Reviewerly/exHarmony huggingface dataset:
Semi-supervised semantic segmentation of prostate and organs-at-risk on 3D pelvic CT images

The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. However, training effective deep learning models usually Requires a large amount of high-quality labeled data, often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep learning methods, the new approach can utilize both annotated and un-annotated data for training. It generates un-annotated synthetic data by a data augmentation scheme using generative adversarial networks (GANs). We applied the new approach to segmenting multiple organs in male pelvic CT images. CT images without annotations and GAN-synthesized un-annotated images were used in semi-supervised learning. Experimental results, evaluated by three metrics (Dice similarity coefficient, average Hausdorff distance, and average surface Hausdorff distance), showed that the new method achieved comparable performance with substantially fewer annotated images or better performance with the same amount of annotated data, outperforming the existing state-of-the-art methods.
Analysis and Application of Verifiable Computation Techniques in Blockchain Systems for the Energy Sector

The energy system is becoming increasingly decentralized. This development requires integrating and coordinating a rising number of actors and small units in a complex system. Blockchain could provide a base infrastructure for new tools and platforms that address these tasks in various aspects—ranging from dispatch optimization or dynamic load adaption to (local) market mechanisms. Many of these applications are currently in development and subject to research projects. In decentralized energy markets especially, the optimized allocation of energy products demands complex computation. Combining these with distributed ledger technologies leads to bottlenecks and challenges regarding privacy requirements and performance due to limited storage and computational resources. Verifiable computation techniques promise a solution to these issues. This paper presents an overview of verifiable computation technologies, including trusted oracles, zkSNARKs, and multi-party computation. We further analyze their application in blockchain environments with a focus on energy-related applications. Applied to a distinct optimization problem of renewable energy certificates, we have evaluated these solution approaches and finally demonstrate an implementation of a Simplex-Optimization using zkSNARKs as a case study. We conclude with an assessment of the applicability of the described verifiable computation techniques and address limitations for large-scale deployment, followed by an outlook on current development trends.
A Pragmatic Study on Movie Recommender Systems Using Hybrid Collaborative Filtering

The Movie Recommendation System (MRS) is part of a comprehensive class of recommendation systems, which categorizes information to predict user preferences. The sum of movies is increasing tremendously day by day, and a reliable recommender system should be developed to increase the user satisfaction. Most of the approaches are made to prevent cold-start, first-rater drawbacks, and gray sheep user problems, nevertheless, in order to recommend the related items, various methods are available in the literature. Firstly, content-based method has some drawbacks like data of similar user could not be achieved, and what category of these items the user likes or dislikes are also not known. Secondly, this paper discusses about collaborative filtering to find both user and item attributes that have been considered. Since there exist some issues pictured with collaborative filtering, so this paper further aims into hybrid collaborative filtering and deep learning with KNN algorithm of ratings of top K-nearest neighbors.
Connecting Students with Research Advisors Through User-Controlled Recommendation

We present Grapevine, a user-controlled recommender that enables undergraduate and graduate students to find a suitable research advisor. This system combines the ideas from the areas of exploratory search, user modeling, and recommender systems by employing state-of-the-art knowledge extraction, grape-based recommendation, and an intelligent user interface. In this paper, we demonstrate the system’s key components and how they work as a whole.
The role of professional certifications in computer occupations

Experienced and aspiring computing professionals need to manage their qualifications according to current market needs. That includes certification achievement as well as formal education, experience, and licenses.
A Hierarchical Inter-Clause Interaction Network for Emotion Cause Extraction

Recently, some methods with inter-clause interaction have achieved promising results on the task of emotion cause extraction. However, the inter-clause modeling modules are only applied on clause-level features rather than word-level features, thus weakening the ability to capture important word-level cues for identifying whether a clause is an emotion cause. In this paper, we propose a framework of Hierarchical Inter-Clause Interaction Network (HICIN), in which inter-clause interaction is applied on both word-level and clause-level features. Word-level interaction can capture the fine-grained semantic cues of each clause by the guidance of all clauses in the document and then generate more powerful clause-level features, while clause-level interaction makes the obtained clause-level features more discriminative. Experimental results show that our model can improve the performance effectively.
An exponential bound on the number of non-isotopic commutative semifields

We show that the number of non-isotopic commutative semifields of odd order <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="p Superscript n"> <mml:semantics> <mml:msup> <mml:mi>p</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> <mml:annotation encoding="application/x-tex">p^{n}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is exponential in <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n"> <mml:semantics> <mml:mi>n</mml:mi> <mml:annotation encoding="application/x-tex">n</mml:annotation> </mml:semantics> </mml:math> </inline-formula> when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n equals 4 t"> <mml:semantics> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>4</mml:mn> <mml:mi>t</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">n = 4t</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="t"> <mml:semantics> <mml:mi>t</mml:mi> <mml:annotation encoding="application/x-tex">t</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is not a power of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="2"> <mml:semantics> <mml:mn>2</mml:mn> <mml:annotation encoding="application/x-tex">2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. We introduce a new family of commutative semifields and a method for proving isotopy results on commutative semifields that we use to deduce the aforementioned bound. The previous best bound on the number of non-isotopic commutative semifields of odd order was quadratic in <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="n"> <mml:semantics> <mml:mi>n</mml:mi> <mml:annotation encoding="application/x-tex">n</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and given by Zhou and Pott [Adv. Math. 234 (2013), pp. 43–60]. Similar bounds in the case of even order were given in Kantor [J. Algebra 270 (2003), pp. 96–114] and Kantor and Williams [Trans. Amer. Math. Soc. 356 (2004), pp. 895–938].
An Interactive Design Space for Wearable Displays

