Reviewerly/exHarmony huggingface dataset:
Combinatorial active contour bilateral filter for ultrasound image segmentation

Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast. Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths. Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches 90.05±5.81% . The evaluation with and without the BF shows that the enhancement procedure improves the framework well. Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.
QScored: An Open Platform for Code Quality Ranking and Visualization

Though abundant source code repositories are available on code repository hosting platforms, their detailed code quality information is not available readily. Software engineering researchers often need to select a set of high-quality repositories. Despite the code quality is an important concern for repository selection, the lack of this information makes researchers depend on alternatives such as the number of issues and the number of stars associated with repositories. Furthermore, practitioners expect user-friendly visual ways to assess the quality of their projects during the evolution of the codebase without putting a considerable effort. We propose an open platform QScored to fill the gap for both researchers and practitioners. The platform hosts detailed code quality analysis information for a large number of repositories (currently more than twelve thousand containing more than 200 million LOC), computes quality score and assigns relative ranking of the hosted repositories based on detected architecture, design, and implementation smells, as well as offers a comprehensive set of visualization aids for code quality aspects. Furthermore, the platform provides REST APIs to search repositories based on their code quality scores and ranking of hosted software projects.Video of the demo: https://youtu.be/-IgvjGV-2X0.
Efficient implementation of modular multiplication over 192-bit NIST prime for 8-bit AVR-based sensor node

Abstract Modular multiplication is one of the most time-consuming operations that account for almost 80% of computational overhead in a scalar multiplication in elliptic curve cryptography. In this paper, we present a new speed record for modular multiplication over 192-bit NIST prime P-192 on 8-bit AVR ATmega microcontrollers. We propose a new integer representation named Range Shifted Representation (RSR) which enables an efficient merging of the reduction operation into the subtractive Karatsuba multiplication. This merging results in a dramatic optimization in the intermediate accumulation of modular multiplication by reducing a significant amount of unnecessary memory access as well as the number of addition operations. Our merged modular multiplication on RSR is designed to have two duplicated groups of 96-bit intermediate values during accumulation. Hence, only one accumulation of the group is required and the result can be used twice. Consequently, we significantly reduce the number of load/store instructions which are known to be one of the most time-consuming operations for modular multiplication on constrained devices. Our implementation requires only 2888 cycles for the modular multiplication of 192-bit integers and outperforms the previous best result for modular multiplication over P-192 by a factor of 17%. In addition, our modular multiplication is even faster than the Karatsuba multiplication (without reduction) which achieved a speed record for multiplication on AVR processor.
Conversational Agents for Learning Foreign Languages a Survey

Conversational practice, while crucial for all language learners, can be challenging to get enough of and very expensive. Chatbots are computer programs developed to engage in conversations with humans. They are designed as software avatars with limited, but growing conversational capability. The most natural and potentially powerful application of chatbots is in line with their fundamental nature - language practice. However, their role and outcomes within (in)formal language learning are currently tangential at best. Existing research in the area has generally focused on chatbots' comprehensibility and the motivation they inspire in their users. In this paper, we provide an overview of the chatbots for learning languages, critically analyze existing approaches, and discuss the major challenges for future work.
A Preliminary Study on the Validity of the Paperless College English Test Based on the Computer Network Platform-Take the iTEST Test System As an Example

Abstract Paperless exams are increasingly used in college English school-based exams, and the effectiveness of these exams cannot be ignored. Computers and modern information technology have promoted the reform of language testing methods. More and more exams are based on computer and network paperless tests. This article first analyzes the feasibility of paperless English test based on the network platform iTEST. Then, this article analyzes the effect of paperless testing. Finally, some suggestions were made.
Virtual Reality: Common Problems of Legal Regulation

