Reviewerly/exHarmony huggingface dataset:
Comparative performance analysis of intelligent controllers for solar tracking system

Solar Energy is the cleanest, cheapest and the most abundant form of renewable energy source that could complement the conventional fossil fuels but the continuous change in the position of the sun relative to earth reduces the power produced by it. In order to overcome this problem, solar tracking system are used to increase the efficiency of the photo voltaic panel. The conventional PID controllers are used for controlling linear and negligible lag system. For controlling non linear system the conventional controllers does not work effectively, therefore for such system intelligent control techniques are needed. This paper elucidates the performance comparative analysis of three intelligent controllers based on Artificial Neural Network which are Model Predictive Controller, Nonlinear Auto Regressive Moving Average (NARMA-L2) Controller and Adaptive-Network-Based Fuzzy Inference System (ANFIS) Controller. The simulation of the above controllers are done using MATLAB/ SIMULINK.
Interactive Query Clarification and Refinement via User Simulation

When users initiate search sessions, their query are often ambiguous or might lack of context; this resulting in non-efficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve documents aligned with users' intents. While some work focus on query disambiguation using users' browsing history, a recent line of work proposes to interact with users by asking clarification questions or/and proposing clarification panels. However, these approaches count either a limited number (i.e., 1) of interactions with user or log-based interactions. In this paper, we propose and evaluate a fully simulated query clarification framework allowing multi-turn interactions between IR systems and user agents.
Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation

In real-world recommendation systems, the preferences of users are often affected by long-term constant interests and short-term temporal needs. The recently proposed Transformer-based models have proved superior in the sequential recommendation, modeling temporal dynamics globally via the remarkable self-attention mechanism. However, all equivalent item-item interactions in original self-attention are cumbersome, failing to capture the drifting of users' local preferences, which contain abundant short-term patterns. In this paper, we propose a novel interpretable convolutional self-attention, which efficiently captures both short- and long-term patterns with a progressive attention distribution. Specifically, a down-sampling convolution module is proposed to segment the overall long behavior sequence into a series of local subsequences. Accordingly, the segments are interacted with each item in the self-attention layer to produce locality-aware contextual representations, during which the quadratic complexity in original self-attention is reduced to nearly linear complexity. Moreover, to further enhance the robust feature learning in the context of Transformers, an unsymmetrical positional encoding strategy is carefully designed. Extensive experiments are carried out on real-world datasets, \eg ML-1M, Amazon Books, and Yelp, indicating that the proposed method outperforms the state-of-the-art methods w.r.t. both effectiveness and efficiency.
ReMeDi

\AcpMDS aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of \acpMDS is hindered because of a lack of resources. In particular. \beginenumerate* [label=(\arabic*) ] \item there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and \item there is no set of established benchmarks for \acpMDS for multi-domain, multi-service medical dialogues. \endenumerate*
Retrieving data constraint implementations using fine-grained code patterns

Business rules are an important part of the requirements of software systems that are meant to support an organization. These rules describe the operations, definitions, and constraints that apply to the organization. Within the software system, business rules are often translated into constraints on the values that are required or allowed for data, called data constraints. Business rules are subject to frequent changes, which in turn require changes to the corresponding data constraints in the software. The ability to efficiently and precisely identify where data constraints are implemented in the source code is essential for performing such necessary changes.
LightSGCN

With both positive and negative links, signed graphs exist widely in the real world. Recently, signed graph neural networks (GNNs) have shown superior performance in the most common signed graph analysis task, i.e., link sign prediction. Existing signed GNNs follow the classic nonlinear-propagation paradigm in unsigned GNNs. However, several recent studies on unsigned GNNs have shown that such a paradigm increases training difficulty and even reduces performance in various unsigned graph analysis tasks. Meanwhile, most of the public real-world signed graph datasets do not provide node features. These motivate us to consider whether the existing complex model architecture is suitable.
Transformer-based highlights extraction from scientific papers

Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art. • We propose a novel Transformer-based Highlights Extractor (THExt, in short). • We achieve performance superior to state-of-the-art highlights extraction methods. • Section-level context encoding turns out to be very effective for sentence ranking.
What Is Immersion? Towards a Phenomenology of Virtual Reality

