Reviewerly/exHarmony huggingface dataset:
Efficient and Robust Filtering Method for Medical CT Images

Abstract This paper introduces a new approach to ensure the certainty of medical diagnosis by eliminating the salt-and-pepper noise (SPN) in medical applications for both gray and coloured computed tomography (CT) images. The proposed approach is based on median filter which utilized for value-preserving and edge-preserving in digital image processing applications, Thus, the proposed approach is called improved adaptive median filter (IAMF). In contrast to the available research in the literature, the introduced method is characterized by the high filtering quality, robust in different noise intensities (low, medium and high) and computed efficiently. The obtained results of the filtering process have been analysed in terms of four main metrics: peak signal to noise ratio (PSNR), structure similarity (SSIM), universal image quality (UIQ) and filter average execution time (AET). The test scenarios were conducted using MATLAB 2019a running on Windows 10 computer. The success of the proposed filter has been validated by the statistical analysis based on the aforementioned metrics using gray and coloured medical CT images. For the worst case scenario in gray and coloured CT images when the noise intensity is 90%, the IAMF enhances the PSNR by 108-129%, the SSIM by 105-153% and the UIQ by 97-100% when they were compared to different filters that existed in the recent literature. These ratios depend on the image quality and image resolution. Moreover, the filter execution time has been improved by five times in gray scenarios and four times in coloured scenarios. Finally, the obtained results are visually verified as well.
Semantics of multiway dataflow constraint systems

Multiway dataflow constraint systems (MDCS) is a programming model where statements are not executed in a predetermined order. Rather, individual methods are selected from specific method sets and then executed to achieve a desired global state. The selection is done by a planner, which typically bases the choice of methods on the history of updates to the global state. MDCS is well suited for describing user interface logic where choosing what code to execute depends in complicated ways on the history of user interactions and on data availability. User interfaces are the domain of examples in this paper. Much of the research into MDCS has been on planning algorithms and their efficiency. Here we investigate a semantic setting for MDCS, introducing dataflow constraints as modules with explicit goals and related method sets. MDCS is defined in a similar manner, with an explicit goal and a set of supporting dataflow constraints. This enables verification and testing of methods and dataflow constraints against the goals. The exposition is based on abstract syntax for an idealised programming language with global variables. On top of this we define a modular reuse mechanism for dataflow constraints based on Goguen-Burstall institution theory. We show how this setup enables reuse in user interfaces; traditionally code that defines user interface logic is almost invariably non-reusable.
SISA News: Software to Support the Swedish Information Systems Community

This paper reports on the design, adoption, and continued use of the SISA News software. The software draws ideas from news syndication, the community of practice concept, ‘leaky knowledge in organizations, and microservice architectures. It provides a news flow for the Swedish information systems community through RSS, Facebook, and the web. The news flow aggregates information systems news from 18/20 Swedish IS departments. We evaluate the software through a multi-level community detection analysis from 24+ months of news consumption among 100 Swedish information systems researchers.
Horizontal Flipping Assisted Disentangled Feature Learning for Semi-supervised Person Re-identification

In this paper, we propose to learn a powerful Re-ID model by using less labeled data together with lots of unlabeled data, i.e. semi-supervised Re-ID. Such kind of learning enables Re-ID model to be more generalizable and scalable to real-world scenes. Specifically, we design a two-stream encoder-decoder-based structure with shared modules and parameters. For the encoder module, we take the original person image with its horizontal mirror image as a pair of inputs and encode deep features with identity and structural information properly disentangled. Then different combinations of disentangling features are used to reconstruct images in the decoder module. In addition to the commonly used constraints from identity consistency and image reconstruction consistency for loss function definition, we design a novel loss function of enforcing consistent transformation constraints on disentangled features. It is free of labels, and can be applied to both supervised and unsupervised learning branches in our model. Extensive results on four Re-ID datasets demonstrate that by reducing 5/6 labeled data, Our method achieves the best performance on Market-1501 and CUHK03, and comparable accuracy on DukeMTMC-reID and MSMT17.
Sequence to Sequence CycleGAN for Non-Parallel Sentiment Transfer with Identity Loss Pretraining

Sentiment transfer has been explored as non-parallel transfer tasks in natural language processing. Previous works depend on a single encoder to disentangle either positive or negative style from its content and rely on a style representation to transfer the style attributes. Utilizing a single encoder to learn disentanglement in both styles might not sufficient due to the different characteristics of each sentiment represented by various vocabularies in the corresponding style. To this end, we propose a sequence to sequence CycleGAN which trains different text generators (encoder-decoder) for each style transfer direction. Learning disentangled latent representations leads previous works to high sentiment accuracy but suffer to preserve the content of the original sentences. In order to manage the content preservation, we pretrained our text generator as autoencoder using the identity loss. The model shows an improvement in sentiment accuracy and BLEU score which indicates better content preservation. It leads our model to a better overall performance compared to baselines.
Extracting User Interests from Operation Logs on Museum Devices for Post-Learning

