Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of subarachnoid haemorrhage with spectrophotometry of cerebrospinal fluid – a comparison of two methods

Abstract Objectives Spectrophotometric absorption curve analysis of cerebrospinal fluid (CSF) for oxyhaemoglobin and bilirubin is necessary to accurately diagnose subarachnoid haemorrhage (SAH) in patients with typical symptoms but with negative findings on X-ray examinations. In this study, we evaluated the performance of two methods for interpreting absorption curves; one method from the United Kingdom National External Quality Assessment Service (UK-NEQAS) and the other from the national quality assurance programme in Sweden (Equalis). Methods Consecutive absorbance curves (n=336) were interpreted with two different methods, and their performance was compared to the diagnosis as stated in the patient records. Results The UK-NEQAS method displayed equal sensitivity to the Equalis method, but the specificity of the UK-NEQAS method was significantly higher than the Equalis method resulting in fewer false positive results. For UK-NEQAS, a positive predictive value (PPV) of 84.6% and a negative predictive value (NPV) of 99.7% were observed, whereas the Equalis method had a PPV of 27.5% and an NPV of 99.7%. Conclusions The semi-automated method based on the guidelines from UK-NEQAS provides an efficient and correct interpretation of absorbance curves with short turn-around times. We propose using this method for the routine interpretation of CSF spectrophotometric curves.
Procedure of functional transposition analysis in the English language

Abstract The paper presents an attempt to develop a procedure of researching functional transposition in English. Functional transposition is interpreted as a diachronicsynchronic functional process and its outcome, which presupposes the ability of lexical units, by means of grammaticalization and lexicalization and without application of any morphological and/or syntactical markers, to acquire and realize functions inherent to other word classes. The procedure consists of four major stages which cover the entire process of transposition of each lexical unit from its origin and up to the current use. The main methods include: definition and componential analyses, historical analysis of origin, diachronic corpus analysis, and synchronic corpus analysis. In the present paper, the procedure is verified with a case study of the lexical unit ‘IN’. It has been established that in the case of ‘IN’, functional transposition started from the category of prepositions. The tendency of its development shows that after a gradual decrease and stabilization, observed in Early and Late Modern English, respectively, functional transposition of ‘IN’ has undergone a dramatic increase in Present-Day English. It is explained by formation of new sociocultural situations and institutionalization of numerous patterns of functionally transposed ‘IN’ in the language.
A Compact SIW-Fed Dielectric Antenna with Equivalently Tapered E-plane Profile

Abstract In this paper, a compact substrate integrated waveguide (SIW) fed dielectric antenna with equivalently tapered E-plane profile is proposed and investigated. Firstly, transition from SIW to broadwall-detached SIW (DSIW) is analyzed, which provides basis for designing a compact SIW-fed dielectric antenna. Secondly, in order to realize an effective radiation structure while keeping geometrical size unchanged, a gradually tapered profile in E-plane is realized through perforating air-vias with different diameters into the DSIW. Effect of transition stage(s) is compared to validate the function of equivalently tapered profile. Results indicate the proposed antenna operates with 27.7 % fractional bandwidth ranging from 20.2 GHz to 26.7 GHz. Moreover, stable end-fire radiation patterns can be observed in the whole operating band. Above all, this antenna shows similarity with E-plane waveguide horn antenna but maintains advantage of planar configuration.
A Learning Framework with Disposable Auxiliary Networks for Early Prediction of Product Success

Consider the scenario in which an investor seeks to identify potential products before they are unveiled to the public. For such a scenario, the investor poses questions such as “What characteristic better represents a product? ” or “What features make a product popular? ” Unlike traditional recommendation problems, in this case, there is no user feedback for the upcoming products, which greatly complicates prediction. To address this challenging yet common scenario, we present an unconventional multi-task learning framework that trains the prediction model on information for mature products that have user feedback, and then uses this model to predict the success of upcoming products for which there is no user feedback. To achieve this goal, we train a main task network to extract product features from their descriptions while at the same time training a disposable auxiliary network to learn domain-specific words and popular trends from user reviews.This disposable auxiliary network is beneficial during the training of the main task network but is unused at the inference stage. Empirical results on two real-world datasets in different languages demonstrate that the proposed framework not only improves the overall rating prediction for products but also identifies the top successful products without any user reviews.
A DQN-based workflow task assignment approach in cloud-fog cooperative considering terminal mobility

