Reviewerly/exHarmony huggingface dataset:
Objective analysis of collagen organization in thyroid nodule capsules using second harmonic generation microscopy images and the Hough transform

Papillary carcinoma is the most prevalent type of thyroid cancer. Its diagnosis requires accurate and subjective analyses from expert pathologists. Here we propose a method based on the Hough transform (HT) to detect and objectively quantify local structural differences in collagen thyroid nodule capsules. Second harmonic generation (SHG) microscopy images were acquired on non-stained histological sections of capsule fragments surrounding the healthy thyroid gland and benign and tumoral/malignant nodules. The HT was applied to each SHG image to extract numerical information on the organization of the collagen architecture in the tissues under analysis. Results show that control thyroid capsule samples present a non-organized structure composed of wavy collagen distribution with local orientations. On the opposite, in capsules surrounding malignant nodules, a remodeling of the collagen network takes place and local undulations disappear, resulting in an aligned pattern with a global preferential orientation. The HT procedure was able to quantitatively differentiate thyroid capsules from capsules surrounding papillary thyroid carcinoma (PTC) nodules. Moreover, the algorithm also reveals that the collagen arrangement of the capsules surrounding benign nodules significantly differs from both the thyroid control and PTC nodule capsules. Combining SHG imaging with the HT results thus in an automatic and objective tool to discriminate between the pathological modifications that affect the capsules of thyroid nodules across the progressions of PTC, with potential to be used in clinical settings to complement current state-of-the-art diagnostic methods.
Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning

Developing a Dialogue/Virtual Agent (VA) that can handle complex tasks (need) of the user pertaining to multiple intents of a domain is challenging as it requires the agent to simultaneously deal with multiple subtasks. However, majority of these end-to-end dialogue systems incorporate only user semantics as inputs in the learning process and ignore other useful user behavior and information. Sentiment of the user at the time of conversation plays an important role in securing maximum user gratification. So, incorporating sentiment of the user during the policy learning becomes even more crucial, more so when serving composite tasks of the user.As a first step towards enabling the development of sentiment aided VA for multi-intent conversations, this paper proposes a new dataset, annotated with its corresponding intents, slot and sentiment (considering the entire dialogue history) labels, named SentiVA, collected from open-sourced dialogue datasets. In order to integrate these multiple aspects, a Hierarchical Reinforcement Learning (HRL) specifically options based VA is proposed to learn strategies for managing multi-intent conversations. Along with task success based immediate rewards, sentiment based immediate rewards are also incorporated in the hierarchical value functions to make the VA user adaptive.Empirically, the paper shows that task based and sentiment based immediate rewards cumulatively are required to ensure successful task completion and attain maximum user satisfaction in a multi-intent scenario instead of any of these rewards alone.The eventual evaluators and consumers of dialogue systems are users. Thus, to ensure a fulfilling conversational experience involving maximum user satisfaction requires VA to consider user sentiment at every time-step in its decision making policy.This work is the first attempt in incorporating sentiment based rewards in the HRL framework.
Four Sensors Bracelet for American Sign Language Recognition based on Wrist Force Myography

Recently, Force myography (FMG) has emerged as an alternate method for gesture recognition applications. It is usually used as raw signal, in combination with data acquired from other sensors or with a large number of sensors for an efficient recognition performances. Only four FMG commercial sensors constitute the proposed gesture recognition system. They are connected as an FSR sensor’s bracelet applied for american sign language(ASL) recognition. In this paper, a comparative study between raw FMG and six commonly extracted features when implemented on the Extreme Learning Machine (ELM) to assess the accuracy of nine ALS alphabet recognition system. 16 trials of the nine gestures were collected from a healthy male wearing the FMG bracelet. In addition, 5 folds cross validation was implemented during the ELM training. As results it was noted that the accuracy based on six commonly features was equal to 89.65%, which over perform not only the raw FMG based gesture recognition that reached only a testing total accuracy of 68.96% by our 4 sensors but also some 8 sensor’s raw data systems in literature.
Empirical studies on software traceability: A mapping study

