Reviewerly/exHarmony huggingface dataset:
Corpus linguistics

Corpus linguistics uses large collections of both spoken and written natural texts that are stored on computers. Corpus linguistics provides an extremely powerful tool for the analysis of natural language and can provide tremendous insights as to how language use varies in different situations, such as spoken versus written, or formal interactions versus casual conversation. One of the most important factors in corpus linguistics is the design of the corpus. A well-designed corpus should aim to be representative of the types of language included in it, but there are many different ways to conceive of and justify representativeness. When creating a corpus, data collection involves obtaining or creating electronic versions of the target texts, and storing and organizing them. Data collection for a written corpus most commonly means using a scanner and optical character recognition software to scan paper documents into electronic text files.
Anomaly IoT Node Detection Based on Local Outlier Factor and Time Series

The heterogeneous nodes in the Internet of Things (IoT) are relatively weak in the computing power and storage capacity. Therefore, traditional algorithms of network security are not suitable for the IoT. Once these nodes alternate between normal behavior and anomaly behavior, it is difficult to identify and isolate them by the network system in a short time, thus the data transmission accuracy and the integrity of the network function will be affected negatively. Based on the characteristics of IoT, a lightweight local outlier factor detection method is used for node detection. In order to further determine whether the nodes are an anomaly or not, the varying behavior of those nodes in terms of time is considered in this research, and a time series method is used to make the system respond to the randomness and selectiveness of anomaly behavior nodes effectively in a short period of time. Simulation results show that the proposed method can improve the accuracy of the data transmitted by the network and achieve better performance.
A Multi-Objective Approach for Materialized View Selection

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.
DevOps

DevOps has become a trending topic in the tech industry over the last decade, gaining the attention of both developers and the C-suite. Despite some legitimate debate over what it does and does not entail, this interest in DevOps has elevated the discussion around software development practices and the impact they can have on how businesses rise and fall. Figure 3-1 gives one indication of the acceleration of interest in the topic of DevOps over time.
The effect of strategic learning for human resources on dynamic capabilities of airlines companies in Kuwait

The aim of this study was to explore the impact of strategic learning on the dynamic capabilities of human resources in airline companies in Kuwait. The study sample consisted of all managers working at different levels in these airline companies. To collect the data, a questionnaire-based survey was used and the model was empirically tested using 110 responses from the airline companies in Kuwait and applying multiple regression tests using SPSS software. It was found that strategic learning had a significant impact on the dynamic capabilities of the human resources workforce in airline companies in Kuwait with regards to the four dimensions studied. Based on the study results, the researchers recommended that managers and decision makers in the airline industry in Kuwait should have the ability through various strategies to seize opportunities from companies that show great importance within the market because of their ability to increase the market share which was in turn reflected by an enhancement of their competitive position.
An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metric
Face Photo Recognition using Sketch Image for Security System

Because of expanding requests in application regions, for example, validation of law requirement, video reconnaissance, banking and access to security frameworks, programmed face acknowledgment has pulled in extraordinary consideration as of late. The acknowledgment of the face has ended up being one of the present essential applications. It is utilized fundamentally for computerized amusement, security and authorization. Face acknowledgment can be for the entire or fractional face, the entire face for the entire face identification and explicit highlights for the incomplete face discovery. The examination in most criminal cases relies upon the portrayals attracted by the observers ' depiction. The programmed recovery of photographs from the police mug-shot database that coordinate this depiction can conceivably diminish the quantity of suspects. Fundamentally, this procedure relies upon the onlooker and precision of the sketch craftsman's memory of the face to catch those subtleties. It can help scientists viably find or diminish potential suspects. By and large, be that as it may, a speculate's photograph picture isn't accessible and the best substitute is frequently a sketch drawing dependent on an observer's memory. We present another photograph recuperation framework utilizing face draws by changing a photograph picture into a sketch, fundamentally lessening the distinction among photograph and sketch, enabling the two to coordinate successfully.
PENGEMBANGAN SISTEM INFORMASI PELATIHAN BERBASIS WEB MENGGUNAKAN TEKNOLOGI WEB SERVICE DAN FRAMEWORK LARAVEL

Pincher ID is a local startup that engaged in the education sector especially for providing training classes. For now, Pincher ID based on a mobile application that causes only a third party (Google Play store/ App Store) who can access it. Furthermore, mobile application Pincher ID is not indexed by search engine so it makes difficult for people to find it. In this research, training information system web-based with web service technology and framework Laravel will be built. Web service technology applied to the system for data integration so data can be stored centrally. The architecture of web service used in the system is REST because accessing REST with URL address on browser application, give easy access to the application. In this research, prototype method used to develop an information system based on a website that can be used by Pincher ID. The test result showed that the training information system worked well and in accordance with the user’s wishes.
Asymmetric cryptosystem based on optical scanning cryptography and elliptic curve algorithm