When the terminal device moves under the cloud-fog cooperative, reasonable task assignment between fog nodes and cloud servers is a difficult problem and uncompleted tasks migration to maintain the continuity of tasks is another difficult problem. To solve these two problems, a workflow task assignment and migration decision algorithm based on Deep Q Network(DQN) is proposed. Firstly, a workflow task assignment optimization model for the minimized latency is constructed. Secondly, a DQN-based algorithm is used to assign tasks reasonably. When the terminal device is moving, a task migration decision program is activated to execute task migration. Finally, a simulation experiment is proposed to verify the approach and the results indicated that the approach reduced the time latency reasonably and effectively by allocating the computing resources.
Single-site passenger flow forecast based on ga-lstm

In short-term passenger flow forecasting, thanks to big data analysis, we can obtain a large number of influencing factors describing the change of station passenger flow. Although this information provides a good basis for passenger flow forecasting, statistical use of passenger flow forecasting in the past are not accurate and stable because of the characteristics of timing, space and trend of passenger flow forecasting. In building an automated passenger flow forecasting system, accuracy and stability are the key points we need to pay attention to. this paper use the optimal parameter passenger flow prediction based on ga-lstm model. After selecting the optimal time step and the number of hidden units by using genetic algorithm (GA), LSTM is used for prediction. In the multi station boarding and alighting number prediction experiment on the real subway data provided by Hangzhou, It is proved that ga-lstm is better than the non optimized RNN model in prediction accuracy.
The Influences of a Knowledge Representation Tool on Searchers with Varying Cognitive Abilities

While current systems are effective in helping searchers resolve simple information needs (e.g., fact-finding), they provide less support for searchers working on complex information-seeking tasks. Complex search tasks involve a wide range of (meta)cognitive activities, including goal-setting, organizing information, drawing inferences, monitoring progress, and revising mental models and search strategies. We report on a lab study (N = 32) that investigated the influences of a knowledge representation tool called the OrgBox, developed to support searchers with complex tasks. The OrgBox tool was integrated into a custom-built search system and allowed study participants to drag-and-drop textual passages into the tool, organize passages into logical groupings called “boxes”, and make notes on passages and boxes. The OrgBox was compared to a baseline tool (called the Bookmark) that allowed participants to save textual passages, but not organize them nor make notes. Knowledge representation tools such as the OrgBox may provide special benefits for users with different cognitive profiles. In this article, we explore two cognitive abilities: (1) working memory (WM) capacity and (2) switching (SW) ability. Participants in the study were asked to gather information on a complex subject and produce an outline for a hypothetical research article. We investigate the influences of the tool (OrgBox vs. Bookmark) and the participant’s working memory capacity and switching ability on three types of outcomes: (RQ1) search behaviors, (RQ2) post-task perceptions, and (RQ3) the quality of outlines produces by participants.
Energy-aware Load Balancing in Heterogeneous Environments

In this paper, we propose an energy saving strategy for heterogeneous clusters. A load balancer is then proposed by using code features and a resource-aware processor selection based on machine learning. The proposed technique identifies energy efficient kernel applicants (from the job pool). It then selects a pair of kernel candidates among all candidates that leads to a reduction in energy consumption and execution time. The proposed kernel technique has achieved good performance compared to the past SOTA models.
A Big Data framework based on Apache Spark for Industry-specific Lexicon Generation for Stock Market Prediction

Research around the process of automatic price prediction of stock markets indicates that published news are an important asset to solve this problem. We further elaborate on an NLP-based approach to generate industry-specific lexicons from news documents exploiting the distributed technology of Apache Spark, with a focus on individuating on a day-to-day scale the correlation between significant stock price variations and the words collected from press releases. Thereafter we apply a binary classification algorithm that builds upon our newly generated lexicons to predict the magnitude of fluctuation of stock market price. Subsequently, by processing news belonging to a large collection of news articles from the most prestigious press agencies, we validate our approach by conducting an experiment on the market history of the US companies belonging to the Standard & Poor 500 index. We also test the performance of the algorithm on a multi-lingual setting, in particular focusing on the Italian stock market and the Italy 40 (FTSE MIB) index. Final data about classification results let us assess the mutual dependence between terms and prices, and help us evaluating the predictive power of our created lexicons.
Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction

Exposing latent structure (graph, tree.) of data is a major challenge to deal with the web of data. Today’s embedding techniques incorporate any data source (noisy graphs, item similarities, plain text) into continuous vector spaces that are typically used as input to classifier. In this work, we are dealing with the opposite task: finding structures (taxonomies) from embedded data. We provide an original unsupervised methodology for taxonomy induction by directly searching for graph structures preserving pairwise distances between items. Contrary to the state-of-the-art (SOTA), our approach does not require to train classifiers; it is also more versatile as it can be applied to any embedding (eg. word embedding, similarity embedding like space-time local embedding.). On standard benchmarks and metrics, our approach yields SOTA performance. As another contribution, we propose better evaluation metrics for taxonomy induction, leveraging graph kernel similarities and edit distance, showing that the structures of our predicted taxonomies are significantly closer to the ground-truth than SOTA solutions.
Event Causal Relationship Retrieval

Analyzing history has numerous benefits, including understanding what the people in the past did for events and what results they obtained and using historical knowledge to the present. Several past studies have analyzed historical events based on the assumption that each event is described in texts. Most of them analyze how similar the words and their categories used in the descriptions are instead of taking care of event-causal relationships. In this study, we propose an algorithm named the Event Causality relationship similarity Measurement (ECM) to measure the similarity between event-causal relationships. The ECM solves a maximum weight matching problem on a bipartite graph, where the weights are the similarities between the event-causal relationships. We evaluated ECM with previous related works and confirmed that the ECM is the best.
Scalability and Generalization of Circuit Training for Chip Floorplanning

Chip floorplanning is a complex task within the physical design process, with more than six decades of research dedicated to it. In a recent paper published in Nature~\citemirhoseini2021graph, a new methodology based on deep reinforcement learning was proposed that solves the floorplanning problem for advanced chip technologies with production quality results. The proposed method enables generalization, which means that the quality of placements improves as the policy is trained on a larger number of chip blocks. In this paper, we describe Circuit Training, an open-source distributed reinforcement learning framework that re-implements the proposed methodology in TensorFlow v2.x. We will explain the framework and discuss ways it can be extended to solve other important problems within physical design and more generally chip design. We also show new experimental results that demonstrate the scaling and generalization performance of Circuit Training.
Offline reinforcement learning application in robotic manipulation with a COG method case

Artificial intelligence now has different applications in various industrial fields. Reinforcement learning (RL) is one of the hot topics in the artificial intelligence, also in robotics. It is an important learning method in the field of robotic manipulation. The training policies of reinforcement learning can be divided into online learning policy and offline learning policy. Besides, the reinforcement learning algorithm of offline policy has great potential in transforming large data sets into powerful decision engine. To solve the problem that most of robot applications involve collecting data from scratch for each new task, offline learning combined with online learning is to make the training more efficient and convenient. The aim of this paper is to clearly introduce the application of offline reinforcement learning in the field of robotic manipulation. The basic formulation of reinforcement learning includes two points: First, it introduces Markov Decision Process and one of method of solution – policy gradients. Then through analyzing an application of offline learning in the field of robotic manipulation - COG algorithm, this paper analyzes the process of offline learning combining the prior data to learn new robotic skills and uses this method to solve specific tasks of robotic, such as the problems of sample efficiency. The results show that the offline learning policy has important research value in the field of robotic manipulation by reducing training time and make process efficient, and it fully embodies its advantages in solving the problems of robotic sample efficiency.
A Novel Deep Learning Approach to the Statistical Downscaling of Temperatures for Monitoring Climate Change

General Circulation Models (GCMs) allow for the simulation of several climate variables through the year 2100. GCM simulations, however, are too coarse to monitor climate change at a local scale in a local region. Hence, one needs to perform spatial downscaling for these simulations, where statistical downscaling is often used. Statistical downscaling is performed by utilizing the large-scale GCM outputs to forecast a local-scale field (e.g., temperature). In this paper, we develop a new deep learning approach, named AIG-TRANSFORMER, which employs a novel attention-based input grouping (AIG) neural network followed by a transformer, for the statistical downscaling of the weekly averages of maximum (Tmax) and minimum (Tmin) temperatures using GCM-simulated climatic fields (climate variables). We formulate the downscaling problem as a multivariate time series forecasting task, with multiple GCM-simulated climatic fields as input features. We employ an attention mechanism within the AIG network to give selective importance to the input features while reducing the size of the input fed to the transformer. To test AIG-TRANSFORMER, we perform the statistical downscaling over the Hackensack-Passaic Watershed, in northeast New Jersey. We compare our new deep learning approach against several existing machine learning methods including random forests, support vector regression and long short-term memory networks. Experimental results show that AIG-TRANSFORMER outperforms the existing methods for downscaling both the maximum and minimum temperatures, with a Nash–Sutcliffe Efficiency coefficient of 0.84 for Tmax, and 0.85 for Tmin. We further apply AIG-TRANSFORMER to produce long-term projections over the 20 years period from 2030 to 2049, and report the annual means for maximum and minimum temperatures.
A Remote Sensing Image Based Convolutional Neural Network for Target Detection of Electric Power

