Reviewerly/exHarmony huggingface dataset:
C-PAK: Correcting and Completing Variable-Length Prefix-Based Abbreviated Keystrokes

Improving keystroke savings is a long-term goal of text input research. We present a study into the design space of an abbreviated style of text input called C-PAK (Correcting and completing variable-length Prefix-based Abbreviated Keystrokes) for text entry on mobile devices. Given a variable length and potentially inaccurate input string (e.g., “li g t m”), C-PAK aims to expand it into a complete phrase (e.g., “looks good to me”). We develop a C-PAK prototype keyboard, PhraseWriter , based on a current state-of-the-art mobile keyboard consisting of 1.3 million n -grams and 164,000 words. Using computational simulations on a large dataset of realistic input text, we found that, in comparison to conventional single-word suggestions, PhraseWriter improves the maximum keystroke savings rate by 6.7% (from 46.3% to 49.4,), reduces the word error rate by 14.7%, and is particularly advantageous for common phrases. We conducted a lab study of novice user behavior and performance which found that users could quickly utilize the C-PAK style abbreviations implemented in PhraseWriter, achieving a higher keystroke savings rate than forward suggestions (25% vs. 16%). Furthermore, they intuitively and successfully abbreviated more with common phrases. However, users had a lower overall text entry rate due to their limited experience with the system (28.5 words per minute vs. 37.7). We outline future technical directions to improve C-PAK over the PhraseWriter baseline, and further opportunities to study the perceptual, cognitive, and physical action trade-offs that underlie the learning curve of C-PAK systems.
Multisensor Fusion Estimation for Systems with Uncertain Measurements, Based on Reduced Dimension Hypercomplex Techniques

The prediction and smoothing fusion problems in multisensor systems with mixed uncertainties and correlated noises are addressed in the tessarine domain, under Tk-properness conditions. Bernoulli distributed random tessarine processes are introduced to describe one-step randomly delayed and missing measurements. Centralized and distributed fusion methods are applied in a Tk-proper setting, k=1,2, which considerably reduce the dimension of the processes involved. As a consequence, efficient centralized and distributed fusion prediction and smoothing algorithms are devised with a lower computational cost than that derived from a real formalism. The performance of these algorithms is analyzed by using numerical simulations where different uncertainty situations are considered: updated/delayed and missing measurements.
Diagnosis of anomalies based on hybrid features extraction in thyroid images

Diagnosing benign and malignant glands in thyroid ultrasound images is considered a challenging issue. Recently, deep learning techniques have significantly resulted in extracting features from medical images and classifying them. Convolutional neural networks ignore the hierarchical structure of entities within images and do not pay attention to spatial information as well as the need for a large number of training samples. Capsule networks consist of different hierarchical capsules equivalent to the same layers in the convolutional neural networks. We propose a feature extraction method for ultrasound images based on the capsule network. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradients and Local Binary Pattern together to form a hybrid feature space. We increase the accuracy percentage of a support vector machine (SVM) by balancing and reducing the data dimensions of samples. Since the SVM provides different training kernels according to the sample distribution method, the extracted textural features were categorized using each of these kernels to obtain the result. The parameters of classification evaluation using the researcher-made model have outperformed the other methods in this field. Experimental results showed that the combination of HOG, LBP, and CapsNet methods outperformed the others, with 83.95% accuracy in the SVM with a linear kernel.
A lightweight access management scheme for the internet of things based smart applications

Abstract The Internet of Things (IoT) houses a diverse range of applications and users, providing different services securely. Privacy and access management are the challenging processes in administering user‐end security measures. User management, density, and behavior levy a complicated security requirement. A lightweight access management scheme (LAMS) is projected in this article to address this issue. User access permissions and privacy are jointly handled by this scheme based on trust evaluation (TE) and access history. In this scheme, an interlinked relationship is constructed between the trust and access permission features in the IoT platform verified using the transfer learning paradigm. The previous TE is used for deciding the access delegation level for the current user application. In the privacy‐preserving process, amendable keys are used for securing the delegated access sessions alone. This key distribution is used for adapting varying session lengths, preventing privacy breaches. In this key generation and distribution, the elliptic curve cryptographic paradigm is used that adapts the session length and access permission relationship defined. The proposed scheme's performance is verified using 12.32% less access time, 7.16% less failure, 10.27% high sustainability, 11.98% less authentication time, and 10.47% less overhead for different session lengths. In addition to this, the method achieves 12.42% less access time, 8.4% less failure, 11.91% high sustainability, 10.9% less authentication time, and 10.34% less overhead for different users.
An Image Style Diversified Synthesis Method Based on Generative Adversarial Networks