The paper is devoted to the analysis of general problems of legal regulation of relations arising in the field of the use of virtual reality technology, as well as problems of technology regulation. The paper analyzes what properties of the technology radically differentiate it from other concepts and create challenges for the development of a system of legal regulation of the use of technology. The author describes the main factors complicating the application of existing mechanisms of legal regulation and does forecasts concerning future problems of regulation. The author comes to the conclusion that this technology is radically different from the existing ones, as it combines the properties of the real world and cyberspace. The virtual reality properties complicating the implementation of legal regulation of relations in the field of the use of this technolog include: high realism, full immersion in the session use and low cybersecurity of hardware and software. The author analyzes several regulatory approaches that could be applied to virtual reality and justifies that they all have significant disadvantages. The results of modern research carried out in the field of the VR safe use in education and leisure are rapidly becoming obsolete, as they do not have time for the technologies development, and can serve as the basis for developing a system of rules only with due regard to this factor.
Irish libraries and COVID-19: first reflections

On Thursday 14 May 2020 Maynooth University Library hosted a seminar via Zoom, entitled 'Irish Libraries and COVID-19: First Reflections'. The seminar explored the response of the library sector to the global pandemic that has impacted every aspect of life. This article presents the case studies from the seminar.
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-Autoregressive Pitch-Dependent Dilated Convolution Model for Parametric Speech Generation

In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG. PWG is a compact non-autoregressive (non-AR) speech generation model, whose generative speed is much faster than real time. While utilizing PWG as a vocoder to generate speech on the basis of acoustic features such as spectral and prosodic features, PWG generates high-fidelity speech. However, when the input acoustic features include unseen pitches, the pitch accuracy of PWG-generated speech degrades because of the fixed and generic network of PWG without prior knowledge of speech periodicity. The proposed QPPWG adopts a pitch-dependent dilated convolution network (PDCNN) module, which introduces the pitch information into PWG via the dynamically changed network architecture, to improve the pitch controllability and speech modeling capability of vanilla PWG. Both objective and subjective evaluation results show the higher pitch accuracy and comparable speech quality of QPPWG-generated speech when the QPPWG model size is only 70 % of that of vanilla PWG.
The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation

Most existing object instance detection and segmentation models only work well on fairly balanced benchmarks where per-category training sample numbers are comparable, such as COCO. They tend to suffer performance drop on realistic datasets that are usually long-tailed. This work aims to study and address such open challenges. Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate classification of object proposals. Based on such an observation, we first consider various techniques for improving long-tail classification performance which indeed enhance instance segmentation results. We then propose a simple calibration framework to more effectively alleviate classification head bias with a bi-level class balanced sampling approach. Without bells and whistles, it significantly boosts the performance of instance segmentation for tail classes on the recent LVIS dataset and our sampled COCO-LT dataset. Our analysis provides useful insights for solving long-tail instance detection and segmentation problems, and the straightforward \emph{SimCal} method can serve as a simple but strong baseline. With the method we have won the 2019 LVIS challenge. Codes and models are available at https://github.com/twangnh/SimCal.
RENERSANs: RELATION EXTRACTION AND NAMED ENTITY RECOGNITION AS SEQUENCE ANNOTATION

In this work we present our system for RuREBus shared task held together with Dialog 2020 conference. The task consisted of 3 subtasks: named entity recognition, relation extraction with provided named entity tags and end-to-end relation extraction. Our system took the first and the second place in the first and the second subtasks respectively. For the third subtask we submitted our solution only in the post-evaluation phase, however, it was among the top 2 best performing systems. The systems for all tasks are based on Transformer models. Relation extraction was solved as a sequence labelling problem. We also used joint task named entity and relation extraction learning.
Block matrix approximation via entropy loss function

The aim of the paper is to present a procedure for the approximation of a symmetric positive definite matrix by symmetric block partitioned matrices with structured off-diagonal blocks. The entropy loss function is chosen as approximation criterion. This procedure is applied in a simulation study of the statistical problem of covariance structureidentification.
Inter‐regional digital forensic knowledge management: needs, challenges, and solutions

