Reviewerly/exHarmony huggingface dataset:
Extending WordNet with UFO foundational ontology

WordNet is a large lexical database used by an uncountable number of applications for computational linguistics. Many proposals have attempted to better describe it in a semantic perspective, especially addressing synonymy, taxonomy and mereology properties, which led to very good results in domain-specific applications. A philosophical shift on this semantic description could, however, improve the scope of these results across different domains. In this direction, this work extends WordNet’s semantic knowledge by addressing philosophical meta-properties. Specifically, we apply the notion of Semantic Types to propose mapping rules between the noun synsets of Wordnet and the top-level constructs of a foundational ontology. For this task we have chosen the Unified Foundational Ontology (UFO), which explicitly exposes philosophical meta-properties of concepts in its structure, leading to a well-founded semantically-enriched version of Wordnet. The proposed rules were validated through an experiment over approximately 5,200 sample mappings, obtaining an average accuracy of 93.5% Furthermore, to show its applicability, the proposal was applied to the task of automatically learning a well-founded domain ontology.
2.5 A 40×40 Four-Neighbor Time-Based In-Memory Computing Graph ASIC Chip Featuring Wavefront Expansion and 2D Gradient Control

Single-source shortest path (SSP) problems have a rich history of algorithm development [1-3]. SSP has many applications including AI decision making, robot navigation, VLSI signal routing, autonomous vehicles and many other classes of problems that can be mapped onto graphs. Conventional algorithms rely on sequentially traversing the search space, which is inherently limited by traditional computer architecture. In graphs which become very large, this slow processing time can become a bottleneck in real world applications. We propose a time-based ASIC to address this issue. Our design leverages a dedicated hardware implementation to solve these problems in linear time complexity with superior energy efficiency. A $40\times40$ four-neighbor grid implements a wavefront (WF) expansion with a first-in lockout mechanism to enable traceback. Outside the array, a programmable resistive ladder provides bias voltages to the edge cells, which enables pulse shaping reminiscent of the A* algorithm [3].
Bayesian inference for uncertainty quantification in point-based deformable image registration

In image guided diagnostics the treatment of patients is often decided based on registered image data. During the registration process errors can occur, e.g., due to incorrect model assumptions or non-corresponding areas due to image artifacts or pathologies. Therefore, the study of approaches that analyze the accuracy and reliability of registration results has become increasingly important in recent years. One way to quantify registration uncertainty is based on the posterior distribution of the transformation parameters. Since the exact computation of the posterior distribution is intractable, variational Bayes inference can be used to efficiently provide an approximate solution. Recently, a probabilistic approach to intensity-based registration has been developed that uses sparse point-based representations of images and shows an intrinsic ability to deal with corrupted data. A natural output are correspondence probabilities between the two point sets which provide a measure for potentially non-corresponding and thus incorrectly deformed regions. In order to perform a comparative analysis of registration uncertainty and correspondence probabilities, we integrate a nonlinear point-based probabilistic registration method in a variational Bayesian framework. The developed method is applied to MR images with brain lesions, where both measures show moderate correlations, but a different behavior with respect to altered regularization. Further, we simulate realistic ground-truth data to allow for a correlation analysis between both measures and local registration errors. In fact, registration errors due to model differences cannot be depicted by registration uncertainty, however, in the presence of corrupted image areas, a strong correlation can be found.
Wide Area Key Distribution Network Based on a Quantum Key Distribution System

The point-to-point quantum key distribution (QKD) system is limited by the transmission distance. So, the wide area QKD network with multiple endpoints is the research focus of this study. The relay-node scenario and key relay protocols provide the solutions to the QKD network. The early key relay protocols require the relay nodes to be reliable. Once the relay nodes become compromised, the whole network is insecure. In this paper, we extend the chain structure of the public-XOR(exclusive OR)-key scheme with two endpoints to the complex network with multiple endpoints. The relay nodes in our scheme do not need encryption actions, decryption actions, or storage XOR keys, which simplifies the system compared with other key distribution schemes based on trusted relay nodes. Our scheme not only improves the practical performance and simplifies the system’s complexity, but it also ensures that the security is not reduced. Specifically, we rigorously demonstrate that an eavesdropper can never access the key shared by the users of the network as long as the process of generating XOR keys and destroying the original keys is secure. In addition, we discuss the information leakage of the practical QKD network from the perspective of the unicity distance.
Exploring hard joints mining via hourglass-based generative adversarial network for human pose estimation

