Reviewerly/exHarmony huggingface dataset:
UWKGM

A knowledge graph becomes a central data hub in the enterprise and the research communities. Nevertheless, the development of knowledge graphs is challenging due to the insufficient functionalities of knowledge graph management platforms. In this paper, we develop a knowledge graph management platform (UWKGM). This platform enables users to integrate arbitrary functionalities as RESTful API services in order to facilitate the knowledge graph development process. In the demonstration, we highlight the main features of UWKGM and its use cases on knowledge graph management tasks.
Vallum-Med

Despite the many advantages of cloud computing, keeping information in such an environment increases the risk of cyber attacks, as well as the possibility of unauthorized access by cloud provider employees. Another critical concern is privacy protection, since depending on data access control, confidential information may be exposed even through authorized access. To solve these issues we have previously proposed Vallum, a platform that leverages Intel SGX protection to ensure the security, confidentiality, and integrity of data at rest and during processing. It also provides tools for privacy protection, following policies set by the data owner. In this demo we present Vallum-Med, an application of Vallum for the protection of medical patient personal data, including imaging results of their cardiac examinations. We will demonstrate that this system fully supports cloud protection of such sensitive data as well as the definition of privacy policies and ensuring that all results of queries are compliant to these policies. All processing, data storage and network traffic are protected using SCONE, a docker container-based technology for seamlessly incorporating SGX protection for applications, which provides a fully encrypted memory environment.
Convolutional Neural Network Based Ensemble Approach for Homoglyph Recognition

Homoglyphs are pairs of visual representations of Unicode characters that look similar to the human eye. Identifying homoglyphs is extremely useful for building a strong defence mechanism against many phishing and spoofing attacks, ID imitation, profanity abusing, etc. Although there is a list of discovered homoglyphs published by Unicode consortium, regular expansion of Unicode character scripts necessitates a robust and reliable algorithm that is capable of identifying all possible new homoglyphs. In this article, we first show that shallow Convolutional Neural Networks are capable of identifying homoglyphs. We propose two variations, both of which obtain very high accuracy (99.44%) on our benchmark dataset. We also report that adoption of transfer learning allows for another model to achieve 100% recall on our dataset. We ensemble these three methods to obtain 99.72% accuracy on our independent test dataset. These results illustrate the superiority of our ensembled model in detecting homoglyphs and suggest that our model can be used to detect new homoglyphs when increasing Unicode characters are added. As a by-product, we also prepare a benchmark dataset based on the currently available list of homoglyphs.
Feature Extraction for Large-Scale Text Collections

Feature engineering is a fundamental but poorly documented component in Learning-to-Rank (LTR) search engines. Such features are commonly used to construct learning models for web and product search engines, recommender systems, and question-answering tasks. In each of these domains, there is a growing interest in the creation of open-access test collections that promote reproducible research. However, there are still few open-source software packages capable of extracting high-quality machine learning features from large text collections. Instead, most feature-based LTR research relies on "canned" test collections, which often do not expose critical details about the underlying collection or implementation details of the extracted features. Both of these are crucial to collection creation and deployment of a search engine into production. So in this regard, the experiments are rarely reproducible with new features or collections, or helpful for companies wishing to deploy LTR systems.
Cost-efficient Integration Process Placement in Multiclouds

Integration as a service (INTaaS) is the centrepiece of current corporate, cloud and device integration processes. Thereby, compositions of integration patterns denote the required integration logic as integration processes, currently running in single-clouds. While multicloud settings gain importance, their promised freedom of selecting the best option for a specific problem is currently not realized as well as security constraints are handled in a cost-intensive manner for the INTaaS vendors, leading to security vs. costs goal conflicts.In this work, we propose a design-time placement for processes in multiclouds that is cost-optimal for the INTaaS vendors, and respects configurable security constraints of their customers. To make the solution tractable for larger, productive INTaaS processes, it is relaxed using local search heuristics. The approach is evaluated on real-world integration processes with respect to cost- and runtime-efficiency, and discusses interesting trade-offs.
Comparison of Algorithms and Action Coordinates Sets in Detection of Slight Differences in Motions like Lock-Picking

