Reviewerly/exHarmony huggingface dataset:
Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world.The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts.We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon.With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute.The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.
Cyber Security Awareness and Behavior of Youth in Smartphone Usage: A Comparative Study between University Students in Hungary and Vietnam

Within the digital culture, the increasing internet consumption and the constant development of technology, especially smartphones have made cyber awareness turn to be increasingly urgent.This study focuses on comparing the level of cyber security awareness, knowledge and behavior among university students in general and between Hungary and Vietnam in particular.Research data was collected, using a set of questionnaires and the 313 responses from University Students, in different school years and fields of study, in Hungary and Vietnam.Quantitative analysis was conducted using SPSS.Results show that all respondents possess a lack of knowledge of cyber security, leading to a low level of cyber threat awareness, beyond the differences in respondent countries.However, there are minor differences in the behavior, between respondents in Hungary and Vietnam, which were measured through four dimensions of cyber security: malware items, password usage issues, social engineering and online scam issues.This research helps to raise awareness of differences in cyber security mindfulness, due to cultural characteristics, that can be considered, when developing global mega-systems, such as, social platforms.
Meta-learning via Language Model In-context Tuning

The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples.Inspired by the recent progress in large language models, we propose in-context tuning (ICT), which recasts task adaptation and prediction as a simple sequence prediction problem: to form the input sequence, we concatenate the task instruction, labeled in-context examples, and the target input to predict; to metatrain the model to learn from in-context examples, we fine-tune a pre-trained language model (LM) to predict the target label given the input sequence on a collection of tasks.We benchmark our method on two collections of text classification tasks: LAMA and Bina-ryClfs.Compared to MAML which adapts the model through gradient descent, our method leverages the inductive bias of pre-trained LMs to perform pattern matching, and outperforms MAML by an absolute 6% average AUC-ROC score on BinaryClfs, gaining more advantage with increasing model size.Compared to non-fine-tuned in-context learning (i.e.prompting a raw LM), in-context tuning meta-trains the model to learn from in-context examples.On BinaryClfs, ICT improves the average AUC-ROC score by an absolute 10%, and reduces the variance due to example ordering by 6x and example choices by 2x.
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

In many real-life problems, it is difficult to acquire or label large amounts of data, resulting in so-called few-shot learning problems. However, few-shot classification is a challenging problem due to the uncertainty caused by using few labeled samples. In the past few years, many methods have been proposed with the common aim of transferring knowledge acquired on a previously solved task, which is often achieved by using a pretrained feature extractor. As such, if the initial task contains many labeled samples, it is possible to circumvent the limitations of few-shot learning. A shortcoming of existing methods is that they often require priors about the data distribution, such as the balance between considered classes. In this paper, we propose a novel transfer-based method with a double aim: providing state-of-the-art performance, as reported on standardized datasets in the field of few-shot learning, while not requiring such restrictive priors. Our methodology is able to cope with both inductive cases, where prediction is performed on test samples independently from each other, and transductive cases, where a joint (batch) prediction is performed.
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization

State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be accommodated on resource constrained embedded devices. Moreover, with the development of technology, more and more embedded devices are available to run a Transformer model. For a Transformer model with different constraints (tight or loose), it can be deployed onto devices with different computing power. However, in previous work, designers did not choose the best device among multiple devices. Instead, they just used an existing device to deploy model, which was not necessarily the best fit and may lead to underutilization of resources. To address the deployment challenge of Transformer and the problem to select the best device, we propose an algorithm <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\leftrightarrows$</tex> hardware closed-loop acceleration framework. Given a dataset, a model, latency constraint LC and accuracy constraint AC, our framework can provide a best device satisfying both constraints. In order to generate a compressed model with high sparsity ratio, we propose a novel pruning technique, hierarchical pruning (HP). We optimize the sparse matrix storage format for HP matrix to further reduce memory usage for FPGA implementation. We design a accelerator that takes advantage of HP to solve the problem of concurrent random access. Experiments on Transformer and TinyBert model show that our framework can find different devices for various LC and AC, covering from low-end devices to high-end devices. Our HP can achieve higher sparsity ratio and is more flexible than other sparsity pattern. Our framework can achieve 37 x, 1.9 x, 1.7x speedup compared to CPU, GPU and FPGA, respectively.
An improved image enhancement framework based on multiple attention mechanism

