Reviewerly/exHarmony huggingface dataset:
SecureLease

Modern software programs have dedicated license-check modules that restrict access to users, who possess valid credentials. They also have a large number of add-on pluggable modules that can be separately purchased and have their dedicated license managers. Sadly, recent work shows that regardless of their complexity, it is possible to break their security using a novel class of attacks known as control flow bending attacks (CFB attacks), where the program is run on a virtual CPU, unbeknownst to it.
Supporting Secure Agile Development: the VIS-PRISE Tool

Privacy by Design and Security by Design are two fundamental aspects in the current technological and regulatory context. Therefore, software development must integrate these aspects and consider software security on one hand, and user-centricity from the design phase on the other. It is necessary to support the team in all stages of the software lifecycle in integrating privacy and security requirements. Taking these aspects into account, the paper presents VIS-PRISE prototype, a visual tool for supporting the design team in the secure agile development.
Jigsaw Self-Supervised Visual Representation Learning: An Applied Comparative Analysis Study

Self-supervised learning has been gaining momentum in the computer vision community as a hopeful contender to replace supervised learning. It aims to leverage unlabeled data by training a network on a proxy task and using transfer learning for a downstream task. Jigsaw is one of the proxy tasks used for learning better feature representations in self-supervised learning. In this work, we comparably evaluated the transferability of jigsaw using different architectures and a different dataset for jigsaw training. The features extracted from each convolutional block were evaluated using a unified downstream task. The best performance was achieved by the shallower architecture of AlexNet where the 2nd block achieved better transferability with a mean average precision of 36.17. We conclude that this behavior could be attributed to the smaller scale of our used dataset, so features extracted from earlier and shallower blocks had higher transferability to a dataset of a different domain.
A joint framework for mining discriminative and frequent visual representation

Discovering visual representation in an image category is a challenging issue, because the visual representation should not only be discriminative but also frequently appears in these images. Previous studies have proposed many solutions, however, all of them separately optimized the discrimination and frequency, which consequently makes the solutions sub-optimal. We propose a method to discover the jointly discriminative and frequent visual representation to address this issue, named as JDFR. To ensure discrimination, JDFR employs a classification task with cross-entropy loss. To achieve frequency, we design a novel similarity concentration (SC) loss to concentrate on the samples with the same representation and pull them closer in the feature space, and then mine the frequent visual representations. Moreover, we utilize an attention module to locate the representative region in the image. Extensive experiments on five benchmark datasets (Place365-20, Travel, VOC2012-10, ImageNet-100, and iNaturalist-100) show that the discovered visual representations have better discrimination and frequency than ones mined by the state-of-the-art (SOTA) method with average improvements of 5.37% on accuracy and 3.06% on frequency.
An enhanced behavioral fingerprinting approach for the Internet of Things

With the growing diffusion of the Internet of Things (IoT) technology across most of the aspects of people daily lives, security concerns have become critical to ensure the exploitation of advantages introduced by this technology. This is even more true in the context of Industry 4.0, for which the IoT is becoming an important driver for automation. The detection of anomalies in IoT systems to ensure the capability of such systems to tolerate attacks to single devices is a crucial aspect. Behavioral fingerprinting is a recent and promising security solution in this context, which still requires research efforts to embrace new challenges in such a complex environment. Existing solutions focus mostly on modeling the behavior of IoT devices by analyzing the information extracted from the header of exchanged networking packets. However, in many application contexts, also attacks on the content of the packets can lead to disruptive results. Our proposal focus on these approaches by addressing a fully distributed scenario in which computation is directly handled by IoT devices, also through delegation, and describes a novel behavioral fingerprinting approach based on features suitably engineered from packet payloads. The effective-ness of our proposed method is assessed by both simulated and experimental results.
Quality prediction through machine learning for the inspection and manufacturing process of blood glucose test strips

Although machine learning for quality prediction of manufacturing processes has attracted attention in the literature, there is a significant lack of case studies from industry, especially in medical sector. This paper proposes a data-driven approach to infer the batch quality of blood glucose test strips. Once the low quality of work in process is detected, unnecessary process waste could be eliminated. Starting from data pre-processing, which consists of Synthetic Minority Over-sampling TEchnique and Random Over-Sampling Example, this project tries to balance the ill-distributed data first. Followed by machine learning aims to classify and predict the quality of blood glucose test strips. Different models are evaluated by the Receiver Operating Characteristic curve and Area Under Curve. Computational results show that the decision tree and random forest after SMOTE perform better than the counterpart of ROSE method. Ensemble learning, such as random forest, out-wins base learner decision tree. To sum up, random forest with SMOTE is the suggested model for accurately predicting the quality of blood glucose test strips. There is a 30% improvement in error rate under random forest and SMOTE for NG class that could be of top concern for prognosis. Several factors, including the direction of applying test reagent onto test strips and the position where the strips are located, that affect quality of test strips have been identified. Explanations in terms of inspection and manufacturing are discussed subsequently. Finally, the prognosis of quality can be attained through big data and statistical machine learning.
Comparing Multiple Models for Section Header Classification with Feature Evaluation

