Reviewerly/exHarmony huggingface dataset:
A Study on the Impact of Gender, Employment Status, and Academic Discipline on Cyber-Hygiene: A Case Study of University of Nigeria, Nsukka

The COVID-19 pandemic has helped amplify the importance of Cyber-hygiene. As the reliance on the Internet and IT services increased during the pandemic, this in turn has introduced a new wave of criminal activities such as cybercrimes. With the emergent of COVID-19 which lead to increase in cyber-attack incidents, the pattern, and sophistication, there is an urgent need to carry out an exploratory study to find out users' level of cyber-hygiene knowledge and culture based on gender, employment status, and academic discipline. Above this, with many organizations providing for dual mode work pattern or remote and in-person as the pandemic subsides, this study still remains very relevant and hence the aim to investigate the cyber-hygiene knowledge and compliance of university students and employees of the University of Nigeria, Nsukka (UNN). In addition, it attempts to verify the relationship between demographics such as gender, employment status, and academic discipline on cyber-hygiene culture among students and employees. The sample population is made of employees and students of UNN, where the employees are either academic staff or non-academic staff. The sample size consisted of three hundred and sixteen (316) participants, one hundred and eight-seven (187) of whom were females and one hundred and twenty-nine (129) were males. The results offer some useful insight on cyber-hygiene practices at the university.
The Data Leakage Sentiment Analysis Using Naive Bayes Algorithm Based on Machine Learning Approach

Data leakage is a case that often occurs anywhere. Indonesia is one of the countries with the most population that is currently having data leakage cases. The leak of data on the COVID-19 PeduliLindung tracking application, triggered a public reaction because it was considered dangerous. Based on this, the aim of the study is to predict the sentiment pattern using Naïve Bayes. This study is important to do sentiment analysis to find out the public's reaction, it can become a recommendation in developing applications that are safer in data storage. The experiment in this study used data from Twitter which was taken for 14 days, between 16–21 May 2022. The data was processed using Google Collab and the Naïve Bayes approach. The experimental results are that negative sentiment is greater than positive sentiment and neutral sentiment, which is 93%. While the accuracy of positive sentiment is 81% and Neutra sentiment is 90%. This means the leak of public data from a COVID-19 tracing application in Indonesia has a greater negative sentiment. The difference in the study is the data testing process was carried out five times to get good accuracy from the model. And the results show that Naïve Bayes is a model that is quite recommended for prediction of sentiment patterns.
Task Allocation and Path Planning Method For Multi-Autonomous Forklift Navigation

The growth of the e-commerce sector necessitates the automation of warehouses. For the optimal automated warehouse, multi-autonomous forklift research is vital. Factors including computing time, safe navigation, and task effectiveness must be carefully taken into account. In this work, we suggest a system that participates in task assignment and conflict resolution in order to address these concerns. The job allocation function uses the Hungarian method to determine the shortest path, and the Conflict Priority Based Search function is used by the conflict solver to resolve the collision. According to tests performed in various warehousing settings and task modes, this method has a marginally 14.44 % lower overall path cost than the previous method.
Text Classification Using Machine Learning Techniques: Comparative Analysis

Sharing short reviews on Social Networking Sites has contributed to rising textual data. For this short review text, needed a text classification model to identify short texts, organize them in structured form to define predefined classes efficiently and accurately. In this paper, Imdb movie review text classification model is designed. The Proposed paper is categorized into three phases: - Pre-processing of textual data, Weighting of text, Development of classifier model. For words weighting process, we considered term frequency-inverse document frequency (Tfidf), N-grams (Bi-grams) and term frequency-inverse document frequency (Tf-idf) with N-grams (Bi-grams). After the phases, evaluation of the classification model is done by comparing different algorithms of classification like Naive Bayes, Support vector machine (SVM), Logistic regression, k-nearest neighbours (k-NN) and Decision tree on statistical parameters like accuracy, f1 score, precision, confusion matrix, recall. Finally, the liberation of each weight processing with the classifier is discussed. Combination approach Tf-idf with Bi-grams perform least in every data split scenario and with every classifier. Combinational approach gives negative results as compared to the other two weighting processes. However, the highest accuracy is Logistic Regression with Ngrams (Bi-grams) and Support Vector Machine (SVM) with term frequency-inverse document frequency (Tf-idf) in an 80:20 data split scenario with every classifier.
Text Summarization using Textrank and Lexrank through Latent Semantic analysis

