Reviewerly/exHarmony huggingface dataset:
Virtual Staining for Mitosis Detection in Breast Histopathology

We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potential of this application to scale the number of training samples without the need for manual annotations.
Efficient and Scalable Job Recommender System Using Collaborative Filtering

Recommendation system is a techniques, which provides users with information, which he/she may be interested in or accessed in past. Traditional recommender techniques such as content and collaborative filtering used in various applications such as education, social media, marketing, entertainment, e-governance and many more. Content-based and collaborative filtering has many advantages and disadvantage and they are useful in specific application. Sparsity and cold start problem are major challenges in content and collaborative filtering. Challenges of content and collaborative filtering can be solved by using hybrid filtering. Hybrid filtering combines the features of two recommender system like content and collaborative; content-based filtering improves the classification accuracy and collaborative model easily gives the best-predicted result of a latent factor model. In this paper, we have presented a brief survey of the recommendation system approaches, techniques and application, one important application of recommendation system in Job Recruitment; in which candidates are elected by using online job recruitment portal based on their profile and job history and behaviour components; wherein it serves millions of candidates with suitable and personifies jobs. As per the recent survey this domain is less explored till now and existing job recommender system has many shortcomings, they use resumes/profile and job descriptions for analysis and new job post and candidate profiles are not matched properly because of cold start problem, sometime potential candidate loses their job due to the incomplete job description and education detail in the ontology. LinkedIn’s Job Ecosystem handles few problems, few are still unsolved that we discussed in result part. In this paper, we have presented a comparative analysis of different job recommender system and their techniques.
A Triple-Stage Self-Guided Network for Kidney Tumor Segmentation

The morphological characteristics of kidney tumor is crucial factor for radiologists to make accurate diagnosis and treatment. Unfortunately, performing quantitative study of the relationship between kidney tumor morphology and clinical outcomes is very difficult because kidney tumor varies dramatically in its size, shape, location, etc. Automatic semantic segmentation of kidney and tumor is a promising tool towards developing advanced surgical planning techniques. In this work, we present a triple-stage self-guided network for kidney tumor segmentation task. The low-resolution net can roughly locate the volume of interest (VOI) from down-sampled CT images, while the full-resolution net and tumor refine net can extract accurate boundaries of kidney and tumor within VOI from full resolution CT images. We innovatively propose dilated convolution blocks (DCB) to replace the traditional pooling operations in deeper layers of U-Net architecture to retain detailed semantic information better. Besides, a hybrid loss of dice and weighted cross entropy is used to guide the model to focus on voxels close to the boundary and hard to be distinguished. We evaluate our method on the KiTS19 (MICCAI 2019 Kidney Tumor Segmentation Challenge) test dataset and achieve 0.9674, 0.8454 average dice for kidney and tumor respectively, which ranked the 2nd place in the KiTS19 challenge.
Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.
Cryptanalysis and Improvement of a Chaotic Map-Based Image Encryption System Using Both Plaintext Related Permutation and Diffusion

In theory, high key and high plaintext sensitivities are a must for a cryptosystem to resist the chosen/known plaintext and the differential attacks. High plaintext sensitivity can be achieved by ensuring that each encrypted result is plaintext-dependent. In this work, we make detailed cryptanalysis on a published chaotic map-based image encryption system, where the encryption process is plaintext Image dependent. We show that some designing flaws make the published cryptosystem vulnerable to chosen-plaintext attack, and we then proposed an enhanced algorithm to overcome those flaws.
RETRACTED: Enhancing the security using SRB18 method of Embedding Computing

Today's world has witnessed the accumulation of data through social media like Twitter and Facebook, which have been growing enormously day by day. Using these social media, users tweet or post on many topics from anyplace in the world through the Internet. Tweets are used to analyze positive and negative messages to drive polarity scores and also to predict future trends. These polarity scores could be extracted from Twitter, even though polarity score data are placed under security. The data can be easily hacked, and changing the score results can lead to lots of issues, such as affecting the worldwide economic status, company brands, and the reputations of firms. To tackle these issues, Daniel Bernstein proposed the implementation of Salsa, which has faster encryption because of the quarter round with better data security. In the present work, a novel algorithm is proposed by modifying the Salsa20/4 to further enhance the security of the polarity scores, which is an important requirement of the present world. The proposed algorithm has two stages. The first stage consists of a secret key and the second stage constructs the diagonal keys in N-1 steps. The proposed algorithm is tested with different file sizes to calculate the encryption time. The results show that the proposed encryption algorithm takes relatively large execution time but provides high data security when compared to that of the existing algorithm of Salsa20/4.
A New Deep Learning Model Selection Method for Colorectal Cancer Classification

