Reviewerly/exHarmony huggingface dataset:
Evaluation of Seven Part-of-Speech Taggers in Tagging Building Codes: Identifying the Best Performing Tagger and Common Sources of Errors

As the number, size, and complexity of building construction projects increase, code compliance checking becomes more challenging because of the time-consuming, costly, and error-prone nature of a manual checking process. A fully automated code compliance checking would be desirable in facilitating a more efficient, cost effective, and human error-proof code checking. Such automation requires automated information extraction from building designs and building codes, and automated information transformation to a format that allows automated reasoning. Natural language processing (NLP) is an important technology to support such automated processing of building codes, because building codes are represented in natural language texts. Part-of-speech (POS) tagging, as an important basis of NLP tasks, must have a high performance to ensure the quality of the automated processing of building codes in such a compliance checking system. However, no systematic testing of existing POS taggers on domain specific building codes data have been performed. To address this gap, the authors analyzed the performance of seven state-of-the-at POS taggers on tagging building codes and compared their results to a manually-labeled gold standard. The authors aim to: (1) find the best performing tagger in terms of accuracy, and (2) identify common sources of errors. In providing the POS tags, the authors used the Penn Treebank tagset, which is a widely used tagset with a proper balance between conciseness and information richness. An average accuracy of 88.80% was found on the testing data. The Standford coreNLP tagger outperformed the other taggers in the experiment. Common sources of errors were identified to be: (1) word ambiguity, (2) rare words, and (3) unique meaning of common English words in the construction context. The found result of machine taggers on building codes calls for performance improvement, such as error-fixing transformational rules and machine taggers that are trained on building codes.
Tracing of Strabismus Detection Using Hough Transform

The Strabismus (squint) is one of the most common vision disorders in children. It can bring a discomfort and serious negative impacts on daily life. A timely diagnosis is needed to prevent from getting worse. However, the traditional diagnosis screening is usually done manually and requires expertise, time, and high cost due to the sophisticated equipment. Thus, the proposed automated strabismus detection using computer aided diagnosis can help to reduce time for the ophthalmologist to diagnose the strabismus and the types. The proposed system consists of early stages for the detection of the strabismus: (1) pre-processing as the early stage to get better visualization by removing the unwanted noise and (2) the feature extraction of the iris position to get the information on types of strabismus. The eyes image from the Columbia Gaze Dataset (CAVE), Kaggle: Eye disease datasets and Siblings Database (SiblingsDB) will be used as the input image for the system. Hence, the proposed method in the early stages gives out the value of Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) of 0.0003 and 84.35% respectively for CAVE dataset slightly higher than Eye disease dataset and SiblingsDB. By utilizing the image processing approach, this system will be able to assists the ophthalmology and health care practitioners as strabismus screening tools.
Extended Magic for Negation: Efficient Demand-Driven Evaluation of Stratified Datalog with Precise Complexity Guarantees

Given a set of Datalog rules, facts, and a query, answers to the query can be inferred bottom-up starting from the facts or top-down starting from the query.For efficiency, top-down evaluation is extended with memoization of inferred facts, and bottom-up evaluation is performed after transformations to make rules driven by the demand from the query.Prior work has shown their precise complexity analysis and relationships.However, when Datalog is extended with even stratified negation, which has a simple and universally accepted semantics, transformations to make rules demanddriven may result in non-stratified negation, which has had many complex semantics and evaluation methods.This paper presents (1) a simple extension to demand transformation, a transformation to make rules demand-driven for Datalog without negation, to support stratified negation, and (2) a simple extension to an optimal bottom-up evaluation method for Datalog with stratified negation, to handle non-stratified negation in the resulting rules.We show that the method provides precise complexity guarantees.It is also optimal in that only facts needed for top-down evaluation of the query are inferred and each firing of a rule to infer such a fact takes worst-case constant time.We extend the precise relationship between top-down evaluation and demand-driven bottom-up evaluation to Datalog with stratified negation.Finally, we show experimental results for performance, as well as applications to previously challenging examples.
Refining Data for Text Generation

Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we propose a learning to rank model to rank the importance of each record which is supervised by a relation extractor. With the noise-reduced data as input, we implement a text generator which sequentially models the input data records and emits a summary. Experiments on the ROTOWIRE dataset verifies the effectiveness of our proposed method in both performance and efficiency.
Listen by Looking: A framework to support the development of serious games for live music

This paper presents an ongoing project focused on the framework Listen by Looking, that includes several tools to support the implementation of digital games, allowing the interactive display of music sheets, piano rolls and dialog windows with contextual information. Using this framework, a serious game for mobile devices was designed to entertain and teach music to users by listening to songs and answering questions about the melodies and musical structures learned during the game. The usefulness of game was assessed in a controlled setup, by comparing the answers of the app users with those of people who listened the same music without the app. Furthermore, an external synchronization tool through a data-over-sound channel has been tested in order to simultaneously manage a wide network of devices. The results are encouraging for future developments of the framework, with the possibility of using it during a public performance and with different games.
Indoor Optical Wireless Communications using Quantum Key Distribution at 1370 nm

Quantum Key Distribution (QKD) can be used to secure indoor optical wireless links. In this paper, we consider an indoor QKD link operating at a wavelength between 1360 - 1370 nm and show that it is robust to the effects of sunlight.
Control Systems for Unmanned Aerial Vehicles

In recent years, unmanned aerial vehicles (UAVs) intended for receiving and processing information and executing control commands have found wide application. The development of methods for designing UAV automatic control systems that change movement parameters depending on the impacts on the control object requires solving a number of scientific and technical problems related to the development of automatic control principles and algorithms, analysis and multifunctional complex of the UAV automatic control system. In this article, modern models of unmanned aerial vehicles were reviewed, and analysis of various control systems of unmanned aerial vehicles was carried out. A review of the current radio frequency bands was carried out in order to identify the optimal one for controlling a UAV.
Contextualizing Map and Geography Library Collections as FAIR Research Data: A Case Study of Historical Landscape Photographs

Collection management techniques used in map and geography libraries to describe and provide access to materials ensure researchers can engage with collections as viable sources of research data. Many practitioners create frameworks to market these collections as potential sources of data, whether used alone or alongside other research data. These frameworks bear similarities to the FAIR data movement, where data producers are encouraged to make their data Findable, Accessible, Interoperable, and Reusable. I frame the historical landscape photograph collections of the American Geographical Society Library and the United States Geological Survey Library as FAIR research data, offering the potential to engage with trends, concerns, or concepts being explored by geographers. When highlighting the FAIRness of these collections, we have an opportunity to better understand how they may be used as standalone sources of data, as well as alongside other FAIR data sources in scientific research. As another FAIR data source, I use the DataONE Data Catalog to contextualize these photographs alongside present-day datasets from fieldwork in similar landscapes. I encourage map and geography libraries to understand how the FAIRness of their collections enable unique research opportunities, leveraging the momentum surrounding FAIR research data in open science/open research communities.
Looking for Software Defects? First Find the Nonconformists

Software defect prediction models play a key role to increase the quality and reliability of software systems. Because, they are used to identify defect prone source code components and assist testing activities during the development life cycle. Prior research used supervised and unsupervised Machine Learning models for software defect prediction. Supervised defect prediction models require labeled data, however it might be time consuming and expensive to obtain labeled data that has the desired quality and volume. The unsupervised defect prediction models usually use clustering techniques to relax the labeled data requirement, however labeling detected clusters as defective is a challenging task. The Pareto principle states that a small number of modules contain most of the defects. Getting inspired from the Pareto principle, this work proposes a novel, unsupervised learning approach that is based on outlier detection. We hypothesize that defect prone software components have different characteristics when compared to others and can be considered as outliers, therefore outlier detection techniques can be used to identify them. The experiment results on 16 software projects from two publicly available datasets (PROMISE and GitHub) indicate that the k-Nearest Neighbor (KNN) outlier detection method can be used to identify the majority of software defects. It could detect 94% of expected defects at best case and more than 63% of the defects in 75% of the projects. We compare our approach with the state-of-the-art supervised and unsupervised defect prediction approaches. The results of rigorous empirical evaluations indicate that the proposed approach outperforms existing unsupervised models and achieves comparable results with the leading supervised techniques that rely on complex training and tuning algorithms.
Smart Contracts in the Automotive Industry

