Reviewerly/exHarmony huggingface dataset:
The Unreasonable Effectiveness of the Final Batch Normalization Layer

Early-stage disease indications are rarely recorded in real-world domains, such as Agriculture and Healthcare, and yet, their accurate identification is critical in that point of time. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. In this work, we extend the study done by [], showing that the final BN layer, when placed before the softmax output layer, has a considerable impact in highly imbalanced image classification problems as well as undermines the role of the softmax outputs as an uncertainty measure. This current study addresses additional hypotheses and reports on the following findings: (i) the performance gain after adding the final BN layer in highly imbalanced settings could still be achieved after removing this additional BN layer in inference; (ii) there is a certain threshold for the imbalance ratio upon which the progress gained by the final BN layer reaches its peak; (iii) the batch size also plays a role and affects the outcome of the final BN application; (iv) the impact of the BN application is also reproducible on other datasets and when utilizing much simpler neural architectures; (v) the reported BN effect occurs only per a single majority class and multiple minority classes – i.e., no improvements are evident when there are two majority classes; and finally, (vi) utilizing this BN layer with sigmoid activation has almost no impact when dealing with a strongly imbalanced image classification tasks.
An Efficient Authentication Scheme for Mobile Online Social Networks

With the immense growth of smartphones and mobile Internet, it has become easier to access online social networking sites, such as Facebook, Twitter, LinkedIn, and so on. At the same time, attackers also utilized this opportunity very well by introducing the innovative idea of attacks. Phishing was a classical technique of information stealing. According to the published reports [], the rate of phishing attacks has increased much more during the last few years. Earlier an adversary sends an email with malicious URLs in it. But in OSN, an attacker creates a scam website of an important website and posts the link on the wall of the OSN users. When the user tries to open and login into the site, the attacker easily steals the private information, such as passwords and credit card numbers. In this paper, we proposed an efficient authentication scheme that can resist phishing attacks and some other known security threats also. Finally, we analyze and show that the scheme provides better security against phishing attacks and other known attacks.
Enhancing Social Recommenders with Implicit Preferences and Fuzzy Confidence Functions

In this paper we explore the use of fuzzy confidence functions for enhancing the performance of social recommenders with implicit preferences, focusing on K-Nearest Neighbors (KNN) collaborative filtering algorithms. Firstly, we measure the effects of including social relations for enhancing the performance of the algorithms with either explicit or implicit preferences, expecting to verify better results when social attributes are considered in the relevant neighborhood estimation. Secondly, it is proposed to enhance the social recomenders with implicit preferences by fuzzy confidence functions. An application is developed to measure the effects of including social relations and to illustrate our proposal on the fuzzy modeling of implicit preferences, recommending courses based on the students socio-demographic and academic information. As a result, the best recommendations are accomplished with socially-enhanced algorithms that make use of implicit preferences and fuzzy confidence functions, obtaining a FCP in test of 0.68.
Extending A+ with Object-Oriented Elements

This work presents a language extension to A+ that allows developers to conveniently handle external objects in A+ code. It discusses a set of object-oriented concepts and investigates their implications for the A+ language. For these new language elements, new symbols are added, and each of them represents basic operations (member access, member modification, type casting, and indexing) on objects. For each new operation the underlying mechanism is presented, and simple examples are provided showing their usage. Using these new operations, various .NET classes can be accessed and used from A+ programs without writing any additional code.The presented language extension in this paper is runtime agnostic and it could enable the interoperation of A+ not only with the .NET framework but with other object-oriented runtimes, too. Additionally this extension adds the basic mechanisms for implementing full object-oriented support for the A+ language in the future.
Image Recognition on Rupiah Currency using Augmented Reality for Learning Media

The use of technology in learning media has been widely used. One of the technologies that can be used to support the learning process is Augmented Reality technology. So far, the way of working done in Augmented Reality is by scanning a marker that is made specifically to be able to bring up scanned information. Therefore, a design was made to create an Augmented Reality application that does not require a special marker but simply by utilizing existing objects, in this case using banknotes in rupiah currency and then displaying information about the images contained in the banknotes. The results of the study indicate that the application can run, and can help in learning the introduction of currency and culture according to the images contained in the banknotes.
Hcdg: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation

Modern deep neural networks struggle to transfer knowledge and generalize across diverse domains when deployed to real-world applications. Currently, domain generalization (DG) is introduced to learn a universal representation from multiple domains to improve the network generalization ability on unseen domains. However, previous DG methods only focus on the data-level consistency scheme without considering the synergistic regularization among different consistency schemes. In this paper, we present a novel Hierarchical Consistency framework for Domain Generalization (HCDG) by integrating Extrinsic Consistency and Intrinsic Consistency synergistically. Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency. To better enhance such consistency, we design a novel Amplitude Gaussian-mixing strategy into Fourier-based data augmentation called DomainUp. For the Intrinsic Consistency, we perform task-level consistency for the same instance under the dual-task scenario. We evaluate the proposed HCDG framework on two medical image segmentation tasks, \ie, optic cup/disc segmentation on fundus images and prostate MRI segmentation. Extensive experimental results manifest the effectiveness and versatility of our HCDG framework.
Psychological Analysis of College Students Based on Improved Boolean Matrix Apriori Algorithm

Mental health directly influences the normal study and life of college students. The problems brought by psychological problems also affect many college students, which has brought many negative effects to them. Therefore, in view of the college students' psychological correlation analysis, it is necessary to put the association rules mining technology for the college students' psychological analysis. To guide college students' mental health education has great practical significance. This article improved algorithm combined with established system of college students' psychological correlation analysis. To collect the college students' psychological evaluation data and the basis of the basic information, we use the improved algorithm for mining. From the results in some regularity and characteristics of college students' psychological factors, it offers a new way for college students' mental health education.
A Channel-Quality Classification Analysis for IoT Communication based on Machine Learning

The Internet of Things (IoT) involves authentication processes that can be prompt to errors due to noise and radiation if they are implemented in hardware, as is the case of Cognitive Radios (CRs). Since noise is related to Channel Quality. In this article is presented an analysis of different ML algorithms for low and high Channel Quality classification. This can serve for future architectures to sense, know and understand the environment and react in accordance with the user necessities. The results showed that the most suitable ML algorithm (from the selected set) is Random Forest, since it achieved the highest score with a 95.54%.
DynaKey: Dynamic Keystroke Tracking Using a Head-Mounted Camera Device

Mobile and wearable devices have become more and more popular. However, the tiny touch screen leads to inefficient interaction with these devices, especially for text input. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DynaKey</i> , which allows people to type on a virtual keyboard printed on a piece of article or drawn on a desk, for inputting text into a head-mounted camera device (e.g., smart glasses). By using the built-in camera and gyroscope, we capture image frames during typing and detect possible head movements, then track keys, detect fingertips, and locate keystrokes. To track the changes of keys’ coordinates in images caused by natural head (i.e., camera) movements, we introduce perspective transformation to transform keys’ coordinates among different frames. To detect and locate keystrokes, we utilize the variation of fingertip’s coordinates across multiple frames to detect possible keystrokes for localization. To reduce the time cost, we combine gyroscope and camera to adaptively track the keys, and introduce a series of optimizations, such as keypoint detection, frame skipping, multithread processing, etc. Finally, we implement DynaKey on Android-powered devices. The extensive experimental results show that our system can efficiently track and locate the keystrokes in real time. Specifically, the average tracking deviation of the keyboard layout is less than 3 pixels and the Intersection over Union (IoU) of a key in two consecutive images is above 93%. The average keystroke localization accuracy reaches 95.5%.
Asynchronous Vector Iteration in Multi-objective Markov Decision Processes

This paper presents new algorithms to solve Multi-Objective Markov Decision Processes (MOMDPs). Namely, we present Multi-objective Dynamic Programming variants of Value Iteration such that the values for every state are updated in some heuristic order. The performance of these algorithms is evaluated applying them to benchmark problems with two and three objectives.
Rare Variants Analysis in Genetic Association Studies with Privacy Protection via Hybrid System

Genomic data are becoming widely used for diagnosis and treatment. Researchers require to study a significant number of genomes in order to analyze patient genomes to figure out some disease-gene associations. Cost-efficient commercial cloud computing services can host genomic data and conduct genome-wide association studies on-demand at considerably lower cost and high availability and scalability. Due to privacy and security issues, genomic data providers are unwilling to send their sensitive data to the cloud service providers without applying any privacy-preserving measures. This paper proposes a novel hybrid privacy-preserving framework that utilizes twofold cryptographic protocols and minimal perfect hash functions to reach maximum efficiency and security for outsourcing genomic data with the least time overhead. Our framework leverages a trusted execution environment (i.e. software guard extensions) to perform rare variant case-control association tests securely. Also, the genomic data outsourced from multiple data providers are encrypted through additive homomorphic encryption. We perform the Weighted-Sum Statistic (WSS) test, a rare variant association test, and we show that our scheme achieves high accuracy results without an increase of computational complexity or time overhead.
Designing Augmented Reality Workflows for Care Specific Tasks