Image enhancement can accentuate image feature and is necessary process in image processing. This work focuses on fusing multi-exposure image sequences low-light image enhancement. Inspired by the classical non-local means in computer vision, we proposed an improved deep neural network framework with attentions for image enhancement. Firstly, the original image was preprocessed in different dimensions. we get the edge images using an edge extracted algorithm and fusion multi exposed images to get an better initial images based on fully convolutional neural network with position and channel attention mechanism. Secondly, the head network is constructed by fully convolutional neural network. For capturing long-range dependencies between features maps, we designed a non-local attention module for head network to get better enhancement image. Finally, emerging the original images, edge image and fusion image as the input of the head network, it can enhance the images to get high-quality images. Experiments show that our framework proposed in this paper is effective and the attention mechanism play a significant hole in the network.
SHSE: A subspace hybrid sampling ensemble method for software defect number prediction

Software defect number prediction (SDNP) helps allocate limited testing resources by ranking software modules according to the predicted defect numbers. However, the highly skewed distribution of defects greatly degrades the performance of SDNP models by preventing SDNP models from ranking software modules accurately. This paper introduces a novel subspace hybrid sampling ensemble (SHSE) method based on feature subspace construction, hybrid sampling, and ensemble learning for building high-performance SDNP models. Specifically, we first construct a series of feature subspace to ensure the diversity of base learners. In each of feature subspace, we then use the proposed hybrid sampling method to balance the training subset without losing too much information and introducing lots of noisy data caused by only using undersampling or oversampling techniques. Finally, we train each base learner and combine them by using the proposed weighted ensemble strategy. Experiments are performed on 27 public defect datasets. We compare SHSE with five state-of-the-art resampling-based models and four zero-inflated/hurdle models in terms of the ranking performance measure fault-percentile-average (FPA). To demonstrate the effectiveness of SHSE, two statistical testing methods including Wilcoxon Signed-rank test and Scott–Knott Effect Size Difference test are utilized. Cliff’s δ is also computed for quantifying the difference when there is significant difference between SHSE and each baseline. The experimental results show that SHSE significantly outperforms the baselines and improves the performance over each baseline with as least medium effect size on most datasets. On average, SHSE improves the performance over the resampling-based methods by 8.7% ∼ 14.4% and the zero-inflate/hurdle models by 10.3% ∼ 15.2%. It can be concluded that SHSE is a more promising alternative for software defect number prediction. • Proposing a novel subspace hybrid sampling ensemble method called SHSE. • Comparing with five sampling-based models and four zero-inflated/hurdle models. • Considering both within-project and cross-project defect number prediction scenarios. • Experimental results on 27 datasets with statistical testing show the superiority.
An entry-exit path planner for an autonomous tractor in a paddy field

A field boundary with a single entrance as well as the entry and exit paths of the tractor based on the entrance location are key design requirements that must be considered when planning an effective path for an autonomous tractor operating in a paddy field to avoid collisions with the embankments that hold floodwater. In creating a complete in-field coverage path, an entry-exit path planner that generates paths to/from the start/end points of the auto-traveled path is needed to achieve effective field operations for a fully autonomous tractor. In this study, a novel path planner for the entry and exit operations of an autonomous tractor was developed using the A* algorithm to enable the tractor located at the entrance to automatically go to the start point of an agricultural task and return to the entrance after completing the agricultural task. An occupancy grid map with virtual obstacles was designed and the A* algorithm was applied to it to create a path for the tractor to reach its destination via the entry-exit path accurately in terms of positioning and heading. Two path-smoothing processes, a line-of-sight path smoother (LOPS) and a collinear-node path smoother (CNS), were performed to reduce redundant turning and control waypoints derived by the A* algorithm. The feasibility of using the developed algorithms was investigated via computer simulations followed by field tests with a 60-kW autonomous tractor. The simulation results confirm that the entry and exit paths generated by the proposed planner effectively guided the tractor to reach the given target points in terms of location and direction under the various entry, start, and end conditions by reducing the lateral and heading errors compared with those obtained without using the proposed search space and smoothers. For validation testing in two real paddy fields, the entry-exit paths were successfully generated in response to different entrance locations and the tractor followed the entry path and started the operation at the start point of the work path with a lateral deviation of ≤8.5 cm. In addition, the tractor navigated the exit path automatically and reached the destination with a lateral deviation of <12 cm and a heading error of <22° to the entrance and the operator was then able to drive the tractor to exit the field easily without backward navigation.
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