This paper's goal is to investigate the applicability of three distinct text summary algorithms to the realm of blogs, with the ultimate goal of identifying the most precise method for indicating a highly trustworthy automatic summarization tool. The most important component of summarizing is selecting a sample that is reflective of the whole and yet stands on its own as a complete entity. Multiple industries now make use of summarization tools. Automatic document summarizing seeks to provide an informative abstract of a text by extracting its most relevant phrases. Document Concisely summarizing online material is a business that's increasingly in demand now. Blogs have a crucial role in the development and dissemination of public sentiment. Blogs are a popular and powerful medium of online expression, with an estimated 152 million sites now online. Because of this, analyzing blog comments is crucial for describing patterns in consumer spending, evaluations in the fields of social science, health, and agriculture, etc. It's possible that studies in fields as diverse as psychology, anthropology, economics, political science, etc. might benefit from this information on consumer expenditure. The tools used to compile these summaries need to be in step with the analysis's needs. There are several tenors, and each one calls for a unique algorithm based on a specific set of mathematics and computational principles. It is thus necessary to examine the algorithms themselves in order to explain the best method for examining blog-based views.
Software Fault Prediction Using Machine Learning Models

In recent years, computers have great role to the society for their reliability which becoms a key essential in day to day life. The role of software and its captious function in computer system for some certain software has appeared as important achievement for certain infrastructure. Exploitation of system perspective which recognise the importance of software that characterized the current state of fault identification research work as it contributes to the reliability of computer systems. In general, different classification algorithms like K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB), Radial Basis Function Support Vector Machine (RBF-SVM), (L-SVM), Polynomial Support Vector Machine (P-SVM), Adaboost, and Random Forest (RF) have been considered to determine classification performance to evaluate the accuracy of classification with ten number of fault-tolerance datasets. In most of the cases, it is noticed that the nature of data have great impact in the performance of the classification algorithm. The evaluation of several performance measures of all the above ML classification algorithms have been analyzed for ten number of fault-tolerance datasets. It is also observed that the classifier Adaboost gives better result as compared to rest of the classification algorithms.
AI-based Block Identification and Classification in the Blockchain Integrated IoT

Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the implementations and further analyzes the adaptability of the AI-based solution in the Blockchain-integrated IoT architecture. This work focuses on identifying the of block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing this, the block's sensitivity can be identified, which can help the system to reduce the energy consumption of the block validation stage by dynamically choosing an appropriate consensus mechanism.
Hierarchical Over-the-Air FedGradNorm

Multi-task learning (MTL) is a learning paradigm to learn multiple related tasks simultaneously with a single shared network where each task has a distinct personalized header network for fine-tuning. MTL can be integrated into a federated learning (FL) setting if tasks are distributed across clients and clients have a single shared network, leading to personalized federated learning (PFL). To cope with statistical heterogeneity in the federated setting across clients which can significantly degrade the learning performance, we use a distributed dynamic weighting approach. To perform the communication between the remote parameter server (PS) and the clients efficiently over the noisy channel in a power and bandwidth-limited regime, we utilize over-the-air (OTA) aggregation and hierarchical fed-erated learning (HFL). Thus, we propose hierarchical over-the-air (HOTA) PFL with a dynamic weighting strategy which we call HOTA-FedGradNorm. Our algorithm considers the channel conditions during the dynamic weight selection process. We conduct experiments on a wireless communication system dataset (RadComDynamic). The experimental results demonstrate that the training speed with HOTA-FedGradNorm is faster compared to the algorithms with a naive static equal weighting strategy. In addition, HOTA-FedGradNorm provides robustness against the negative channel effects by compensating for the channel conditions during the dynamic weight selection process.
Rate Distortion Tradeoff in Private Read Update Write in Federated Submodel Learning

