search space for different datasets. In this process, the values generated by training algorithms are used as network parameters and are directly effective in the success of the neural network (NN). In classical training techniques, problems such as local optimum and slow convergence are encountered. Meta-heuristic algorithms for the training of ANNs in the face of this negative situation have been used in many studies as an alternative. In this study, a new hybrid algorithm namely MVOSANN is suggested for the training of ANNs, using Simulated annealing (SA) and Multi-verse optimizer (MVO) algorithms. The suggested MVOSANN algorithm has been experimented on 12 prevalently classification datasets. The productivity of MVOSANN has been compared with 12 well-recognized and current meta-heuristic algorithms. Experimental results show that MVOSANN produces very successful and competitive results.