Deep learning is one of the most commonly used techniques in computer-aided diagnosis systems. Their exploitation for histopathological image analysis is important because of the complex morphology of whole slide images. However, the main limitation of these methods is the restricted number of available medical images, which can lead to an overfitting problem. Many studies have suggested the use of static ensemble learning methods to address this issue. This article aims to propose a new dynamic ensemble deep learning method. First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.
Robust sensor fusion with heavy-tailed noises

In this paper, the problem of multi-sensor centralized state fusion with heavy-tailed process and measurement noises is considered. In order to improve the sensor fusion estimation, a novel identify-fusion strategy is proposed. An indicator, which is modelled by a Bernoulli prior, for each measurement in each sensor to identify whether the measurement is an outlier. A Student-t based hierarchical Gaussian state space model is then constructed, and the fusion is formulated as a state space estimation problem. The state and unknown parameters in the Student-t based hierarchical Gaussian state space model are jointly inferred by the variational Bayesian technique. Computer simulations are provided to demonstrate the effectiveness of the proposed method.
The effects of STEAM-based mobile learning on learning achievement and cognitive load

In order to explore whether STEAM education in combination with elementary school curriculums can help students' learning performance, this study developed a digital learning system. This digital learning system combined STEAM concepts with elements of gamebased learning. This study developed the curriculum content based on the unit of transportation vehicles and presented it in a digital learning manner. In the experiment, the students were divided into an experimental group and a control group. The experimental group students were given the system developed in this study to assist their learning, and the control group students the lecturer-centered narrative curriculum. The study results show that the experimental group was superior to the control group in learning effectiveness. In the aspect of cognitive load, the cognitive load generated by the experimental group in their learning activities was also lower than that of the control group. By classifying students according to their learning styles, the visual type students had better learning effectiveness than the verbal type students when learning through the system developed by this graduate institute. As for the sequential type and global type students learning with the system developed, there were no significant differences in their learning effectiveness.
SCRIPT

Power side-channel attacks (SCAs) have been proven to be effective at extracting secret keys from hardware implementations of cryptographic algorithms. Ideally, the power side-channel leakage (PSCL) of hardware designs of a cryptographic algorithm should be evaluated as early as the pre-silicon stage (e.g., gate level). However, there has been little effort in developing computer-aided design (CAD) tools to accomplish this. In this article, we propose an automated CAD framework called SCRIPT to evaluate information leakage through side-channel analysis. SCRIPT starts by defining the underlying properties of the hardware implementation that can be exploited by side-channel attacks. It then utilizes information flow tracking (IFT) to identify registers that exhibit those properties and, therefore, leak information through the side-channel. Here, we develop an IFT-based side-channel vulnerability metric ( SCV ) that is utilized by SCRIPT for PSCL assessment. SCV is conceptually similar to the traditionally used signal-to-noise ratio (SNR) metric. However, unlike SNR, which requires thousands of traces from silicon measurements, SCRIPT utilizes formal methods to generate SCV-guided patterns/plaintexts, allowing us to derive SCV using only a few patterns (ideally as low as two) at gate level. SCV estimates PSCL vulnerability at pre-silicon stage based on the number of plaintexts required to attain a specific SCA success rate. The integration of IFT and pattern generation makes SCRIPT efficient, accurate, and generic to be applied to any hardware design. We validate the efficacy of the SCRIPT framework by demonstrating that it can effectively and accurately determine SCA success rates for different AES designs at pre-silicon stage. SCRIPT is orders of magnitude more efficient than traditional pre-silicon PSCL assessment (SNR-based), with an average evaluation time of 15 minutes; whereas, traditional PSCL assessment at pre-silicon stage would require more than a month. We also analyze the PSCL characteristic of the multiplication unit of RISC processor using SCRIPT to demonstrate SCRIPT’s applicability.
One-sided Differential Privacy

