Reviewerly/exHarmony huggingface dataset:
Compressed Image Quality Assessment Based on Saak Features

Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.
Guided Similarity Separation for Image Retrieval

Despite recent progress in computer vision, image retrieval remains a challenging open problem. Numerous variations such as view angle, lighting and occlusion make it difficult to design models that are both robust and efficient. Many leading methods traverse the nearest neighbor graph to exploit higher order neighbor information and uncover the highly complex underlying manifold. In this work we propose a different approach where we leverage graph convolutional networks to directly encode neighbor information into image descriptors. We further leverage ideas from clustering and manifold learning, and introduce an unsupervised loss based on pairwise separation of image similarities. Empirically, we demonstrate that our model is able to successfully learn a new descriptor space that significantly improves retrieval accuracy, while still allowing efficient inner product inference. Experiments on five public benchmarks show highly competitive performance with up to 24\% relative improvement in mAP over leading baselines. Full code for this work is available here: https://github.com/layer6ai-labs/GSS.
Traffic-Aware and Reliability-Guaranteed Virtual Machine Placement Optimization in Cloud Datacenters

With the increasing scale of cloud datacenters and rapid development of virtualization technologies, many cloud-based services have been deployed to meet requirements. Virtual machines (VMs) are placed on physical servers, and often provide virtual environment for cloud services. Therefore, virtual machines placement (VMP) problem has gradually attracted many attentions. It is meaningful that how to effectively and efficiently place VMs on servers to guarantee the service reliability and reduce the bandwidth consumption. In this paper, we first formulate VMP with a reliability model and a bandwidth consumption model, and analyse its complexity. Then we propose a VMP optimization approach to solve the problem and prove its effectiveness and efficiency. The core algorithm of our approach is an approximation algorithm to get VM partitions under the constraint of a specified reliability parameter. Then placement problem is transformed into matching problem between VM partitions with physical servers. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.
Asymmetric Distances for Approximate Differential Privacy

Differential privacy is a widely studied notion of privacy for various models of computation, based on measuring differences between probability distributions. We consider (epsilon,delta)-differential privacy in the setting of labelled Markov chains. For a given epsilon, the parameter delta can be captured by a variant of the total variation distance, which we call lv_{alpha} (where alpha = e^{epsilon}). First we study lv_{alpha} directly, showing that it cannot be computed exactly. However, the associated approximation problem turns out to be in PSPACE and #P-hard. Next we introduce a new bisimilarity distance for bounding lv_{alpha} from above, which provides a tighter bound than previously known distances while remaining computable with the same complexity (polynomial time with an NP oracle). We also propose an alternative bound that can be computed in polynomial time. Finally, we illustrate the distances on case studies.
Clustering of Argument Graphs Using Semantic Similarity Measures

Research on argumentation in Artificial Intelligence recently investigates new methods that contribute to the vision of developing robust argumentation machines. One line of research explores ways of reasoning with natural language arguments coming from information sources on the web as a foundation for the deliberation and synthesis of arguments in specific domains. This paper builds upon arguments represented as argument graphs in the standardized Argument Interchange Format. While previous work was focused on the development of semantic similarity measures used for the case-based retrieval of argument graphs, this paper addresses the problem of clustering argument graphs to explore structures that facilitate argumentation interpretation. We propose a k-medoid and an agglomerative clustering approach based on semantic similarity measures. We compare the clustering results based on a graph-based semantic measure that takes the structure of the argument into account with a semantic word2vec measure on the pure textual argument representation. Experiments based on the Microtext corpus show that the graph-based similarity is best on internal evaluation measures, while the pure textual measure performs very well for identifying topic-specific clusters.
Image processing based Tracking and Counting Vehicles

In this research work, we explore the vehicle detection technique that can be used for traffic surveillance systems. This system works with the integration of CCTV cameras for detecting the cars. Initial step will always be car object detection. Haar Cascades are used for detection of car in the footage. Viola Jones Algorithm is used in training these cascade classifiers. We modify it to find unique objects in the video, by tracking each car in a selected region of interest. This is one of the fastest methods to correctly identify, track and count a car object with accuracy up to 78 percent.
A model for creating a virtual library in Iranian universities of medical sciences

