Reviewerly/exHarmony huggingface dataset:
Hide and seek in pathology- A research on game-based histopathology learning

To know the effectiveness of a Game-Based Histopathology learning method. &#x0D; To create a new Image oriented Game Based Histopathology slide reading method. To know the usefulness of the new method. To compare the conventional learning and game-based learning methods. Two groups of undergraduate students were tested with two different learning methods to identify pathology slide sections of four lesions. For each group, sixteen Undergraduate dental students were selected randomly. A game was created by using histopathology images of those four lesions, and circulated among the group one. The conventional method of slide reading was given to the group two. Both the group was tested for diagnosing the four pathology lesions through light microscopy spotter identification. Results were calculated accordingly. A questionnaire survey was done based on the pattern and pathology features oriented diagnostic capacity. Results of the questionnaire survey were also analyzed. Statistically, the results of both the two groups were analyzed. An Independent t-test was done and found out there was no significant difference between the two learning methods. The questionnaire survey revealed that the group learned through game-based pathology slide learning method learned the morphological features better than that of the conventional slide learning method. The image oriented game based pathology slide learning helps the undergraduate students to diagnose the pathology lesions with proper knowledge of morphological features than the conventional slide learning method, which is mainly pattern oriented. Though it is a very complicated procedure, the game based slide learning method is fun, creative and involves a majority of the student’s attention towards morphological features of any pathological lesions.
Extrinsic Factors Influencing Internal Auditors’ Effectiveness in Jordanian Public Sector

This study focuses on the extrinsic factors, namely, top management support, complexity, independence, and internal audit department&amp;rsquo;s size that are out of internal auditors&amp;#39; control affecting their effectiveness in the Jordanian public sector. The current study also tries to improve understanding of the extrinsic factors affecting internal auditors&amp;rsquo; ability to achieve the assigned goals in order to highlight internal auditors&amp;rsquo; effectiveness. Resource-based and agency theories were used in developing the research model. Two sets of questionnaires were distributed among the financial managers and internal audit managers. The results reveal that top management support, independence, and the size of internal audit department play a significant and positive role on the effectiveness of internal auditors, whereas complexity of the task has been found to make a negative impact on the level of their performance. Given the significance of the public sector within the Jordanian economy, the findings are valuable for the internal audit function, regulators, and decision-makers in proposing new legislation and regulations of an internal audit function. Future studies may look into other factors that may restrict internal audit performance, such as organizational culture and pay satisfaction.
Multilabel Deep Visual-Semantic Embedding

Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. We propose a new learning paradigm for multilabel image classification, in which labels are ranked according to its relevance to the input image. In contrast to conventional CNN models that learn a latent vector representation (i.e., the image embedding vector), the developed visual model learns a mapping (i.e., a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, the proposed model achieves state-of-the-art results on three public benchmark datasets.
libmpk: Software Abstraction for Intel Memory Protection Keys (Intel MPK).

Intel Memory Protection Keys (MPK) is a new hardware primitive to support thread-local permission control on groups of pages without requiring modification of page tables. Unfortunately, its current hardware implementation and software support suffer from security, scalability, and semantic problems: (1) vulnerable to protection-key-use-after-free; (2) providing the limited number of protection keys; and (3) incompatible with mprotect()’s process-based permission model. In this paper, we propose libmpk, a software abstraction for MPK. It virtualizes the hardware protection keys to eliminate the protection-key-use-after-free problem while providing accesses to an unlimited number of virtualized keys. To support legacy applications, it also provides a lazy inter-thread key synchronization. To enhance the security of MPK itself, libmpk restricts unauthorized writes to its metadata. We apply libmpk to three real-world applications: OpenSSL, JavaScript JIT compiler, and Memcached for memory protection and isolation. Our evaluation shows that it introduces negligible performance overhead (
Augmented Interval List: a novel data structure for efficient genomic interval search