The promise of on-body interactions has led to widespread development of wearable displays. They manifest themselves in highly variable shapes and form, and are realized using technologies with fundamentally different properties. Through an extensive survey of the field of wearable displays, we characterize existing systems based on key qualities of displays and wearables, such as location on the body, intended viewers or audience, and the information density of rendered content. We present the results of this analysis in an open, web-based interactive design space that supports exploration and refinement along various parameters. The design space, which currently encapsulates 129 cases of wearable displays, aims to inform researchers and practitioners on existing solutions and designs, and enable the identification of gaps and opportunities for novel research and applications. Further, it seeks to provide them with a thinking tool to deliberate on how the displayed content should be adapted based on key design parameters. Through this work, we aim to facilitate progress in wearable displays, informed by existing solutions, by providing researchers with an interactive platform for discovery and reflection.
Mapping experience research across disciplines: who, where, when

Abstract Human experiences have been studied in multiple disciplines, Human–Computer Interaction (HCI) being one of the largest research fields with its user experience (UX) research. Currently, there is little interaction between experience researchers from different disciplines, although cross-disciplinary knowledge sharing has the potential to accelerate the development of UX and other experience research fields to the next level. This article reports a research profiling study of almost 52,000 experience publications over 125 years, showing the breadth of experience research across disciplines. The data analysis reveals the disciplines that study experiences, the prominent authors, institutions and countries in experience research, the most cited works by experience researchers across disciplines, and how UX research is situated on the map of experience research. This descriptive research profiling study is a necessary first step on the journey of mapping the landscape of experience research, guiding researchers towards understanding experience as a multidisciplinary concept, and establishing a more coherent experience research field.
A novel quantitative measurement method for irregular tubules in breast carcinoma

Tubule detection is very crucial and is one of the important criterions for tubule formation assessment in breast carcinoma. The output of tubule formation is essential to determine the grade of breast carcinoma based on the Nottingham Histological Grading (NHG) system. Very few studies have been done on the tubule quantification in breast carcinoma. In this study, the geometrical pattern and regularity measurement in tubule and non-tubule were investigated. Spatial Angle (SA), Distribution (D), and Harmonic Mean (HMκ) indices were proposed to quantify the tubule and non-tubule. The information of the spatial angle and distribution of neighborhood nuclei surrounding the central lumen were considered in the proposed measurements. The main advantage of the proposed measurements over the other existing findings is that the proposed measurements provide a clear geometrical meaning for the irregular tubule. The proposed Harmonic Mean Index (HMκ) was found significant in discriminating the tubule and non-tubule (p value < 0.05). Sensitivity (Sen), accuracy (Acc), and F1-score (F1) of 1.00, 0.98, and 0.98 respectively were achieved when classifying the independent dataset using support vector machine.
Variational Bayes on manifolds

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods. This paper extends the scope of VB to the case where the variational parameter space is a Riemannian manifold. We develop an efficient manifold-based VB algorithm that exploits both the geometric structure of the constraint parameter space and the information geometry of the manifold of VB approximating probability distributions. Our algorithm is provably convergent and achieves a convergence rate of order $${\mathcal {O}}(1/\sqrt{T})$$ and $$\mathcal O(1/T^{2-2\epsilon })$$ for a non-convex evidence lower bound function and a strongly retraction-convex evidence lower bound function, respectively. We develop in particular two manifold VB algorithms, Manifold Gaussian VB and Manifold Wishart VB, and demonstrate through numerical experiments that the proposed algorithms are stable, less sensitive to initialization and compares favourably to existing VB methods.
V-Elliot: Design, Evaluate and Tune Visual Recommender Systems

The paper introduces Visual-Elliot (V-Elliot), a reproducibility framework for Visual Recommendation systems (VRSs) based on Elliot. framework provides the widest set of VRSs compared to other recommendation frameworks in the literature (i.e., 6 state-of-the-art models which have been commonly employed as baselines in recent works). The framework pipeline spans from the dataset preprocessing and item visual features loading to easily train and test complex combinations of visual models and evaluation settings. V-Elliot provides an extended set of features to ease the design, testing, and integration of novel VRSs into V-Elliot. The framework exploits of dataset filtering/splitting functions, 40 evaluation metrics, five hyper-parameter optimization methods, more than 50 recommendation algorithms, and two statistical hypothesis tests. The files of this demonstration are available at: github.com/sisinflab/elliot.
Tunnel Vision – Dynamic Peripheral Vision Blocking Glasses for Reducing Motion Sickness Symptoms

