Reviewerly/exHarmony huggingface dataset:
Evaluating Natural Language Inference Models: A Metamorphic Testing Approach

Natural language inference (NLI) is a fundamental NLP task that forms the cornerstone of deep natural language understanding. Unfortunately, evaluation of NLI models is challenging. On one hand, due to the lack of test oracles, it is difficult to automatically judge the correctness of NLI's prediction results. On the other hand, apart from knowing how well a model performs, there is a further need for understanding the capabilities and characteristics of different NLI models. To mitigate these issues, we propose to apply the technique of metamorphic testing (MT) to NLI. We identify six categories of metamorphic relations, covering a wide range of properties that are expected to be possessed by NLI task. Based on this, MT can be conducted on NLI models without using test oracles, and MT results are able to interpret NLI models' capabilities from varying aspects. We further demonstrate the validity and effectiveness of our approach by conducting experiments on five NLI models. Our experiments expose a large number of prediction failures from subject NLI models, and also yield interpretations for common characteristics of NLI models.
Fractal Neural Networks

This work is aimed to gain artificial neural network architecture that allows to recognize such events in video stream data that merit higher attention. The convolutional neural networks, being used commonly in image recognition, are rather inappropriate for those purposes due to their slowness and poor re-training abilities for real-time systems. Other known architectures suffer from overfitting while re-training. This paper contains some new ideas that proposed to be solution for the several problems at once. The self-similarity allows to replicate the training process. The network is not fully connected that leads to the possibility to consider partial training procedures (explained in text). The training procedure does not use gradient descents, but built using fast and precise linear algebra methods (although the nonlinearity preserved in activation functions). Finally, there is special property that allows the proposed network to be suitable for video first of all: when the frames have small difference between them, the recognition and even re-training are fast due to just channels with changed signals are in use. The first experiments confirm the efficiency of the architecture for the simple video streams.
Visual Question Answering Using Deep Learning

Visual Question Answering (VQA) in recent times challenges fields that have received an outsized interest from the areas of Natural Language Processing and Computer Vision. VQA aims to establish an intelligent system to predict the answers for the natural language questions raised related to the image. The questions about the abstract or real word images are appealed to the VQA system; The system understands the image, and questions using Natural Language Processing (NLP) and Computer Vision which aims to predict the answer in natural language. The main issues which affect the performance of the VQA system is the inability to deal with the open-ended question acquired from the user. The proposed system is developed with a Graphical User Interface (GUI) that extracts the image features using pretrained VGG 16, and Golve embedding and Long Short- Term Memory (LSTM) are used in order to extract question features. By merging the characteristics of the images and the questions using pointwise multiplication the ultimate result is obtained. The acquired result is passed through a softmax layer to find the top 5 predictions about the image question. The proposed system has been experimented with various open-ended questions to show the robustness of the system. VQA finds its application in various real-world scenarios such as self-driving cars and guiding visually impaired people. Visual questions aim different parts of an image, including underlying context and background details.
Measuring Programmer Quality from Complexity Point of View

Quality codes reflects the quality of the one who is behind the keyboard. Acknowledging quality codes is useful for either companies or learning institutions in finding prospect employee or assessing the students' learning process. In this paper, we propose an approach to find quality programmers from their contributions in the crowdsourcing projects (Git-based). This approach measures the complexity level of every contribution committed contributors (or programmers) from the beginning of the project to date. This will help us to find quality programmers and see when they start improving. We use cyclomatic complexity (CC) to decide the complexity level of a contribution. In practice, we could use this approach to assess the quality of a programmer based on his/her previous contributions.
First Experimental Results on Real-Time Cleaning Activity Monitoring System

The COVID-19 pandemic has presented social challenges to establish the new normal lifestyle in our daily lives. The goal of this paper is to enable easy and low-cost monitoring of cleaning activity to keep a clean environment for preventing infection. Although human activity recognition has been a hot research topic in pervasive computing, existing schemes have not been optimized for monitoring cleaning activities. To address this issue, this paper provides an initial concept and preliminary experimental results of cleaning activity recognition using accelerometer data and RFID tags. In the proposed scheme, machine learning technologies and short range wireless communication are employed for recognizing the time and place of wiping as an example of cleaning activities, because it is an important activity for shared places to avoid infection. This paper reports the evaluation results on the recognition accuracy using the proof-of-concept (PoC) implementation to clarify the required sampling rate and time-window size for further experiments. Also, a real-time feedback system is implemented to provide the monitoring results for users. The proposed scheme contributes for efficient monitoring of cleaning activities for creating the new normal era.
Investigating Federated Learning strategies for Pneumonia Image Classification

