Reviewerly/exHarmony huggingface dataset:
BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis

Radiologists make the diagnoses of bone fractures through examining X-ray radiographs and document them in radiology reports. Applying information extraction techniques on such radiology reports to retrieve the information of bone fracture diagnosis could yield a source of structured data for medical cohort studies, image labelling and decision support concerning bone fractures. In this study, we proposed an information extraction system of Bone X-ray radiology reports to retrieve the details of bone fracture detection and diagnosis, based on a bio-medically pre-trained Bidirectional Encoder Representations from Transformers (BERT) natural language processing (NLP) model by Google. The model, named as BoneBert, was first trained on annotations automatically generated by a handcrafted rule-based labelling system using a dataset of 6,048 X-ray radiology reports and then fine-tuned on a small set of 4,890 expert annotations. Thus, the model was trained in a "semi-supervised" fashion. We evaluated the performance of the proposed model and compared it with the conventional rule-based labelling system on two typical tasks: Assertion Classification (AC) for bone fracture status detection (positive, negative or uncertainty) and Named Entity Recognition (NER) related to the fracture type, the bone type and location of a fracture occurs. BoneBert outperformed the rule-based system in both tasks, showing great potential for automated information extraction of the detection and diagnosis of bone fracture from radiology reports, such as, the clinical status, type and location of bone fracture, and more related observations.
Integrating Knowledge Into End-to-End Speech Recognition From External Text-Only Data

Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained with speech-text paired data. It is challenging to incorporate external text-only data into AED models. Another issue of the AED model is that it does not use the right context of a text token while predicting the token. To alleviate the above two issues, we propose a unified method called LST (Learn Spelling from Teachers) to integrate knowledge into an AED model from the external text-only data and leverage the whole context in a sentence. The method is divided into two stages. First, in the representation stage, a language model is trained on the text. It can be seen as that the knowledge in the text is compressed into the LM. Then, at the transferring stage, the knowledge is transferred to the AED model via teacher-student learning. To further use the whole context of the text sentence, we propose an LM called causal cloze completer (COR), which estimates the probability of a token, given both the left context and the right context of it. Therefore, with LST training, the AED model can leverage the whole context in the sentence. Different from fusion based methods, which use LM during decoding, the proposed method does not increase any extra complexity at the inference stage. We conduct experiments on two scales of public Chinese datasets AISHELL-1 and AISHELL-2. The experimental results demonstrate the effectiveness of leveraging external text-only data and the whole context in a sentence with our proposed method, compared with baseline hybrid systems and AED model based systems.
Optimization of short-arc ellipse fitting with prior information for planetary optical navigation

Autonomous navigation technology has played an increasingly important role during modern planetary exploration missions. The image-based optical navigation method is one of the most promising solutions for autonomous navigation. Previous studies have mainly focused on image processing and navigation filtering algorithms. Classic ellipse fitting approaches rely on the least square method, which may fail in the case of short-arc ellipse fitting. This paper proposes an optimization method with a priori shape information to achieve a high-precision short-arc ellipse fitting. First, by querying the ephemeris and using the projection principle, the projected ellipse of the entire planet can be obtained. Second, the shape information of the ellipse that represents the useful priori information can be extracted using the least squares ellipse fitting algorithm. Third, the shape information is introduced into the standard elliptic equation, whose parameters are optimized to fit the planetary edge arc obtained by the navigation camera. Finally, simulations are conducted and the results show that the proposed method performs better in the accuracy of short-arc ellipse fitting compared to the traditional least squares method.
MagAuth: Secure and Usable Two-Factor Authentication With Magnetic Wrist Wearables

Secure and usable user authentication is the first line of defense against cyber attacks on smart end-user devices. Advanced hacking techniques pose severe threats to the traditional authentication systems based on the password/PIN/fingerprint. We propose MagAuth, a secure and usable two-factor authentication scheme with commercial off-the-shelf (COTS) wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for mobile touchscreen devices. In MagAuth, a user enrolls a self-chosen unlock pattern or touch gesture into his touchscreen device by performing it with the same hand the magnetic wrist wearable is on. The chosen unlock pattern or touch gesture serves as the first authentication factor, and the user’s behavioral features manifested in the magnetic field changes during his finger movement correspond to the second factor. The user can unlock his touchscreen device only when both authentication factors can be validated. Comprehensive user experiments confirm the high security and usability of MagAuth. In particular, MagAuth achieves an average true-positive rate up to 96.3 percent and a false-positive rate no larger than 8.4 percent. Moreover, we show that MagAuth is highly resilient to various attacks.
StimulEye: An intelligent tool for feature extraction and event detection from raw eye gaze data