Abstract Although the importance of the concept of immersion in game studies is indisputable, its meaning remains imprecise and ambiguous. My goal here is to develop a phenomenological clarification of this concept. I begin by clarifying how immersion has been understood in game studies. I further contend that immersion in digital games should be recognized as one modality of immersion among others. This basic realization allows one to open a dialogue between game studies and phenomenology. I develop a phenomenological conception of immersion, which relies on Alfred Schutz’s phenomenology of multiple realities and Theodor Conrad’s phenomenology of immersion. Although such an approach provides us with a general conception of immersion, it does not clarify what specific features characterize immersion in digital games. I argue that this form of immersion is a hybrid phenomenon, which shares certain features with immersion in non-digital games and other features with immersion in other types of digital media. I further demonstrate that immersion in digital games is characterized by a specific function of embodiment. With this in mind, I conclude my analysis by introducing a phenomenologically grounded distinction between actual and virtual embodiment, thereby clarifying in which sense immersion in digital games is an embodied and a disembodied experience.
Multi-path Convolutional Neural Network to Identify Tumorous Sub-classes for Breast Tissue from Histopathological Images

Malignancy is one of the leading causes of death. It is on the rise in the developed and low-income countries with survival rates of less than 40%. However, early diagnosis may increase survival chances. Histopathology images acquired from the biopsy are a popular method for cancer diagnosis. In this work, we propose a deep convolutional neural network-based method that helps classify breast cancer tumor subtypes from histopathology images. The model is trained on the BreakHis dataset but is also tested on images from other datasets. The model is trained to recognized eight different tumor subtypes, and also to perform binary classification (malignant/non-malignant). The CNN model combines an encoder–decoder architecture and a parallel feed-forward network with attention mechanism. The proposed model provides state-of-the-art scores. Comparing with the other models, the accuracy of the proposed model is higher at different magnification and patient levels. The implementation is available at github.com/rangan2510/Residual $$\_$$ Unet
Indoor Navigation System for Visually Impaired People using Computer Vision

This paper studies an indoor navigation guidance system for visually impaired people using Artificial Intelligence (AI) and computer vision techniques to guide users via optimal path based on quick response (QR) code markers and collision avoidance system supported by the monocular depth estimation algorithm. The proposed system utilizes a set of QR code markers, as location beacons, to generate an optimal path for the user to reach the destination. The identified QR code markers are used to correlate and compare information from the online database containing valuable information such as current location information, and the next possible locations from the current location. In addition, our system embraces a safety feature, a collision avoidance system, by utilizing the monocular depth estimation algorithm to identify obstacles in the path to the desired destination. All obtained information is provided to the user through a text-to-speech engine, where the system can direct the user to the optimal path via audio output.
Continuous situation-specific development of business models: knowledge provision, method composition, and method enactment

Abstract The development of new business models is essential for startups to become successful, as well as for established companies to explore new business opportunities. However, developing such business models is a continuous challenging activity where different tasks need to be performed, and business decisions need to be made. Both have to fit the constantly changeable situation in which the business model is developed to reduce the risk of developing ineffective business models with low market penetration. Therefore, a method for developing situation-specific business models is needed. As a solution, we refine the concept of situational method engineering (SME) to business model development. SME, in turn, provides means to construct situation-specific development methods out of fragments from a method repository. We develop a concept for the continuous situation-specific development of business models based on design science. The approach uses the roles of a domain expert, a method engineer, and a business developer together with a repository with method fragments for developing business models and a repository with modeling artifacts for supporting the development. Both repositories are filled by utilizing the experience of domain experts. Out of these repositories, situation-specific development methods for developing business models can be continuously composed based on the changeable situation by the method engineer and enacted by the business developer. We implement it as an open-source tool and evaluate its applicability in an industrial case study of developing a business model for a local event platform. Our results show that situation awareness supports the continuous development of business models.
Patterns of interest change in stack overflow

Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user's interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user's interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists' interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users' reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.
Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.
Training pre-service teachers to enhanced digital education