Human pose estimation has broad application prospects in the fields of human behavior recognition and human-computer interaction. Although the current human pose estimation methods have made tremendous progress, the partial occlusion of human bodies still remains a challenging problem. In this paper, we address the challenging joints in human bodies by the hard joints mining technique. The proposed hard joints mining method is based on the generative adversarial network, which consists of two stacked hourglasses with a similar architecture: the generator and the discriminator. During the training period, the discriminator distinguishes the generated heatmaps from the ground-truth heatmaps and introduces the adversarial loss to the generator through back-propagation to induce generator generates a more reasonable prediction. Moreover, the hard joints mining technique is used to focus the training attention on the difficult joint points in the generator. Finally, the experimental results demonstrate the effectiveness of the proposed approach for human pose estimation on Leeds Sports Pose (LSP) Dataset, LSP-extended datasets and MPII Human Pose Datasets.
Physically Colliding with Music

A Very Real Looper (AVRL) is an audio-only virtual reality (VR) interface inside of which a performer triggers and controls music through full-body movement. Contrary to how musical interfaces in VR are normally used, a performer using AVRL is not disconnected from their surrounding environment through immersion, nor is their body restrained by a head-mounted display. Rather, AVRL utilizes two VR sensors and the Unity game engine to map virtual musical sounds onto physical objects in the real world. These objects help the performer locate the sounds. Using two handheld VR controllers, these sounds can be triggered, looped, acoustically affected, or repositioned in space. AVRL thus combines the affordances of the physical world and a VR system with the reconfigurability of a game engine. This integration results in an expansive and augmented performance environment that facilitates full-body musical interactions.
Assessment of Histogram-Based Medical Image Contrast Enhancement Techniques; An Implementation

Medical image contrast enhancement is an important issue playing a prominent role in the digital image processing applications in some areas such as Medical Imaging, Biometric Image Recognition, Satellite, and etc. Basically, image enhancement on the basis of Histogram Equalization fails to provide the better image contrast enhancement and brightness preservation which might cause loss inaccurate diagnostic information of medical images. Hence, in order to improve image quality in computer processing, it can modify the intensity of image pixels by image contrast enhancement. In this research, some important various image contrast enhancement methods such as HE, BBHE, DSIHE, GHE, CLAHE, QWAGC-FIL, are simulated based on retinal image. This practical study attempts to focus upon the comparative analysis of various image contrast enhancement techniques.
HAIR

Alcoholics Anonymous (AA) is the largest grassroots peer support group for any health condition. While AA meeting attendance is particularly important for people who are newly sober, newcomers often have trouble finding meetings because of a lack of global up-to-date meeting list due to preference for regional autonomy in AA's organizational structure. Detection of regional webpages containing meetings and extraction of day, time, and address of meetings from those pages are essential steps in making the information available and up-to-date in a global meeting list. However, varied structure of the webpages and the meetings pose challenges in achieving the goal with traditional information retrieval methods. In this paper we propose HAIR: a semi-automated human-aided information retrieval technique and explore its potential to solve this problem. We describe future directions in developing this critical tool and discuss major implications of our work in pointing to the importance of context-specific rather than context-agnostic semi-automated in-formation retrieval techniques by conceptualizing the proposed methods and results in a broader context.
Development of a dimensions measurement system based on depth camera for logistic applications

In this work a dimension estimation system is developed for logistic applications. The purpose of the system is to find the oriented minimum bounding box of a package. The volume of the bounding box and the volumetric weight of the package were determined. Intel® RealSense™ Depth Camera D415 was used to obtain point cloud of the package view. Then smoothing and filtering algorithms were applied to eliminate the noise and the distortion. The object is isolated from the background and the minimum bounding box is determined. Different geometric shapes were tested including: hardboard calibrated cube, cube with an uneven top, uncalibrated box, cube with a sloped side, small cylinder, tube and cylinder with irregular top. Statistical analysis of the measurements revealed an average error rates less than 0.5cm for normal work conditions. This error rate is acceptable for most logistic operations
Cryptographic construction using coupled map lattice as a diffusion model to enhanced security