We study the problem of privacy-preserving data sharing, wherein only a subset of the records in a database is sensitive, possibly based on predefined privacy policies. Existing solutions, viz, differential privacy (DP), are over-pessimistic as they treat all records as sensitive. Alternatively, techniques like access control and personalized differential privacy that reveal all non-sensitive records truthfully indirectly leak whether a record is sensitive and consequently the record's value. In this work we introduce one-sided differential privacy (OSDP) that offers provable privacy guarantees to the sensitive records. In addition, OSDP satisfies the sensitivity masking property which ensures that any algorithm satisfying OSDP does not allow an attacker to significantly decrease his/her uncertainty about whether a record is sensitive or not. We design OSDP algorithms that can truthfully release a sample of non-sensitive records. Such algorithms can be used to support applications that must output true data with little loss in utility, especially when using complex types of data like images or location trajectories. Additionally, we present OSDP algorithms for releasing count queries, which leverage the presence of nonsensitive records and are able to offer up to a 6× improvement in accuracy over state-of-the-art DP-solutions.
Challenges of Designing Consent: Consent Mechanics in Video Games as Models for Interactive User Agency

This paper argues for a conceptual framework that treats user consent in interactive technologies as a design challenge necessitating careful, culturally-informed consideration. We draw on recent work in HCI as well as queer and feminist theory that understands consent as rooted in negotiating agency in order to frame our exploration of unique difficulties and potential solutions to meaningful opportunities for user consent in the design of computational technologies. Through a critical analysis of three video games that offer different models of consent-each of which communicates different values through its design-we introduce the concept of consent mechanics. Consent mechanics describe designed interactions that allow players to consent to or opt out of in-game experiences, often those related to sexuality or intimacy. Here, we approach video games as windows onto design considerations surrounding interactive technologies more broadly, suggesting crucial questions and tactics for how to design user agency ethically into computational systems.
Modelling a Crawler Robot Using Wheels as Pseudo-Tracks: Model Complexity vs Performance

Crawler mobile robots have a large variety of applications, including urban search and rescue. Before new algorithms and concepts could be integrated into a real robot control system and tested in field experiments, extensive virtual simulations should be performed in order to verify them carefully. This requires a proper modelling of a robot in a virtual environment, but a specific construction of crawler robot crawlers complicates the modelling. In this paper, in order to provide a high level of physical interaction similarity of a crawler with a supporting surface we model crawlers of mobile robot Servosila Engineer with a set of virtual wheels and study a relationship between a model complexity and a simulation performance. The model complexity is reflected by a number of virtual wheels that approximate each crawler of the robot. Verification experiments were performed in ROS/Gazebo simulator.
PenSight: Enhanced Interaction with a Pen-Top Camera

We propose mounting a downward-facing camera above the top end of a digital tablet pen. This creates a unique and practical viewing angle for capturing the pen-holding hand and the immediate surroundings which can include the other hand. The fabrication of a prototype device is described and the enabled interaction design space is explored, including dominant and non-dominant hand pose recognition, tablet grip detection, hand gestures, capturing physical content in the environment, and detecting users and pens. A deep learning computer vision pipeline is developed for classification, regression, and keypoint detection to enable these interactions. Example applications demonstrate usage scenarios and a qualitative user evaluation confirms the potential of the approach.
Replicate and Reuse: Tangible Interaction Design for Digitally-Augmented Physical Media Objects

Technology has transformed our physical interactions into infinitely more scalable and flexible digital ones. We can peruse an infinite number of photos, news articles, and books. However, these digital experiences lack the physical experience of paging through an album, reading a newspaper, or meandering through a bookshelf. Overlaying physical objects with digital content using augmented reality is a promising avenue towards bridging this gap. In this paper, we investigate the interaction design for such digital-overlaid physical objects and their varying levels of tangibility. We first conduct a user evaluation of a physical photo album that uses tangible interactions to support physical and digital operations. We further prototype multiple objects including bookshelves and newspapers and probe users on their usage, capabilities, and interactions. We then conduct a qualitative investigation of three interaction designs with varying tangibility that use three different input modalities. Finally, we discuss the insights from our investigations and recommend design guidelines.
3D-Auth: Two-Factor Authentication with Personalized 3D-Printed Items