Teachers must be digitally competent at the beginning of their career. The main aim of this paper is to study whether specific training is necessary to enable pre-service teachers in Catalonia with the required level of teacher digital competence (TDC). Three cases are discussed. In the first case, TDC is self-assessed among first-year pre-service teachers (N = 1166). In the second, TDC is assessed among second year pre-service teachers who received specific training (N = 33). The third case measures TDC among third-year, pre-service teachers without any specific training in TDC (N = 151). Results reveal that pre-service teachers who are given specific training in TDC get better scores in the assessment compared to those who do not receive training. Practical implications on formative assessment emerge: specific training in TDC in higher education curricula must be practical and address methodological aspects of TDC.
Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.
ROGUE: A System for Exploratory Search of GANs

Image retrieval from generative adversarial networks (GANs) is challenging for several reasons. First, there are no clear mappings between the GAN's latent space and useful semantic features, making it difficult for users to navigate. Second, the number of unique images that can be generated is exceptionally high, taxing the scaling properties of existing search algorithms. In this article, we present ROGUE, a system to support exploratory search of images generated from GANs. We demonstrate how to implement features that are commonly found in exploratory search interfaces, such as faceted search and relevance feedback, in the context of GAN search. We additionally use reinforcement learning to help users navigate the image space [8], trading off exploration (showing diverse images) and exploitation (showing images predicted to receive positive relevance feedback). Finally, we present a usability study where participants were situated in the role of a casting director who needs to explore actors' headshots for an upcoming movie. The system obtained an average SUS score of 72.8 and all participants reported being either satisfied or very satisfied with the images they identified with the system. The system is shown in this accompanying video: https://vimeo.com/680036160.
I Want to Know Your Hand

We present HoldPass, the first system that can authenticate a user while they simply hold their phone. It uses the heart activity as biometric trait sensed via the hand vibrations in response to the cardiac cycle - a process known as ballistocardiography (BCG). While heart activity has been used for biometric authentication, sensing it through hand-based ballistocardiography (Hand-BCG) using standard sensors found on commodity mobile phones is an uncharted territory. Using a combination of in-depth qualitative analysis and large-scale quantitative analysis involving over 100 volunteers, we paint a detailed picture of opportunities and challenges. Authentication based on Hand-BCG is shown to be feasible but the signal is weak, uniquely prone to motion artifacts and does not land itself to the common approach of alignment-based authentication. HoldPass addresses these challenges by introducing a novel alignment-free authentication scheme that builds on asynchronous signal slicing and a data-driven algorithm for identifying a reduced set of features for characterizing a user. We implement HoldPass and evaluate it using a multi-modal approach: a large-case study involving 112 volunteers and targeted studies with a smaller set of volunteers over a period of several months. The data shows that HoldPass provides an authentication accuracy and user experience on par with or better than state-of-the-art systems with stronger requirements on hardware and/or user participation.
Data Breaches: Vulnerable Privacy

Data breaches are becoming the common news headlines nowadays. The organizations are incurring bulk amount of money and efforts, right after the data breaches, expenses, and efforts from lawsuits, installing cyber security measures and organization policy changes. This study looks at a few of the most prominent data breaches that have occurred in recent history and provides a detailed analysis of some of the factors that lead up to the breaches. With a higher frequency of data breaches taking place, this study also focuses on statistics regarding the size and number of breaches that have occurred over time and provides some of the technique’s companies use to secure their data. It was found that neither the size nor frequency of data breaches had increased over time, but it is possible that their impact has.
Educational Behaviour Analysis Using Convolutional Neural Network and Particle Swarm Optimization Algorithm

With the continuous development of online technology, online education has become a trend. To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. At the same time, the hyperparameters of the convolutional neural network model are adaptively adjusted based on the particle swarm algorithm to improve the model recognition accuracy further. Through the experimental validation on NTU-RGB + D and NTU-RGB + D120 data set, the recognition accuracy of this paper is 88.8% for cross-subject (CS), 94.7% for cross-view (CV), 82.8% for cross-subject (CSub) 83.2%, and 84.3% for cross-setup (CSet), respectively. The experimental results show that the algorithm in this paper is an effective method for educational behaviur recognition.