Deep Learning has emerged as a promising approach for building accurate and robust models in healthcare. This paper explores federated learning strategies for pneumonia classification using X-ray images and analyzes how it can address privacy issues. The performance of federated training has been compared and proved to be on par with traditional centralized training methodology. Three aggregation techniques, namely, Federated Averaging (FedAvg), Coordinate-wise median (COMED) and Geometric median (GEOMED) have been implemented and analyzed for two real-world scenarios. COMED and GEOMED have proved to be more resilient to the presence of outliers when compared to FedAvg. Layer shuffling protocol is proposed for implementing data encryption while sharing the model weights.
Fuzzy Kalman Filter using Linear Matrix Inequalities

The Kalman filter has been extensively used in different applications due to its strengths in estimating the system states under noisy observations. In this paper, a modification of the classical Kalman filter for nonlinear state estimation is presented; firstly, a polytopic set of linear discrete-time models based on a Takagi-Sugeno inference system is used to describe the nonlinear operating region. The stabilizing gains of the linear filters are calculated using Linear Matrix Inequalities (LMI), the proposal is evaluated through simulations.
Optimized Design of ALU Using Reversible Gates

This paper presents a new approach to enhance the speed of the Arithmetic logical unit using different basic reversible gates through instruction management. Generally, Arithmetic logical unit is divided into three fundamental components, which include the Arithmetic, Logical, and Control unit. The proposed architecture of Arithmetic and logical unit yields efficient results compared with traditional design in terms of various performance parameters such as Ancilla input, Garbage output, and Quantum cost. The Ancilla input, Garbage output, and Quantum cost of the proposed design are much better as compared to the existing design in the order of 64, 37 and 35%, respectively.
Using Deep Learning for Urban Pedestrian Counting

Overcrowding in urban areas makes pedestrian travel slower and more dangerous: disease transmission and theft occur more frequently in crowds. This paper proposes a deep learning system designed to analyze traffic camera footage, measuring crowd densities at five intersections in Manhattan. The results were plotted on a mapping model in MATLAB. The system measured crowd densities in clear weather with an average accuracy of 75&#x0025; to 83&#x0025; during the daytime and 59&#x0025; to 79 &#x0025; during the nighttime. The results demonstrate the viability of a pedestrian counting system for monitoring foot traffic in urban settings.
FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning

Machine learning (ML) has been drawn to attention from both academia and industry thanks to outstanding advances and its potential in many fields. Nevertheless, data collection for training models is a difficult task since there are many concerns on privacy and data breach reported recently. Data owners or holders are usually hesitant to share their private data. Also, the benefits from analyzing user data are not distributed to users. In addition, due to the lack of incentive mechanism for sharing data, ML builders cannot leverage the massive data from many sources. Thus, this paper introduces a collaborative approach for building artificial intelligence (AI) models, named FedChain to encourage many data owners to cooperate in the training phase without sharing their raw data. It helps data holders ensure privacy preservation for the collaborative training right on their premises, while reducing the computation load in case of centralized training. More specifically, we utilize federated learning (FL)and Hyperledger Sawtooth Blockchain to set up a prototype framework that enables many parties to join, contribute and receive rewards transparently from their training task results. Finally, we conduct experiments of our FedChain on cyber threat intelligence context, where AI model is trained within many organizations on each their private datastore, and then it is used for detecting malicious actions in the network. Experimental results with the CICIDS-2017 dataset prove that the FL-based strategy can help create effective privacy-preserving ML models while taking advantage of diverse data sources from the community.
Garbage Classification Method Based on YOLOv3