Abstract During the last decades, software traceability has been studied in a large number of studies, from different perspectives (e.g., how to create traces and what are its benefits). This large body of knowledge needs to be better explored and exploited by both practitioners and researchers: We need an overview of different aspects of traceability and a structured way to assess and compare existing work in order to extend it with new research or apply it in practice, Thus, we have conducted a secondary study on this large corpus of primary studies, focusing on empirical studies on software traceability, without setting any further restrictions in terms of investigating a specific domain or concrete artifacts. The study explores the goals of existing approaches and the empirical methods used for their evaluation. Its main contributions are the investigation of (a) the type of artifacts linked through traceability approaches; (b) the benefits of using artifact traceability approaches; (c) the ways of measuring their benefit; and (d) the research methods used. The results of the study suggest that (i) requirements artifacts are dominant in traceability; (ii) the research corpus focuses on the proposal of novel techniques for establishing traceability; and (iii) the main benefits are the improvement of software correctness and maintainability. Finally, although many studies include some empirical validation, there is still room for improvements and research methods that can be used more extensively. The obtained results are discussed under the prism of both researchers and practitioners and are compared against the state‐of‐the‐art.
Emergent nonlinear phenomena in discrete-time quantum walks

Quantum walks are important tools for the development of quantum algorithms and carrying out quantum simulations. Recent interest in nonlinear discrete-time quantum walks aims to use it as a shortcut through dynamical regimes hard to obtain using current methods. We introduce a model featuring a modified conditional shift operator to carry dependence on the local occupation probability with a given strength we are able to control. It accounts for a third-order nonlinear contribution which is found in many physical contexts. We find a rich set of dynamical profiles, including solitonlike propagation, self-trapping, and chaos, all these arising from rather simple rules. Our tool set goes beyond unitary transformations, thus broadening the possibilities for controlling quantum dynamics.
Meta-Semi: A Meta-Learning Approach for Semi-Supervised Learning

Deep learning based semi-supervised learning (SSL) algorithms have led to promising results in recent years.However, they tend to introduce multiple tunable hyper-parameters, making them less practical in real SSL scenarios where the labeled data is scarce for extensive hyper-parameter search.In this paper, we propose a novel meta-learning based SSL algorithm (Meta-Semi) that requires tuning only one additional hyper-parameter, compared with a standard supervised deep learning algorithm, to achieve competitive performance under various conditions of SSL.We start by defining a meta optimization problem that minimizes the loss on labeled data through dynamically reweighting the loss on unlabeled samples, which are associated with soft pseudo labels during training.As the meta problem is computationally intensive to solve directly, we propose an efficient algorithm to dynamically obtain the approximate solutions.We show theoretically that Meta-Semi converges to the stationary point of the loss function on labeled data under mild conditions.Empirically, Meta-Semi outperforms state-of-the-art SSL algorithms significantly on the challenging semi-supervised CIFAR-100 and STL-10 tasks, and achieves competitive performance on CIFAR-10 and SVHN.
Play Design as a Relational Strategy to Intensify Affective Encounters in the Art Museum

This paper presents an exploration of play design as a relational strategy to intensify affective encounters during an art museum visit. Theoretically, the paper presents a foundation emphasising the relational aspects of designing playful museum experiences. Based on a detailed and contextual analysis of a mobile web app entitled 'Never let me go', designed to be used in art museums, we show how the app and infrastructure catalysed affective encounters and put the relations between the players, the architecture and the exhibited artworks into motion. In our analysis, we highlight four ways through which the players' experiences were intensified. Finally, we discuss the potential and concerns arising from working with relational play strategies in the design of affectively engaging museum experiences, emphasising emergence, intimacy, ambiguity and trust as key elements.
Project Us

Enhancing the empathy of our human interactions has been the object of intensive psychological studies for decades. The emergence of affective computing has opened the door towards technologically-enabled solutions. Yet, existing techniques struggle to attain their desired impact, often being difficult and expensive to deliver, and disconnected from daily life. Project Us' goal is to help overcome these challenges through a pair of wearable devices (in this case wristbands) that aim to trigger an empathy-enhancing effect, when being worn by two people during day-to-day conversations. The small-sized, wireless devices sense each person's electrodermal activity, associated with their level of emotional arousal, and share it to the other partner (when a threshold is exceeded) through a discreet, haptic nudge, creating a real-time feedback loop. The user study performed with 18 participants (nine romantically engaged couples) revealed that most of them found the wristbands to increase their level of awareness of the partner's emotional experience. Their interaction was analyzed based on interviews (qualitatively), and natural language processing techniques (quantitatively).
Image Enhancement Using Exposure and Standard Deviation-Based Sub-image Histogram Equalization for Night-time Images