Existing research shows that there are many mature methods for image conversion in different fields. However, when the existing methods deal with images in multiple image domains, the robustness and scalability of images are often limited. We propose a novel and scalable approach, using a generative adversarial networks (GANs) model that can transform images across multiple domains, to address the above limitations. Our model can be trained on image datasets with different domains in a single network, with the ability to translate images and the ability to flexibly translate input images to any desired target domain. Our model is mainly composed of a generator, discriminator, style encoder, and a mapping network. The datasets use the celebrity face dataset CelebA-HQ and the animal face dataset AFHQ, and the evaluation criteria use FID and LPIPS to evaluate the images generated by the model. Experiments show that our model can generate a rich variety of high-quality images, and there is still some room for improvement.
Privacy-Preserving, Scalable Blockchain-Based Solution for Monitoring Industrial Infrastructure in the Near Real-Time

This paper proposes an improved monitoring and measuring system dedicated to industrial infrastructure. Our model achieves security of data by incorporating cryptographical methods and near real-time access by the use of virtual tree structure over records. The currently available blockchain networks are not very well adapted to tasks related to the continuous monitoring of the parameters of industrial installations. In the database systems delivered by default (the so-called world state), only the resultant or the last value recorded by the IoT device is stored. Effective use of measurement values recorded in the past requires each time viewing the entire chain of recorded events for a given IoT device. The solution proposed in the article introduces the concept of dependent wallets, the purpose of which is the aggregation and indexation of changes in machine parameters, recorded in the original wallets. As a result, we can easily get data from a certain sensor or sensors in the specified date range, even if the chain of transactions is very long. Our contribution is a universal mechanism that improves the efficiency of the infrastructure monitoring process, which uses blockchains to record measurements from sensors. The proposed model has been experimentally tested on two types of blockchains: Stellar and Hyperledger Fabric.
On turning black - into dark gray-optimization with the direct empirical linkage discovery and partition crossover

Gray-box optimization employs the knowledge about the true direct gene dependencies represented by the Variable Interaction Graph (VIG). This knowledge is utilized in many ways, e.g., for improving the fitness computation efficiency and proposing more effective operators for Genetic Algorithms (GAs). In the Black-box optimization, the underlying problem structure is not known. Therefore, linkage learning techniques were proposed to at least approximate gene relations and improve the evolutionary search. However, since these techniques are based on predictions, they were not suitable for building VIG. The proposition of Empirical Linkage Learning (ELL) has changed the situation. In ELL, the prediction that two genes are dependent is replaced by certainty. Additionally, ELL techniques are proven never to mark two independent genes as dependent. Therefore, we use ELL to build the empirical VIG and propose the fusion of ELL and the Gray-box operators. On this base, we propose two new mechanisms that detect (with certainty) the missing linkage between two groups of genes and the fact that the population is stuck. We integrate these mechanisms with a Gray-box operator (partition crossover) in the proposed Dark Gray Genetic Algorithm that is shown highly competitive to other state-of-the-art GAs.
Blockchain Technology in Supply Chain Management

Supply chain management has existed for thousands of years. Technology started evolving with the industrial revolution and yet, paper-based bills of lading are still widely used in the international trade today. This chapter explores what problems exist in today’s supply chains, and whether blockchain technology can help solve them. The problems are categorized into three categories: The origin of the raw material or the product is not obvious; trust issues can exist between seller and buyer; and the supply chain execution is inefficient. A brief explanation of blockchain technology follows as the second part, with a focus on the technology that is useful for this purpose of this article. The two parts come together in the third part, showing how blockchain technology can solve the problems. Finally, a critical view is taken to show the limitations of blockchain technology as a problem solver.
Improved regression models for algorithm configuration