We propose an asymmetric cryptosystem based on optical scanning cryptography (OSC) and elliptic curve cryptography (ECC) algorithm. In the encryption stage of OSC, an object is encrypted to cosine and sine holograms by two pupil functions calculated via ECC algorithm from sender's biometric image, which is sender's private key. With the ECC algorithm, these holograms are encrypted to ciphertext, which is sent to the receiver. In the stage of decryption, the encrypted holograms can be decrypted by receiver's biometric private key which is different from the sender's private key. The approach is an asymmetric cryptosystem which solves the problem of the management and dispatch of keys in OSC and has more security strength than the conventional OSC. The feasibility of the proposed method has been convincingly verified by numerical and experiment results.
Using IoT Assistive Technologies for Older People Non-Invasive Monitoring and Living Support in Their Homes

Many western societies are confronted with issues in planning and adapting their health policies due to an ageing population living alone. The "NOt Alone at Home-NOAH" project aimed to involve older people in the Agile co-creation of services for a collaborative monitoring and awareness notification for remote caregivers. Our research aim was to create a scalable and modern information system that permitted a non-invasive monitorization of the users for keeping their caregivers up to date. This was done via a cloud IoT (Internet of Things), which collects and processes data from its domotic sensors. The notifications generated by the system, via the three applications we developed (NOAH/NOAH Care/Admin Centre), offer caregivers an easy way of detecting changes in the day-to-day behaviour and activities of their patients, giving them time to intervene in case of abnormal activity. Such an approach would lead to a longer and more independent life for the older people. We evaluated our system by conducting a year-long pilot-study, offering caregivers constant information from the end-users while still living independently. For creating our pilot groups, we used the ABAS (Adaptive Behaviour Assessment System) II, which we then matched with the pre-profiled Behavioral Analysis Models of older people familiar with modern communication devices. Our results showed a low association between daily skills and the sensors we used, in contrast with the results from previous studies done in this field. Another result was efficiently capturing the behaviour changes that took place due to the COVID-19 Lockdown measures.
Mineralogical mapping using EO-1 Hyperion data for iron mine identification

The Tamera open pit mine is located in the Nefza mining district (north-west of Tunisia) where the reddish soil reveals its strong potential for heavy Fe mineralization, along with other metals (Pb, Zn, and Mn). This study explored Iron oxide mineralization in a particular nappe zone using multiscale field visible and near infrared/short waves infrared and EO-1 Hyperion hyperspectral data. Thus, spectral features fittings were conducted on Hyperion data for mineral mapping using scene-extracted endmembers. A first minimum noise fraction algorithm was used to reduce the hyperspectral data's dimensionality, followed by the Pixel Purity Index and n-dimensional visualization to extract the pure pixels (endmembers) from the cluster of pixels in the data cube. Furthermore, 56 soil samples were collected from the test site and spectrally measured using Analytical Spectral Devices FieldSpecHiRes spectrometer, which covers the spectral range between 350 and 2500 nm. All samples were analyzed in the laboratory with x-ray diffraction. Endmember extracted spectra were compared with field measurements before EO-1 Hyperion processing. Mapping results revealed the occurrence of a mineralogy composition consisting particularly of iron oxide minerals (hematite and goethite) and bearing minerals, such as clays (kaolinite), within all samples.
The stereo matching algorithm based on an improved adaptive support window

Abstract In binocular stereo matching, there has been a problem of low matching accuracy and noise immunity in discontinuous regions and weak-textured regions. This paper proposes a stereo matching algorithm based on an improved adaptive support window. In the cost computation stage, first, according to the preset arm length and colour threshold, a cross-based arm is obtained, which centres on the pixel to be matched; then the adaptive regions of the vertical arm and the horizontal arm are constructed respectively, which have different shape and size. Finally, the union of the two adaptive regions is used as the final support window of Census transform. Performance evaluations on Middlebury stereo data sets demonstrate that the proposed algorithm outperforms other seven most challenging stereo matching algorithms. The mismatching rate of this algorithm is greatly reduced, and the anti-noise performance is also improved considerably. Because the construction of the adaptive region is based on strict criteria and comprehensive consideration, the algorithm proposed in the paper can improve the matching accuracy in the weak-textured regions and discontinuous disparity regions.
Does Regulating Dietary Supplements as Food in a World of Social Media Influencers Promote Public Safety?