Extraction of eye gaze events is highly dependent on automated powerful software that charges exorbitant prices. The proposed open-source intelligent tool StimulEye helps to detect and classify eye gaze events and analyse various metrics related to these events. The algorithms for eye event detection in use today heavily depend on hand-crafted signal features and thresholding, which are computed from the stream of raw gaze data. These algorithms leave most of their parametric decisions on the end user which might result in ambiguity and inaccuracy. StimulEye uses deep learning techniques to automate eye gaze event detection which neither requires manual decision making nor parametric definitions. StimulEye provides an end to end solution which takes raw streams of data from an eye tracker in text form, analyses these to classify the inputs into the events, namely saccades, fixations, and blinks. It provides the user with insights such as scanpath, fixation duration, radii, etc.
Deep Light Field Acquisition Using Learned Coded Mask Distributions for Color Filter Array Sensors

Compressive light field photography enables light field acquisition using a single sensor by utilizing a color coded mask. This approach is very cost effective since consumer-level digital cameras can be turned into light field cameras by simply placing a coded mask between the sensor and the aperture plane. This paper describes a deep learning architecture for compressive light field acquisition using a color coded mask and a sensor with Color Filter Array (CFA). Unlike previous methods where a fixed mask pattern is used, our deep network learns the optimal distribution of the color coded mask pixels. The proposed solution enables end-to-end learning of the color-coded mask distribution and the reconstruction network, taking into account the sensor CFA. Consequently, the resulting network can efficiently perform joint demosaicing and light field reconstruction of images acquired with color-coded mask and a CFA sensor. Compared to previous methods based on deep learning with monochrome sensors, as well as traditional compressive sensing approaches using CFA sensors, we obtain superior color reconstruction of the light fields.
Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.
Extended supersingular isogeny Diffie–Hellman key exchange protocol: Revenge of the SIDH

The supersingular isogeny Diffie–Hellman key exchange protocol (SIDH) was introduced by Jao and De Feo in 2011. SIDH operates on supersingular elliptic curves defined over F p 2 , where p is a large prime number of the form p = 4 e A 3 e B − 1 and eA and eB are positive integers such that 4 e A ≈ 3 e B . A variant of the SIDH protocol, dubbed extended SIDH (eSIDH), is presented. The eSIDH makes use of primes of the form p = 4 e A ℓ B e B ℓ C e C f − 1. Here ℓB and ℓC are two small prime numbers; f is a cofactor; and eA, eB, and eC are positive integers such that 4 e A ≈ ℓ B e B ℓ C e C . It is shown that for many relevant instantiations of the SIDH protocol, this new family of primes enjoys faster field arithmetic than the one associated with traditional SIDH primes. Furthermore, its richer opportunities for parallelism yield a noticeable speed-up factor when implemented on multicore platforms. A supersingular isogeny key encapsulation (SIKE) instantiation using the prime eSIDH-p765 yields an acceleration factor of 1.06, 1.15 and 1.14 over a SIKE instantiation with the prime SIKE-p757 when implemented on k = {1, 2, 3}-core processors. To the authors’ knowledge, this work reports the first multicore implementation of SIDH and SIKE.
AG-GAN: An Attentive Group-Aware GAN for pedestrian trajectory prediction

Understanding human behaviors in crowded scenarios requires analyzing not only the position of the subjects in space, but also the scene context. Existing approaches mostly rely on the motion history of each pedestrian and model the interactions among people by considering the entire surrounding neighborhood. In our approach, we address the problem of motion prediction by applying coherent group clustering and a global attention mechanism on the LSTM-based Generative Adversarial Networks (GANs). The proposed model consists of an attentive group-aware GAN that observes the agents' past motion and predicts future paths, using (i) a group pooling module to model neighborhood interaction, and (ii) an attention module to specifically focus on hidden states. The experimental results demonstrate that our proposal outperforms state-of-the-art models on common benchmark datasets, and is able to generate socially-acceptable trajectories.
Towards an Intelligent Framework for Cloud Service Discovery

The variety of cloud services (CSs) that are described, their non-uniform naming conventions, and their heterogeneous types and features make cloud service discovery a difficult problem. Therefore, an intelligent cloud service discovery framework (CSDF) is needed for discovering the appropriate services that meet the user's requirements. This study proposes a CSDF for extracting cloud service attributes (CSAs) based on classification, ontology, and agents. Multiple-phase classification with topic modeling has been implemented using different machine learning techniques to increase the efficiency of CSA extraction. CSAs that are represented in different formats have been extracted and represented in a comprehensive ontology to enhance the efficiency and effectiveness of the framework. The experimental results showed that the multiple-phase classification methods with topic modeling for CSs using a support vector machine (SVM) obtained a high accuracy (87.90%) compared to other methods. In addition, the results of extracting CSAs showed high values for precision, recall, and f-measure of 99.24%, 99.24%, and 99.24%, respectively, for Java script object notation(JSON) format, followed by 99.05%, 97.20%, and 98.11% for table formats, and with lower accuracy for text format (90.63%, 86.57%, and 88.55%).
Automorphism groups of some families of bipartite graphs