Offline algorithm configuration methods search for fixed parameter values for a given set of problem instances. For each parameter, such methods perform an equivalent to a constant regression, since the parameter value remains constant for any problem instance. However, optimal parameter values may depend on instance features, such as the instance size. In this paper, we represent parameters by non-constant models, which set the parameter values according to the instance size. Instead of searching for parameter values directly, the configuration process calibrates such models. In particular, we propose a simple yet effective linear model, which approximates linear relations between instance size and optimal parameter values. For modeling nonlinear relations, we propose piecewise and log-log linear models. The evaluation of the proposed methods on four configuration scenarios show good performance gains in comparison to traditional instance-independent algorithm configuration with comparable tuning effort.
Using phylogenetic analysis to enhance genetic improvement

Genetic code improvement systems (GI) start from an existing piece of program code and search for alternative versions with better performance according to a metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas are remembered and avoided, or formal analysis which attempts to find high-utility parameterizations for the GI process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers affect the fitness of a population over time for a particular problem; we use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20% improved fitness compared to a baseline GI process.
Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression

Shape-constrained Symbolic Regression integrates prior knowledge about the function shape into the symbolic regression model. This can be used to enforce that the model has desired properties such as monotonicity, or convexity, among others. Shape-constrained Symbolic Regression can also help to create models with better extrapolation behavior and reduced sensitivity to noise. The constraint evaluation can be challenging because exact evaluation of constraints may require a search for the extrema of non-convex functions. Approximations via interval arithmetic allow to efficiently find bounds for the extrema of functions. However, interval arithmetic can lead to overly wide bounds and therefore produces a pessimistic estimation. Another possibility is to use sampling which underestimates the true range. Sampling therefore produces an optimistic estimation. In this paper we evaluate both methods and compare them on different problem instances. In particular we evaluate the sensitivity to noise and the extrapolation capabilities in combination with noise data. The results indicate that the optimistic approach works better for predicting out-of-domain points (extrapolation) and the pessimistic approach works better for high noise levels.
Hierarchical attention and transformers for automatic movie rating

The MPAA rating provides a guide for parents to decide if a movie is suitable for their children, and determines who is allowed into movie screenings. If the assigned rating does not match with that intended by the movie makers, the movie has to go through extra changes. Predicting this rating from the movie scripts would allow for the changes to be done even before the shooting starts, when they are the cheapest. Furthermore, automatizing this reviewing process would allow for cheaper large scale classification of videos from other sources, such as social media and streaming platforms. In this paper we propose RNN and Transformer based hierarchical architecture well suited to analyze movie scripts as large text sequences. The proposed RNN architecture outperforms the State-of-the-art (SOTA) by around 3 points in the F1 score, while our Hierarchical Transformer outperformed the SOTA in around 5 points. Furthermore, we devise a visualization strategy to address the problem of interpretability of transformers, which is particularly hard for large sequences.
Dual Gaussian Modeling for Deep Face Embeddings

Most currently existing face recognition methods model face images as deterministic points in the latent space. However, it could encounter performance drop inevitably in a fully unconstrained scenario for the intrinsic noise of the images. In order to mitigate the detrimental impact of noisy data on model training, distribution estimation is introduced to face recognition, which models each face image as a Gaussian distribution and improves the robustness against noise effectively. But the uncertainty (variance) learned only relates to one attribute: the quality of the image. We propose dual Gaussian modeling (DGM) for deep face embeddings. For an input image, the network learns two Gaussian distributions simultaneously. The main Gaussian branch focuses on learning easy samples in the training dataset, while the other one mainly deals with faces with large pose. The uncertainty is not only correlated with image quality, but also with facial pose. During training, a sample-specific adaptive weight is learned to integrate the two sub-Gaussian features into a more compact discriminant embedding for classification. Besides, we introduce weighted Euclidean distance and minimize the entropy of the adaptive weight to regulate the relationship between the two distributions. Comprehensive experiments and analysis demonstrate that our method can boost the performance of face recognition under common or more unconstrained benchmarks, such as IJB-C.
AutoVideo: An Automated Video Action Recognition System

Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https://github.com/datamllab/autovideo
Spectral unmixing analysis for the lunar surface mineralogy using Chandrayaan-2 IIRS imagery