The automotive industry has undoubtedly significantly changed our societies and daily life. It is among the most advanced, complicated, and innovative industries. The automotive industry is an essential driving factor of many other advanced industries, it requires the contribution of many other technologies like advanced manufacturing systems, cyber-physical systems, and robotics. Blockchain technology can be highly beneficial for the automotive industry, to enhance its data security, integrity and reliability, tracking and location management, enhanced connectivity, mobility-as-a-service, tamper prevention, and fraud detection. One of the emerging blockchain capabilities is smart contract enforcement and autonomy. In this article, a basic overview of smart contracts is given, early history, definition and concepts, relation with the blockchain, listing its benefits, data sources, design, and describing its importance, finally we highlighted some serious, outstanding and worth to mention steps toward activation and enabling the smart contracts and blockchain roles in the automotive industry specifically and the industry in general.
Automated detection of lesion in computer tomography images based on Cascade R-CNN

Computed tomography (CT) images-based early disease screening is of high significance for the detection of the occurrence of cancer. It has been demonstrated that lesion detection using deep learning in CT images are significantly effective for the early stage of cancer. In this study, an improved Cascade R-CNN is proposed. In the proposed network, the Feature Pyramid Network (FPN) is introduced to complete automatic computer-aided lesion detection. Based on this trick, numerous tiny lesions in CT images can be well detected. Experimental results on the DeepLesion show that the proposed method can achieve the mAP of 0.598 with a threshold of 0.5 and 85.2% sensitivity with 4 false positives per image.
Analysis of the Randomness Generation for PoS-Based Blockchains with Verifiable Delay Functions

With the development of Ethereum 2.0, the proof-of-stake-based blockchain has become more and more popular. Although not commonly deployed in existing blockchains, many PoS or its variants’ consensus protocols have been proposed. As the same in many other cryptographic systems, the trustworthy randomness is crucial in PoS-based blockchains such as the selection of the block proposer. Since Boneh proposed the primitive of verifiable delay functions in 2018, it has received intensive attention and been used for many applications, among which the most interesting one is to make an unpredictable, unbiased and unstoppable randomness as the Ethereum Minimal VDF randomness beacon. In this paper, we analyze it in an algorithmic aspect, concentrating on the RANDAO scheme with verifiable delay functions to generate unbiased and public-verifiable randomness for such PoS-based blockchains. We analyze Pietrzak’s verifiable delay function and give improvements to the Ethereum 2.0 Randomness beacon based on the benchmark results. We further propose some new ideas to prevent quantum attack and ASICs to break the scheme where verifiable delay functions are used.
Improved image resolution during zooming in ultrasound image using deep learning technique

In the ultrasound display image, users, such as sonographers or doctors, use the zoom function to observe a specific area in more detail. However, the zoom function enlarges the corresponding area image using some simple algorithms, such as linear interpolation. The zoomed image quality is usually quite poor and blurry. Such poor image quality can be improved using deep learning technology. In this study, the applied Super-Resolution Using Very Deep Convolution Networks (VDSR) [1] prepared with natural images (1,800) and ultrasound images (200) was tested on both an experimental phantom image and a scanned ultrasound image of the liver. The resolution (axial/lateral) of the output and input image was 0.42/1.94 and 1.04/2.52, respectively. In addition, the peak signal-to-noise ratio of output and input images were 35.91/35.70/35.52/34.58(dB) and 32.24/32.86/32.57/31.89(dB), respectively. The contrast of the output image compared to the input one was also excellent. Overall, the output images that were exposed to the applied network were perfect compared to the input images.
GeoMakeIt!: A Platform for Developing Location Based Games

