Reviewerly/exHarmony huggingface dataset:
Progressive Local Filter Pruning for Image Retrieval Acceleration

This paper focuses on network pruning for image retrieval acceleration. Prevailing image retrieval works target at the discriminative feature learning, while little attention is paid to how to accelerate the model inference, which should be taken into consideration in real-world practice. The challenge of pruning image retrieval models is that the middle-level feature should be preserved as much as possible. Such different requirements of the retrieval and classification model make the traditional pruning methods not that suitable for our task. To solve the problem, we propose a new Progressive Local Filter Pruning (PLFP) method for image retrieval acceleration. Specifically, layer by layer, we analyze the local geometric properties of each filter and select the one that can be replaced by the neighbors. Then we progressively prune the filter by gradually changing the filter weights. In this way, the representation ability of the model is preserved. To verify this, we evaluate our method on two widely-used image retrieval datasets,i.e., Oxford5k and Paris6K, and one person re-identification dataset,i.e., Market-1501. The proposed method arrives with superior performance to the conventional pruning methods, suggesting the effectiveness of the proposed method for image retrieval.
Class-Prototype Discriminative Network for Generalized Zero-Shot Learning

We present a novel end-to-end deep metric learning model named Class-Prototype Discriminative Network (CPDN) for Generalized Zero-Shot Learning (GZSL). It consists of a generative network for producing the visual prototype of each class by feeding its semantic representation, and a metric network for measuring the similarities between the sample and the generated class-prototypes to accomplish the classification. In CPDN, a query sample intends to posses a higher similarity with its homogenous class-prototypes while the lower similarities with the inhomogenous ones, and the class-prototypes also intend to be distinguished with each other through the metric network. Moreover, a discriminative version of Relation Network (RN) named Discriminative Relation Network (DRN) is presented by incorporating the aforementioned idea into the conventional RN model for further achieving the complementation CDPN and RN in metric learning. Extensive experimental results on standard benchmarks demonstrate that our proposed approaches consistently outperform RN, and achieve the competitive performances compared with the state-of-the-arts in GZSL.
Improving Access to Design Information of Paper-Based Floor Plans Using Augmented Reality

Despite the advancement of technologies and tools used to design and display building elements in construction, design communication on construction sites still heavily relies on 2-dimensional drawings. Paper-based floor plans often reference other paper drawings to deliver design intent to construction site crews. This complicated process sometimes results in the poor transfer of design intent between the designers and construction crews, which can cause quality, safety, and productivity issues in construction sites. How can the process of access to information in 2D drawings be improved to reduce the need to reference different sheets? This study responds to this question by implementing an AR workflow that provides more straightforward access from paper-based floor plans to digital design information without the need to refer to other sheets. In this paper, the current process of viewing and accessing information using 2D drawings was investigated. Then an AR workflow was created to facilitate access to the design information. Finally a case study was used to test the AR workflow against the conventional method of using 2D drawings, considering variables such as time, error, and user preference. The results showed that the AR workflow is more favorable and could improve time and reduce errors.
Multi-Task Network Representation Learning

Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and facilitate downstream network analysis. The existing embedding methods commonly endeavor to capture structure information in a network, but lack of consideration of subsequent tasks and synergies between these tasks, which are of equal importance for learning desirable network representations. To address this issue, we propose a novel multi-task network representation learning (MTNRL) framework, which is end-to-end and more effective for underlying tasks. The original network and the incomplete network share a unified embedding layer followed by node classification and link prediction tasks that simultaneously perform on the embedding vectors. By optimizing the multi-task loss function, our framework jointly learns task-oriented embedding representations for each node. Besides, our framework is suitable for all network embedding methods, and the experiment results on several benchmark datasets demonstrate the effectiveness of the proposed framework compared with state-of-the-art methods.
Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks

Sign language is the main method for the deaf-mute to communicate with each other. However, normal people cannot understand the meaning of specific gestures without training. In this study, a real-time Chinese Sign Language (CSL) recognition model based on surface electromyographic (sEMG) signals and Artificial Neural Networks (ANN) is proposed. The model achieves an average accuracy at 88.7% on 15 CSL gestures with average response time around 300ms (timing at movement begins). In our approach, the MYO armband is used for acquiring raw sEMG signals. Signal preprocessing includes rectification, filter and extracting the muscle activation section. We apply a sliding window and a muscle detection approach to segment the signals and extract features. A test method is used to recognize the gesture when the totality of same label, which returned by ANN classifier, reach the activation times threshold.
Analysis of adversary activities using cloud-based web services to enhance cyber threat intelligence

Abstract The understanding of cyber threats to a network is challenging yet rewarding as it allows an organisation to prevent a potential attack. Numerous efforts have been made to predict cyber threat before they occur. To build a threat intelligence framework, an organisation must understand attack data collected from the network events and analyse them to identify the cyber attack artefacts such as IP address, domain name, tools and techniques, username and password, and geographic location of the attacker, which could be used to understand the nature of attack to a system or network. However, it is very difficult or dangerous to collect and analyse live data from a production system. Honeypot technology is well known for mimicking the real system while collecting actual data that can be in near real time in order to monitor the activities on the network. This paper proposes a threat intelligence approach analysing attack data collected using cloud-based web service in order to support the active threat intelligence.
Cost-Efficient Outsourced Decryption of Attribute-Based Encryption Schemes for Both Users and Cloud Server in Green Cloud Computing

To reduce a user's decryption cost and protect the private information from being leaked, Green et al. proposed an approach outsourcing the decryption of the attribute based encryption (ABE) scheme to the cloud server. Later, almost all ABE schemes with outsourced decryption (ABE-OD) used their model or approach. However, the cloud server needs to repeat the outsourced decryption service of the same ciphertext for distinct users satisfying the same access policy in these schemes. Green computing is the atmosphere conscientious and recyclable utilization of resources. The green cloud networks can reduce their cost or energy requirements by adapting its performance, optimizing resources management and services. The method is not efficient for the cloud server in the green cloud networks. In this article, to take into account recyclable utilization of resources for the cloud server, we put forward a new and secure approach to reduce total overhead of the cloud server when many users satisfying an access policy require the outsourced decryptions for the same ciphertext besides decreasing the decryption computation cost for users. Compared with the existing ABE-OD schemes, our total overhead of the cloud server is independent of the number of the users who satisfy an access policy and request the outsourcing decryption service. Finally, we extend our approach to a RCCA-secure ABE-OD scheme.
Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.
Relational reference attribute grammars: Improving continuous model validation

Just like current software systems, conceptual models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated, validated and serialized. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform incremental analysis efficiently using dynamic dependency tracking. However, not all features used in conceptual modelling are directly available in RAGs. In particular, support for noncontainment model relations is only available through encodings. We present an approach called Relational RAGs to directly model uni- and bidirectional noncontainment relations in RAGs and provide efficient means for navigating and editing them. Furthermore, we discuss the efficient and inter-operable serialization and deserialization of such model instances. This approach is evaluated using a scalable benchmark for incremental model editing and the JastAdd RAG system. Our work demonstrates the suitability of RAGs for validating complex and continuously changing models of current software systems.
Readability of Cancer Clinical Trials Websites

Clinical trials are critically important for the development of new cancer treatments. According to recent estimates, however, clinical trial enrollment is only about 8%. Lack of patient understanding or awareness of clinical trials is one reason for the low rate of participation. The purpose of this observational study was to evaluate the readability of cancer clinical trial websites designed to educate the general public and patients about clinical trials. Nearly 90% of Americans use Google to search for health-related information. We conducted a Google Chrome Incognito search in 2018 using the keywords "cancer clinical trial" and "cancer clinical trials." Content of the 100 cancer clinical trial websites was analyzed using an online readability panel consisting of Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning-Fog Index, Coleman-Liau Index, and Simple Measure of Gobbledygook scales. Reading level difficulty was assessed and compared between commercial versus non-commercial URL extensions. Content readability was found to be "difficult" (10.7 grade level). No significant difference in readability, overall, and between commercial and non-commercial URL extensions was found using 4/5 measures of readability; 90.9% of commercial versus 49.4% of non-commercial websites were written at a >10th grade (P = .013) using Gunning-Fog Index. Written cancer clinical trials content on the Internet is written at a reading level beyond the literacy capabilities of the average American reader. Improving readability to accommodate readers with basic literacy skills will provide an opportunity for greater comprehension that could potentially result in higher rates of clinical trial enrollment.
HSV Color Histogram Based Image Retrieval with Background Elimination