In this paper, a novel exposure and standard deviation-based sub-image histogram equalization technique is proposed for the enhancement of low-contrast nighttime images. Initially, the histogram of the input image is clipped to avoid the over-enhancement. The clipped histogram is partitioned into three sub-histograms depending on the exposure threshold and standard deviation values. After that, the individual sub-histogram is equalized independently. At last, a new enhanced image is produced after combining each equalized sub-images. The simulation results reveal that our proposed method outperforms over other histogram equalized techniques by providing a good visual quality image. The proposed method minimizes the entropy loss and preserves the brightness of the enhanced image efficiently by reducing the absolute mean brightness error (AMBE). It also maintains the structural similarity with the input image and controls the over-enhancement rate effectively.
Combined Covers and Beth Definability

Uniform interpolants were largely studied in non-classical propositional logics since the nineties, and their connection to model completeness was pointed out in the literature. A successive parallel research line inside the automated reasoning community investigated uniform quantifier-free interpolants (sometimes referred to as "covers") in first-order theories. In this paper, we investigate cover transfer to theory combinations in the disjoint signatures case. We prove that, for convex theories, cover algorithms can be transferred to theory combinations under the same hypothesis needed to transfer quantifier-free interpolation (i.e., the equality interpolating property, aka strong amalgamation property). The key feature of our algorithm relies on the extensive usage of the Beth definability property for primitive fragments to convert implicitly defined variables into their explicitly defining terms. In the non-convex case, we show by a counterexample that cover may not exist in the combined theories, even in case combined quantifier-free interpolants do exist.
Joint Over and Under Exposures Correction by Aggregated Retinex Propagation for Image Enhancement

Since the interference of ambient light and the limitation of physical devices, it is quite a common phenomenon that images taken in real-world scenarios turn out to be incorrectly exposed. Most existing techniques emphasize underexposed image correction. On one hand, these works ignore the correction of over-exposure regions in the original input. On the other hand, it is likely to generate over-exposure images. To mitigate these issues, we have developed a novel aggregated Retinex propagations to simultaneously correct over and under-exposure correction of a single image. Concretely, we first manifest the necessity of concurrently correcting under and over-exposure appearances. We establish a Retinex image propagation framework with shared weights to correct different levels of exposure. Then by introducing the fusion computational module, we achieve the accurate exposure correction for a single image. Plenty of quantitative and qualitative comparisons are conducted to fully indicate our superiority against other state-of-the-art algorithms. The elaborated algorithmic analyses show our effectiveness. Experiments on face detection further verify our practicability.
A framework of smart pedagogy based on the facilitating of high order thinking skills

Purpose The purpose of this study is to verify the effect of smart pedagogy to facilitate the high order thinking skills of students and to provide the design suggestion of curriculum and intelligent tutoring systems in smart education. Design/methodology/approach A smart pedagogy framework was designed. The quasi-experiment was conducted in a junior high school. The experimental class used the smart pedagogy and smart learning environment. The control class adopted conventional teaching strategies. The math test scores of these two classes were compared to verify the effectiveness of smart pedagogy. Findings The smart pedagogy framework contains three sections including the situated learning (S), mastery learning (M), adaptive learning (A), reflective learning (R) and thinking tools (T) (SMART) key elements model, the curriculum design method and detailed teaching strategy. The SMART key elements model integrates the situated learning, mastery learning, adaptive learning, reflective learning and thinking tools to facilitate the high order thinking. The curriculum design method of smart pedagogy combines the first five principles of instruction and the SMART key elements model to design the curriculum. The detailed teaching strategies of smart pedagogy contain kinds of innovative learning methods. The results of the quasi-experiment proved that the learning outcome was significantly promoted by using smart pedagogy. Originality/value This research investigates a general framework that can be used to cultivate the high order thinking skills in different subjects and grades was one of the first to introduce high order thinking skills into smart education. The framework of smart pedagogy was innovative and effect in practice.
Periodicity of lively quantum walks on cycles with generalized Grover coin