We investigate the rate distortion tradeoff in private read update write (PRUW) in relation to federated submodel learning (FSL). In FSL a machine learning (ML) model is divided into multiple submodels based on different types of data used for training. Each user only downloads and updates the submodel relevant to its local data. The process of downloading and updating the required submodel while guaranteeing privacy of the submodel index and the values of updates is known as PRUW. In this work, we study how the communication cost of PRUW can be reduced when a pre-determined amount of distortion is allowed in the reading (download) and writing (upload) phases. We characterize the rate distortion tradeoff in PRUW along with a scheme that achieves the lowest communication cost while working under a given distortion budget.
Convolutional Block Design for Learned Fractional Downsampling

The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values. However, in many image and video processing applications, the ability to resize by a fractional factor would be advantageous. One example is conversion between resolutions standardized for video compression, such as from 1080p to 720p. To solve this problem, we propose an alternative building block, formulated as a conventional convolutional layer followed by a differentiable resizer. More concretely, the convolutional layer preserves the resolution of the input, while the resizing operation is fully handled by the resizer. In this way, any CNN architecture can be adapted for non-integer resizing. As an application, we replace the resizing convolutional layer of a modern deep downsampling model by the proposed building block, and apply it to an adaptive bitrate video streaming scenario. Our experimental results show that an improvement in coding efficiency over the conventional Lanczos algorithm is attained, in terms of PSNR, SSIM, and VMAF on test videos.
Inverse Cognition in Nonlinear Sensing Systems

Inverse cognition aims to estimate the ‘estimate’ of a defender's state inferred by an adversarial cognitive system such as radar when the defender observes the attacker's actions in a noisy environment. We develop an inverse extended Kalman filter (IEKF) to address inverse cognition in a non-linear system setting. We consider general non-linear system dynamics, wherein the attacker employs an extended Kalman filter to compute its estimate of the defender's state. We further provide sufficient conditions for the boundedness of the IEKF's estimation error. We validate our model and methods through numerical experiments using recursive Cramer- Rao lower bound as a benchmark.
CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints

We present a new solver for non-convex trajectory optimization problems that is specialized for robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point SOlver, combines several strategies for constrained numerical optimization to natively handle second-order cones and complementarity constraints. It reliably solves challenging motion planning problems that include contact-implicit formulations of impacts and Coulomb friction, thrust limits subject to conic constraints, and state-triggered constraints where general-purpose nonlinear programming solvers like SNOPT and Ipopt fail to converge. Additionally, CALIPSO supports efficient differentiation of solutions with respect to problem data, enabling bi-level optimization applications like auto-tuning of feedback policies. Reliable convergence of the solver is demonstrated on a range of problems from manipulation, locomotion, and aerospace domains. An open-source implementation of this solver is available.
Visuo-Textual Explanations of a Robot's Navigational Choices

With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.
Creative AI for HRI Design Explorations

Design fixation, a phenomenon describing designers' adherence to pre-existing ideas or concepts that constrain design outcomes, is particularly prevalent in human-robot interaction (HRI), for example, due to collectively held and stabilised imaginations of what a robot should look like or behave. In this paper, we explore the contribution of creative AI tools to overcome design fixation and enhance creative processes in HRI design. In a four weeks long design exploration, we used generative text-to-image models to ideate and visualise robotic artefacts and robot sociotechnical imaginaries. We exchanged results along with reflections through a digital postcard format. We demonstrate the usefulness of our approach to imagining novel robot concepts, surfacing existing assumptions and robot stereotypes, and situating robotic artefacts in context. We discuss the contribution to designerly HRI practices and conclude with lessons learnt for using creative AI tools as an emerging design practice in HRI research and beyond.
New wafer shape measurement technique for 300mm blank vertically held silicon wafer