Nowadays, a variety of information on museum collections online has been stored as digital archives. With the increasing use of smartphones and tablets in daily life, visitors can obtain various knowledge of museum exhibits for pre-learning by using mobile devices and applications. Also, interactive learning systems in museums are very active in the field of information engineering, and interactive on-site learning is necessary for recent education. However, existing learning support systems mainly focused on support for pre-learning or on-site learning, and they are not enough to provide more advanced learning in per-learning or to deepen user interests in on-site learning. Therefore, it is necessary to support diverse knowledge levels of users on museum education for post-learning. In this paper, we aim to utilize video materials related to museums to support post-learning based on user interests by analyzing user interactions for exhibits on multimedia museum devices. For this, we propose a scoring method based on four features of user operation log data: keyword appearance frequency, keyword transition, media type, and media transition. Finally, we verified and discussed the effectiveness of our proposed scoring method through a user study.
Class-Balanced Loss for Scene Text Detection

To address class imbalance issue in scene text detection, we propose two novel loss functions, namely Class-Balanced Self Adaption Loss (CBSAL) and Class-Balanced First Power Loss (CBFPL). Specifically, CBSAL reshapes Cross Entropy (CE) loss to down-weight easy negatives and up-weight positives. However, CBSAL ignores gradient imbalance that CE gives positives and negatives different gradients. Since text detectors need to identify text and background simultaneously, positives and negatives have same importance and should possess equivalent gradients. Thus CBFPL provides equal but opposite gradients for positives and negatives to eliminate this gradient imbalance. Then, CBFPL abandons easy negatives and makes their gradients zero to handle class imbalance. Both CBSAL and CBFPL can focus training on positives and hard negatives. Experimental results show that on the basis of CBSAL and CBFPL, the efficient and accurate scene text detector (EAST) can achieve higher F-score on ICDAR2015, MSRA-TD500 and CASIA-10K datasets.
A comparison of abstract and concrete mass nouns in terms of their interaction with quantificational determiners

In this paper, I compare concrete mass nouns such as water with abstract mass nouns derived from gradable adjectives like generosity in terms of their interaction with quantificational determiners. The main focus is on vague quantifiers such as a lot and little, on the one hand, and specificity markers such as a certain, on the other. In both cases the crucial factor setting the abstract mass nouns apart from the concrete ones is that the latter make available only a quantity/cardinality related scale for measurement and identification. The former, in contrast, give rise to an additional reading since they are associated with a second scale – namely one that orders the states denoted by the respective noun according to the degree with which they instantiate the corresponding property.
ESENet: A Human Behavior Recognition Model Based on Extended Squeeze-and-Excitation Network

Human behavior recognition in video sequences is an important research problem in computer vision, and has attracted a lot of researchers’ attention in recent years. Because of its powerful ability in feature channels recalibration, the Squeeze-and-Excitation network (SE network) has been widely used in video behavior recognition models. However, most of these methods use the SE network directly to recalibrate different feature channels of a single frame image, but ignore the temporal dimension of videos. To address this problem, a new behavior recognition model based on extended Squeeze-and-Excitation network (named as ESENet) is proposed in this study. In order to improve the attention capability, the integration strategy of the 3DSE is also discussed in this study. Experiment results show that the proposed method in this paper achieves competitive performance on the challenging HMDB51, UCF101, and Something-Something v1 datasets.
A Proposal of Latent Interest Analysis by Geo-tagged SNS for Advertisement Recommendation

advertisement (ad) recommendation services for mobile users is rapidly increasing. The conventional ways of recommending ads are based on the analysis of users' explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on an analysis of the user's latent interest on web browsing history which categorized positive and negative behaviors. In this paper, we adapt the method of the linked pages to real world locations using geo-tagged tweets. By several evaluations, we discuss the possibility to recommend ads according to the user's current location.
Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter

This paper presents preliminary findings regarding automated bots mentioning scientific papers about COVID-19 publications on Twitter. A quantitative approach was adopted to characterize social and posting patterns of bots, in contrast to other users, in Twitter scholarly communication. Our findings indicate that bots play a prominent role in research dissemination and discussion on the social web. We observed 0.45% explicit bots in our sample, producing 2.9% of tweets. The results implicate that bots tweeted differently from non-bot accounts in terms of the volume and frequency of tweeting, the way handling the content of tweets, as well as preferences in article selection. In the meanwhile, their behavioral patterns may not be the same as Twitter bots in another context. This study contributes to the literature by enriching the understanding of automated accounts in the process of scholarly communication and demonstrating the potentials of bot-related studies in altmetrics research.
Fast Ellipse Fitting Implementation on USG Mobile Telehealth Application

Fetal head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, measuring the fetal head is not an easy task. This study aims to create an automatic fetal head measurement system. This system is expected to run on mobile devices as part of telehealth system. HC measurement can be done with object detection method, followed by edge detection, then using every edge pixel, fetal head can be approximated using ellipse fitting. Evaluations are carried out using hit rates and error rates for ellipse fitting. From each method that was tested, the evaluation result showed that the Adaptive Boosting and Fast Ellipse Fitting (ElliFit) method had the best performance. This method also had a relatively fast execution time for a mobile device, which is 3-5 seconds.
Digital forensic organisational capability

Digital evidence and digital forensic capabilities have become core investigational capabilities in law enforcement agencies over the past two decades. The importance and need for digital evidence capability has now, however, gone beyond a ubiquitous requirement for law enforcement and regulatory agencies to become a necessary capability in a much wider range of industries and organisations. Any organisation, whether public, private, not-for-profit, or government, has a need to access digital evidence capabilities, whether in-house or through a service provider. I cannot think of an organisation that does not have assets of value, such as intellectual property, personal and private data concerning its employees and the management of funds that is not held, or can be accessed, by a computer system. Digital evidence principles and capabilities are key to not only protecting those valuable assets but also determining if the assets have been subjected to unauthorised access or have been misused.
Lightweight Short-term Photovoltaic Power Prediction for Mobile Edge Computing

To meet the needs for energy savings in Internet of Things (IoT) and mobile systems, solar energy has been increasingly exploited to serve as a green and renewable source to allow systems to better operate in an energy-efficient way. In this respect, accurate photovoltaics (PV) power output prediction is a prerequisite for any energy saving scheme employed in these systems [1].
Distinguishing Attacks on Linearly Filtered NFSRs with Decimated Output

This paper presents an investigation into the resistance of linearly filtered nonlinear feedback shift registers (LF-NFSRs) against distinguishing attacks. We formalise the method described by Orumiehchiha, Pieprzyk, Steinfeld and Bartlett and then extend it to develop a more efficient, systematic framework for accurately distinguishing an arbitrary LF-NFSR. Our method is then generalised to distinguish arbitrary LF-NFSRs with regularly decimated output sequences. The proposed method is demonstrated through application to the example LF-NFSR used by Orumiehchiha et al. with improved results. Additionally, our new method can be accurately applied to much larger registers and can predict how much output is needed to find the strongest bias. To demonstrate this, we derive time and keystream requirement estimates for our attacks on each variant of the Grain family of stream ciphers under weak key-IV pairs.
Finding EFL and EQL Allocations of Indivisible Goods

Fair resource allocation has become an emerging research topic in Computer Science and Artificial Intelligence. We can judge whether the allocation is “fair” from two aspects. First, if each agent prefers his own set of bundle, that is, envy-free, then we say that the allocation is fair; If each agent's valuation of his own item set is equal to other agents' valuation of its own item-equitability (EQ), then we also say that this allocation is fair. Solving the problem of envy-free (EF) or equitability (EQ) fair allocation is proved to be NP-hard, so this paper mainly studies the problem of approximate fair allocation i.e., approximate envy-free and approximate equitability. Our contribution are as follows. 1. We proved that equitable up to one less-preferred good (EQL) allocation always exists and can be found in polynomial time; 2. We proved that the allocation that satisfies both equitable up to one less-preferred good (EQL) and Pareto optimality (PO) exists and provides a pseudo-polynomial time algorithm that can find the allocation when the valuation function is additive with strictly positive; 3. We proved that when the allocation with specific valuation that satisfies equitable up to one less-preferred good (EQL), envy-free up to one less-preferred good (EFL) and Pareto optimality (PO) exists then it can be found in polynomial time.
Application of IAFSA-BP neural network in face orientation recognition