Purpose This paper aims to propose an integrating model for creating virtual libraries in Iranian universities of medical sciences. Design/methodology/approach This study was conducted with an analytic survey method. The statistical population comprised 66 Iranian universities of medical sciences, of which 59 libraries participated in the study. A researcher-made checklist was used for data collection. To ensure the accuracy of data, interviews and, in some cases, observations were also performed. Statistical estimates, including frequency, percentage, cumulative frequency and diagrams, were used for data analysis, and the system analysis method was used for modeling. Findings Results demonstrated that the library software programs of the studied universities of medical sciences do not have desirable interoperability capabilities. Only Azarsa program can exchange information with other systems. In terms of metadata and its standards, the studied libraries use programs with various standards, with MARC and Dublin Core standards being the most frequently used ones in the studied sample. Originality/value The model proposed here for integration is a hybrid model which can translate metadata standards and use the Z39.50 and OEI protocol to transfer data.
Progress in Research Data Services

University libraries have played an important role in constructing an infrastructure of support for Research Data Management at an institutional level. This paper presents a comparative analysis of two international surveys of libraries about their involvement in Research Data Services conducted in 2014 and 2018. The aim was to explore how services had developed over this time period, and to explore the drivers and barriers to change. In particular, there was an interest in how far the FAIR data principles had been adopted.&#x0D; Services in nearly every area were more developed in 2018 than before, but technical services remained less developed than advisory. Progress on institutional policy was also evident. However, priorities did not seem to have shifted significantly. Open ended answers suggested that funder policy, rather than researcher demand, remained the main driver of service development and that resources and skills gaps remained issues. While widely understood as an important reference point and standard, because of their relatively recent publication date, FAIR principles had not been widely adopted explicitly in policy.
A Comparative Analysis of the Implementation of the Software Basic Profile of ISO/IEC 29110 in Thirteen Teams That Used Predictive Versus Adaptive Life Cycles

The growing of the software demand has created opportunities for software development organizations. Especially for very small entities (VSEs), because a large number of them produce software for medium and large companies as part of a production chain that provides products and services to satisfy market needs. This situation highlights the increasing need for improving their software development processes in order to stay in business by developing quality software products and services with limited resources. Unfortunately, a common issue faced by most of the VSEs is the lack of knowledge and practical experience regarding the implementation of current SPI models and software engineering standards. In this context, the ISO/IEC 29110 series of standards and guides have been developed to help VSEs to improve their development processes. One of the main features of this standard is that it can be adapted to the software lifecycle of VSEs. This paper presents the results of a comparative analysis of 13 software organizations with teams that work using predictive methodologies and those using adaptive methodologies during the implementation of the software Basic profile of the ISO/IEC 29110. The results show that after executing the method teams have a high level of coverage regarding the ISO/IEC 29110.
Physical Adversarial Attacks by Projecting Perturbations

Research on adversarial attacks analyses how to slightly manipulate patterns like images to make a classifier believe it recogises a pattern with a wrong label, although the correct label is obvious to humans. In traffic sign recognition, previous physical adversarial attacks were mainly based on stickers or graffity on the sign’s surface. In this paper, we propose and experimentally verify a new threat model that projects perturbations onto street signs via projectors or simulated laser pointers. No physical manipulation is required, which makes the attack difficult to detect. Attacks via projection imply new constraints like exclusively increasing colour intensities or manipulating certain colour channels. As exemplary experiments, we fool neural networks to classify stop signs as priority signs only by projecting optimised perturbations onto original traffic signs.
A participatory platform supporting awareness and empathy building between tourists and locals

In this paper, we describe the conceptualization, design and preliminary evaluation of Há-Vita, a participatory platform populated with video interviews about nature and biodiversity preservation, designed primarily for tourists and visitors of a popular Touristic Island destination. The content was selected to raise awareness towards environmental sustainability related. As a strategy to engage the public with the local biodiversity, we envisioned to foster interaction between tourists and locals. Evaluating our strategy with tourists revealed itself to be a research challenge on its own. Nevertheless, our results shed light on content creation and design choices of the platform, as well as reflecting on the methodological challenges of conducting HCI research with real tourists in situ.
Updating the Data Curation Continuum