Wave Front Phase Imaging (WFPI) is a new technique for measuring the free shape of a silicon wafer. To avoid the effects of gravity affecting the wafer shape, the silicon wafer is held vertically while measured using a custom made three-point wafer holder. The wave front phase is measuring using a non-coherent light source that is collimated and then reflected off the silicon wafer surface. The wave front phase is measured using a unique new method that only needs to record the intensity of the reflected light at two or more distances along the optical path. Since only intensity images are used to generate the phase, commercially available CMOS sensors with very high pixel count are used, which enables very high number of data points to be collected at the time required by the cameras shutter speed when using a dual camera setup with simultaneous image acquisition. In the current lab system, a single camera on a linear translation stage is used that acquires 16.3 million data points in 12 seconds, including the stage motion, on a full 300mm wafer providing lateral pixel resolution of 65&mu;m. The flatness of the silicon wafers used to manufacture integrated circuits (IC) is controlled to tight tolerances to help ensure that the full wafer is sufficiently flat for lithographic processing. Advanced lithographic patterning processes require a detailed map of the free, non-gravitational wafer shape, to avoid overlay errors caused by depth-of-focus issues. We present WFPI as a new technique for measuring the free shape of a silicon wafer with high resolution and high data count acquired at very high-speed using a system where the wafer is held vertically without the effects of gravity.
Space-to-speed architecture supporting acceleration on VHR image processing

One of the major focuses in the remote sensing community is the rapid processing of deep neural networks (DNNs) on very high resolution (VHR) aerial images. Few studies have investigated the acceleration of training and prediction by optimizing the architecture of the DNN system rather than designing a lightweight DNN. Parallel processing using multiple graphics processing units (GPUs) increases VHR image processing performance. It drives extremely large and frequent data transfers (input/output(I/O)) from random access memory (RAM) to GPU memory. As a result, the system bus congestion causes the system to hang, resulting in long latency in training/predicting. In this paper, we evaluate the causes of long latency and propose a space-to-speed (S2S) DNN system to overcome the aforementioned challenges. A three-level memory system aiming to reduce data transfer during system operation is presented. Distribution optimization with parallel processing was used to accelerate the training. Training optimizations on VHR images (such as hot-zone searching and image/ground truth queues for data saving) were used to train the VHR images efficiently. Inference optimization was performed to speed up prediction in the release mode. To verify the efficiency of the proposed system, we used aerial image labeling from the Institut National de Recherche en Informatique et en Automatique (INRIA) and benchmarks from the Massachusetts Institute of Technology Aerial Imagery for Roof Segmentation (MITAIRS) to test the system performance and accuracy. Without the loss of accuracy, the S2S system improved prediction speed on the testing dataset by eight GPUs in a normal setting in both the INRIA dataset (from 534 to 72 s) and the MITAIRS dataset (818 to 120 s). With the prediction in half-float (using float-16 data), an 8-GPU parallel processing increased the speed to 38 s in the INRIA dataset and 83 s in the MITAIRS dataset. In a pressure test, our proposed system operated on 18,000 images with a size of 5000 × 5000 from 18.2 to 1.8 h with the prediction in full-float (using float-32 data) and 43 min with the prediction in half-float, increasing the speed by a factor of 9.78 and 25.3, respectively, when compared to system runs without optimization.
Counterfactual attention alignment for visible-infrared cross-modality person re-identification

Visible-infrared person re-identification (VI-ReID) copes with cross-modality matching between the daytime visible and night-time infrared images. Existing methods try to use attention modules to enhance multi-modality feature representations, but ignore measures of attention quality and lack direct and effective supervision of the attention learning process. To solve these problems, we propose a counterfactual attention alignment (CAA) strategy by mining intra-modality attention information with counterfactual causality and aligning the cross-modality attentions. Specifically, a self-weighted part attention module is designed to extract the pairwise attention information in local parts. The counterfactual attention alignment strategy obtains the learning results of the attention module through counterfactual intervention, and aligns the attention maps of the two modalities to find better shared cross-modality attention regions. Then the effect of the aligned attention on network prediction is used as a supervision signal to directly guide the attention module to learn more effective attention information. Extensive experimental results demonstrate that the proposed approach outperforms other state-of-the-art methods on two standard benchmarks.
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In this paper, by Gauss sums, we determine Hamming weight distributions of generalized quasi-cyclic (GQC) codes over the finite chain ring Fq+uFq, where u2=0. As applications, some new infinite families of minimal linear codes with WminWmax≤q−1q and projective two-weight codes are constructed.