Reviewerly/exHarmony huggingface dataset:
Evolution Support for Custom Variability Artifacts Using Feature Models: A Study in the Cyber-Physical Production Systems Domain

Cyber-Physical Production Systems (CPPSs) are constantly evolving, highly configurable, complex software-intensive systems interacting with their environment. The variability of CPPSs must be well-documented to foster reuse, for which the Software Product Line (SPL) community proposed variability models. Unfortunately, industry is mostly unaware of existing variability modeling approaches and frequently develops custom artifacts to document variability, e.g., spreadsheets or Domain-Specific Languages (DSLs). In contrast to SPL variability models, the evolution of these custom artifacts is hardly researched and evolving them remains a tedious and error-prone manual task in practice. In this paper, using two CPPS case studies, we investigate the impact of system evolution on custom artifacts and feature models as a basis for further research. We discuss how feature models could benefit the evolution of DSL-based variability artifacts.
“Hey CAI” - Conversational AI Enabled User Interface for HPC Tools

HPC system users depend on profiling and analysis tools to obtain insights into the performance of their applications and tweak them. The complexity of modern HPC systems have necessitated advances in the associated HPC tools making them equally complex with various advanced features and complex user interfaces. While these interfaces are extensive and detailed, they require a steep learning curve even for expert users making them harder to use for novice users. While users are intuitively able to express what they are looking for in words or text (e.g., show me the process transmitting maximum data), they find it hard to quickly adapt to, navigate, and use the interface of advanced HPC tools to obtain desired insights. In this paper, we explore the challenges associated with designing a conversational (speech/text) interface for HPC tools. We use state-of-the-art AI models for speech and text and adapt it for use in the HPC arena by retraining them on a new HPC dataset we create. We demonstrate that our proposed model, retrained with an HPC specific dataset, can deliver higher accuracy than the existing state-of-the-art pre-trained language models. We also create an interface to convert speech/text data to commands for HPC tools and show how users can utilize the proposed interface to gain insights quicker leading to better productivity. To the best of our knowledge, this is the first effort aimed at designing a conversational interface for HPC tools using state-of-the-art AI techniques to enhance the productivity of novice and advanced users alike.
Methods of Tracks for Training Juniors in Olympiad Informatics: The ISIJ Experience

The article describes methodological approaches to the formation of olympiad competencies for juniors, children 12–15 years old, based on five training tracks in olympiad informatics in accordance with the IOI Syllabus. An integrated approach to the preparation of rounds for children in the framework of the experience of the International School in Informatics for Juniors (ISIJ) is also described.
Augmentation of Handwritten Devanagari Character Dataset Using DCGAN

Nannapaneni, Rajasekhar Chakravarti, Aravind Sangappa, Shilpa Bora, Parinita Kulkarni, V.Many machine learning applications require training datasets containing enormous amounts of data. Generating such huge amounts of data is quite a formidable task in machine learning tasks such as handwritten character recognition. Generative models are emerging as machines having human-like cognitive abilities. They are helpful in augmenting the handwritten character datasets. A deep convolution generative adversarial network (DCGAN) has been developed in this study to generate Devanagari characters. The characters generated by the DCGAN would augment the handwritten character dataset used in training and testing learning machines for character recognition applications. The DCGAN has been tested for numerical and characters of the Devanagari script. The details of the developed DCGAN and those of its implementation have been presented in this paper. The results show that images generated by the DCGAN closely resemble with the handwritten characters.
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective

Metamorphic testing has recently been used to check the safety of neural NLP models.Its main advantage is that it does not rely on a ground truth to generate test cases.However, existing studies are mostly concerned with robustness-like metamorphic relations, limiting the scope of linguistic properties they can test.We propose three new classes of metamorphic relations, which address the properties of systematicity, compositionality and transitivity.Unlike robustness, our relations are defined over multiple source inputs, thus increasing the number of test cases that we can produce by a polynomial factor.With them, we test the internal consistency of state-of-theart NLP models, and show that they do not always behave according to their expected linguistic properties.Lastly, we introduce a novel graphical notation that efficiently summarises the inner structure of metamorphic relations.
On the Impact of Uncertainties in Silicon-Photonic Neural Networks

