Reviewerly/exHarmony huggingface dataset:
Real-Time Gaze Tracking with Event-Driven Eye Segmentation

Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking algorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a real-time rate (&#x003E; 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates at 30 Hz on a mobile processor, achieves 0.1&#x00B0;&#x2013;0.5&#x00B0; gaze accuracies, all the while requiring only 30K parameters, one to two orders of magnitude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto ROI mode, which continuously predicts the Regions of Interest (ROIs) of near-eye images and judiciously processes only the ROIs for gaze estimation. To that end, we introduce a novel, lightweight ROI prediction algorithm by emulating an event camera. We discuss how a software emulation of events enables accurate ROI prediction without requiring special hardware. The code of our paper is available at https://github.com/horizon-research/edgaze.
SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Less Labeled Data

Capsule network is a recent new deep network architecture that has been applied successfully for medical image segmentation tasks. This work extends capsule networks for volumetric medical image segmentation with self-supervised learning. To improve on the problem of weight initialization compared to previous capsule networks, we leverage self-supervised learning for capsule networks pre-training, where our pretext-task is optimized by self-reconstruction. Our capsule network, SS-3DCapsNet, has a UNet-based architecture with a 3D Capsule encoder and 3D CNNs decoder. Our experiments on multiple datasets including iSeg-2017, Hippocampus, and Cardiac demonstrate that our 3D capsule network with self-supervised pre-training considerably outperforms previous capsule networks and 3D-UNets. Code is available at here. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Multimodal Transformer for Nursing Activity Recognition

In an aging population, elderly patient safety is a primary concern at hospitals and nursing homes, which demands for increased nurse care. By performing nurse activity recognition, we can not only make sure that all patients get an equal desired care, but it can also free nurses from manual documentation of activities they perform, leading to a fair and safe place of care for the elderly.In this work, we present a multimodal transformer-based network, which extracts features from skeletal joints and acceleration data, and fuses them to perform nurse activity recognition. Our method achieves state-of-the-art performance of 81.8% accuracy on the benchmark dataset available for nurse activity recognition from the Nurse Care Activity Recognition Challenge. We perform ablation studies to show that our fusion model is better than single modality transformer variants (using only acceleration or skeleton joints data).Our solution also outperforms state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is available at https://github.com/Momilijaz96/MMT_for_NCRC.
A hybrid k-prototypes clustering approach with improved sine-cosine algorithm for mixed-data classification

• Develop a novel clustering-based classification algorithm for mixed data. • Use a sine–cosine algorithm (SCA) to find attribute weights and initial centroids. • The objective function of the algorithm is formulated as a sum-up purity. • Four mutation strategies are embedded into the original SCA. • The results showed that the proposed algorithm can achieve superior accuracy. When dealing a classification problem with mixed data, most of conventional supervised learning algorithms cannot perform well due to their numerical characteristics. However, some clustering algorithms, such as k -prototypes algorithm, show their potential in clustering mixed data. Therefore, the current study intends to develop a novel clustering-based classification algorithm for mixed data to have both merits of classification and clustering. The proposed algorithm employs a sine-cosine algorithm (SCA) to find attribute weights and initial centroids for a k -prototypes algorithm. The objective function of the algorithm is formulated as a sum-up purity. To have better performance for SCA, a mutation strategy, containing Gaussian mutation, Cauchy mutation, Levy mutation, and single-point mutation, is embedded into the original SCA. The proposed algorithm is compared with some metaheuristic-based classification algorithms and existing classification algorithms. Based on the 10 data sets from UCI, the experimental results indicated that the proposed algorithm can achieve superior classification performance in terms of accuracy and Cohen's Kappa. In addition, mutation mechanism can make SCA have better performance.
Real Time Malicious URL Detection on twitch using Machine Learning