Abstract Motivation Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary. Results We present a new data structure, the Augmented Interval List (AIList), to enumerate intersections between a query interval q and an interval set R. An AIList is constructed by first sorting R as a list by the interval start coordinate, then decomposing it into a few approximately flattened components (sublists), and then augmenting each sublist with the running maximum interval end. The query time for AIList is O(log2N+n+m), where n is the number of overlaps between R and q, N is the number of intervals in the set R and m is the average number of extra comparisons required to find the n overlaps. Tested on real genomic interval datasets, AIList code runs 5–18 times faster than standard high-performance code based on augmented interval-trees, nested containment lists or R-trees (BEDTools). For large datasets, the memory-usage for AIList is 4–60% of other methods. The AIList data structure, therefore, provides a significantly improved fundamental operation for highly scalable genomic data analysis. Availability and implementation An implementation of the AIList data structure with both construction and search algorithms is available at http://ailist.databio.org. Supplementary information Supplementary data are available at Bioinformatics online.
Research and analysis of threshold segmentation algorithms in image processing

Abstract Image segmentation is one of the most difficult and important tasks in digital image processing. The accuracy and effect of segmentation determine the final success or failure of the calculation and analysis process. Therefore, analysis and improvement on basic image segmentation algorithms should be paid attention to in a quite wide range of applications. This paper mainly introduces and studies the principle and characteristics of threshold segmentation algorithm, and analyses the application scenarios of global threshold segmentation and adaptive local threshold segmentation related algorithms, which has certain reference significance for digital image processing related research.
Attention Neural Model for Temporal Relation Extraction

Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction.
DICER

Workload consolidation has been shown to achieve improved resource utilisation in modern datacentres. In this paper we focus on the extended problem of allocating resources when co-locating High-Priority (HP) and Best-Effort (BE) applications. Current approaches either neglect this prioritisation and focus on maximising the utilisation of the server or favour HP execution resulting to severe performance degradation for BEs. We propose DICER, a novel, practical, dynamic cache partitioning scheme that adapts the LLC allocation to the needs of the HP and assigns spare cache resources to the BEs. Our evaluation reveals that DICER successfully increases the system's utilisation, while at the same time minimising the impact of co-location on HP's performance.
A Holistic Forensic Model for the Internet of Things

The explosive growth of the Internet of Things offers numerous innovative applications such as smart homes, e-healthcare, smart surveillance, smart industries, smart cities and smart grids. However, this has significantly increased the threat of attacks that exploit the vulnerable surfaces of Internet of Things devices. It is, therefore, immensely important to develop security solutions for protecting vulnerable devices and digital forensic models for recovering evidence of suspected attacks. Digital forensic solutions typically target specific application domains such as smart wearables, smart surveillance systems and smart homes. What is needed is a holistic approach that covers the diverse application domains, eliminating the overhead of employing ad hoc models. This chapter presents a holistic forensic model for the Internet of Things that is based on the ISO/IEC 27043 international standard. The model has three phases – forensic readiness (proactive), forensic initialization (incident) and forensic investigation (reactive) – that cover the entire lifecycle of Internet of Things forensics. The holistic model, which provides a customizable and configurable environment that supports diverse Internet of Things applications, can be enhanced to create a comprehensive framework.
An empirical study of real-world variability bugs detected by variability-oblivious tools

Many critical software systems developed in C utilize compile-time configurability. The many possible configurations of this software make bug detection through static analysis difficult. While variability-aware static analyses have been developed, there remains a gap between those and state-of-the-art static bug detection tools. In order to collect data on how such tools may perform and to develop real-world benchmarks, we present a way to leverage configuration sampling, off-the-shelf "variability-oblivious" bug detectors, and automatic feature identification techniques to simulate a variability-aware analysis. We instantiate our approach using four popular static analysis tools on three highly configurable, real-world C projects, obtaining 36,061 warnings, 80% of which are variability warnings. We analyze the warnings we collect from these experiments, finding that most results are variability warnings of a variety of kinds such as NULL dereference. We then manually investigate these warnings to produce a benchmark of 77 confirmed true bugs (52 of which are variability bugs) useful for future development of variability-aware analyses.
A Cognitive Model of Human Bias in Matching