As the basic content of urban construction and development, electric power facilities target detection can guarantee the basic work of urban electricity safety. In the rapid development of remote sensing technology, there are more and more remote sensing image data information, which plays a positive role in the processing and application of remote sensing image. Therefore, how to efficiently and accurately identify typical targets in remote sensing images has become the focus of electric power exploration at this stage, which can not only effectively solve the problem of low accuracy of traditional remote sensing image target detection methods, but also further grasp the application of power facilities. In this paper, the deep convolutional neural network is combined with remote sensing image target detection, and the ftL-RCNN neural network model is used to train the VGG16 convolutional network on the relevant data machine, and the input remote sensing image is marked with the regional recommendation network, so as to realize the remote sensing image target detection and analysis. The final results show that the actual test data sets have obtained high detection accuracy and confidence, so the relevant research results can be applied to power remote sensing image analysis
An End-to-End Spatial-Aware Attention Method for Multi-Line License Plate Spotting

Previous scene text methods in text spotting often include two tasks: text detection and text recognition. Recently, some methods have been proposed to combine these two tasks into a unified model. However, most of these methods usually detect and recognize text line-by-line. This work proposes a novel spatial-aware attention method for multi-line text spotting and applies it to license plate recognition. Our method provides an end-to-end trainable architecture, significantly reducing the time needed to train the model and inference. Notably, our approach incorporates the 2D positional encoding into Transformer architecture for the recognition branch. The experiments show that our method is very efficient for complex license plate datasets with skew, curve, and multi-line texts.
Hardware Security: Physical Design versus Side-Channel and Fault Attacks

What is "hardware" security? How can we improve trustworthiness in hardware circuits? Is there a design method for secure hardware design? To answer these questions, different communities have different expectations of trusted (expecting trustworthy) hardware components upon which they start to build a secure system. At the same time, electronics shrink: sensor nodes, IOT devices, smart electronics are becoming more and more available. In the past, adding security was only a concern for locked server rooms or now cloud servers. However, these days, our portable devices contain highly private and secure information. Adding security and cryptography to these often very resource constraint devices is a challenge. Moreover, they can be subject to physical attacks, including side-channel and fault attacks [1][2]. This presentation aims at bringing some order in the chaos of expectations by introducing the importance of a design methodology for secure design [3][5]. We will illustrate the capabilities of current side EM and laser fault passive and active attacks. In this context, we will also reflect on the role of physical design, place and route [4][6].
Evolving Multi-view Autoencoders for Text Classification

Text classification has received more and more attention from researchers in recent years, due to its wide range of helpful applications in the real world. Its performance has significantly benefited from the rapid development of Deep Neural Networks (DNNs). However, designing the network architecture of DNNs is by no means an easy task and can increase operating costs, since it requires domain knowledge from both the deep learning and text classification areas, which is often not available. In addition, existing methods typically use one representation which is a single view of text documents, and hence, they may have difficulty capturing all the discriminative features for text classification. To resolve these issues, this paper proposes a novel method named Evolving Multi-view Autoencoder (EvoMAE), which can automatically design optimal architectures of Autoencoders to extract multi-view features for text classification. The multi-view features are obtained from (i) Word, (ii) Emotion, and (iii) Sentence embeddings. We also introduce a novel objective function that allows the training of Autoencoders and the classifier simultaneously. The proposed system is tested on two benchmark datasets, and the experimental results show that it achieves significantly better performance than the current state-of-the-art automatically-designed approaches.
Reaching Everyone by Integrating Computing Everywhere

The inventors of the term “computer science” meant for it to be something that was taught to everyone, to facilitate learning other subjects. Today, we mostly teach computer science to people who want to become professional software developers or computer scientists. Worse yet, we get a narrow range of people who are even willing to take a CS class. If we wanted to reach the original and more general goal, we would have to change how we teach computer science. In this talk, we consider the history of “computer science” and its earlier purpose, describe and demonstrate new kinds of languages, tools, and media for learning computer science.