Live streaming systems like twitch enable broadcasters to live stream content to a huge audience. The Chatrooms are an important part of Twitch, allowing users to interact with the broadcaster, and among themselves freely. This means that these chatrooms are an ideal place for cybercrime attack and get sensitive information of the twitch user with the help of a malicious Uniform Resource Locators (URL). Harmful websites are responsible for a number of online cyber-attacks and scams today. The attacker tries to persuade unsuspecting twitch users to click on harmful URLs via Twitch Chatroom. Clicking on these URLs may result in the e-mail spam, the introduction of phishing scams, and the download of malicious software, adware, and ransomware, which may result in significant financial losses. As a result, it is very important to detect and prevent these threats effectively.The most common and fastest way to find malicious URLs is to help compare the URLs to the restricted list. However, blocking lists lack the ability to remove reproduced URLs. Unable to detect new bugs in the invisible URL of a strategy based on a completely restricted list, the machine-based reading strategy is integrated here to mitigate the above mentioned threats. The method uses lexical and semantic features extracted from a series of URLs, this feature extraction method is very secure and fast as it does not involve high computation as real-time protection is required.
AGSDE: Archive guided speciation-based differential evolution for nonlinear equations

Solving nonlinear equations (NEs) has been obtained considerable attentions in recent years. However, it is still a difficult problem to improve the efficiency of the algorithm to find multiple roots of NEs. Aiming to deal with this issue, an archive guided speciation-based differential evolution (AGSDE) is presented in this paper. It contains three main components: (i) an archive construction approach is used to save the historical individual with poor fitness values in the selection phase; (ii) a reusing historical individual mechanism is implemented to guide the evolution; (iii) a local search method for solving NEs is performed on different subpopulations to refine the accuracy of the candidate solutions. The performance of AGSDE is tested on 30 NEs problems with different characteristics. Experimental results of AGSDE are competitive with those of other state-of-the-art methods in terms of root rate and success rate. In addition, AGSDE also shows its superiority for solving the other 10 complex NEs problems.
CODH++: Macro-semantic differences oriented instance segmentation network

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available.
Efficient and Secure Measure-Resend Authenticated Semi-Quantum Key Distribution Protocol against Reflecting Attack

In 2021, Chang et al. proposed an authenticated semi-quantum key-distribution (ASQKD) protocol using single photons and an authenticated channel. However, an eavesdropper can launch a reflective attack to forge the receiver’s identity without being detected. In addition, Chang et al.’s ASQKD protocol assumes an authenticated classical channel between the sender and the receiver. It is considered illogical to have an authenticated channel in the ASQKD protocol. If these security issues are not addressed, the ASQKD protocol will fail to deliver the secret key. Therefore, this study proposes an efficient and secure ASQKD protocol to circumvent these problems using only single photons. Security analysis proves that the proposed ASQKD protocol can effectively avoid reflecting attacks, collective attacks, and other typical attacks. Compared with the existing ASQKD protocols, this study has the following advantages: based on a single photon, it demands less advanced quantum devices, the communication efficiency is higher than most protocols, it reduces the length of the required pre-shared keys, endures reflecting attacks, collective attacks, and there is no need for the classical channel.
WebRobot: web robotic process automation using interactive programming-by-demonstration

It is imperative to democratize robotic process automation (RPA), as RPA has become a main driver of the digital transformation but is still technically very demanding to construct, especially for non-experts. In this paper, we study how to automate an important class of RPA tasks, dubbed web RPA, which are concerned with constructing software bots that automate interactions across data and a web browser. Our main contributions are twofold. First, we develop a formal foundation which allows semantically reasoning about web RPA programs and formulate its synthesis problem in a principled manner. Second, we propose a web RPA program synthesis algorithm based on a new idea called speculative rewriting. This leads to a novel speculate-and-validate methodology in the context of rewrite-based program synthesis, which has also shown to be both theoretically simple and practically efficient for synthesizing programs from demonstrations. We have built these ideas in a new interactive synthesizer called WebRobot and evaluate it on 76 web RPA benchmarks. Our results show that WebRobot automated a majority of them effectively. Furthermore, we show that WebRobot compares favorably with a conventional rewrite-based synthesis baseline implemented using egg. Finally, we conduct a small user study demonstrating WebRobot is also usable.
An improved K-medoids clustering approach based on the crow search algorithm