Abstract In order to improve the recognition accuracy of BP neural network in face orientation recognition, an improved artificial fish swarm algorithm is proposed to optimize the weights and thresholds of BP neural network face orientation recognition model. The improved artificial fish swarm algorithm is based on the standard algorithm, introducing adaptive factors to make the horizon and step size adaptive change, and at the same time learning the solution before the result announcement, so as to improve the accuracy of the final result. Finally, the experimental results show that the effective combination of the improved artificial fish swarm algorithm and the BP neural network algorithm can improve the output accuracy of face orientation recognition of the BP neural network, and the running speed of the improved algorithm is significantly higher than that of the standard artificial fish swarm algorithm.
Resource management in client-server computing systems based on trust assessment

Abstract The article describes a trust assessment model and a method based on it for managing client access to scarce resources in a client-server computing system. Trust in a client is the degree to which it meets the standard. The trust assessment score is based on an ordinal preference scale with a limited normalized weight of criteria. The model allows you to get a numerical assessment of the client’s compliance with a given set of criteria. The method of managing computer system resources is based on a controlled impact on the communication channel between the client and the server of the system. This method describes how to select a threshold value for the trust assessment score. The method allows you to reasonably limit the total number of clients in the system and thereby increase the probability of access to scarce computing resources of the server for trusted clients.
Round Based Extension Algorithm for Gossip Learning

Distributed training algorithms represent a promising direction in providing specially crafted machine learning models, personalized on more appropriate user data samples producing greater overall user experience.We propose an extension for the fully distributed, peer-to-peer based Gossip Learning algorithm, enhancing it with additional memory for storing local caches of model updates. In our simulations, the extension algorithm achieved better performance for both accuracy and convergence time. One inherent advantage of the extension is that it makes the algorithm better suited for deployment on mobile devices. We also examine some technical aspects of deploying a Gossip Learning framework onto mobile devices, defining some basic building blocks, followed by technical implementation solutions for the Android operating system.
An Embedding-Based Approach to Repairing Question Semantics

A question with complete semantics can be answered correctly. In other words, it contains all the basic semantic elements. In fact, the problem is not always complete due to the ambiguity of the user’s intentions. Unfortunately, there is very little research on this issue. In this paper, we present an embedding-based approach to completing question semantics by inspiring from knowledge graph completion based on our proposed representation of a complete basic question as unique type and subject and multiple possible constraints. Firstly, we propose a back-and-forth-based matching method to acknowledge the question type as well as a word2vec-based method to extract all constraints via question subject and its semantic relevant in knowledge bases. Secondly, we introduce a time-aware recommendation to choose the best candidates from vast possible constraints for capturing users’ intents precisely. Finally, we present constraint-independence-based attention to generate complete questions naturally. Experiments verifies the effectiveness of our approach.
Kernel-controlled DQN based CNN Pruning for Model Compression and Acceleration

Apart from the accuracy, the size of Convolutional Neural Networks (CNN) model is another principal factor for facilitating the deployment of models on memory, power and budget constrained devices. Conventional compression techniques require human expert to setup parameters to explore the design space and iterative based pruning requires heavy training which is sub-optimal and time consuming. Given a CNN model, we propose deep reinforcement learning [8] DQN based automated compression which effectively turned off kernels on each layer by observing its significance. Observing accuracy, compression ratio and convergence rate, proposed DQN model can automatically re- activate the healthiest kernels back to train it again to regain accuracy which greatly ameliorate the model compression quality. Based on experiments on MNIST [3] dataset, our method can compress convolution layers for VGG-like [10] model up to 60% with 0.5% increase in test accuracy within less than a half the number of initial amount of training (speed-up up to 2.5×), state- of-the-art results of dropping 80% of kernels (compressed 86% parameters) with increase in accuracy by 0.14%. Further dropping 84% of kernels (compressed 94% parameters) with the loss of 0.4% accuracy. The first proposed Auto-AEC (Accuracy-Ensured Compression) model can compress the network by preserving original accuracy or increase in accuracy of the model, whereas, the second proposed Auto-CECA (Compression-Ensured Considering the Accuracy) model can compress to the maximum by preserving original accuracy or minimal drop of accuracy. We further analyze effectiveness of kernels on different layers based on how our model explores and exploits in various stages of training.
A successful long-term relationship: Three years of LTI integrations in Canvas

As Florida State University transitioned to Canvas in 2017, the Distance Library Services team planned and implemented Springshare LTI, subject-specific Library Tools within the Learning Management System. These tools present invaluable teaching and learning opportunities for the libraries within digital classrooms and offer options for connecting students to library staff and services. With heightened interest from the distance library community in LMS integration, the goal of this paper is to examine an experience with LTI integration after three years of partnership. Furthermore, recommendations will be offered for other libraries interested in implementing LTI integrations in their LMS to support distance learning and on-campus populations.