In this study, a new content based image retrieval (CBIR) method, which uses HSV histogram data is proposed. The model uses the HSV histogram to find the background from the image by analyzing the peaks in the histogram data and performing a moving window algorithm to identify the region within the histogram that belongs to the background colors. After identifying the background information, the sections of the image that are part of the background are removed from the original image and the remaining foreground or content information is stored for comparison with other images. In order to verify the methodology, a graphical user interface is developed and 1000 different images from 10 different groups from the coral database are put into the image database for comparison. The analysis and preliminary tests show that comparing only the foreground information of the images pro- vided better results than comparing images themselves, especially when searching for particular objects within the images. This algorithm can also be used as a background elimination technique to reduce the storage requirements of images and the comparison time between images can be reduced significantly.
Detection of Customer Satisfaction on Unbalanced and Multi-Class Data Using Machine Learning Algorithms

With the increase of internet technologies, the opinions and suggestions of the previous customers are very important for both organizations and the people who supply them when purchasing goods and services. In particular, the spread of bad comments about goods and service providers on the internet is a harbinger of a great disaster for these companies. For this reason, companies want to compensate for their lack of customer feedback. They compete with each other to maximize customer satisfaction. However, as the number of feedbacks increases, their work becomes more difficult. In this study, it is aimed to determine customer satisfaction automatically in order to overcome this difficulty. The study was conducted on the text data of six different airline companies. These data consist of three different class labels: positive, negative and neutral. Natural Language Processing (NLP) steps, which we can call as text mining, are processed on these data. Using different Machine Learning (ML) algorithms, the results are examined with tables and graphs. The unbalanced distribution of data make difficult the success of the algorithms used in the study. It is estimated that this study is a reference source for literature that will attract the attention of researchers interested in natural language processing and machine learning algorithms, and importantly most many companies.
Propaedeutics of Solving Extreme Problems in the School Course of Mathematics

This article discusses the propaedeutics of solving extreme problems. As is known, such tasks play an important role in various fields of science and technology. They often reduce many of the problems that arise in the economy, industry and agriculture. As a rule, the solution of these problems requires the use of a fairly complex mathematical apparatus studied in higher education. However, many problems on the maximum and minimum can be solved by elementary means without the use of higher mathematics. The solution of such problems is very useful for students as propaedeutics of the relevant subjects.
It is about time: Bias and its mitigation in time-saving decisions in software development projects

Estimates of completion times in software development projects are frequently inaccurate, potentially resulting in failure to meet project objectives. The present work aims at empirically investigating whether the time-saving bias, describing the human failure to correctly estimate the relationship between speed increase and time saving, can inform our understanding of the decades-long problem of time estimation in software development. In particular, this work examines whether a decision to save time in a software development project by increasing development speed is biased, whether this bias is observed when the decision is framed using plan-based and agile terminology, and whether the availability of relevant information mitigates this bias. These objectives are addressed in three experimental studies, in which senior information systems students (Study 1) and professional software project managers (Studies 2 and 3) are asked to make time-saving decisions about two similar scenarios, with and without relevant information. The findings confirm the existence of the bias and show that it is more likely to occur under an agile framing than under a plan-based framing, although students are highly biased in both cases. The findings also show that while the bias is mitigated, but not eliminated, when relevant information is included in the scenario, this effect dissipates once the information is no longer included in the following scenario. The accumulated evidence reported here contributes to research on the consequences of cognitive biases for project management decisions.
Acceptance of Blockchain Based Supply Chain Management System: Research Model Proposal