K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve many clustering problems. Instead of using the mean point as the centre of a cluster, K-medoids uses an actual point to represent it. Medoid is the most centrally located object of the cluster, with a minimum sum of distances to other points. K-medoids can correctly represent the cluster centre as it is robust to outliers. However, the K-medoids algorithm is unsuitable for clustering arbitrary shaped groups of objects and large scale datasets. This is because it uses compactness as a clustering criterion instead of connectivity. An improved k-medoids algorithm based on the crow search algorithm is proposed to overcome the above problems. This research uses the crow search algorithm to improve the balance between the exploration and exploitation process of the K-medoids algorithm. Experimental result comparison shows that the proposed improved algorithm performs better than other competitors.
Digitization of Data from Invoice using OCR

Optical Character Recognition (OCR) is a predominant aspect to transmute scanned images and other visuals into text. Computer vision technology is extrapolated onto the system to enhance the text inside the digitized image. This preliminary provisional setup holds the invoice's information and converts it into JSON and CSV configurations. This model can be helpful in divination based on knowledge engineering and qualitative analysis in the nearing future. The existing system contains data extraction and nothing more. In a paramount manner, image pre-processing techniques like black and white, inverted, noise removal, grayscale, thick font, and canny are applied to escalate the quality of the picture. With the enhanced image, more OpenCV procedures are carried through. In the very next step, three different OCRs are used: Keras OCR, Easy OCR, and Tesseract OCR, out of which Tesseract OCR gives the precise result. After the initial steps, the undesirable symbols (/t, /n) are cleared to get the escalated text as an output. Eventually, a unique work that is highly accurate in giving JSON and CSV formats is developed.Impact statement&#x2014; In our protrude, a front-end android app is developed which takes input from the user and stores the output onto the database. The JSON and CSV files can be viewed through an app by the end.
Integrating artificial intelligence for knowledge management systems – synergy among people and technology: a systematic review of the evidence

This paper analyses Artificial Intelligence (AI) and Knowledge Management (KM) and focuses primarily on examining to what degree AI can help companies in their efforts to handle information and manage knowledge effectively. A search was carried out for relevant electronic bibliographic databases and reference lists of relevant review articles. Articles were screened and the eligibility was based on participants, procedures, comparisons, outcomes (PICO) model, and criteria for PRISMA (Preferred Reporting Items for Systematic Reviews). The results reveal that knowledge management and AI are interrelated fields as both are intensely connected to knowledge; the difference reflects in how – while AI offers machines the ability to learn, KM offers a platform to better understand knowledge. The research findings further point out that communication, trust, information systems, incentives or rewards, and the structure of an organization; are related to knowledge sharing in organizations. This systematic literature review is the first to throw light on KM practices & the knowledge cycle and how the integration of AI aids knowledge management systems, enterprise performance & distribution of knowledge within the organization. The outcomes offer a better understanding of efficient and effective knowledge resource management for organizational advantage. Future research is necessary on smart assistant systems thus providing social benefits that strengthen competitive advantage. This study indicates that organizations must take note of definite KM leadership traits and organizational arrangements to achieve stable performance through KM.
Estimating Hourly Surface Solar Irradiance from GK2A/AMI Data Using Machine Learning Approach around Korea

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m−2, a bias of −0.007 MJ m−2, and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications.
Resource Assessment of a Floating Solar Photovoltaic (FSPV) System with Artificial Intelligence Applications in Lake Mainit, Philippines