The schema matching problem is at the basis of integrating structured and semi-structured data. Being investigated in the fields of databases, AI, semantic Web and data mining for many years, the core challenge still remains the ability to create quality matchers, automatic tools for identifying correspondences among data concepts (e.g., database attributes). In this work, we investigate human matchers behavior using a new concept termed match consistency and introduce a novel use of cognitive models to explain human matcher performance. Using empirical evidence, we further show that human matching suffers from predictable biases when matching schemata, which prevent them from providing consistent matching.
Multi-Label Remote Sensing Scene Classification Using Multi-Bag Integration

For remote sensing (RS) scene classification, most of the existing techniques annotate a scene image with merely a single semantic label.However, with the recent advance of remote sensing technology, more abundant information is contained in high-resolution scenes, making a scene image having multiple semantic meanings (i.e., multilabels).Since multi-label RS scene image annotation is a domain full of challenges due to the ambiguities between complicated scene contents and labels, it motivates us to present a novel algorithm which is based on multi-bag integration.First, to describe the semantic content of RS scene image, we propose to partition a scene image into image patches, defined by a regular grid, and extract the heterogeneous features within each.Second, two kinds of image instance bag, namely segmented instance bag (SIB) and layered instance bag (LIB), are designed to represent the scene image.Third, a Mahalanobis distance-based K-Medoids approach is applied to cluster SIB and LIB, respectively, to convert the multi-instance into single-instance, and then the obtained two single-instances are concatenated to generate more powerful scene-aware representation.At last, a multi-class classification technique is used to make predictions on the class labels.Experiments are performed on real remote sensing images and the results show that the proposed method is valid and can achieve superior performance to a number of stateof-the-art approaches.
Asking Images: Hybrid Recommendation System for Tourist Spots by Hierarchical Sampling Statistics and Multimodal Visual Bayesian Personalized Ranking

Data sparseness'' is a key issue in current research works on recommendation systems.However, additional information, such as texts, images, knowledge graph, and audios, that is correlated to items helps alleviate the problem to some extent.We focus our research on designing a novel hybrid recommendation system for tourist spots.Tourist spot images are utilized to suppress the ''data sparseness'' problem in the recommendation procedure.First, a novel multimodal visual bayesian personalized ranking algorithm is proposed to fully utilize the cross-modal semantic correlations among different image features.Then, a new recommendation list called L A is generated accordingly from the multimodal perspective.Second, user preference is acquired using the hierarchical sampling statistics model.A new recommendation list called L H is generated in turn from the statistical perspective.Finally, hybrid recommendation results are obtained on the basis of L H and L A .Experimental results demonstrate that the proposed hybrid recommendation system for tourist spots is effective and robust.It is superior to other competitive baselines.More importantly, the proposed hybrid recommendation system is good at recommending a group of tourist spots and more stable than baselines, indicating its high practical value.
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce

Homepage is the first touch point in the customer’s journey and is one of the prominent channels of revenue for many e-commerce companies. A user’s attention is mostly captured by homepage banner images (also called Ads/Creatives). The set of banners shown and their design, influence the customer’s interest and plays a key role in optimizing the click through rates of the banners. Presently, massive and repetitive effort is put in, to manually create aesthetically pleasing banner images. Due to the large amount of time and effort involved in this process, only a small set of banners are made live at any point. This reduces the number of banners created as well as the degree of personalization that can be achieved. This paper thus presents a method to generate creatives automatically on a large scale in a short duration. The availability of diverse banners generated helps in improving personalization as they can cater to the taste of larger audience. The focus of our paper is on generating a wide variety of homepage banners that can be made as an input for a user-level personalization engine. Following are the main contributions of this paper: (1) We introduce and explain the need for large scale banner generation for e-commerce companies (2) We present on how we utilize existing deep learning based detectors which can automatically annotate the required objects/tags from the image. (3) We also propose a Genetic Algorithm based method to generate an optimal banner layout for the given image content, input components and other design constraints. (4) Further, to aid the process of picking the right set of banners, we designed a ranking method and evaluated multiple models. All our experiments have been performed on data from Myntra ( http://www.myntra.com ), one of the top fashion e-commerce players in India.
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

