Reviewerly/exHarmony huggingface dataset:
Blockchain Frameworks

The blockchain is fascinating in many areas such as finance, healthcare, governance, security and many more. The underlying principle of the blockchain is based on distributed ledgers that solves many existing problems. Every blockchain framework has a different objective and application. There are many important criteria that must be considered during the selection of blockchain framework. Every blockchain framework and development platform has different pros and cons. In this chapter we present some of the leading enterprise frameworks for implementing blockchain solution. The enterprise support, pros and cons and transaction model are few important parameters to select the framework. In this chapter we present detailed investigation on blockchain frameworks that are publicly available.
Scikit-Learn Classifier Tuning from Simple Training Sets

Tuning is the process of maximizing an algorithm’s performance without overfitting, underfitting, or creating high variance. Overfitting is when an algorithm trains data so exactly that it may fail to fit new data or predict future results reliably. Overfitting usually occurs when a model is too complex for the data it is trying to train. An overly complex model trains data very well, but also fits noise that is not a part of the data. So, when used to train new data, noise is introduced causing unpredictable results.
Ontological Categories and the Problem of Expressibility with Øystein Linnebo

Frege famously held that ontological categories correspond to logico-syntactic types. Something is an object just in case it can be referred to by a singular term, and likewise for all the other ontological categories. However, this view faces an expressibility problem. In order to express the view, we need to generalize across ontological categories; but according to the view itself, any one variable can only range over a single ontological category. This chapter provides a sharp formulation of the problem of expressibility. It shows that there is no easy way out, and then explores some of the hard ways out.
SoK: Authentication in Augmented and Virtual Reality

Augmented reality (AR) and virtual reality (VR) devices are emerging as prominent contenders to today’s personal computers. As personal devices, users will use AR and VR to store and access their sensitive data and thus will need secure and usable ways to authenticate. In this paper, we evaluate the state-of-the-art of authentication mechanisms for AR/VR devices by systematizing research efforts and practical deployments. By studying users’ experiences with authentication on AR and VR, we gain insight into the important properties needed for authentication on these devices. We then use these properties to perform a comprehensive evaluation of AR/VR authentication mechanisms both proposed in literature and used in practice. In all, we synthesize a coherent picture of the current state of authentication mechanisms for AR/VR devices. We draw on our findings to provide concrete research directions and advice on implementing and evaluating future authentication methods.
Revealing the state of the art of large-scale agile development research: A systematic mapping study

Success with agile methods in the small scale has led to an increasing adoption also in large development undertakings and organizations. Recent years have also seen an increasing amount of primary research on the topic, as well as a number of systematic literature reviews. However, there is no systematic overview of the whole research field. This work identifies, classifies, and evaluates the state of the art of research in large-scale agile development. We conducted a systematic mapping study and rigorously selected 136 studies. We designed a classification framework and extracted key information from the studies. We synthesized the obtained data and created an overview of the state of the art. This work contributes with (i) a description of large-scale agile endeavors reported in the industry, (ii) a systematic map of existing research in the field, (iii) an overview of influential studies, (iv) an overview of the central research themes, and (v) a research agenda for future research. This study portrays the state of the art in large-scale agile development and offers researchers and practitioners a reflection of the past thirteen years of research and practice on the large-scale application of agile methods.
Applications of artificial intelligence in B2B marketing: Challenges and future directions

With the growing popularity of artificial intelligence (AI) transforming business-to-business (B2B) marketing, there is a growing demand to comprehensively understand the adoption and application of AI to advance B2B marketing. This study examines AI methods and their applications in B2B marketing across the four customer life cycle stages of reach, acquisition, conversion, and retention. The paper also analyzes and synthesizes the findings of five B2B industry surveys conducted to do the following: 1) examine B2B marketers' knowledge and attitudes toward using AI in their businesses, 2) determine the various ways in which AI is used in B2B marketing, and 3) investigate the perceived merits and challenges of using AI in B2B marketing. The findings reconcile various machine learning (ML) techniques suitable for use by B2B marketers. Employing the technology acceptance model (TAM), the paper identifies how B2B marketers perceive the benefits of AI adoption. Furthermore, this study discusses the perceived barriers to AI adoption, including data privacy challenges and the replacement of human workforces. To further highlight the benefits of AI, the study showcases three examples of successful AI adoption in B2B marketing. The paper concludes by summarizing the theoretical and managerial implications of AI adoption in B2B marketing and directions for future studies.
Underwater Target Detection Based on Improved YOLOv7

Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks.
Understanding and Improving Consumer Reactions to Service Bots

Abstract Many firms are beginning to replace customer service employees with bots, from humanoid service robots to digital chatbots. Using real human–bot interactions in lab and field settings, we study consumers’ evaluations of bot-provided service. We find that service evaluations are more negative when the service provider is a bot versus a human—even when the provided service is identical. This effect is explained by consumers’ belief that service automation is motivated by firm benefits (i.e., cutting costs) at the expense of customer benefits (such as service quality). The effect is eliminated when firms share the economic surplus derived from automation with consumers through price discounts. The effect is reversed when service bots provide unambiguously superior service to human employees—a scenario that may soon become reality. Consumers’ default reactions to service bots are therefore largely negative but can be equal to or better than reactions to human service providers if firms can demonstrate how automation benefits consumers.
Beyond Whittle: Nonparametric Correction of a Parametric Likelihood with a Focus on Bayesian Time Series Analysis

Nonparametric Bayesian inference has seen a rapid growth over the last decade but only few nonparametric Bayesian approaches to time series analysis have been developed.Most existing approaches use Whittle's likelihood for Bayesian modelling of the spectral density as the main nonparametric characteristic of stationary time series.It is known that the loss of efficiency using Whittle's likelihood can be substantial.On the other hand, parametric methods are more powerful than nonparametric methods if the observed time series is close to the considered model class but fail if the model is misspecified.Therefore, we suggest a nonparametric correction of a parametric likelihood that takes advantage of the efficiency of parametric models while mitigating sensitivities through a nonparametric amendment.We use a nonparametric Bernstein polynomial prior on the spectral density with weights induced by a Dirichlet process and prove posterior consistency for Gaussian stationary time series.Bayesian posterior computations are implemented via an MH-within-Gibbs sampler and the performance of the nonparametrically corrected likelihood for Gaussian time series is illustrated in a simulation study and in three astronomy applications, including estimating the spectral density of gravitational wave data from the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO).
A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women over age 50 with BI-RADS 3, 4, or 5 Assessment

Abstract Purpose: With improvements in breast cancer imaging, there has been a corresponding increase in false-positives and avoidable biopsies. There is a need to better differentiate when a breast biopsy is warranted and determine appropriate follow-up. This study describes the design and clinical performance of a combinatorial proteomic biomarker assay (CPBA), Videssa Breast, in women over age 50 years. Experimental Design: A BI-RADS 3, 4, or 5 assessment was required for clinical trial enrollment. Serum was collected prior to breast biopsy and subjects were followed for 6–12 months and clinically relevant outcomes were recorded. Samples were split into training (70%) and validation (30%) cohorts with an approximate 1:4 case:control ratio in both arms. Results: A CPBA that combines biomarker data with patient clinical data was developed using a training cohort (469 women, cancer incidence: 18.5%), resulting in 94% sensitivity and 97% negative predictive value (NPV). Independent validation of the final algorithm in 194 subjects (breast cancer incidence: 19.6%) demonstrated a sensitivity of 95% and a NPV of 97%. When combined with previously published data for women under age 50, Videssa Breast achieves a comprehensive 93% sensitivity and 98% NPV in a population of women ages 25–75. Had Videssa Breast results been incorporated into the clinical workflow, approximately 45% of biopsies might have been avoided. Conclusions: Videssa Breast combines serum biomarkers with clinical patient characteristics to provide clinicians with additional information for patients with indeterminate breast imaging results, potentially reducing false-positive breast biopsies.
A nonlinear second-order filtering strategy for state estimation of uncertain systems

