Reviewerly/exHarmony huggingface dataset:
Re-Examining Calibration: The Case of Question Answering

For users to trust model predictions, they need to understand model outputs, particularly their confidence-calibration aims to adjust (calibrate) models' confidence to match expected accuracy.We argue that the traditional calibration evaluation does not promote effective calibrations: for example, it can encourage always assigning a mediocre confidence score to all predictions, which does not help users distinguish correct predictions from wrong ones.Building on those observations, we propose a new calibration metric, MACROCE, that better captures whether the model assigns low confidence to wrong predictions and high confidence to correct predictions.Focusing on the practical application of open-domain question answering, we examine conventional calibration methods applied on the widely-used retrieverreader pipeline, all of which do not bring significant gains under our new MACROCE metric.Toward better calibration, we propose a new calibration method (CONSCAL) that uses not just final model predictions but whether multiple model checkpoints make consistent predictions.Altogether, we provide an alternative view of calibration along with a new metric, re-evaluation of existing calibration methods on our metric, and proposal for a more effective calibration method. 1
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling

Existing research generally treats Chinese character as a minimum unit for representation.However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each individual character has to be represented by a unique vector.In this paper, we introduce a novel representation method for Chinese characters to break the bottlenecks, namely StrokeNet, which represents a Chinese character by a Latinized stroke sequence (e.g., "凹(concave)" to "ajaie" and "凸(convex)" to "aeaqe").Specifically, StrokeNet maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar Latin representations.With the introduction of StrokeNet to neural machine translation (NMT), many powerful but not applicable techniques to non-Latin languages (e.g., shared subword vocabulary learning and ciphertextbased data augmentation) can now be perfectly implemented.Experiments on the widelyused NIST Chinese-English, WMT17 Chinese-English and IWSLT17 Japanese-English NMT tasks show that StrokeNet can provide a significant performance boost over the strong baselines with fewer model parameters, achieving 26.5 BLEU on the WMT17 Chinese-English task which is better than any previously reported results without using monolingual data.
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation

Code-switching has seen growing interest in recent years as an important multilingual NLP phenomenon.Generating code-switched text for data augmentation has been sufficiently well-explored.However, there is no prior work on generating code-switched text with fine-grained control on the degree of codeswitching and the lexical choices used to convey formality.We present COCOA, an encoder-decoder translation model that converts monolingual Hindi text to Hindi-English code-switched text with both encoder-side and decoder-side interventions to achieve finegrained controllable generation.COCOA can be invoked at test-time to synthesize codeswitched text that is simultaneously faithful to syntactic and lexical attributes relevant to code-switching.COCOA outputs were subjected to rigorous subjective and objective evaluations.Human evaluations establish that our outputs are of superior quality while being faithful to desired attributes.We show significantly improved BLEU scores when compared with human-generated code-switched references.Compared to competitive baselines, we show 10% reduction in perplexity on a language modeling task and also demonstrate clear improvements on a downstream code-switched sentiment analysis task.
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning

While transferring a pretrained language model, common approaches conventionally attach their task-specific classifiers to the top layer and adapt all the pretrained layers.We investigate whether one could make a task-specific selection on which subset of the layers to adapt and where to place the classifier.The goal is to reduce the computation cost of transfer learning methods (e.g.fine-tuning or adapter-tuning) without sacrificing its performance.We propose to select layers based on the variability of their hidden states given a taskspecific corpus.We say a layer is already "wellspecialized" in a task if the within-class variability of its hidden states is low relative to the between-class variability.Our variability metric is cheap to compute and doesn't need any training or hyperparameter tuning.It is robust to data imbalance and data scarcity.Extensive experiments on the GLUE benchmark demonstrate that selecting layers based on our metric can yield significantly stronger performance than using the same number of top layers and often match the performance of fine-tuning or adapter-tuning the entire language model.
Appropriate Detection of HAM and Spam Emails Using Machine Learning Algorithm