Computer vision has been widely used in various fields, but garbage classification based on object detection has received little attention. This paper has carried out the practice on the garbage classification in dataset of the real scene by using the network structure of object detection of YOLOv3 (you only look once version 3). First of all, two types of datasets are established, one contains only bottles, the other contains multiple categories, such as plastic, paper, etc., which are composed of pictures with complex backgrounds in real scene. Then, the principle and architecture of YOLOv3 network are described in detail, and the practice is carried out on the garbage classification dataset in the real scene. Finally, the experimental results show that the method has high accuracy in the dataset containing bottles, the recognition rate reaches 95.33%, and the recognition speed reaches 50 FPS. In addition, the yolov4 network is also applied to the dataset containing multiple garbage.
Structure-Preserving Deep Autoencoder-based Dimensionality Reduction for Data Visualization

Here, we propose a structure-preserving deep autoencoder-based dimensionality reduction scheme for data visualization. For this, we introduce two regularizers for regularizing autoencoders. The proposed regularizers help the encoded feature space preserve the local and global structures present in the original feature space. A chosen reduced dimensionality of two or three for the encoded feature space enables us to visualize the extracted latent representations of the data using scatterplots. The proposed method has two variants, depending on which regularizer it uses. The proposed approach, moreover, is unsupervised and has predictability. We use three synthetic datasets and one real-world dataset to illustrate the effectiveness of the proposed method. We also visually compare it with three state-of-the-art data visualization schemes and discuss several future research directions.
Collaborative Creative Coding Through Drawing Robots

With the development of end-user fabrication technology, plotters have become increasingly accessible and attractive to a broader community. Since their invention, these 3-axis CNC machines have been connecting digital and analog creation. Plotters have great potential as a tool to bridge the digital-physical divide, allowing learners from a variety of backgrounds to gain immediate satisfaction from their programming by translating digital output into physical artifacts. In addition, due to their roots in art, plotters may be more inviting to students from artistic backgrounds new to programming. In this studio, we investigate plotters’ potential in introducing creative programming in collaborative learning environments. By introducing collaborative creative programming through the lens of plotters, we explore solutions to challenges in programming education, collaborative programming, and personal fabrication.
Cyber Attacks, Contributing Factors, and Tackling Strategies

This article describes how as access to the Internet has increased, cybersecurity has become important, with businesses and the government spending much time and resources to combat cyber attacks. The purpose of this article was to review the existing literature related to cybersecurity. Specifically, the review synthesizes the empirical research in (1) various types of cyber attacks, (2) contributing factors related to cybersecurity behavior, and (3) strategy to improve cybersecurity behavior. The most developed line of research in this area has been focusing on the strategies to improve cybersecurity behavior, showing a questionable trend of quickly creating solutions before fully conceptualizing the problem.
An Image Sentence Generation Based on Deep Neural Network Using RCNN-LSTM Model

Image captioning refers to generating a sentence description by analyzing the image. The objective of image captioning is to automatically generate these captions for an image to gain a deeper knowledge by using deep learning algorithms. In this paper, an image sentence generation based on deep neural network using RCNN-LSTM model is proposed. In the proposed model an image is taken as input and generate sentence as an output by making use of natural language processing for describing the contents of the image. We have developed this model by consistently analyzing a deep neural networks and image sentence generation methodologies. The scheme uses image datasets and their sentence descriptions to train and test the model and have balance between language and visual data. This research paper uses Recurrent Convolutional Neural Networks (RCNN) a combination of recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). RNN is used to process language part and CNN is used to process image part for obtaining feature vectors. Additionally, Long Short Term Memory (LSTM) is used for textual sentence generation. In this model, RCNN works as an encoder to retrieve features for the images by making use of Keras VGG16 and LSTM works as a decoder to obtain textual sentences which describes the images. In our approach we have used Flickr-8k, Flickr30K and MSCOCO dataset to train the model. The model image sentence generation achieved a very good accuracy for generating captions.
Double distillation for class incremental learning