In this paper, a new strategy referred to as the nonlinear second-order (NSO) filter is presented and used for estimation of linear and nonlinear systems in the presence of uncertainties. Similar to the popular Kalman filter estimation strategy, the proposed strategy is model-based and formulated as a predictor-corrector. The NSO filter is based on variable structure theory that utilizes a switching term and gain that ensures some level of estimation stability. It offers improvements in terms of robustness to modeling uncertainties and errors. The proof of stability is derived based on Lyapunov that demonstrates convergence of estimates towards the true state values. The proposed filtering strategy is based on a second-order Markov process that utilizes information from the current and past two time steps. An experimental system was setup and characterized in order to demonstrate the proposed filtering strategy's performance. The strategy was compared with the popular Kalman filter (and its nonlinear form) and the smooth variable structure filter (SVSF). Experimental results demonstrate that the proposed nonlinear second-order filter provides improvements in terms of state estimation accuracy and robustness to modeling uncertainties and external disturbances.
Taming the Cross Entropy Loss

We present the Tamed Cross Entropy (TCE) loss function, a robust derivative of the standard Cross Entropy (CE) loss used in deep learning for classification tasks. However, unlike other robust losses, the TCE loss is designed to exhibit the same training properties than the CE loss in noiseless scenarios. Therefore, the TCE loss requires no modification on the training regime compared to the CE loss and, in consequence, can be applied in all applications where the CE loss is currently used. We evaluate the TCE loss using the ResNet architecture on four image datasets that we artificially contaminated with various levels of label noise. The TCE loss outperforms the CE loss in every tested scenario.
PAVAL: A location-aware virtual personal assistant for retrieving geolocated points of interest and location-based services

information retrieval Geoparsing Geocoding A B S T R A C TToday most of the users on the move require contextualized local and georeferenced information.Several solutions aim to meet these trends, thus assisting users and satisfying their needs and preferences, such as virtual assistants and Location-Aware Recommender Systems (LARS), both in commercial and research literature.However, general purpose virtual assistants usually have to manage large domains, dealing with big amounts of data and online resources, losing focus on more specific requirements and local information.On the other hand, traditional recommender systems are based on filtering techniques and contextual knowledge, and they usually do not rely on Natural Language Processing (NLP) features on users' queries, which are useful to understand and contextualize users' necessities on the spot.Therefore, comprehending the actual users' information needs and other key information that can be included in the user query, such as geographical references, is a challenging task which is not yet fully accomplished by current state-of-the-art solutions.In this paper, we propose Paval (Location-Aware Virtual Personal Assistant 2 ), a semantic assisting engine for suggesting local points of interest (POIs) and services by analyzing users' natural language queries, in order to estimate the information need and potential geographic references expressed by the users.The system exploits NLP and semantic techniques providing as output recommendations on local geolocated POIs and services which best match the users' requests, retrieved by querying our semantic Km4City Knowledge Base.The proposed system is validated against the most popular virtual assistants, such as Google Assistant, Apple Siri and Microsoft Cortana, focusing the assessment on the request of geolocated POIs and services, showing very promising capabilities in successfully estimating the users' information needs and multiple geographic references.
MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis

The visual analysis of peripheral blood samples is an important test for blood illnesses diagnosis, like leukaemia or malaria. Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome these issues, numerous methods of automatic malaria diagnosis have been proposed so far. Unfortunately, no public image dataset is available to test and compare such algorithms. The aim of this paper is to present the first public dataset of blood samples afflicted by malaria, specifically designed to evaluate and compare algorithms for segmentation and classification of malaria parasite species. Every image is provided with its related ground truth and parasite’s classification of type and stage of life. Our purpose is to offer a new comparative test tool to the image processing and pattern matching communities, in order to encourage and improve computer-aided malaria parasites analysis.
Wireless sensing for a solar power system