An clever and automatic anti-unsolicited mail framework is vital because of the excessive increase of unsolicited e-mail assaults and the inherent malevolent dynamic inside the ones assaults on numerous social, personal, and expert work. There is an increased risk of identity theft, theft of sensitive information, financial loss, damage to reputation, and other crimes that threaten the privacy of the victim. When taking into account the multidimensional feature set of email, current methods are rather fallible. We believe that an artificial intelligence-based strategy is the most effective one going forward, particularly unsupervised machine learning. Exploring the application of unsupervised learning for ham and spam clustering in the mail by comparing these Random Forest, Logistic, Random Tree, Bayes Net, and Naïve Bayes algorithms with LTSM Algorithms by using frequency weightage of words and validating the best accuracy is the purpose of this study.
A Comparative Study of Deep Learning Robustness for Sensor-based Human Activity Recognition

Recognition of human activities has become a highly significant area of research in the current era of the Internet of Things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. The integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. This study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. The study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. The results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels.
The Essence of Software Engineering Framework-based Model for an Agile Software Development Method

Agile development's rapid growth is due to its ability to address complex problems and facilitate a smooth transition from traditional methods.However, no single Agile method can fit every organization, which leads to a lack of adoption guidelines.It triggers this investigation by proposing an Agile development method model based on the Essence of software engineering framework and incorporating the common ground of popular methods such as Scrum, Kanban, Extreme programming, SAFe, Less, Nexus, Spotify Agile, Scrum of Scrums, and Disciplined Agile.The Essence of software engineering framework provides an approach for organizations to develop software development methods based on common ground or shared understanding among methods.We enhance this approach for Agile methods, resulting in a model to support organizations in developing their Agile methods and practices.Moreover, Design Science Research (DSR) was employed as a methodology to construct the artifact, demonstration, and evaluation.We demonstrated the model in an Agile product development at a national-wide bank in Indonesia.This investigation enhances Agile methods in SWEBOK's Software Engineering Models and Methods knowledge area, benefiting academics and practitioners.Practitioners can use the model as a reference to implement their Agile projects.
PIDray: A Large-Scale X-ray Benchmark for Real-World Prohibited Item Detection

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. In specific, PIDray collects 124, 486 X-ray images for 12 categories of prohibited items, and each image is manually annotated with careful inspection, which characterizes it, to our best knowledge, with the largest volume and varieties of annotated images with prohibited items to date. Meanwhile, we propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray. Specifically, we adopt the tree-like structure to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories. Based on this simple yet effective scheme, we offer strong task-specific baselines across object detection, instance segmentation, and multi-label classification tasks and verify the generalization ability on common datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDray demonstrate that the proposed method performs favorably against current state-of-the-art methods, especially for deliberately hidden items. Our benchmark and codes are available at https://github.com/lutao2021/PIDray .
cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing

Software development of high-performance graph algorithms is difficult on modern parallel computers. To simplify this task, we have designed and implemented a collection of C++ graph primitives, basic building blocks, within cuGraph to assist graph analytics software developers on parallel computers, ranging from desktops to large clusters. This graph primitives API provides a vertex/edge-centric C++ Standard Template Library (STL)-like interface, allowing users to pick a primitive algorithm, and specify desired operations on vertices and edges and how to reduce the output of such operations through C++ functors. The API implementation is responsible for executing these functors on the underlying hardware. In this case, the graph primitives are implemented to run on NVIDIA GPU systems, from a single-GPU to multi-GPUs in a distributed cluster. RAPIDS cuGraph is NVIDIA’s graph analytics solution for data scientists and software integrators. Algorithms in cuGraph are either implemented using the cuGraph C++ primitives API or being migrated over to using the primitives API. The Louvain and PageRank algorithms have been tested on clusters with over 1000 GPUs.
A Dataset for recognition of Norwegian Sign Language