Abstract Increasing demand for digital evidence in criminal investigations is driving decentralization of forensic capabilities closer to the crime scene. Law enforcement agencies are struggling to keep pace with technological developments, cybercrime growth, and scientific advances. In federated environments, digital forensic knowledge and practices vary widely across regions. To reduce delays, wasted resources, missed opportunities, mistakes, and misinterpretations, there is a pressing need to balance the democratization of digital forensic capabilities with knowledge management and sharing between decentralized regions. There are multiple forms of knowledge to be managed, including procedural, technical, investigative, scientific, behavioral, crime analysis, and forensic intelligence. In addition, there are multiple knowledge producers and consumers, including police investigators, digital forensic practitioners, criminal intelligence analysts, attorneys, and judges. Knowledge management becomes even more challenging when multiple interdependent regions are involved, speaking different languages. Taking all of these factors into consideration, this work presents an inter‐regional knowledge management solution for improving the quality, consistency, reliability, efficiency, cost‐effectiveness, and return on investment of digital forensic capabilities. The basis of this work is a community‐driven initiative of Swiss regional police authorities. Interviews were conducted with 15 digital forensic units to determine their current knowledge management practices and needs. The results were then generalized into a prioritized set of requirements for inter‐regional digital forensic knowledge management that may be applicable in other countries. These requirements were used to evaluate knowledge management platforms, and one was selected. Implementation, operations, and maintenance challenges of an inter‐regional digital forensic knowledge management platform are discussed.
Are RGB-based salient object detection methods unsuitable for light field data?

Abstract Considering the significant progress made on RGB-based deep salient object detection (SOD) methods, this paper seeks to bridge the gap between those 2D methods and 4D light field data, instead of implementing specific 4D methods. We observe that the performance of 2D methods changes dramatically with the input refocusing on different depths. This paper attempts to make the 2D methods available for light field SOD by learning to select the best single image from the 4D tensor. Given a 2D method, a deep model is proposed to explicitly compare pairs of SOD results on one light field sample. Moreover, a comparator module is designed to integrate the features from a pair, which provides more discriminative representations to classify. Experiments over 13 latest 2D methods and 2 datasets demonstrate the proposed method can bring about 24.0 % and 5.3 % average improvement of mean absolute error and F -measure, and outperform state-of-the-art 4D methods by a large margin.
Verifying the output of quantum optimizers with ground-state energy lower bounds

The authors show how to benchmark the output of quantum devices designed to solve optimization problems encoded in the ground-state configuration of classical spin systems by means of lower bounds to the ground-state energy obtained by semi-definite programming.
Semantic Technologies for Situation Awareness

Abstract The project “Semantic Technologies for Situation Awareness” was concerned with detecting certain critical situations from data obtained by observing a complex hard- and software system, in order to trigger actions that allow this system to save energy. The general idea was to formalize situations as ontology-mediated queries, but in order to express the relevant situations, both the employed ontology language and the query language had to be extended. In this paper we sketch the general approach and then concentrate on reporting the formal results obtained for reasoning in these extensions, but do not describe the application that triggered these extensions in detail.
Using Persuasion science to improve COVID-19 contact tracing

In the battle against the spread of COVID-19, contract tracing is widely seen as a crucial requirement (www.preventepidemics.org) to reopen society. Since its first call with a COVID-19 patient ("case") on March 27, the Contact Tracing program at Penn State Health, previously described in this journal,1 has completed calls with 87% of all cases. In the past few weeks, as the performance of other programs has been reported, it appears that our contact tracers are unusually successful. In neighboring Lancaster County, with demographics similar to ours, calls with only 48% of cases are being completed.
A Study on the Effect of a Table’s Involvement in Foreign Keys to its Schema Evolution

In this paper, we study the evolution of tables in a schema with respect to the structure of the foreign keys to which tables are related. We organize a hierarchy of topological complexity for the structure of foreign keys, based on a modeling of schemata as graphs, where tables are classified in increasing order of complexity as: isolated (not involved in foreign keys), source (with outgoing foreign keys only), lookup (with incoming foreign keys only) and internal (with both kinds). Our study reveals that this hierarchy reflects also the update behavior of tables: topologically simple tables are more likely to have a life with few or zero schema updates, whereas, topologically complex tables are more likely to undergo high numbers of updates. Early versions of the database attract the large majority of births of complex tables, as opposed to the simple ones, demonstrating a pattern of reducing the introduction of complex, heavily updated constructs in the schema as time progresses.
Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters

During the last years, deep learning trackers achieved stimulating results while bringing interesting ideas to solve the tracking problem. This progress is mainly due to the use of learned deep features obtained by training deep convolutional neural networks (CNNs) on large image databases. But since CNNs were originally developed for image classification, appearance modeling provided by their deep layers might be not enough discriminative for the tracking task. In fact, such features represent high-level information, that is more related to object category than to a specific instance of the object. Motivated by this observation, and by the fact that discriminative correlation filters (DCFs) may provide a complimentary low-level information, we present a novel tracking algorithm taking advantage of both approaches. We formulate the tracking task as a two-stage procedure. First, we exploit the generalization ability of deep features to coarsely estimate target translation, while ensuring invariance to appearance change. Then, we capitalize on the discriminative power of correlation filters to precisely localize the tracked object. Furthermore, we designed an update control mechanism to learn appearance change while avoiding model drift. We evaluated the proposed tracker on object tracking benchmarks. Experimental results show the robustness of our algorithm, which performs favorably against CNN and DCF-based trackers.
Comparison of Image Quality Assessment Metrics for Evaluation of Performance of Anisotropic Diffusion Filter for SAR Images

The reduction of speckle noise is an important part of synthetic aperture radar image processing. In the process, it is necessary to select the values of some filter parameters that will give the highest processing quality. To select such parameters, many authors use image quality assessment metrics. The question, however, is which of the many available metrics is optimal for this task. The article presents a comparison of image quality assessment metrics applied to filtering speckle noise on SAR images. For this purpose, a specially designed test image and algorithm for finding optimal parameters of speckle noise reduction filters, developed by the authors, are used. The study is carried out using the anisotropic diffusion filter as an example.
An Improved Random Forest Algorithm

Abstract A random forest algorithm with an improved decision tree and an optimal tree integration is proposed. First, an improved ID3 decision tree is proposed. Next, when the trees are integrated, each decision tree is assigned a voting right consistent with the out-of-bag accuracy of the decision tree. Finally, the experimental results show that the improved random forest algorithm is efficient and the classification performance is improved compared with the traditional random forest algorithm.
A Novel Path-Based Entity Relatedness Measure for Efficient Collective Entity Linking

Collective entity linking is a core natural language processing task, which consists in jointly identifying the entities of a knowledge base (KB) that are mentioned in a text exploiting existing relations between entities within the KB. State-of-the-art methods typically combine local scores accounting for the similarity between mentions and entities, with a global score measuring the coherence of the set of selected entities. The latter relies on the structure of a KB: the hyperlink graph of Wikipedia in most cases or the graph of an RDF KB, e.g., BaseKB or Yago, to benefit from the precise semantics of relationships between entities. In this paper, we devise a novel RDF-based entity relatedness measure for global scores with important properties: (i) it has a clear semantics, (ii) it can be calculated at reasonable computational cost, and (iii) it accounts for the transitive aspects of entity relatedness through existing (bounded length) property paths between entities in an RDF KB. Further, we experimentally show on the TAC-KBP2017 dataset, both with BaseKB and Yago, that it provides significant improvement over state-of-the-art entity relatedness measures for the collective entity linking task.
Energy Yield Generated by a Small Building Integrated Photovoltaic Installation

Abstract In the recent years photovoltaic (PV) industry has experienced a major growth, caused by the ever present annual decrease in module production prices and the expanding awareness of the general public in terms of renewable energy. There are numerous ways to implement PV modules as an additional energy source for a building, be it mounted on the rooftop, or building integrated (BIPV). An analysis of BIPV consisting of 8 modules with the power of 250 W p each was carried out for the building of the Chemistry Faculty of Gdansk University of Technology (GUT). It included monthly irradiance and energy generation values and compared them to data obtained by the means of PV-GIS system, after inserting site specific coordinates. Additional research on the same type of a single module with the power of 270 W p was conducted to provide more insight in this matter. A comprehensible analysis allows for defining a final conclusion for the decrease in energy yield for GUT BIPV installation. Data outputs are lower than expected based on PV-GIS values, as for the most time the facade mounted PV system experiences partial soft shading from the nearby park. Furthermore, it is not located directly facing south, but rather south-east which does not prompt ideal working conditions.