Wireless sensing is an excellent approach for remotely operated solar power system. Not only being able to get the sensor data, such as voltage, current, and temperature, the system can also have a proper control for tracking the Sun and sensing real-time data from a controller. In order to absorb the maximum energy by solar cells, it needs to track the Sun with proper angles. Arduino, H-bridge motor driver circuit, and Direct Current (DC) motor are used to alter the tilt angle of the solar PhotoVoltaic (PV) panel following the Sun while the azimuth and the elevation angles are fixed at noon. Unlike the traditional way, the tilt rotation is proposed to be stepped hourly. The solar PV panel is tilted 7.5∘ in advance of current time to the west to produce more output voltage during an hour. As a result, the system is simple while providing good solar-tracking results and efficient power outputs.
Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets

In this paper, we propose a novel deep model for unbalanced distribution Character Recognition by employing focal loss based connectionist temporal classification (CTC) function. Previous works utilize Traditional CTC to compute prediction losses. However, some datasets may consist of extremely unbalanced samples, such as Chinese. In other words, both training and testing sets contain large amounts of low-frequent samples. The low-frequent samples have very limited influence on the model during training. To solve this issue, we modify the traditional CTC by fusing focal loss with it and thus make the model attend to the low-frequent samples at training stage. In order to demonstrate the advantage of the proposed method, we conduct experiments on two types of datasets: synthetic and real image sequence datasets. The results on both datasets demonstrate that the proposed focal CTC loss function achieves desired performance on unbalanced datasets. Specifically, our method outperforms traditional CTC by 3 to 9 percentages in accuracy on average.
Blind Image Watermarking Based on Adaptive Data Spreading in<i> n</i>-Level DWT Subbands

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n -level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.
Mobile Learning System for Egyptian Higher Education Using Agile-Based Approach

Nowadays, due to easiness and expansion in property of smart mobile devices, it is becoming inevitable for mobile applications to have an important role in higher education systems. The Egyptian public universities are facing the problem of students’ large number enrolled in each year. Thus, we lack proper communication between educators and learners. Mobile learning can solve that problem, and it enables adjustment of the curriculum to meet students' learning time and life situations. It provides different solutions better than traditional educational methods. Students and professors could exchange educational material or information even if they are not in the same class. Furthermore, the cost of universities’ materials reduced, as all course materials can be found online through mobile applications. This paper proposes a mobile learning system named “Easy-Edu.” The proposed system intended to make the learning process easier, focus on students’ needs, and encourage communication and collaboration between students and professors and supports collaborative scenario-based learning for university students. Unlike other traditional systems, the proposed “Easy-Edu” was built using an Agile-based approach that delivers sustainable and high-quality mobile learning system. In addition, it eliminates the chances of absolute system failure and detects and fixes issues faster. Summarily, everything related to the design and implementation of “Easy-Edu” is discussed.
Rearranging Online Tubes for Streaming Video Synopsis: A Dynamic Graph Coloring Approach

To efficiently browse long surveillance videos, the video synopsis technique is often used to rearrange tubes (i.e., tracks of moving objects) along the temporal axis to form a much shorter video. In this process, two key issues need to be addressed, i.e., the minimization of spatial tube collision and the maximization of temporal video condensation. In addition, when a surveillance video comes as a stream, an online algorithm with the capability of dynamically rearranging tubes is also required. Toward this end, this paper proposes a novel graph-based tube rearrangement approach for online video synopsis. The relationships among tubes are modeled with a dynamic graph, whose nodes (i.e., object masks of tubes) and edges (i.e., relationships) can be progressively inserted and updated. Based on this graph, we propose a dynamic graph coloring algorithm to efficiently rearrange all tubes by determining when they should appear. Extensive experimental results show that our approach can condense online surveillance video streams in real time with less tube collision and high compact ratio.