A security guard must always watch all monitors when he manually detects a malicious person by surveillance cameras. Actions with big motions can be automatically classified by machine learning in some researches but the detection of slight differences in motions is difficult. Therefore, we tried to distinguish lock-picking from unlocking by a key by machine learning and evaluated what data augmentation increased the accuracy. As a result, we found that the data augmentation was not so effective while algorithms of machine learning were more important. We also considered that selection of action coordinates might influence the accuracy. In this paper, we compare CNN with SVM by applying them to our method. In addition, we evaluate the relation between F-measure and selected action coordinates. In a result of an experiment, the average F-measure of two examinees was higher than 90%. Moreover, the score was kept when action coordinates were limited to the right arm. That is, actual users are not forced to be limited their standing position and their posture. Our method releases a security guard from watching all monitors every time.
Human-Centric Preference Modeling for Virtual Agents

Intelligent virtual agents increasingly use machine learning to model the needs and preferences of their users in order to give personalized support. In this paper we present a vision of "human-centric modeling" that moves this practice beyond the automated estimation of users' current preferences based on observable behaviors to a modeling process that a) considers users' hidden characteristics rather than observable behaviors, b) models possible futures rather than users' current state, and c) gives users an opportunity to interactively control this modeling process. This vision focuses on the long-term needs of individual humans and a genuine betterment of society.
Ultra short-term forecast of photovoltaic generation based on time-division LSTM neural networks

Accurate forecast of photovoltaic generation is important for power system operation. This paper proposes a new ultra short-term forecast of PV generation based on Spearman correlation coefficient analysis and time-division long short-term memory network. The method first uses the Spearman correlation coefficient to analyze the factors affecting photovoltaic power at each moment. The influencing factors with high correlation are selected as the input variables of the long short-term memory network model. A forecasting model based on long short-term memory network is established for each moment to realize the forecasting of time-division photovoltaic generation. Finally, case study is performed using historical generation output of a practical PV plant. Study results indicate that the proposed method has higher forecast precision than the single long short-term memory network forecast model.
Explainable Cluster-Based Rules Generation for Image Retrieval and Classification

In this paper, we propose a method for rules generation for images, which can be further used to enhance the classification accuracy of a convolutional neural network as well as to increase the level of explainability of neural network decisions. For each image from the training set, a descriptor is created based on the information contained in the convolutional layers of the network. The descriptors of images of the same class are then used to create a single-class prototype, which is characteristic of that class. The prototypes of different but semantically similar prototypes are then grouped into major categories using the DBSCAN algorithm. The analysis is carried out using the ILSVRC dataset and the VGG16 net is used. The experimental verification performed on the validation set demonstrated the validity and general nature of the rules obtained using the proposed method.
Chapter 8. Working with images and emoji in the 🦆 Dukki Facebook Corpus

In this chapter, I demonstrate how researchers can incorporate image content and emoji into corpus approaches to social media data. I present data from the Facebook Page of Dukki Gifts & Souvenirs, an independent business based in Nottingham, U.K. and, using a simple annotation scheme, I show how researchers can incorporate image content and emoji into frequency and collocation analysis in order to examine the patterns of use for images in this dataset. I discuss some of the adjustments required to extend approaches conventionally applied to written forms of data to image content, including how this might affect the way we think of core concepts, such as collocation.
Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation

Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is challenging to apply parallel computing or a pipeline structure to update the weights in different layers simultaneously. In this letter, we introduce a novel learning structure, associated learning (AL), that modularizes the network into smaller components, each of which has a local objective. Because the objectives are mutually independent, AL can learn the parameters in different layers independently and simultaneously, so it is feasible to apply a pipeline structure to improve the training throughput. Specifically, this pipeline structure improves the complexity of the training time from [Formula: see text], which is the time complexity when using BP and stochastic gradient descent (SGD) for training, to [Formula: see text], where [Formula: see text] is the number of training instances and [Formula: see text] is the number of hidden layers. Surprisingly, even though most of the parameters in AL do not directly interact with the target variable, training deep models by this method yields accuracies comparable to those from models trained using typical BP methods, in which all parameters are used to predict the target variable. Consequently, because of the scalability and the predictive power demonstrated in the experiments, AL deserves further study to determine the better hyperparameter settings, such as activation function selection, learning rate scheduling, and weight initialization, to accumulate experience, as we have done over the years with the typical BP method. In addition, perhaps our design can also inspire new network designs for deep learning. Our implementation is available at https://github.com/SamYWK/Associated_Learning .
Creating an Academic Conversational Agent for Dynamic Information Retrieval