This article presents a method of criticality assessment to identify susceptible components of silicon-photonic neural networks. —Fei Su, Intel Corporation
Simulating Bandit Learning from User Feedback for Extractive Question Answering

We study learning from user feedback for extractive question answering by simulating feedback using supervised data.We cast the problem as contextual bandit learning, and analyze the characteristics of several learning scenarios with focus on reducing data annotation.We show that systems initially trained on a small number of examples can dramatically improve given feedback from users on modelpredicted answers, and that one can use existing datasets to deploy systems in new domains without any annotation, but instead improving the system on-the-fly via user feedback.
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data

Identifying sections is one of the critical components of understanding medical information from unstructured clinical notes and developing assistive technologies for clinical notewriting tasks.Most state-of-the-art text classification systems require thousands of indomain text data to achieve high performance.However, collecting in-domain and recent clinical note data with section labels is challenging given the high level of privacy and sensitivity.This paper proposes an algorithmic way to improve the task transferability of meta-learningbased text classification in order to address the issue of low-resource target data.Specifically, we explore how to make the best use of the source dataset and propose a unique task transferability measure named Normalized Negative Conditional Entropy (NNCE).Leveraging the NNCE, we develop strategies for selecting clinical categories and sections from source task data to boost cross-domain meta-learning accuracy.Experimental results show that our task selection strategies improve section classification accuracy significantly compared to meta-learning algorithms.
Smart Surveillance Video Monitoring for Home Intruder Detection Using Deep Neural Network

Home security for elderly persons is a cause of concern in today’s world. Monitoring through CCTV is becoming a popular choice to ensure home safety for senior citizens. Automated analysis of surveillance video captured by CCTV at the entrance of a house can be used to ensure home safety of elderly or physically challenged persons. This work proposes a smart surveillance video monitoring system for detection of suspicious activities from real-time CCTV video. The proposed system has a convolutional neural network (CNN) and long short term memory (LSTM) based architecture. The system analyses the human activity present in the input video and categorizes it as any of the four major suspicious activities or normal activity. The proposed system uses CNN to perform feature extraction and LSTM to perform sequence learning from time-series based movement of human body. Training and testing with existing dataset and real-time videos exhibit satisfactory results with training accuracy of 94.2% and testing accuracy of 88.75% respectively.
Method for Generating Synthetic Images of Masked Human Faces

This study is devoted to the topical problem of generating synthetic images of human faces.The paper presents a new method for generating images of human faces in protective masks.The proposed method is based on the combined use of a neural network method for detecting three-dimensional facial landmarks (3D-FAN) and three-dimensional modeling tools.Approbation and quality assessment of the proposed method was conducted on a test dataset, which includes 3836 images.The dataset included human faces images of different gender and age, taken at different distances and at various angles relatively to the camera lens.To assess generation results, the method of multi-criteria assessment was used with the involvement of an expert group.For each generated image final scores were formed by averaging the obtained ratings, both by criteria and by experts.During the experiment, the developed method has demonstrated a high and stable quality for the following ranges of face orientations [-20; +55], [-60; +60] and [-70; +80] along the OX, OY and OZ axes, respectively.The final proportion of correctly generated images of masked human faces turned out to be 95.9%.
Aligned Weight Regularizers for Pruning Pretrained Neural Networks

While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on the choice of pruning criteria.This pruning setup is particularly important for cross-lingual models that implicitly learn alignment between language representations during pretraining, which if distorted via pruning, not only leads to poorer performance on language data used for retraining but also on zero-shot languages that are evaluated.In this work, we show that there is a clear performance discrepancy in magnitude-based pruning when comparing standard supervised learning to the zero-shot setting.From this finding, we propose two weight regularizers that aim to maximize the alignment between units of pruned and unpruned networks to mitigate alignment distortion in pruned cross-lingual models and perform well for both non zero-shot and zero-shot settings.We provide experimental results on cross-lingual tasks for the zero-shot setting using XLM-RoBERTa Base , where we also find that pruning has varying degrees of representational degradation depending on the language corresponding to the zero-shot test set.This is also the first study that focuses on cross-lingual language model compression.
A Sandwich Structure Sensor Based on Ag-Silk Fibroin-Ag Co-Modification for Trace Water Determination in Visible Light Band