In this paper we extend the study of three state lively quantum walks on cycles by considering the coin operator as linear sum of permutation matrices, which is a generalization of the Grover matrix. First we provide a complete characterization of orthogonal matrices of order 3×3 which are linear sum of permutation matrices. Consequently, we determine several groups of complex, real and rational orthogonal matrices. We establish that an orthogonal matrix of order 3×3 is a linear sum of permutation matrices if and only if it is permutative. Finally we determine period of lively quantum walk on cycles when the coin operator belongs to the orthogonal group of (real) linear sum of permutation matrices.
Attention-based deep neural network for Internet platform group users’ dynamic identification and recommendation

Under the Internet background, group recommendation has become a major interest in the study of recommendation systems. In the method of group recommendation, the existing researches are mostly conducted using cluster analysis and similarity analysis. The group characteristics studied are relatively generalized, and the group objects studied are mostly fixed, so the group cannot change in real time according to the different attributes and characteristics of the different products. At the same time, for the research object of group recommendation, the existing research mainly consider the recommended project group or user group, but seldom consider recommending the appropriate project group to the appropriate user group to improve the recommendation efficiency and user satisfaction. In view of these problems, this paper proposes a deep neural network that integrates the attention mechanism for group users’ dynamic identification and recommendation on the Internet platform. This paper uses an attention mechanism and deep neural networks to generate the attention preference weights for the group users according to the product attributes. Doing so achieves the purpose of recommending many types of projects to different groups to adapt to their preferences. We compare this method with other baseline methods on two public datasets to validate the effectiveness of the proposed method, which achieves better performance than the most advanced methods.
Advancing User Quality of Experience in 360-degree Video Streaming

Conventional streaming solutions for streaming 360-degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach. We also develop an end-to-end testbed using standards-compliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques - two for conventional adaptive video streaming and two for 360-degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality.
Deep Melanoma classification with K-Fold Cross-Validation for Process optimization

Deep convolution neural networks (DCNNs) enable effective methods to predict the melanoma classes otherwise found with ultrasonic extraction. However, gathering large datasets in local hospitals in Sweden can take years. Small datasets will result in models with poor accuracy and insufficient generalization ability, which has a great impact on the result. This paper proposes to use a K-Fold cross validation approach based on a DCNN algorithm working on a small sample dataset. The performance of the model is verified via a Vgg16 extracting the features. The experimental results reveal that the model built by the approach proposed in this paper can effectively achieve a better prediction and enhance the accuracy of the model, which proves that K-Fold can achieve better performance on a small skin cancer dataset.
Gradient-based discriminative modeling for blind image deblurring

Blind image deconvolution is a fundamental task in image processing, computational imaging, and computer vision. It has earned intensive attention in the past decade since the seminal work of Fergus et al. [1] for camera shake removal. In spite of the recent great progress in this field, this paper aims to formulate the blind problem with a simpler modeling perspective. What is more important, the newly proposed approach is expected to achieve comparable or even better performance towards the real blurred images. Specifically, the core critical idea is the proposal of a pure gradient-based discriminative prior for accurate and robust blur kernel estimation. Numerous experimental results on both the benchmark datasets and real-world blurred images in various imaging scenarios, e.g., natural, manmade, low-illumination, text, or people, demonstrate well the effectiveness and robustness of the proposed approach.
Virtual Mouse Using Object Tracking

With new changes seen in computer technology day by day, it has become quite essential for us to find specific new ways of interaction with computer systems as its need is increasing in society every day. Today, every device is making the use of touch screen technology on its systems, which isn't affordable to be used in all applications. A specific interactive module like a virtual mouse that makes use of Object Tracking and Gestures that will help us to interact can be an alternative way for the traditional touch screen and the physical mouse. The objective is to create an Object Tracking application that interacts with the system. This system proposed is a Computer Vision-based mouse cursor control system, which uses hand gestures that are being captured from a webcam through an HSV color detection technique. This system allows the user to navigate the system cursor using their hand bearing color caps or tapes that the computer webcam tracks and perform mouse operations like left-click, right-click, and double click using different hand gestures. Python and OpenCV library is used for realtime computer vision to implement the system. The camera output is displayed on the monitor.
Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates

Abstract The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.