The popularization and evolution of online conversational agents, also known as chatbots, provoked disruptive changes in the communication between institutions and people. Motivated by the growing need to provide more efficient mechanisms for information retrieval from legislation, manuals, and operational procedures, as well as taking advantage of recent technological developments, we propose an architectural model for conversational agents specialized in institutional information retrieval from unstructured documents (e.g., PDFs, DOCX). Besides, we present a conversational agent, created from our architectural model, to assist in the search and retrieve for information related to resolutions and internal regulations at our university. An evaluation performed with users using the System Usability Score (SUS) method revealed that our conversational agent presented itself as an effective alternative, achieving almost the best imaginable usability (SUS Score 89), and speeding up the search and retrieval for institutional information.
Dual Task Training for Face Expression Recognition

Due to the increased interest in human-computer interaction, Facial Expression Recognition (FER) is among the most popular research fields in computer vision and state of the solutions are based on machine learning techniques. In this paper, we propose the use of a deep neural network trained with a joint loss for better discrimination of action units (AUs). The main idea is to label the action units images with what we have entitled pseudo-expressions. In addition to the classic Mean Squared Error (MSE) loss used for a multi-class, multiple instance leaning problem, we added a Cross Entropy term to gain advantage of pseudo-emotions to better learn the AUs. Last part of the experiment consisted of using the center loss in order to better cluster the embeddings harvested on fully-connected layers.
ML Suite: An Auto Machine Learning Tool

In today’s age, it is important for some businesses to upgrade to machine learning techniques. The aim of this project is to create an autonomous platform for researchers/laymen who operate on data, which would auto-clean the data and suggest machine learning approach to understand and get better value out of data. The proposed project includes a minimalistic user interface and complex backend to cater to user’s needs such as better data visualization and summarization, using pandas and matplotlib operations in python. The ultimate goal is to obtain an autonomy such that the user does not have to write a single line of code. The data cleaning has been generalized for different structures of datasets in Django backend. The algorithms are allotted according to target columns which will be set by user in UI. User can try different ML approaches by just choosing options in UI. The project also includes complementary features such as auto web scraping to gather similar advancements in user’s research area, Automatic report generation using semantics of natural language processing and dynamic file management. The project also features a chatbot which can be used to query various document and can give answers using semantic querying. This way a layman can be introduced to power of computing using machine learning to speed up businesses and scholar work.
Starting a search: Exploring the differences in two digital libraries

Abstract We conducted an eye‐tracking study in two digital libraries to examine how users orientate themselves on the digital library homepages, how fast and by which actions they initiate the search process. Results showed that participants typically started their task by querying. However, there were some differences in the elements they fixated on first, the way they started the search and the time they needed to orient on the page. These differences seem to have been caused by the homepage layout, especially by the search box position. This shows eye‐tracking can be used to examine first actions in digital libraries and could be useful for designing better interfaces.
Speckle Denoising of the Multipolarization Images by Hybrid Filters

Speckle reduction in synthetic aperture radar (SAR) images and SAR polarimetry (PolSAR) is important for the extraction of important information for homogeneous extended areas. Finding good balance between spatial detail preservation and high equivalent number of looks is a challenge. In this paper, we validate the use of the infinite number of looks filter (INLP) method for the denoising of the multipolarization channels (i. e. hh, hv and vv) by representing their analogy with the eigenvalues of the coherency matrix. The denoised pixels obtained by a traditional filtering method served as inputs for the INLP method. Results demonstrated that the proposed method outperformed traditional filters i. e. boxcar and the refined sigma filter in terms of spatial detail preserving and speckle reduction.
An Approach Towards Hotel Recommender System

Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable hotel from available database and shows on the map by using a leaflet map package.
Imitating Agents in A Complex Environment by Generative Adversarial Imitation Learning