A sensor based on silver- Silk Fibroin -silver sandwich structure is proposed to detect trace water in organic solvents. The side Section of D-shaped optical fiber is coated with silver, silk fibroin, and silver film in turn. The inherent nanopores in the top silver membrane allow water molecules to penetrate the middle layer, then the middle silk fibroin layer absorbs water and expands, resulting in the shift of the surface plasmon resonance peak. The sensitivity of the sensor is improved by the strong local electric field enhancement effect between the double-layer metals. The simulation results show that when the initial thickness of silk is 140 nm, the upper silver thickness is 30 nm, the lower silver thickness is 21 nm and the air hole spacing is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.55 \mu \text{m}$ </tex-math></inline-formula> , the sensing effect is the best, final sensitivity can reach 1.92 nm/ppt. In the visible band, the sensor proposed in this paper has the potential to be used for high sensitivity and rapid detection in organic solvents.
Analytical Study of YOLO and Its Various Versions in Crowd Counting

Crowd counting is one of the main concerns of crowd analysis. Estimating density map and crowd count in crowd videos and images has a large application area such as traffic monitoring, surveillance, crowd anomalies, congestion, public safety, urbanization, planning and development, etc. There are many difficulties in crowd counting, such as occlusion, inter and intra scene deviations in perception and size. Nonetheless, in recent years, crowd count analysis has improved from previous approaches typically restricted to minor changes in crowd density and move up to recent state-of-the-art systems, which can successfully perform in a broad variety of circumstances. The recent success of crowd counting methods can be credited mostly to the deep learning and different datasets published. In this paper, a CNN-based technique named You Only Look Once (YOLO), and its various versions have been studied, and its latest version, YOLOv5, is analyzed in the crowd counting application. This technique is studied on three benchmark datasets with different crowd densities. It is being observed that YOLOv5 gives favorable results in crowd counting applications with density ranges from low to medium but not in a very dense crowd.
LTDTS: A Lightweight Trash Detecting and Tracking System

Community environment is one of the focuses of urban governance. And the phenomenon of residents littering in violation of community regulations is one of the most prominent problems in the management of the community environment. However, the existing methods, whether it is time-consuming and labor-intensive human management or action detection-based system that still cannot achieve a good balance between accuracy and real-time performance, cannot solve this outstanding problem well. At the same time, the more commonly used object detection-based systems also have poor performance due to the complexity of the model and system architecture. To address these issues, we propose a novel lightweight trash detection system. The system uses an improved yolov5 algorithm, which is more suitable for the detection of small targets like litter. In addition, we novelly proposed two methods called tracking object transmission and video backtracking, combined with the tracking algorithm based on kernelized correlation filter, we successfully achieved accurate localization of littering pedestrians. So far, our model has been experimented on several small public datasets and our self-designed community littering dataset. What's more, our system has also been put into use in some communities, with initial results that are acceptable, which successfully verified the feasibility of our method.
Multi-Core Implementation of Geometric Multidimensional Scaling for Large-Scale Data

A well-known and widely used technique for mapping data from high-dimensional space to lower-dimensional space is multidimensional scaling (MDS). Although MDS, as a dimensionality reduction method used for data visualization, demonstrates great versatility, it is computationally demanding, especially when the data set is not fixed and its size is constantly growing. Traditional MDS approaches are limited when analyzing very large data sets, as they require very long computation times and large amounts of memory. A way to minimize MDS stress, which can be used to reduce the dimensionality of large-scale data, has been developed using the ideas of Geometric MDS, where all points in a low-dimensional space change their coordinates simultaneously and independently during a single iteration of stress minimization. It is shown in this paper that Geometric MDS allows the implementation of parallel computing for the dimensionality reduction process of large-scale data using multithreaded multi-core processors. We explore how the computational time consumption of data dimensionality reduction and multidimensional data visualization depends on the number of processor cores or processor threads used.
Detection of Multi-column Text in Related Sequence Using Object Detection Technique

Newspapers contain multi-column text with complex structure containing articles, advertisements and much more. All the existing technologies detect text row-wise without any consideration of different columns. In this paper, we propose a model based on YOLO object detection technique to detect the multi-column text using bounding boxes of contents of a newspaper article in real-time inference. Object detection using deep learning has achieved excellent performance. We have created bounding boxes around columns and further classified them into different classes like heading, text, image, etc. in nine important classes. A novel reading sequence algorithm based on n-ary tree concept is also proposed for reading the related text in sensible order like human. We have compared the accuracy of our proposed model with the existing techniques. It has been found that the proposed model is able to extract and read the related text with superior speed without compromising the accuracy.
The Dimensionality of the Cyber Warrior