Augmented Reality Head Mounted Devices have the potential to provide digital information to people while they are working. Besides other advantages, this enables the provision workers with instructions how to carry out certain tasks both for training and while they are working on the task. While using HMDs for the purposes has been shown to be beneficial in practice, the creation and adaptation of instructions and corresponding workflows is still a manual, time-consuming task. In this paper, we present a tool that enables the modelling and HMD specific configuration of such workflows, and that automatically instantiates these workflows on HMDs. This provides opportunities for creating user-specific workflows on HMDs and to adapt existing workflows to the needs of users. We illustrate the potential of our solution by the example of workflows in care
A Novel Human Parsing Method Driven by Multi-Scale Feature Blend Network

In recent years, human parsing has been developed a lot for its valuable utilization. However, existing methods have not fully solved semantic errors and incomplete semantic predictions. In this regard, a Multi-Scale Feature Blend Network(MFBNet) is proposed to deal with these problems from the respective of fusing multi-scale features. Specifically, we creatively introduce the Context Embedding module which uses the feature pyramid as the main structure to blend multi-scale feature information. Besides, ResNet-101 is applied as the backbone network to train and optimize shared weights and map the generated feature maps to the Context Embedding module. Experimental results on several wide-used datasets show that the proposed method outperforms than the state-of-art methods in human parsing.
The Face of Trust: Using Facial Action Units (AUs) as Indicators of Trust in Automation

Adaptive automation is pivotal for the calibration of trust between automation and its users. This paper presents an exploratory analysis into the use of facial expressions to trust in automation. This was done by analysing facial expressions of distrust and classifying into facial AUs composites, and comparing these to the 6 basic emotions. Results suggests the existence of two prototypical expressions of distrust that can be distinguished from the basic emotions based on AUs combinations. This shows promise into using facial AUs as a means for detecting distrust.
Eight Cutting-Edge Dilemmas and Strategic Projects

This chapter discusses eight key dilemmas that typically tend to be faced by leading business schools. Each of these call for carful trade-offs and balancing. These dilemmas might include:
Accelerating disaggregated data centers using unikernel

Resource disaggregation is a new hardware and system paradigm to split computation, memory, and storage into individual resource pools. Compared with the traditional single machine design, resource disaggregation allows independent scaling and managed grouping of hardware resources beyond the boundary of a single machine. It is a key driver to solve the low resource utilization [2] and to hide intermittent hardware failures [13, 14] in cloud systems. Cloud service providers are building disaggregated data centers (DDC) to take full advantage of resource disaggregation [8].
Dual Channel Among Task and Contribution on OSS Communities: An Empirical Study

Open Source Software (OSS) community has attracted a large number of distributed developers to work together, e.g. reporting and discussing issues as well as submitting and reviewing code. OSS developers create links among development units (e.g. issues and pull requests in GitHub), share their opinions and promote the resolution of development units. Although previous work has examined the role of links in recommending high-priority tasks and reducing resource waste, the understanding of the actual usage of links in practice is still limited. To address the research gap, we conduct an empirical study based on the 5W1H model and data mining from five popular OSS projects on GitHub. We find that links originating from a PR are more common than the other three types of links, and links are more frequently created in Documentation. We also find that average duration between development units’ create time in a link is half a year. We observed that link behaviors are very complex and the duration of link increases with the complexity of link structure. We also observe that the reasons of link are very different, especially in P–P and I–I. Finally, future works are discussed in conclusion.
The new frontier of platform policy

Platform policies aimed at the misbehaviour of users that occurs off of the platform, especially offline abuse, are a relatively new and understudied phenomenon that may represent a new frontier of platform policy.However, policies of this nature raise unique problems in comparison to on-platform content moderation that exacerbate existing concerns about the accountability of platforms by demanding both heightened due process for the accused and strong privacy and safety protections for complainants.This article discusses these challenges through the case study of Twitch.tv, and argues that further steps must be taken to ensure accountability to users and the public when such policies are implemented.
Pyramid Fusion Convolutional Network for Image Super-Resolution