The generative adversarial imitation learning (GAIL) shows the ability to find reward functions to explain expert players' behaviors in some low-dimensional environments using hand-crafted features as inputs. In this research, we aim to extend GAIL to complex environments and using raw images as inputs. We propose to (1) use convolutional neural networks to deal with image inputs, (2) adopt a structure called global-local discriminator to GAIL, and (3) represent trajectories as state-state pairs instead of state-action pairs. Our approach successfully imitates given players in Super Mario Bros. To our knowledge, the results are the first to have successful imitations in complex environments based on image inputs.
Structural fragmentation in scene graphs

Despite continuous performance improvements, contemporary Scene Graph (SG) systems tend to generate ‘fragmented’ graphs. A central problem is that standard metrics only measure similarity to ground truth graphs at the triplet level and may not fully capture image relevance or semantic correctness. In particular, multiple triplet predictions are usually made for the same ground truth regions, which can be considered as a trivial method to improve the standard evaluation metric, i.e. recall. The central purpose of our work is to reveal the inherent drawback of current SG evaluation methods and the resultant redundancy issue. We investigate different types of graph artifacts in SGs generated by existing models and propose two graph quality metrics to evaluate the level of fragmentation. Detailed analysis is given to show how SG model architectures contributes to graph fragmentation. We study these problems in the context of graph semantic quality assessment. Qualitative assessment via human study is conducted to evaluate the semantic consistency between the proposed metrics and human perception. To further clarify the validity of the new source of error, a simple but effective method which targets graph fragmentation is presented. Systematic experiments are conducted with the standard Visual Genome (VG) dataset and the Visual Relationship Detection (VRD) dataset. Experimental results show that our proposed system significantly improves the scene graph quality in terms of the new metrics as well as the traditional Top-N recall values.
An Interdependency between Symmetric Ciphers and Hash Functions: A Survey

Symmetric ciphers are one of the most famous cryptographic paradigms in cryptography. Its simplistic algorithm makes it easy to understand, and it has not very complex implementation. Hash functions, on the other hand, are the fundamental algorithm in managing the integrity of the transmitted data. It is also widely popular. Symmetric ciphers and hash functions are very much dependent on each other to make them more secure and robust application. In this paper, we will see how symmetric ciphers and hash functions are connected, and their different use cases in real life. This paper also includes different symmetric ciphers, hash functions, and we will see their relationship. First, this paper will introduce what a security service is, then the classes of cryptographic algorithms are illustrated where we have discussed different paradigms of cryptography. After that, we have addressed different types of symmetric ciphers and hash functions. Based on some use cases, we have shown the inter-dependency of symmetric cipher and hash function. Next, we have presented a comparison in various approaches to dependency.
Quantum Factoring Algorithm: Resource Estimation and Survey of Experiments

Abstract It is known that Shor’s algorithm can break many cryptosystems such as RSA encryption, provided that large-scale quantum computers are realized. Thus far, several experiments for the factorization of the small composites such as 15 and 21 have been conducted using small-scale quantum computers. In this study, we investigate the details of quantum circuits used in several factoring experiments. We then indicate that some of the circuits have been constructed under the condition that the order of an element modulo a target composite is known in advance. Because the order must be unknown in the experiments, they are inappropriate for designing the quantum circuit of Shor’s factoring algorithm. We also indicate that the circuits used in the other experiments are constructed by relying considerably on the target composite number to be factorized.
Binary GPU-Planning for Thousands of NPCs

Game Artificial Intelligence Engines of commercial games run on CPUs and not on GPUs. With more and more powerful GPUs and Cloud gaming, our vision is that GPUs will become the dedicated Game AI hardware, just as it now provides computing power for Game Physics (e.g. nVidia PhysX). In particular, we believe GPUs can run Game AI Planning, which computes plans in order to control the behaviors of Non-Player Characters (NPCs) in video-games. Our objective is an efficient online GPU-based Game AI Planning component with Cloud gaming as a target. We here report on our most recent implementation which can control thousands of NPCs each frame with only one GTX 1080 on the AI server, pushing thousands of plans to the client (PC or console).