The Floating Solar Photovoltaic (FSPV) system is an emerging solar PV installation, gaining traction primarily due to its distinct advantages over other forms of installations. FSPV mainly solves the problem when land area is scarce and the power plant capacity is on the megawatt (MW) scale. This paper investigates the resource potential of FSPV, specifically in Lake Mainit, Caraga Region, Philippines. This study implemented a descriptive research design to identify the resources needed to implement an FSPV system in the said lake. The Lake Mainit area can generate 762.96MWh per year. Accounting for the needs of the community, the resources needed to put up the FSPV should satisfy the 35,640Whr daily energy requirement of the community. Based on the analysis, the computed FSPV system size is 9.90kWp. The components required to implement an Artificial Intelligence (AI) integrated monitoring and data processing system for fault diagnosis and detection to help mitigate impact to the FSPV system with the undesirable weather conditions were also identified.
Scale-Aware Automatic Augmentations for Object Detection With Dynamic Training

Data augmentation is a critical technique in object detection, especially the augmentations targeting at scale invariance training (scale-aware augmentation). However, there has been little systematic investigation of how to design scale-aware data augmentation for object detection. We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and instance-level augmentations are designed for maintaining scale robust feature learning. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilitate efficient augmentation policy search. In experiments, Scale-aware AutoAug yields significant and consistent improvement on various object detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with strong multi-scale training baselines. Our searched augmentation policies are generalized well to other datasets and instance-level tasks beyond object detection, e.g., instance segmentation. The search cost is much less than previous automated augmentation approaches for object detection, i.e., 8 GPUs across 2.5 days versus. 800 TPU-days. In addition, meaningful patterns can be summarized from our searched policies, which intuitively provide valuable knowledge for hand-crafted data augmentation design. Based on the searched scale-aware augmentation policies, we further introduce a dynamic training paradigm to adaptively determine specific augmentation policy usage during training. The dynamic paradigm consists of an heuristic manner for image-level augmentations and a differentiable copy-paste-based method for instance-level augmentations. The dynamic paradigm achieves further performance improvements to Scale-aware AutoAug without any additional burden on the long tailed LVIS benchmarks. We also demonstrate its ability to prevent over-fitting for large models, e.g., the Swin Transformer large model. Code and models are available at https://github.com/dvlab-research/SA-AutoAug.
Artificial Intelligence Techniques for Chatbot Applications

Nowadays in a variety of businesses, the use of chatbots has grown including cultural heritage, entertainment, education, marketing, health care, and support systems. Let’s discuss what chatbots are in general and later know about them in detail. In a detailed explanation first, we must know how dialogue management will be done in chatbots later we will focus on how chatbots can be an entertainment source for elderly people. How using bio-inspired chatbots can increase e-learning efficiency will also be studied. Not only that, as information cannot be conveyed in a un fashioned manner, so to give the information in a neat and detailed matter we will learn about polishing the generated response in chatbots. And also in this paper, we discuss chatbots whose data neither be manipulated nor their training data is used but end users want to test the trustworthiness which can be explained in detail. Whenever the usage of chatbots comes they are only restricted to certain languages for example like English but due to rapid growth in technology, the development of chatbots or upgradation of chatbots is needed for other regional languages too. Chatbots can also be used to communicate with humans and also clarify their queries mainly across the internet. Chatbots can replace the human power needed in sectors like call centers, etc and many tech giants are also investing in it
Contrastive Learning for Image Registration in Visual Teach and Repeat Navigation

Visual teach and repeat navigation (VT&R) is popular in robotics thanks to its simplicity and versatility. It enables mobile robots equipped with a camera to traverse learned paths without the need to create globally consistent metric maps. Although teach and repeat frameworks have been reported to be relatively robust to changing environments, they still struggle with day-to-night and seasonal changes. This paper aims to find the horizontal displacement between prerecorded and currently perceived images required to steer a robot towards the previously traversed path. We employ a fully convolutional neural network to obtain dense representations of the images that are robust to changes in the environment and variations in illumination. The proposed model achieves state-of-the-art performance on multiple datasets with seasonal and day/night variations. In addition, our experiments show that it is possible to use the model to generate additional training examples that can be used to further improve the original model's robustness. We also conducted a real-world experiment on a mobile robot to demonstrate the suitability of our method for VT&R.