Two-factor authentication is a widely recommended security mechanism and already offered for different services. However, known methods and physical realizations exhibit considerable usability and customization issues. In this paper, we propose 3D-Auth, a new concept of two-factor authentication. 3D-Auth is based on customizable 3D-printed items that combine two authentication factors in one object. The object bottom contains a uniform grid of conductive dots that are connected to a unique embedded structure inside the item. Based on the interaction with the item, different dots turn into touch-points and form an authentication pattern. This pattern can be recognized by a capacitive touchscreen. Based on an expert design study, we present an interaction space with six categories of possible authentication interactions. In a user study, we demonstrate the feasibility of 3D-Auth items and show that the items are easy to use and the interactions are easy to remember.
Design and Implementation of Augmented Reality for English Language Education

Computer- and mobile-assisted language learning (CALL and MALL) have been gaining mainstream acceptance in second language education across the globe over the past two decades. Recently, Augmented Reality (AR)-supported learning has become a new frontier in MALL attributing to the pervasiveness of smartphones and the development of mobile technologies. However, one major research gap is that the previous studies on mobile AR, primarily relying on the case study approach to verify the effectiveness of various mobile technologies and AR products, often lack strong theoretical support such as frameworks and models. Against this backdrop, this chapter first reviews mainstream language learning theories, and then examines recent studies of AR in English Language Education (ELE). We then introduce three existing AR design frameworks in the mobile learning context, aiming to offer theoretical insights into designing and learning technology-enhanced language learning tasks. We end the chapter with design and learning principles for language teachers so as to promote the integration of this novel technology into English Language Education.
Characterizing Twitter Users Who Engage in Adversarial Interactions against Political Candidates

Social media provides a critical communication platform for political figures, but also makes them easy targets for harassment. In this paper, we characterize users who adversarially interact with political figures on Twitter using mixed-method techniques. The analysis is based on a dataset of 400 thousand users' 1.2 million replies to 756 candidates for the U.S. House of Representatives in the two months leading up to the 2018 midterm elections. We show that among moderately active users, adversarial activity is associated with decreased centrality in the social graph and increased attention to candidates from the opposing party. When compared to users who are similarly active, highly adversarial users tend to engage in fewer supportive interactions with their own party's candidates and express negativity in their user profiles. Our results can inform the design of platform moderation mechanisms to support political figures countering online harassment.
LiveClip

Recent years have witnessed great success of mobile short-form video apps. However, most current video streaming strategies are designed for long-form videos, which cannot be directly applied to short-form videos. Especially, short-form videos differ in many aspects, such as shorter video length, mobile friendliness, sharp popularity dynamics, and so on. Facing these challenges, in this paper, we perform an in-depth measurement study on Douyin, one of the most popular mobile short-form video platforms in China. The measurement study reveals that Douyin adopts a rather simple strategy (called Next-One strategy) based on HTTP progressive download, which uses a sliding window with stop-and-wait protocol. Such a strategy performs poorly when network connection is slow and user scrolling is fast. The results motivate us to design an intelligent adaptive streaming scheme for mobile short-form videos. We formulate the short-form video streaming problem and propose an adaptive short-form video streaming strategy called LiveClip using a deep reinforcement learning (DRL) approach. Trace-driven experimental results prove that LiveClip outperforms existing state-of-the-art approaches by around 10%-40% under various scenarios.
Meter classification of Arabic poems using deep bidirectional recurrent neural networks

Poetry is an important component of any language. Much of a nation's history and culture are documented in poems. A poem has a rhythmic flow which is quite different as compared to a prose. Each language has its own set of rhythmical structures for poems, called meters. Identifying the meters of Arabic poems is a lengthy and complicated process. To classify a poem's meter, the text of the poem should be encoded in a special Arudi form which needs complex rule-based transformations before another set of rules can be used to finally classify the meters. This paper introduces a novel method for classifying poem meters of Arabic poems using RNN-based deep learning. It bypasses the need to transform the poem to the Arudi form as well as the need to explicitly encode the complex rules that are usually followed to determine the meter. The presented method was evaluated on a large dataset collected specifically for this purpose. We are able to classify the poem meters with an accuracy of 94.32% on an independent test set.