Machine learning is a powerful tool in developing sign language recognition models, significantly enhancing communication accessibility for the deaf and hard-of-hearing community. However, such models' creation relies on the availability of comprehensive datasets, which are currently scarce for specific sign languages, particularly for Norwegian Sign Language (NSL). This paper introduces a unique dataset created to address this gap. The dataset, comprising 24,300 images of 27 NSL letters, was captured under varying conditions to represent each sign comprehensively. A man and a woman performed the signs. The goal of creating this dataset was to provide a robust foundation for further research in NSL recognition.&#x0D; The dataset is hosted by the Department of Applied Data Science at Noroff University College, Norway, and is freely accessible [1] at https://data.mendeley.com/datasets/3cfrj4vd4m/1.
Networkization of higher educational institutions as a factor in increasing the innovative development of the region

Transformation of the Russian system of higher education resulting from an escalation in international relations, and as a result, an unreasonable severance of scientific and educational ties with many European universities and research centers (for example, the termination of support for domestic scientists by the German academic exchange service DAAD), exit and from the Bologna system of education, have become a kind of bifurcation point for the further development of the national higher education. At present, it is necessary to concentrate on the use of the country's internal scientific resources, as well as the search for new and promising solutions. This issue is currently one of the most pressing and requires special attention from the state. The purpose of the article is to analyze the level of development of Russian universities and propose directions for networking higher education institutions in accordance with the requests of various subjects. The analysis of the competitiveness of Russian universities in international rankings was carried out and the ranking of regions with the best universities was given. Data on the integration of universities are presented on the example of clusters as one of the forms of networking with various subjects of the ecosystem. The research methodology is based on the analysis of theoretical and practical work in the field of university management in order to identify the most promising areas for the development of network links. The scientific novelty consists in consolidating the developed and proposed areas for practical application by universities in order to intensify the processes of interaction through the development of network links. The proposed areas of cooperation between universities are practically applicable regardless of the place in the rankings in order to increase both the prestige of the educational institution and the innovative attractiveness of the region.
The extended digital image correlation based on intensity change model

Digital image correlation is used to measure deformation and shape. In some complex scenes, the illumination of the measured surface may vary unevenly before and after deformation. The intensity change model is added to correlation function and a family of speckle image matching algorithms is obtained. In integer pixel matching, linear least square estimation is used to calculate the initial values of the intensity coefficients and integer pixel position; in subpixel matching, cyclic variable method is proposed to make the iteration converge. Compared with the current mainstream algorithm, the proposed method makes the physical meaning of correlation matching clearer, so that the correlation function can be defined according to the actual lighting environment, and correlation matching can be more accurate. The experimental results indicate that the proposed matching algorithm is suitable for applications where ambient light is constantly changing.
AI-Based Portable Gesture Recognition System for Hearing Impaired People Using Wearable Sensors

Recently, there has been a remarkable interest in sign language recognition techniques. Especially in the field of sensor-based besides the extensive employment of open-source platforms in research and development testbeds. Sign language recognition has attracted considerable attention from academic scholars and the industry because deafness recognized as a severe and worldwide health concern. However, most studies in recognition have only focused on vision-based or image-based systems that were not suitable for outdoor usage and lack mobility features. This paper introduces a smart glove that is based on wearable sensors to achieve portable standalone system working in a real-time environment with a user-friendly interface. The presented system utilized modern approaches to collect and generate new datasets using two kinds of sensors only. This dataset was employed to develop an artificial neural network (ANN) model that was capable of predicting the alphabetic letters based on hand gestures and orientation. The ANN model was trained using Scaled Conjugate Gradient (SCG) algorithm. The obtained results showed a remarkable performance in terms of ANN accuracy for both Arabic Sign Language (ArSL) and American Sign Language (ASL) which were 96%, 98% respectively. The performance of the developed ANN model ensured its usability in real-time scenario.
Distributed Database Design and Performance Tuing

it is necessary to design a distributed database for real-time data storage to meet the needs of rapid data collection, for the operation and maintenance of large and complex systems. The system adopts the distributed database integrated with three databases to meet the storage of different types of data such as structured data and time series data of the operation and maintenance system. Based on the characteristics of data collection, the system adopts three methods: horizontal database partitioning, horizontal table partitioning, and horizontal partitioning to reduce the amount of table data and meet the performance requirements of the system.
Penggunaan Negative Pressure Wound Therapy (NPWT) terhadap Diabetic Foot Ulcer