The Data Curation Continuum was developed as a way of thinking about data repository infrastructure. Since its original development over a decade ago, a number of things have changed in the data infrastructure domain. This paper revisits the thinking behind the original data curation continuum and updates it to respond to changes in research objects, storage models, and the repository landscape in general. &#x0D;
How to Empower Disease Diagnosis in a Medical Education System Using Knowledge Graph

Disease diagnosis is an important function in a medical training system, an integrated system which is aimed at providing the necessary skills and know-how to health practitioners. As one of the most vital features of a medical training system, many researchers and industry alike have channelled time and resources to engage several techniques and practices in a bid to find a way to accurately predict diseases with minimal margin of error. This has motivated several variations in feature selection, data representations and techniques in machine learning. In this paper, we explore some of these variations with prime focus on how knowledge graphs have helped address issues like insufficient data and interpretation to help empower the construction of a disease diagnosis feature in a medical training systems.
Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) are another specialized scheme of neural network architectures. RNNs are developed to solve learning problems where information about the past (i.e., past instants/events) is directly linked to making future predictions. Such sequential examples play up frequently in many real-world tasks such as language modeling where the previous words in the sentence are used to determine what the next word will be. Also in stock market prediction, the last hour/day/week stock prices define the future stock movement. RNNs are particularly tuned for time series or sequential tasks.
An Amharic Syllable-Based Speech Corpus for Continuous Speech Recognition

Speech recognition systems play an important role in solving problems such as spoken content retrieval. Thus, we are interested in the task of speech recognition for low-resource languages, such as Amharic. The main challenges in solving Amharic speech recognition are the limited availability of corpora and complex morphological nature of the language. This paper presents a new corpus for the low-resource Amharic language which is suitable for training and evaluation of speech recognition systems. The corpus prepared contains 90 h of speech data with word and syllable-based annotation. Moreover, the use of syllable units for acoustic and language model in comparison with a morpheme-based model is presented. Syllable-based triphone speech recognition system provides a lower word error rate of 16.82% on the subset of the dataset. Moreover, syllable-based hybrid deep neural network with hidden Markov model provides a 14.36% word error rate.
Optimal Path Planning Method of Marine Sailboat Based on Fuzzy Neural Network

Sun, K.; Wu, J.; Sun, Z., and Cao, Z., 2019. Optimal path planning method of marine sailboat based on fuzzy neural network. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 911–916. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the automatic planning and adaptive scheduling ability of sailing route and guide the management of sailing route, an optimal path planning model based on fuzzy neural network is proposed. The control instruction information of sailing route planning is extracted by using wireless sensor networking information scheduling technology, and the sailing route planning is carried out by combining the network information recognition and pattern scheduling of sailing route at sea. The statistical big data is extracted, the fuzzy scheduling of the associated feature set after information sampling is carried out, and the fuzzy neural network control technology is used to automatically screen and optimize the route of sailing route planning at sea. The information management platform of the Internet of things is constructed to carry out the automatic planning and distributed cooperative scheduling of the sailing route at sea, and the statistical data of the route planning of the sailing ship collected by all the member nodes are evenly configured in the cluster of things. The fuzzy PID neural network learning method is used to plan the sailing route of marine sailboat, and the distributed cooperative scheduling of sailing route planning is realized according to the result of automatic optimization. The simulation results show that the system can effectively realize the automatic planning and optimal scheduling of sailing route, and the reliability of route planning is high and the convergence is good.
2D Henon-Chebyshev Chaotic Map for Image Encryption