A cyber ready military is critical to national security. Yet, traditional kinetic warfare techniques for selecting and evaluating combatants do not necessarily apply directly to the abilities necessary to perform as an outstanding cyber warrior. Cyber warfare represents a fundamentally distinct form of warfare, and it is necessary to define the leadership competencies and skills required for cyber warfare to prepare for engagement in the multi-faceted cyberwarfare domain. The cyber domain is multi-faceted and multi-disciplinary. It integrates the use of computer science, mathematics, economics, law, psychology, engineering, and others. We present an analysis of the skills required for cyber warrior readiness. In actuality, the cyber warrior of the future cannot be described as a single type of individual or single set of training. Rather, the cyber warfare theatre comprises a complex interplay of technologies, strategies, policies, and warriors with vastly varying skillset. Using current literature, we present a multi-dimensional view of cyber warfare abilities and propose evaluations for those abilities based on validated psychometric evaluations.
Compaz: Exploring the Potentials of Shared Dictionary Compression on the Web

In this demonstration, we present Compaz, an extensible benchmarking tool for web compression that enables evaluating approach-es before they have been fully implemented and deployed. Compaz makes this possible by collecting all relevant data from user journeys on live websites first and then performing the benchmark analysis as a subsequent step with global knowledge of all transmitted resources. In our demonstration scenario, the audience can witness how current websites could improve their compression ratio and save bandwidth. They can choose from standard and widespread approaches such as Brotli or gzip and advanced approaches like shared dictionary compression that are currently not even supported by any browser.
An Optimized Convolutional Neural Network Model for Wild Animals Detection Using Filtering Techniques and Different Opacity Levels

Despite the fact that there are numerous ways for object identification, these techniques under-perform in real-world conditions. For example, heavy rains and fog at night. As a result, this research work has devised a new convolutional neural network for identifying animals in low-light environments. In the proposed system, images of different animals (containing both domestic and wild animals) are collected from various resources in the form of images and videos. The overall number of samples in the dataset is 2300; however, because convolutional neural networks require more samples for training, a few data augmentation techniques are employed to raise the number of samples in the dataset to 6700. Horizontal flip, rotation, and padding are the data augmentation techniques. The proposed model has achieved an accuracy of 0.72 on the testing set and 0.88 on training set, respectively, without applying the edge detection techniques. The proposed model has achieved 0.81 accuracy after using Canny edge detection technique on animal dataset for outperforming the state-of-the-art models with ResNet-50 and EfficientNet-B7.
Learning to Rank Visual Stories From Human Ranking Data

Visual storytelling (VIST) is a typical vision and language task that has seen extensive development in the natural language generation research domain.However, it remains unclear whether conventional automatic evaluation metrics for text generation are applicable on VIST.In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST ranker), a novel reference-free VIST metric for story evaluation. 1 We first show that the results from commonly adopted automatic metrics for text generation have little correlation with those obtained from human evaluation, which motivates us to directly utilize human evaluation results to learn the automatic evaluation model.In the experiments, we evaluate the generated texts to predict story ranks using our model as well as other reference-based and reference-free metrics.Results show that Vrank prediction is significantly more aligned to human evaluation than other metrics with almost 30% higher accuracy when ranking story pairs.Moreover, we demonstrate that only Vrank shows human-like behavior in its strong ability to find better stories when the quality gap between two stories is high.Finally, we show the superiority of Vrank by its generalizability to pure textual stories, and conclude that this reuse of human evaluation results puts Vrank in a strong position for continued future advances.* * denotes equal contribution 1 Dataset VHED and metric Vrank can be found on GitHub: https://github.com/AcademiaSinicaNLPLab/VHED.git the city was very busy.there were many different kinds of bikes.some were very unique.they were all very fast.i had a great time.i went to the park station.it was a train trip to the museum.the train was very long.we had to go on our way out of the trains.this dog is so happy to see us.Reference: i decided my dog would like a train ride.off to the train station we go.this is the train we will be taking our short trip on.my friend is the conductor.he is getting ready to attach the cars.