Convolutional neural network has been proved to be an effective architecture for single image super-resolution. However, some of the high-frequency features are still not exhibited enough in the reconstructed image. To address this issue, we propose the Pyramid Fusion Convolutional Network to progressively improve performance. In the pre-trained module, our network constructs pyramid fusion architecture to exploit the inherent multi-scale information. At each feature level, we design a parallel convolutional skip-connections module to ensure the integrity of information. After that, a novel loss function with edge preservation is defined to train and seek optimal parameters. Unlike most typical approaches that only employ the final feature maps as input in the mapping module, we extract effective mapping results from different levels of features to deliver more accurate estimation. Extensive quantitative and qualitative evaluations have been demonstrated that our method performs better than state-of-the-art algorithms on a massive number of benchmarked images.
Simulation of quantum delayed-choice experiment through a single shot

A fundamental feature of micro objects is the wave-particle duality which is addressed by Bohr’s complementarity principle. To observe the wave and particle behaviours, quantum delayed-choice experiments based on linear optics have been realized at the single-photon level. Since they were performed by using a single photon as the input, repeating measurements were required in order to obtain different experimental data and adjusting experimental parameters was necessary prior to each of measurements. Different from the previous works, we here realize a simulation of quantum delayed-choice experiment through a single shot, which employs a classical intense light beam as the input instead of a single photon. Experimentally, we demonstrate the trade-off between distinguishability and visibility of photons in a two-arm interferometer in an intuitive way by utilizing the finite beam profile of the light. We observe the morphing between wave and particle natures of photons via a single shot of a charged-coupled-device camera. Since the image is captured within the exposure time which is several milliseconds, the phase fluctuation is negligible, and therefore our experimental setup is robust against the noise. This work provides a simple and new route to inspect quantum duality, which does not require adjusting experimental parameters frequently and only needs performing measurement once.
Evaluation Method of Physical Education Teaching in Higher Vocational Colleges Based on Analytic Hierarchy Process

It is difficult to evaluate the teaching quality of the traditional teaching methods, such as quantitative evaluation of the teaching quality of the students. The purpose of this paper is to construct the analytic hierarchy process (AHP) model of the teaching evaluation system of physical education teachers. According to the influence of various indexes on the quality of physical education, a comprehensive evaluation system of multi factor weight is established, and the corresponding weight of corresponding factors is calculated. Then, a simple and reliable evaluation calculation method is established. It provides a quantitative index for how to evaluate the teaching quality of physical education teachers more reasonably. The results of empirical test show that the evaluation index system can effectively measure the differences between the teaching ability of physical education teachers, and can comprehensively evaluate the teaching ability of physical education teachers in colleges.
Hybrid engineering on permeable groins to reduce the longshore current

Abstract This study aims to determine the effectiveness of hybrid engineering technology to be applied to the groin to reduce longshore currents. The application of hybrid engineering as a breakwater and sediment trap in coastal areas has given good results so that it can be developed in permeable groin structures. The research method used is laboratory experimental with physical modeling to make prototypes and conduct tests on the wave flume to determine changes in current velocity against the existence of a hybrid engineering structure with several model scenarios by changing the characteristics of brushwood groin such as length and distance between groin to surface height. water. The expected result of this study is an engineering model of the permeable groin that can function as an efficient and effective hybrid engineering in reducing longshore currents.
CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection

The robustness of gaze and head pose estimation models is highly dependent on the amount of labeled data. Recently, generative modeling has shown excellent results in generating photo-realistic images, which can alleviate the need for annotations. However, adopting such generative models to new domains while maintaining their ability to provide fine-grained control over different image attributes, e.g., gaze and head pose directions, has been a challenging problem. This paper proposes CUDA-GHR, an unsupervised domain adaptation framework that enables fine-grained control over gaze and head pose directions while preserving the appearance-related factors of the person. Our framework simultaneously learns to adapt to new domains and disentangle visual attributes such as appearance, gaze direction, and head orientation by utilizing a label-rich source domain and an unlabeled target domain. Extensive experiments on the benchmarking datasets show that the proposed method can outperform state-of-the-art techniques on both quantitative and qualitative evaluations. Furthermore, we demonstrate the effectiveness of generated image-label pairs in the target domain for pretraining networks for the downstream task of gaze and head pose estimation. The source code and pre-trained models are available at https://github.com/jswati31/cuda-ghr.