Chaotic systems is widely employed in image encryption on account of its momentous properties including nonperiodicity, ergodicity, randomness and initial state sensitivity. However, some chaos-based cryptosystems still exhibit security problems due to the weak performance of their applied chaotic maps. To address those problems, a new two-dimensional HenonChebyshev map (2D-HCM) is first proposed in this paper. It is generated by connecting Henon map and Chebyshev map where the output of one map is used to adapt the input of the others. It results in a better chaotic performance in 2D-HCM, including better chaotic behaviors, higher chaotic index, wider chaotic range and finer ergodicity than other extant chaotic maps. Using the 2D-HCM, we further design an image encryption algorithm. The proposed algorithm disorganize the pixel positions of the input plain image with random sequence generated by 2D-HCM, and then convert it into DNA-planes using DNA operation rules. Finally, the cipher image is acquired by employing the newly defined DNA-level 2D cellular automata (DNA-CA) to update the DNA-planes in each iteration where 2D-HCM produces the rule number sequences applied in the update. Our scheme has proven to be highly efficient and secure by the experimental results and security analysis.
Monitorability over Unreliable Channels

In Runtime Verification (RV), monitoring a system means checking an execution trace of a program for satisfactions and violations of properties. The question of which properties can be effectively monitored over ideal channels has mostly been answered by prior work. However, program monitoring is often deployed for remote systems where communications may be unreliable. In this work, we address the question of what properties are monitorable over an unreliable communication channel. We describe the different types of mutations that may be introduced to an execution trace and examine their effects on program monitoring. We propose a fixed-parameter tractable algorithm for determining the immunity of a finite automaton to a trace mutation and show how it can be used to classify $$\omega $$ -regular properties as monitorable over channels with that mutation.
A Comprehensive Teaching Reform Model for a Computer Networks Course Based on Integrated Information Systems

in integrating and realigning the applied skills training at Minnan Normal University (MNNU), we analyzed the current situation and problems in the teaching of the Computer Networks course based on national standards on teaching quality and the Engineering Education Accreditation standards of China. Guided by a self-developed educational management system, teaching ex-perimentation platform and questionnaire survey system, based in a cloud com-puting platform, we adopted a problem-oriented research method to explore comprehensive teaching reform from four perspectives: teaching methods, teacher development, resource guarantees and continual improvement. The teaching reform cases show that the students’ ability to solve problems was sig-nificantly improved, and the goal of cultivating student knowledge, ability and quality has been achieved.
An Improved Object Detection Method for Mitosis Detection

Breast cancer grading is important for patient prognosis, and the mitosis count is one of the most important indicators for breast cancer grading. Traditional methods use handcraft features and deep learning based methods to detect mitosis in a classified model. These methods are time-consuming and difficult for practical clinical practice application. For this reason, this paper proposes an improved object detection method for automatic mitosis detection from histological images. First, we use a convolutional neural network (CNN) to automatically extract mitosis features. Then, we use the region proposed network (RPN) to locate a set of class-agnostic mitosis proposals. Finally, we use the improved R-CNN subnet to screen for mitosis from these proposals. Our approach achieved the best results in the ICPR2012 mitosis detection competition test dataset. Additionally, our proposed method is fast enough to be potentially used in clinical and health centers.
Storing Object-Dependent Sparse Codes in a Willshaw Associative Network

Willshaw networks are single-layered neural networks that store associations between binary vectors. Using only binary weights, these networks can be implemented efficiently to store large numbers of patterns and allow for fault-tolerant recovery of those patterns from noisy cues. However, this is only the case when the involved codes are sparse and randomly generated. In this letter, we use a recently proposed approach that maps visual patterns into informative binary features. By doing so, we manage to transform MNIST handwritten digits into well-distributed codes that we then store in a Willshaw network in autoassociation. We perform experiments with both noisy and noiseless cues and verify a tenuous impact on the recovered pattern's relevant information. More specifically, we were able to perform retrieval after filling the memory to several factors of its number of units while preserving the information of the class to which the pattern belongs.
“SkyMap”: World-Scale Immersive Spatial Display

To relate typical survey map features to the real world during navigation, users must make time-consuming, error-prone cognitive transformations in scale and rotation and make frequent realignments over time. In this paper, we introduce SkyMap, a novel immersive display device method that presents a world-scaled and world-aligned map above the user that evokes a huge mirror in the sky. This approach, which we have implemented in a VR-based testbed, potentially reduces cognitive effort associated with survey map use. We discuss first-hand observations and further areas of research. User evaluations to compare performance under various task scenarios are currently under way.