Blockchain technology provides emerging solutions including decentralized management, security, privacy and robustness. In Supply Chain Management (SCM) applications, blockchain technology enable us to increase customer satisfaction, operational excellence, and to decrease operational costs and risks. Despite of these significant benefits, there are limited number of studies that combines SCM and blockchain in the literature. The main aim of this study is to investigate the factors influencing the adoption and acceptance of the blockchain based SCM in organizations. To this end, a comprehensive systematic literature review is conducted and a research model based on Technology-Organization-Environment (TOE) framework is proposed. The proposed research model consists of factors of the relative advantage, complexity, interoperability/compatibility, standardization, trust and scalability in the context of technology; organization's IT resource, top management support, organization size and financial resources in the context of organization; competitive pressure, trading partner pressure, government policy and regulations and inter-organizational trust in the context of environment.
A dissimilarity measure for mixed nominal and ordinal attribute data in k-Modes algorithm

Among the existing clustering algorithms, the k-Means algorithm is one of the most commonly used clustering methods. As an extension of the k-Means algorithm, the k-Modes algorithm has been widely applied to categorical data clustering by replacing means with modes. However, there are more mixed-type data containing categorical, ordinal and numerical attributes. Mixed-type data clustering problem has recently attracted much attention from the data mining research community, but most of them fail to notice the ordinal attributes and establish explicit metric similarity of ordinal attributes. In this paper, the limitations of some existing dissimilarity measure of k-Modes algorithm in mixed ordinal and nominal data are analyzed by using some illustrative examples. Based on the idea of mining ordinal information of ordinal attribute, a new dissimilarity measure for the k-Modes algorithm to cluster this type of data is proposed. The distinct characteristic of the new dissimilarity measure is to take account of the ordinal information of ordinal attribute. A convergence study and time complexity of the k-Modes algorithm based on this new dissimilarity measure indicates that it can be effectively used for large data sets. The results of comparative experiments on nine real data sets from UCI show the effectiveness of the new dissimilarity measure.
Infrastructure data fusion for validation and future enhancements of autonomous vehicles' perception on Austrian motorways

The electronic horizon of a vehicle is limited by the range of the different sensor technologies. In contrast, infrastructure sensor data can track the traffic flow and even single vehicles from a bird's perspective, and this data can be used to post-validate driving maneuvers by comparing a vehicle's on-board sensor data with the infrastructure data. In the future, this data could even be used to enhance autonomous vehicles' perception. This paper highlights the achievements in data fusion of state-of-the-art and novel infrastructure sensor data and their planned use for the vehicle-to-infrastructure communication.
A Practicable Copula-Based Approach for Power Forecasting of Small-Scale Photovoltaic Systems

Photovoltaic (PV) systems are well known as one of the most significant sources of distributed generations in the world. Nowadays, the usage of small-scale PVs by households in residential homes has increased noticeably. The output power of PVs is associated with uncertainty, so there is a need for a practicable method to forecast the output power that can be used by households. The current article presents a feasible method to predict the power of small-scale PVs in a day-ahead pattern. This applicable model does not have the complexities of other forecasting methods and can be applied by households in general. On the other hand, power produced by PVs depends on meteorological characteristics. Meanwhile, one important point to be noted is the fact that in many models the dependency of the PVs output power on meteorological characteristics is taken into account, but the correlation among these meteorological features is neglected. Therefore, in this article, the copula is used in order to model stochastic correlation structure among meteorological characteristics, such as ambient temperature, wind speed, and solar irradiance. Subsequently, by using a mathematical model for obtaining the output power of the PVs in parallel with utilizing the Monte Carlo method the prediction is performed. Here, to study the proposed method, a PV unit of a residential home in Swanson region in New Zealand is considered. The simulation results demonstrated high accuracy of the proposed method in diverse meteorological conditions.