Social media influencers promote a wide variety of products, including dietary supplements. Dietary supplements are regulated as foods, not drugs, by the US Food and Drug Administration and the Federal Trade Commission. This article details weaknesses in administrative and common law regulatory approaches to addressing some influencers' negligent misrepresentation claims about dietary supplements.
Clustering compositional data using Dirichlet mixture model

A model-based clustering method for compositional data is explored in this article. Most methods for compositional data analysis require some kind of transformation. The proposed method builds a mixture model using Dirichlet distribution which works with the unit sum constraint. The mixture model uses a hard EM algorithm with some modification to overcome the problem of fast convergence with empty clusters. This work includes a rigorous simulation study to evaluate the performance of the proposed method over varied dimensions, number of clusters, and overlap. The performance of the model is also compared with other popular clustering algorithms often used for compositional data analysis (e.g. KMeans, Gaussian mixture model (GMM) Gaussian Mixture Model with Hard EM (Hard GMM), partition around medoids (PAM), Clustering Large Applications based on Randomized Search (CLARANS), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) etc.) for simulated data as well as two real data problems coming from the business and marketing domain and physical science domain, respectively. The study has shown promising results exploiting different distributional patterns of compositional data.
Neural network training with limited precision and asymmetric exponent

Abstract Along with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage and operational memory, but also computational power. Increasing complexity and variety of neural network architectures are vivid examples of such trends in the modern data-driven industry. In response to this situation, focusing on less demanding operations for inference and training of neural networks became a popular approach among many researchers to overcome resources related issues. This work aims to investigate one of the paths associated with the mentioned efficiency problems and shows the impact of floating-point precision limitation on convolutional neural networks, including experiments on various exponent and mantissa sizes. Additionally, authors explore floating-point numbers utilization and optimization techniques in the scope of neural network training. Based on conducted research a novel method of asymmetric exponent utilization is presented achieving almost identical accuracy of 32-bit floating-point parameters while training a neural network with only 12-bit variables without additional rounding.
Towards Universal Backward-Compatible Representation Learning

Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as “backfill”), which is time-consuming and expensive, especially in large-scale applications. The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features. Despite the success, previous works only investigated a close-set training scenario (i.e., the new training set shares the same classes as the old one), and are limited by more realistic and challenging open-set scenarios. To this end, we first introduce a new problem of universal backward-compatible representation learning, covering all possible data split in model upgrades. We further propose a simple yet effective method, dubbed as Universal Backward-Compatible Training (UniBCT) with a novel structural prototype refinement algorithm, to learn compatible representations in all kinds of model upgrading benchmarks in a unified manner. Comprehensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C fully demonstrate the effectiveness of our method. Source code is available at https://github.com/TencentARC/OpenCompatible.
Multi-perspective label based deep learning framework for cerebral vasculature segmentation in whole-brain fluorescence images

The popularity of fluorescent labelling and mesoscopic optical imaging techniques enable the acquisition of whole mammalian brain vasculature images at capillary resolution. Segmentation of the cerebrovascular network is essential for analyzing the cerebrovascular structure and revealing the pathogenesis of brain diseases. Existing deep learning methods use a single type of annotated labels with the same pixel weight to train the neural network and segment vessels. Due to the variation in the shape, density and brightness of vessels in whole-brain fluorescence images, it is difficult for a neural network trained with a single type of label to segment all vessels accurately. To address this problem, we proposed a deep learning cerebral vasculature segmentation framework based on multi-perspective labels. First, the pixels in the central region of thick vessels and the skeleton region of vessels were extracted separately using morphological operations based on the binary annotated labels to generate two different labels. Then, we designed a three-stage 3D convolutional neural network containing three sub-networks, namely thick-vessel enhancement network, vessel skeleton enhancement network and multi-channel fusion segmentation network. The first two sub-networks were trained by the two labels generated in the previous step, respectively, and pre-segmented the vessels. The third sub-network was responsible for fusing the pre-segmented results to precisely segment the vessels. We validated our method on two mouse cerebral vascular datasets generated by different fluorescence imaging modalities. The results showed that our method outperforms the state-of-the-art methods, and the proposed method can be applied to segment the vasculature on large-scale volumes.
AuxChannel

Payment channels have been a promising solution to blockchain scalability. While payment channels for script-empowered blockchains (such as Bitcoin and Ethereum) have been well studied, developing payment channels for scriptless blockchains (such as Monero) is considered challenging. In particular, nabling bidirectional payment on scriptless blockchains remains an open challenge.
SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.
Watermarking PRFs Against Quantum Adversaries

We initiate the study of software watermarking against quantum adversaries. A quantum adversary generates a quantum state as a pirate software that potentially removes an embedded message from a classical marked software. Extracting an embedded message from quantum pirate software is difficult since measurement could irreversibly alter the quantum state. In software watermarking against classical adversaries, a message extraction algorithm crucially uses the (input-output) behavior of a classical pirate software to extract an embedded message. Even if we instantiate existing watermarking PRFs with quantum-safe building blocks, it is not clear whether they are secure against quantum adversaries due to the quantum-specific property above. Thus, we need entirely new techniques to achieve software watermarking against quantum adversaries. In this work, we define secure watermarking PRFs for quantum adversaries (unremovability against quantum adversaries). We also present two watermarking PRFs as follows. We develop a quantum extraction technique to extract information (a classical string) from a quantum state without destroying the state too much. We also introduce the notion of extraction-less watermarking PRFs as a crucial building block to achieve the results above by combining the tool with our quantum extraction technique.