Location based or pervasive games are played outdoors from users with GPS enabled smartphone devices and involve physical visits of various geographical locations by the player. The fact that the playing area combines both the digital and the natural world, gives unique advantages to location based games that were explored by the relevant literature in domains such as: education, marketing etc. Furthermore, financial aspects cannot be overlooked, given the success of certain genre representatives in the gaming market, especially in the youth target group. However, developing such games is far from being trivial requiring high programming skills, while the administration overhead induced by large scale deployment is also an impediment. Motivated by the above, in this paper we present an overview of the system architecture and discuss design concepts of the GeoMakeIt! platform, which targets at facilitating location based game development by lowering the gap between the background of potential game creators and the programming skills required. Contrary to efforts existing in the literature that target at customizing specific game plots, the proposed platform is designed in an open ended manner to act as a marketplace of plugin components whose customization and synthesis can output diverse game experiences. As such, we envision that GeoMakeIt! can attract the interest of both experienced game developers and amateurs.
Retrofitting Soft Rules for Knowledge Representation Learning

Recently, a significant number of studies have focused on knowledge graph completion using rule-enhanced learning techniques, supported by the mined soft rules in addition to the hard logic rules. However, due to the difficulty in determining the confidence of the soft rules without the global semantics of knowledge graph such as the semantic relatedness between relations, the knowledge representation may not be optimal, leading to degraded effectiveness in its application to knowledge graph completion tasks. To address this challenge, this paper proposes a retrofit framework that iteratively enhances the knowledge representation and confidence of soft rules. Specifically, the soft rules guide the learning of knowledge representation, and the representation, in turn, provides global semantics of the knowledge graph to optimize the confidence of soft rules. Extensive evaluation shows that our method achieves state-of-the-art results on link prediction and triple classification tasks, brought by the fine-tuned soft rules.
Facial Expression Recognition with an Attention Network Using a Single Depth Image

In the facial expression recognition field, RGB image-involved models have always achieved the best performance. Since RGB images are easily influenced by illumination, skin color, and cross-databases, the effect of these methods decreases accordingly. To avoid these issues, we propose a novel facial expression recognition framework in which the input only relies on a single depth image since depth image performs very stable in cross-situations. In our framework, we pretrain an RGB face image synthesis model by a generative adversarial network (GAN) using a public database. This pretrained model can synthesize an RGB face image under a unified imaging situation from a depth face image input. Then, introducing the attention mechanism based on facial landmarks into a convolutional neural network (CNN) for recognition, this attention mechanism can strengthen the weights of the key parts. Thus, our framework has a stable input (depth face image) while retaining the natural merits of RGB face images for recognition. Experiments conducted on public databases demonstrate that the recognition rate of our framework is better than that of the state-of-the-art methods, which are also based on depth images.
Domain Concretization From Examples: Addressing Missing Domain Knowledge Via Robust Planning

The assumption of complete domain knowledge is unwarranted for robot planning and decision-making in the real world. Incompleteness in domain knowledge may come from design flaws or arise from domain ramifications or qualifications. In such cases, traditional planning methods can produce highly undesirable behaviors. Addressing the planning problem under incomplete domain knowledge is challenging since the agent has no clue about what information is missing. This is a type of unknown unknowns, which differs significantly from partial observability, a type of known unknowns. In this work, we assume that the missing information is encoded in a set of examples or teacher demonstrations. We formulate the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain concretization</i> with these examples as an inverse problem to domain abstraction. Given a domain model provided initially, when the model does not conform with the teacher demonstrations, our method searches for a candidate model set that refines the initial model under a minimalistic and deterministic model assumption. For new problems, it generates a robust plan with the maximum probability of success under the set of candidate models. Together with a standard search formulation in the model-space, we propose a heuristic-based search method and also an online version of it to reduce the search time. We evaluated our approach with several International Planning Competition (IPC) domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete models. Results show that our methods increase the success rate of planning without significantly impacting the plan cost.
A Label-Enhanced Text Classification Model

Abstract In the field of text classification, most of the previous work only uses one-hot labels, ignoring the correlations between labels. The paper proposes a novel label-enhanced text classification model, which utilizes the semantic correlation between sentences and category labels in the Natural Language Processing (NLP) classification task to integrate label information. We measure the similarity between instance and the category with the correlations among the labels. We test the proposed model on text classification tasks in two levels: text classification (document level) and sentiment analysis (sentence level). Experimental results show that the label-enhanced text classification model achieves great performance in multiple text classification tasks. In addition, experiment results on the unbalanced datasets show that our model is able to mitigate the impact of unbalanced data in classification tasks.