This paper discusses the automorphism group of a class of weakly semiregular bipartite graphs and its subclass called WSB END graphs. It also tries to analyse the automorphism group of the SM sum graphs and SM balancing graphs. These graphs are weakly semiregular bipartite graphs too. The SM sum graphs are particular cases of bipartite Kneser graphs. The bipartite Kneser type graphs are defined on n -sets for a fixed positive integer n . The automorphism groups of the bipartite Kneser type graphs are related to that of weakly semiregular bipartite graphs. Weakly semiregular bipartite graphs in which the neighbourhoods of the vertices in the SD part having the same degree sequence, possess non trivial automorphisms. The automorphism groups of SM sum graphs are isomorphic to the symmetric groups. The relationship between the automorphism groups of SM balancing graphs and symmetric groups are established here. It has been observed by using the well known algorithm Nauty, that the size of automorphism groups of SM balancing graphs are prodigious. Every weakly semiregular bipartite graphs with k-NSD subparts has a matching which saturates the smaller partition.
Comparative Analysis for Content Defined Chunking Algorithms in Data Deduplication

Data deduplication works on eliminating redundant data and reducing storage consumption. Nowadays more data generated and it was stored in the cloud repeatedly, due to this large volume of storage will be consumed. Data deduplication tries to reduce data volumes disk space and network bandwidth can be to reduce costs and energy consumption for running storage systems. In the data deduplication method, data broken into small size of chunk or block. Hash ID will be calculated for all the blocks then it’s compared with existing blocks for duplication. Blocks may be fixed or variable size, compared with a fixed size of block variable size chunking gives a better result. So the chunking process is the initial task of deduplication to get an optimal result. In this paper, we discussed various content defined chunking algorithms and their performance based on chunking properties like chunking speed, processing time, and throughput.
Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Despite the development of technical means, the processes associated with the search for complete and accurate scientific information in a huge number of data sources are becoming more complicated. To reach a new level in the use of information processing technologies, first of all, a transition to a semantically meaningful representation is necessary for scientific knowledge extracted from information in a digital environment. In modern conditions, characterized by multidisciplinary research, the desired effect can be achieved by developing universal approaches to the storage and presentation of scientific knowledge. These approaches are reflected in the concept of the Common Digital Space of Scientific Knowledge. The paper presents an overview of the basic concepts in this area, which are used both to represent the elements of space and to provide access to them not only for humans, but also for software agents. Semantic libraries are considered as tools for constructing the knowledge space.
Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.
A Systematic Approach to Develop an Advanced Insider Attacks Detection Module

The insider threat is one of the most moving issues to identify because of its perplexing nature and huge effect on associations. Insiders represent an extraordinary threat to associations because of their insight on the association and its security conventions, their approved access to the association's assets, and the trouble of perceiving the conduct of an insider threat from an ordinary representative's conduct. Thus, the insider-threat field faces the test of creating recognition arrangements that can identify threats without producing an incredible number of bogus positives and can mull over the non-specialized part of the issue. A possibility to concentrate on threat location was led to assess the recognition execution of the proposed arrangement and its ease of use. The field can profit from our proposed systematic approach that is scientific classification and novel arrangement of research that adds to the association and disambiguation of insider threat occurrences and the protection arrangements utilized against them. Clients with genuine access to delicate and confidential information have been authorized by insiders, who may know about the vulnerabilities of the systems and business types submitted. Numerous cyber-attacks brought about by malicious insiders are progressively hard to recognize contrasted with those of outside assailants whose impressions are more enthusiastically to cover up. The paper aims to propose a systematic approach to develop an advanced insider attacks detection module. The approach proposed in this paper will help the organization to early detect the insider threat and help them in performing more effectively in cyberspace.
A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high-quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar-shaped “shadows” will be generated in the three-dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&amp;PatchGAN model based on transfer learning. Since Dual3D&amp;PatchGAN is set based on transfer learning, there is no need for one-to-one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar-shaped “shadows” produced by DualGAN’s generated images and can also perform two-way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&amp;PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.
Multi-UAV Cooperative Task Assignment Based on Orchard Picking Algorithm

The multi-unmanned aerial vehicle (UAV) must autonomously perform reconnaissance-attack-evaluation tasks under multiple constraints in the battlefield environment.This paper proposes a nearest neighbor method designed with the shortest neighboring distance as an indicator which quickly solves the optimal sequence of multiple tasks for cooperative execution.Each target to be destroyed requires a different quantity of ammunition; a cooperative task assignment model for heterogeneous UAVs is established accordingly.Based on the nearest neighbor method, and with reference to fruit-picking techniques currently in use, a novel "orchard picking algorithm (OPA)" is investigated as well.This algorithm proposed in this paper is a heuristic algorithm, which has a broad application prospect in complex task assignment.A cooperative attack task assignment is simulated to test the performance of the algorithm.In essence, it balances the assignment of tasks, works within a brief execution time, and exhibits high flexibility, strong robustness, and scalability.