Motion sickness affects roughly a third of all people. Narrowing the field of view (FOV) can help to reduce motion sickness symptoms. In this paper, we present Tunnel Vision, a type of smart glasses that can dynamically block a wearer’s peripheral vision area using switchable polymer dispersed liquid crystal (PDLC) film. We evaluate the prototype in a virtual reality environment. Our experiments (n=19) suggest that Tunnel Vision statistically significantly reduces the following Simulator Sickness Questionnaire (SSQ) related motion sickness symptoms without impacting immersion: ”difficulty concentrating” (F(2,35) = 4.121, p = 0.025), ”head feeling heavy” (F(2,35) = 3.231, p = 0.051) and ”nausea” (F(2,35) = 3.145, p = 0.055).
Gaussian-guided feature alignment for unsupervised cross-subject adaptation

Human activities recognition (HAR) and human intent recognition (HIR) are important for medical diagnosis and human-robot interaction. HAR and HIR usually rely on the signals of some wearable sensors, such as inertial measurement unit (IMU), but these signals may be user-dependent, which degrades the performance of the recognition algorithm on new subjects. Traditional supervised learning methods require labeling signals and training specific classifiers for each new subject, which is burdensome. To deal with this problem, this paper proposes a novel non-adversarial cross-subject adaptation method called Gaussian-guided feature alignment (GFA). The proposed GFA metric quantifies the discrepancy between the labeled features of source subjects and the unlabeled features of target subjects so that minimizing the GFA metric leads to the alignment of the source and target features. The GFA metric is estimated by calculating the divergence between the feature distribution and Gaussian distribution, as well as the mean squared error of the mean and variance between source and target features. This paper analytically proves the effect of the GFA metric and validates its performance using three public human activity datasets. Experimental results show that the proposed GFA achieves 1% higher target classification accuracy and 0.5% lower variance than state-of-the-art methods in case of cross-subject validation. These results indicate that the proposed GFA is feasible for improving the generalization of the HAR and HIR.
A Relabeling Approach to Handling the Class Imbalance Problem for Logistic Regression

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Short Message Service Filtering with Natural Language Processing in Indonesian Language

As the amount of spam on messaging platforms such as emails have increased, the same has happened within Short Message Service (SMS) services as well. Within this study, Natural Language Processing was used on SMS in Indonesian Language (Bahasa), to create an Artificial Intelligence (AI) model capable of distinguishing between spam and other types of messages that are not spam. Within this study, we compared the performance of the Multinomial Naive Bayes Classifier and the Bi-Directional LSTM algorithm. We demonstrated this using code written in Python and the TensorFlow and Scikit libraries to generate reports, graphs and an application to test the performance of the models. Our results reveal that these methods are effective in filtering Bahasa Indonesia spam within SMS inboxes. In addition, we also published the SMS dataset in Bahasa with this paper.
A Survey on Smart Home Authentication: Toward Secure, Multi-Level and Interaction-Based Identification

With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.
Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition

Attention-based encoder-decoder framework is widely used in the scene text recognition task. However, for the current state-of-the-art (SOTA) methods, there is room for improvement in terms of the efficient usage of local visual and global context information of the input text image, as well as the robust correlation between the scene processing module (encoder) and the text processing module (decoder). In this paper, we propose a Representation and Correlation Enhanced Encoder-Decoder Framework (RCEED) to address these deficiencies and break performance bottleneck. In the encoder module, local visual feature, global context feature, and position information are aligned and fused to generate a small-size comprehensive feature map. In the decoder module, two methods are utilized to enhance the correlation between scene and text feature space. 1) The decoder initialization is guided by the holistic feature and global glimpse vector exported from the encoder. 2) The feature enriched glimpse vector produced by the Multi-Head General Attention is used to assist the RNN iteration and the character prediction at each time step. Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's generalization towards changeable texts. Extensive experiments on the benchmarks demonstrate the advantageous performance of RCEED in scene text recognition tasks, especially the irregular ones. The source code will be available https://github.com/Mona9955/RCEED-ICDAR2021.
MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content

Cyberbullying is a growing and significant problem in today’s workplace. Existing automated cyberbullying detection solutions rely on machine learning and deep learning techniques. It is proven that the deep learning-based approaches produce better accuracy for text-based classification than other existing approaches. A novel decentralized deep learning approach called MaLang is developed to detect abusive textual content. MaLang is deployed at two levels in a network: (1) the System Level and (2) the Cloud Level, to tackle the usage of toxic or abusive content on any messaging application within a company’s networks. The system-level module consists of a simple deep learning model called CASE that reads the user’s messaging data and classifies them into abusive and non-abusive categories, without sending any raw or readable data to the cloud. Identified abusive messages are sent to the cloud module with a unique identifier to keep user profiles hidden. The cloud module, called KIPP, utilizes deep learning to determine the probability of a message containing different categories of toxic content, such as: ‘Toxic’, ‘Insult’, ‘Threat’, or ‘Hate Speech’. MaLang achieves a 98.2% classification accuracy that outperforms other current cyberbullying detection systems.
Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.