Incremental learning models belong to an area of machine learning algorithms dealing with streams of data arriving in sequential order over time. As such, these algorithms are vulnerable to catastrophic forgetting, which is the tendency of the incremental models to forget past information when new data is added. This phenomenon could be attenuated with the use of a knowledge distillation component and with the support of a sample memory from past classes. In here, we propose a double distillation incremental learning recipe for the class incremental learning scenario, starting from a proof of the classification limits of the relaxed SoftMax function and coupling it with a modified version of the iCaRL algorithm in which we have remodeled the last classification layer by varying the temperature parameter. We performed extensive experiments to outline the advantages of using our method compared to the standard algorithm.
Early Detection of Suicidal Tendencies from Text Data using LSTM

Social networking sites provide us with an abun-dance of textual data for analysis, when combined with a neural network to consume data to any level depending on its architecture, it makes the frontier of social media suitable for textual analysis. One such analysis is the ability to detect intentions of suicide from text content posted by users on such platforms. Although conventional algorithms in machine learning serve this purpose to a certain degree, neural networks that mainly specialize in handling text content such as Long Short Term Memory networks perform this function more efficiently and accurately preserving temporal properties, while still being flexible enough to neither over-fit nor under-fit through the control of the depth of the network. This suits to the varying volume of data available for training, which is collected through the scraping of publicly available content posted by actual users, so that the trained model&#x0027;s performance resembles the real world, when compared against conventional classifying algorithms, prove its efficiency through several standard metrics.
On increasing password security awareness using a serious game

Passwords are the base of almost all authentication systems. Hence, it is essential that users choose strong passwords, to prevent attackers from guessing them by using the most common passwords. In this paper, we present a serious game developed with the aim of increasing the awareness of players on the (in)security of passwords. It has been developed using standard web technologies and employing the MEAN stack. To gain points, players have to guess which of the two proposed passwords is the most used one. Our prototype has three different game modalities both single and multi-player. A preliminary evaluation session has been conducted to evaluate the effectiveness of our prototype and its usability.
Design and FPGA Implementation of High-Speed Area and Power Efficient 64-bit Modified Dual CLCG based Pseudo Random Bit Generator

In Internet of Things (IOT) based applications, security is given the highest importance and cryptography plays a major role in maintaining the data safety. Encryption and decryption in cryptography requires a Pseudo random bit generator (PRBG) for key generation. Modified dual coupled linear congruential generator (MDCLCG) based PRBG is the highly efficient PRBG algorithm as it clears a1115 National Institute of Standards and Technology (NIST) tests and has maximum period of 2<sup>n</sup> for a n-bit design. In this paper, complete design and testing of a 64-bit MDCLCG based PRBG is proposed and it&#x2019;s implementation on Kintex-7 XC7K160TFBG676-3 field-programmable gate array (FPGA) is presented. Main components of MDCLCG based PRBG includes a Three operand adder, Barrel shifter, Comparator and an Encoder. Further, a High speed area efficient three operand adder (HSAEA) is used to improve performance of the proposed 64-bit MDCLCG architecture. It&#x2019;s performance is compared with 64-bit MDCLCG designed using three operand Ultra fast adder (UFA) and three operand Carry save adder (CSA) architectures. The post-implementation results of the proposed 64-bit MDCLCG are carried out and from the analysis, it is reported that the proposed 64-bit MDCLCG designed using HSAEA has 25.1&#x0025;, 9.2&#x0025; reduction in Area/Maximum frequency $(A/F_{Max})$ value when compared to UFA and CSA based 64-bit MDCLCG architectures respectively. Also, it has 17.7&#x0025;, 4.4&#x0025; reduction in Power/Maximum frequency $(P/F_{Max})$ value over UFA and CSA based 64-bit MDCLCG architectures respectively. Moreover, the proposed 64-bit MDCLCG ensures more security than 32-bit MDCLCG proposed in literature as the pseudo random bit sequence has a period of 2<sup>64</sup> bits instead of 2<sup>32</sup> bits.
Text Detection and Language Identification on Natural Scene Images using Faster R-CNN

In a world filled with a great deal of linguistic diversity, it can sometimes be difficult to identify signs, markings, or documents that are perhaps in another language. Text detection and language identification are the most critical steps for a language translator working with natural scene images. Here, we present a method for detecting text and language in images by utilizing a Faster Region-based Convolutional Neural Network trained on labelled natural scene images of text in multiple languages.
Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.