Hyperspectral imagery (HSI) in the visible to near-infrared wavelength region has a high potential for deciphering mineral compositions of terrestrial and planetary surfaces. Thus, ISRO's Imaging Infrared Spectrometer (IIRS) sensor onboard Chandrayaan-2 (Ch-2) orbiter provides an opportunity to utilise the hyperspectral observations to characterise the lunar surface minerals and composition significantly. Hence, a sensitivity study of hyperspectral observations of the IIRS sensor was carried out for the detection and mapping of various lunar minerals using spectral unmixing analysis. A common region within the hyperspectral image obtained from Chandrayaan-1's (Ch-1) Moon Mineralogy Mapper (M3) sensor and Ch-2 IIRS sensor has been identified in the South-East of the Taurus-Littrow valley nearby Gardner crater for comparison and validation purposes. Ch-2 IIRS is an advanced hyperspectral imaging spectrometer capable of collecting spectra over a wider spectral range (800 ​nm ​− ​5000 ​nm) with high spatial (~80 m) and spectral resolution (20 n m ​− ​25 ​n m ). To examine spectral unmixing analysis, an electromagnetic range (800 ​nm ​− ​2500 ​nm) in the near-infrared region was chosen for the Ch-2 IIRS sensor. An L 1 ​− ​ norm based denoising algorithm is used for mixed noise removal from both the IIRS and M3 reflectance datasets. Endmember extraction and fractional abundance estimation are carried out using the N-Findr algorithm and the fully-constrained least square method, respectively. Our results reveal different minerals such as high-calcium pyroxene (Clinopyroxene), low-calcium pyroxene (Orthopyroxene), and regolith surface (matured and younger). The results obtained from the IIRS and M3 observations are in strong agreement. Hence, the hyperspectral observations made by the IIRS sensor are extremely effective for describing the mineral and chemical compositions of the lunar surface. • Spectral unmixing analysis is executed to determine lunar surface mineralogy and their abundance using Hyperspectral data. • For comparative analysis, co-located datasets from Chandrayaan-2 (Ch-2) IIRS and Chandrayaan-1(Ch-1) M3 are investigated. • An L1-norm-based denoising algorithm is introduced to significantly improve the data quality by removing the complex noise. • The methodology can be automated to develop the lunar mineralogical and chemical map in a time-efficient and systematic way.
Defining Recommendations to Guide User Interface Design: Multimethod Approach

For the development of digital solutions, different aspects of user interface design must be taken into consideration. Different technologies, interaction paradigms, user characteristics and needs, and interface design components are some of the aspects that designers and developers should pay attention to when designing a solution. Many user interface design recommendations for different digital solutions and user profiles are found in the literature, but these recommendations have numerous similarities, contradictions, and different levels of detail. A detailed critical analysis is needed that compares, evaluates, and validates existing recommendations and allows the definition of a practical set of recommendations.This study aimed to analyze and synthesize existing user interface design recommendations and propose a practical set of recommendations that guide the development of different technologies.Based on previous studies, a set of recommendations on user interface design was generated following 4 steps: (1) interview with user interface design experts; (2) analysis of the experts' feedback and drafting of a set of recommendations; (3) reanalysis of the shorter list of recommendations by a group of experts; and (4) refining and finalizing the list.The findings allowed us to define a set of 174 recommendations divided into 12 categories, according to usability principles, and organized into 2 levels of hierarchy: generic (69 recommendations) and specific (105 recommendations).This study shows that user interface design recommendations can be divided according to usability principles and organized into levels of detail. Moreover, this study reveals that some recommendations, as they address different technologies and interaction paradigms, need further work.
Blind Image Quality Measurement via Data-Driven Transform-Based Feature Enhancement

Blind image quality measurement (BIQM) attempts to measure image perceptual quality in the absence of reference ones. Based on the types of features, BIQM methods can be divided into two categories: hand-crafted features and learning-based features. In this paper, we present a data-driven transform based feature enhancement approach for BIQM that combines the portability of hand-crafted features with the high performance of learning-based features. Specifically, we first extract the image structural features from Karhunen-Lo&#x00E9;ve transform (KLT), phase congruency (PC) and gradient magnitude (GM) coefficients, and then natural scene statistics (NSS) feature from the local normalized coefficient. Then, we utilize KLT as features enhancement process to enhance the structural and NSS features, with Weibull distribution and generalized Gaussian distribution (GGD) modeling the distributions of transform coefficient in all frequency bands as quality-aware features. Finally, support vector regression is adopted to map the features to subjective scores. The proposed method has been compared favorably with state-of-the-art BIQM methods on seven widely utilized databases on both artificial and authentic distortion types and achieves highly competitive performance.