This study aims to conduct a literature review related to research results that discusses the effectiveness of NPWT on diabetic foot ulcers (DFU). The method used is a systematic review method from online databases such as Scopus, ClinicalKey, ClinicalKey Nursing, EBSCOhost, Proquest, ScienceDirect, Springer Link, Pubmed, and SAGE Journals using the keywords diabetes mellitus, negative pressure wound therapy, NPWT, and diabetic foot ulcer. The search uses the "AND" and "OR" logic booleans. The results of a systematic review study obtained six articles describing showing that NPWT can help speed up the healing process of DFU wounds. In conclusion, the use of Negative Pressure Wound Therapy (NPWT) in wound care is a relatively effective treatment method for diabetic foot wounds. Keywords: Diabetes Mellitus, Diabetic Foot Wounds, NPWT, Negative Pressure Wound Therapy
Stabilization and Control of Inverted Pendulum Cart System using Fuzzy Logic Controller

The inverted pendulum cart system (IPCS) be similar to number of real-world systems such as transport vehicles, spacecraft and medical fields equipment’s robots and many more, however it is a traditional problem for the control engineers as it is highly non-linear with more than one equilibrium points. Therefore, the paper investigates a fuzzy logic controller to control cart position while maintaining the balance of an inverted pendulum. Furthermore, the length of inverted pendulum is varied and the performance is evaluated under both controllers’ application simultaneously. The performances of both controllers are compared with number of error indices such as Root mean square error (RMSE), Integral square error (ISE), Integral time square error (ITSE), Integral absolute error (IAE) and Integral time absolute error (ITAE). The fuzzy controlled system shows better results as compared with the classical control approach with PID controller in terms of settling time, rise time, overshoot and undershoot under MATLAB/Simulink environment.
Sentiment Classification of Drug Reviews Using Machine Learning Techniques

Sentiment analysis extracts people's feelings and attitudes about a certain subject. It has recently received a lot of interest in a variety of applications. In general, the sentiment analysis of healthcare, especially of drug experiences of users, might give substantial importance to how to enhance public health and make sound judgments. In this paper, new approaches have been developed that are based on patient reviews to predict sentiment to improve data analysis. Then, use Term Frequency-Inverse Document Frequency (TF-IDF) to extract the features. The experimental findings show that the Random Forest Classifier (RFC) beats all results of other existing models from the literature in terms of Precision, Recall, F1-Score, and Accuracy of 93 % accuracy.
Analysis of Feature Selection and Phishing Website Classification Using Machine Learning

Phishing website detection is the task of classifying websites as phishing or legitimate based on URL parameters and certain behaviour of the site. In today's world, dependency on websites has become inevitable. With the increase in website users population and the rise of the internet, cyber-attacks have become a common thing. Attackers across the globe target innocent users to steal their personal classified information such as login credentials, credit or debit card information, which may lead to serious monetary and identity damage for the users. One of the main challenges with this problem is the constant change in phishing URLs. Due to this, there is a constant need to update the detection mechanism, which may be extinct in a short period of time. Most of the current phishing detection tools utilise the black box method, where phishing URLs are stored and queried for verification. This may not be an efficient way due to the constant change in the URLs. In this study, a machine learning based approach is proposed along with a feature selection method to select the right set of features that may contribute to higher detection accuracy. The proposed model is also aimed at being simple, faster, and interpretable. Efficiency, accuracy, and model execution time will be evaluated against the final model.