Despite some exciting progress on high-quality image generation from structured (scene graphs) or free-form (sentences) descriptions, most of them only guarantee the image-level semantical consistency, i.e. the generated image matching the semantic meaning of the description. They still lack the investigations on synthesizing the images in a more controllable way, like finely manipulating the visual appearance of every object. Therefore, to generate the images with preferred objects and rich interactions, we propose a semi-parametric method, PasteGAN, for generating the image from the scene graph and the image crops, where spatial arrangements of the objects and their pair-wise relationships are defined by the scene graph and the object appearances are determined by the given object crops. To enhance the interactions of the objects in the output, we design a Crop Refining Network and an Object-Image Fuser to embed the objects as well as their relationships into one map. Multiple losses work collaboratively to guarantee the generated images highly respecting the crops and complying with the scene graphs while maintaining excellent image quality. A crop selector is also proposed to pick the most-compatible crops from our external object tank by encoding the interactions around the objects in the scene graph if the crops are not provided. Evaluated on Visual Genome and COCO-Stuff dataset, our proposed method significantly outperforms the SOTA methods on Inception Score, Diversity Score and Frechet Inception Distance. Extensive experiments also demonstrate our method’s ability to generate complex and diverse images with given objects. The code is available at https://github.com/yikang-li/PasteGAN.
Longitudinal Prediction Modeling of Alzheimer Disease using Recurrent Neural Networks

This paper proposes an implementation of Recurrent Neural Networks (RNNs) for (a) predicting future Mini-Mental State Examination (MMSE) scores in a longitudinal study and (b) deploying a multiclass multimodal neuroimaging classification process that involves three different known stages of Alzheimer's progression, cognitively normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). This multimodal data is fed into two well-studied variations of the RNNs; Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The accuracy, F-score, sensitivity, and specificity of the models are reported for the classification task as well as the root mean square error (RMSE) and correlation coefficient for the regression task. The results demonstrate the superiority of the proposed model over state-of-the-art classification and regression techniques of Support Vector Machine (SVM), Support Vector Regression (SVR) and Ridge Regression.
A novel True Random Bit Generator design for image encryption

Random Numbers are popular research topic due to the performance in encryption techniques. Owing to the protection of communication data against eavesdroppers, randomness is quiet important. The aim of this paper is to introduce an easily applicable True Random Bit Generator for image encryption. For this purpose, electromagnetic pollution is used as entropy source. Normalization procedure with very simple mathematical equations are applied on electric field strength values. Additionally, XOR post-processing procedure is performed. Different randomness test are studied for Random Numbers: NIST 800-22 Test Suite, Autocorrelation Test, Scale-Index Test. Subsequently, Tests are applied on normalized data and post-processed data separately. NIST Tests and Scale-Index Test are accomplished for both data, but Autocorrelation Test is successful for only Post-Processed values. Encryption of images are made with generated bits. This paper proves that, performing only NIST Tests are not trustworthy in True Random Bit Generators. High Frequency Electromagnetic Pollution firstly used as an entropy source in this work. Encryption process of sample images is concluded successfully.
Medical Handwritten Prescription Recognition Using CRNN

Reading a doctor's handwritten prescription is a challenge that most patients and some pharmacists face; an issue that, in some cases, lead to negative consequences due to wrong deciphering of the prescription. Part of the reason why doctor's prescriptions are so difficult to decipher is that doctors make use of Latin abbreviations and medical terminology that most people don't understand. This paper demonstrates how Artificial Neural Networks (ANN) is used to develop a system that can recognize handwritten English medical prescriptions. Using the Deep Convolution Recurrent Neural Network to train this supervised system, input images are segmented and processed to detect characters and classify them into the 64 different predefined characters. The results show that the proposed system yields good recognition rates and an accuracy of %98.
Experience

Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? an open-hardware, open-software wrist-worn computing device? and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.