In this study, a new color image encryption model that uses an intertwining logistic map based confusion mechanism and coupled map lattice based diffusion process. It incorporates mixing based on pseudo random number generator to ensure shuffling incase of same pixels, intertwining logistic map based confusion mechanism enhance the key sensitivity in the model. Finally, Couple map lattice based diffusion is utilized in the model to change the pixels in a way that even one-bit change in a pixel of plain image leads to a change in large number of pixels of the cipher image. The proposed encryption method enhance the security and results in better Number of pixel change rate (NPCR) and Unified average change intensity (UACI) scores. The correlation among the pixels of cipher image is reduced to be negligible. Corresponding results suggest that the proposed model is highly secure as compared with chaos based algorithms for image encryption application.
A study of public key ‘e’ in RSA algorithm

RSA Algorithm is public-key cryptography which exposed from different attack based on the public key 'e' and modulus 'n'. Different studies on RSA Algorithm modify only the modulus 'n' and disregard the public key 'e'. The study aimed to modify an RSA Algorithm based on generation of different value of public key 'e' and show different result based on original equation of RSA Algorithm. A modified RSA Algorithm was developed to generate different value of public key 'e' and compared the result based on the output value and speed of execution. The results show that the modification of RSA Algorithm suggested to give importance on the public key 'e' which creates a new scheme in the encryption and decryption process.
A deep learning method for named entity recognition in bidding document

Named entity recognition is a fundamental task of Natural Language Processing and belongs to the category of sequence labeling problems. In the text, the named entity is the main carrier of information, which is used to express the main content of the text. Accurately identifying these contents is essential for implementing various natural language processing techniques such as information extraction, information retrieval, machine translation, and question and answer system. Named entities in business documents contain a lot of important information that can bring significant business value to the business. In this paper, we propose the method of combining Bi-directional long-short-term memory network and conditional random field, combining n-gram features and character features, and introducing attention mechanism to identify the tenderee, bidding agent and project number three entities in the bidding documents. Compared with the LSTM, BiLSTM can obtain the context information better and extract more features. The CRF uses the features obtained by BiLSTM to decode and obtain the final labeling result. In the comparative experiment of the collected data sets of 20,000 bidding documents, the BiLSTM-CRF model proposed in this paper can produce better labeling effect than other models and meet our expectations.
Understanding Node Change Bugs for Distributed Systems

Distributed systems are the fundamental infrastructure for modern cloud applications and the reliability of these systems directly impacts service availability. Distributed systems run on clusters of nodes. When the system is running, nodes can join or leave the cluster at anytime, due to unexpected failure or system maintenance. It is essential for distributed systems to tolerate such node changes. However, it is also notoriously difficult and challenging to handle node changes right. There are widely existing node change bugs which can lead to catastrophic failures. We believe that a comprehensive study on node change bugs is necessary to better prevent and diagnose node change bugs. In this paper, we perform an extensive empirical study on node change bugs. We manually went through 6,660 bug issues of 5 representative distributed systems, where 620 issues were identified as node change bugs. We studied 120 bug examples in detail to understand the root causes, the impacts, the trigger conditions and fixing strategies of node change bugs. Our findings shed lights on new detection and diagnosis techniques for node change bugs. In our empirical study, we develop two useful tools, NCTrigger and NPEDetector. NCTrigger helps users to automatically reproduce a node change bug by injecting node change events based on user specification. It largely reduces the manual efforts to reproduce a bug (from 2 days to less than half a day). NPEDetector is a static analysis tool to detect null pointer exception errors. We develop this tool based on our findings that node operations often lead to null pointer exception errors, and these errors share a simple common pattern. Experimental results show that this tool can detect 60 new null pointer errors, including 7 node change bugs. 23 bugs have already been patched and fixed.
Information visualization skills for academic librarians

Purpose The purpose of this paper is to investigate how scholars in the digital humanities employ information visualization techniques in their research, and how academic librarians should prepare themselves to support this emerging trend. Design/methodology/approach This study adopts a content analysis methodology, which further draws techniques from data mining, natural language processing and information visualization to analyze three peer-reviewed journals published within the last five years and ten online university library research guides in this field. Findings To successfully support and effectively contribute to the digital humanities, academic librarians should be knowledgeable in more than just visualization concepts and tools. The content analysis results for the digital humanities journals reflect the importance of recognizing the wide variety of applications and purposes of information visualization in digital humanities research. Practical implications This study provides useful and actionable insights into how academic librarians can prepare for this emerging technology to support future endeavors in the digital humanities. Originality/value Although information visualization has been widely adopted in digital humanities research, it remains unclear how librarians, especially academic librarians who support digital humanities research, should prepare for this emerging technology. This research is the first study to address this research gap through the lens of actual applications of information visualization techniques in digital humanities research, which is compared against university LibGuides for digital humanities research.
Real-Time Energy-Conserving VM-Provisioning Framework for Cloud-Data Centers