Recent studies have shown that prompts improve the performance of large pre-trained language models for few-shot text classification.Yet, it is unclear how the prompting knowledge can be transferred across similar NLP tasks for the purpose of mutual reinforcement.Based on continuous prompt embeddings, we propose TransPrompt, a transferable prompting framework for few-shot learning across similar tasks.In TransPrompt, we employ a multitask meta-knowledge acquisition procedure to train a meta-learner that captures cross-task transferable knowledge.Two de-biasing techniques are further designed to make it more task-agnostic and unbiased towards any tasks.After that, the meta-learner can be adapted to target tasks with high accuracy.Extensive experiments show that TransPrompt outperforms single-task and cross-task strong baselines over multiple NLP tasks and datasets.We further show that the meta-learner can effectively improve the performance on previously unseen tasks.TransPrompt also outperforms strong fine-tuning baselines when learning with full training sets.
Multimodal Sentiment Analysis Using Multi-tensor Fusion Network with Cross-modal Modeling

With the rapid development of social networks, more and more people express their emotions and opinions via online videos. However, most of the current research on multimodal sentiment analysis cannot do well with effective emotional fusion in multimodal data. To deal with the problem, we propose a multi-tensor fusion network with cross-modal modeling for multimodal sentiment analysis. In this study, the multimodal feature extraction with cross-modal modeling is utilized to obtain the relationship of emotional information between multiple modalities. Moreover, the multi-tensor fusion network is used to model the interaction of multiple pairs of bimodal and realize the emotional prediction of multimodal features. The proposed approach performs well in regression and different dimensions of classification experiments on the two public datasets CMU-MOSI and CMU-MOSEI.
Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes.
Pill Detection Model for Medicine Inspection Based on Deep Learning

This paper proposes a deep learning algorithm that can improve pill identification performance using limited training data. In general, when individual pills are detected in multiple pill images, the algorithm uses multiple pill images from the learning stage. However, when there is an increase in the number of pill types to be identified, the pill combinations in an image increase exponentially. To detect individual pills in an image that contains multiple pills, we first propose an effective database expansion method for a single pill. Then, the expanded training data are used to improve the detection performance. Our proposed method shows higher performance improvement than the existing algorithms despite the limited imaging and data set size. Our proposed method will help minimize problems, such as loss of productivity and human error, which occur while inspecting dispensed pills.
Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Understanding the situation is a critical component of any self-driving system. Accurate real-time visual signal processing to create pixelwise classed pictures, also known as semantic segmentation, is critical for scenario comprehension and subsequent acceptance of this new technology. Due to the intricate interaction between pixels in each frame of the received camera data, such efficiency in terms of processing time and accuracy could not be achieved prior to recent advances in deep learning algorithms. We present an effective approach for semantic segmentation for self-driving automobiles in this study. We combine deep learning architectures like convolutional neural networks and autoencoders, as well as cutting-edge approaches like feature pyramid networks and bottleneck residual blocks, to develop our model. The CamVid dataset, which has undergone considerable data augmentation, is utilised to train and test our model. To validate the suggested model, we compare the acquired findings to various baseline models reported in the literature.
Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.
New Acoustic Features for Synthetic and Replay Spoofing Attack Detection

With the rapid development of intelligent speech technologies, automatic speaker verification (ASV) has become one of the most natural and convenient biometric speaker recognition approaches. However, most state-of-the-art ASV systems are vulnerable to spoofing attack techniques, such as speech synthesis, voice conversion, and replay speech. Due to the symmetry distribution characteristic between the genuine (true) speech and spoof (fake) speech pair, the spoofing attack detection is challenging. Many recent research works have been focusing on the ASV anti-spoofing solutions. This work investigates two types of new acoustic features to improve the performance of spoofing attacks. The first features consist of two cepstral coefficients and one LogSpec feature, which are extracted from the linear prediction (LP) residual signals. The second feature is a harmonic and noise subband ratio feature, which can reflect the interaction movement difference of the vocal tract and glottal airflow of the genuine and spoofing speech. The significance of these new features has been investigated in both the t-stochastic neighborhood embedding space and the binary classification modeling space. Experiments on the ASVspoof 2019 database show that the proposed residual features can achieve from 7% to 51.7% relative equal error rate (EER) reduction on the development and evaluation set over the best single system baseline. Furthermore, more than 31.2% relative EER reduction on both the development and evaluation set shows that the proposed new features contain large information complementary to the source acoustic features.
Wrapper feature selection with partially labeled data

In this paper, we propose a new feature selection approach with partially labeled training examples in the multi-class classification setting. It is based on a new modification of the genetic algorithm that creates and evaluates candidate feature subsets during an evolutionary process, taking into account feature weights and recursively eliminating irrelevant features. To increase the variety of data, unlabeled observations are employed in the feature selection process, namely by pseudo-labeling them using a self-learning algorithm with a recently proposed transductive policy. Empirical results on different data sets show the effectiveness of our method compared to several state-of-the-art semi-supervised feature selection approaches.