This paper proposes a real-time virtual machine (VM) provisioning framework to minimize energy consumption in a cloud data center with an efficient and novel data pre-processing, real-time clustering, instant multivariate time-series prediction and placement algorithms. The proposed framework represents an enhanced version of our recent work and it is suitable for real-time VM consolidation as it takes into consideration user behavior and VM historical usage to forecast the number of future VMs and to improve consolidation process with minimum energy consumption. The proposed real-time framework is evaluated using real data with different duration and sampling rate. The results obtained show up to 80% improvement on average.
Manipulating Narrative Salience in Interactive Stories Using Indexter's Pairwise Event Salience Hypothesis

The salience of a narrative event is defined as the ease with which an audience member can recall that past event. This paper describes a series of experiments investigating the use of salience as a predictor of player behavior in interactive narrative scenarios. We utilize Indexter, a plan-based model of narrative for reasoning about salience. Indexter defines a mapping of five event indices identified by cognitive science research onto narrative planning event structures. The indices-protagonist, time, space, causality, and intentionality-correspond to the “who, when, where, how, and why” of a narrative event, and represent dimensions by which events can be linked in short-term memory. We first evaluate Indexter's claim that it can effectively model the salience of past events in a player's mind. Next, we demonstrate that salience can be used to predict players' choices for endings in an interactive story, and finally, we demonstrate that the same technique can be applied to influence players to choose certain endings.
A randomized CPA-secure asymmetric-key chaotic color image encryption scheme based on the Chebyshev mappings and one-time pad

In this paper, a novel randomized chaotic asymmetric-key algorithm is proposed for color image encryption. Using the multiplicative coupled Chebyshev-based encryption algorithm, a novel chaotic key establishment algorithm using Chebyshev polynomials is proposed. This key establishment algorithm is used to generate three chaotic pseudo-random number sequences to permute the rows and the columns of the plain image randomly. Then, each pixel is XORed with respect to a random value obtained based on its new position to mask its value. The whole encryption algorithm is randomized, so it can resist chosen plaintext attacks. The experimental and security analysis of the proposed algorithm shows that the proposed algorithm has a large keyspace, a good level of security, and an acceptable level of robustness.
Quantum communication through chains with diluted disorder

We investigate a single-qubit state transfer protocol along a channel featuring diagonal diluted disorder. In the regime where the source and destination sites are weakly coupled to the channel, we report the possibility of transmitting quantum states with high fidelity as well as establishing end-to-end entanglement in that sort of configuration. We further discuss how the performance of the protocol depends upon the availability of extended states within the disordered channel.
Benchmarking static code analyzers

We show that a widely used benchmark set for the comparison of static"=analysis tools exhibits an impressive number of weaknesses, and that the internationally accepted quantitative"=evaluation metrics may lead to useless results. The weaknesses in the benchmark set were identified by applying a sound static analysis to the programs in this set and carefully interpreting the results. We propose how to deal with weaknesses of the quantitative metrics and how to improve such benchmarks and the evaluation process, in particular for external evaluations, in which an ideally neutral institution does the evaluation, whose results potential clients can trust. We also show that sufficiently high quality of the test cases makes an automatic result evaluation possible.
Knotted-line: A Visual explorer for uncertainty in transportation system

Public transport system(PTS) is a complex system, and there are various uncertainties caused by traffic congestion, varying departure interval of buses, etc. In this paper, taking bus as an example, a novel visualization model knotted-line for transportation system uncertainties is proposed. The knotted-line visualization model consists of three parts, the violin plot, multi-layer ring and risk indicator. The violin plot shows the uncertainty of passengers' waiting time. The multi-layer ring describes the uncertainty of bus arrival time. The risk indicator expresses the possibility of passenger spending time on the trip. A user study is conducted to evaluate the proposed visualization method. The results show that the proposed knotted-line visualization model is helpful for passengers to understand the uncertainty in transportation system and help users to make decisions.