We present on the performance evaluation of machine learning (ML) and Natural Language Processing (NLP) based Section Header classification. The section headers classification task was performed as a two-pass system. The first pass detects a section header while the second pass classifies it. Recall, precision, and F1-measure metrics were reported to explore the best approach for ML based section header classification for use in downstream NLP tasks.
WRS: Workflow Retrieval System for Cloud Automatic Remediation

Remediation systems in modern information infrastructure gradually take over the increasing workload of unexpected events from operators. The core logic in such systems — remediation rule <symptom, workflow> — still needs operators to fill in manually, which is a tedious and error-prone process. We propose Workflow Retrieval System (a.k.a. WRS), which helps the operator to build remediation rules.WRS is a recommendation system that recommends structures from existing rules to operators when they are creating new ones. WRS formalizes the workflows in remediation rules as trees and extracts representative atomic structures from them. Then WRS organizes atomic structures in two ways to accelerate the later retrieval — a two-level hierarchy which helps searching similar structures, and a keyword indexing structure which helps searching by words. With the two retrieval structures, we build two applications: one is real-time workflow auto completion application which recommends remaining structures based on the existing partial structure, and another is workflow recommendation where WRS recommends atomic structures based on the description of the remediation symptoms.We prototype WRS and evaluate it with legacy device vendors’ operation manual. Our evaluation shows that WRS reasonably extracts and organizes atomic structures in remediation workflows, and the two applications atop it show fast execution time and high accuracy.
Traffic Signs Recognition using R-CNN

Traffic signs are considered as an important source of information for both vehicles and pedestrians. To ensure safety, they represent the rules that have been set in place and help communicate information to pedestrians and drivers, which can help maintain order and reduce accidents. Neglecting them is considered as a dangerous practice. The majority of signs employ visuals rather than text to make them more generally comprehensible. As a result, it is critical to comprehend the significance of each image and utilize it to guide in the driving process. If the rider has not followed any rules, the rider may be either penalized or involved in a serious accident. According to a recent research, the significant number of accidents were caused by the driver's errors or bad judgement. It is possible to reduce the number of traffic accidents by using the driverless/automated vehicles without requiring any human operator to drive them to their destination. Driverless cars should also be conscious of traffic rules. Traffic signs should be interpreted by automated cars and their decisions should be based on this information. This research addresses the design and development of a neural network model capable of classifying traffic signs into multiple categories to assist self-driving automobiles in better interpretation of traffic signs. Although there are several existing models on TSR, the proposed model outperforms the existing models in terms of accuracy.
Gender Classification from Tweets Using LSTMs and Transformers

With the rise of social media platforms and personalised Web 2.0, the study of sociolinguistic factors in text such as gender bias has had a huge influx of new data from users that has since been used by consumer-based organizations for data analytics to streamline their digital models and cluster their customers to better cohort their services, and by social scientists and linguistics researchers to further improve their hypotheses and work. In this work, I have gathered data from the social media platform “Twitter”, and used existing data from the web and preprocessing it to train and develop 7 different models for Gender Classification based on short snippet of text using LSTMs and Transformers in conjunction with other techniques including transfer learning using feature extraction and fine-tuning. The results and statistics of the models are discussed, and illustrated in confusion matrices, and a discussion/comparison between the 7 models is performed.
Instruction pictograms for experience-based displays and their use cases

Along with the advancement in devices and cognitive technologies, experience-based displays (exhibits) reacting to user behaviors are gaining popularity. These systems use sensors to recognize users’ upper arm and forearm movements to switch images or the environment within the images, but it is difficult to communicate exactly what user action is required for the display to respond. This paper proposes means to instruct behaviors through pictograms for the purpose of having viewers (users) behave in a manner intended by experience-based displays incorporated in media art and museum exhibits. Upon elaborating on forms and compositions that specify desired behaviors, the study produced still (frozen) and moving pictograms and compared the correct answer rates and average time spent to achieve the desired behaviors among users in their twenties up to users in their seventies. In the assessment experiment targeting users in their twenties, the correct answer rates varied among different still images, while all moving images successfully navigated over 88.5% users to behave correctly. Pictograms successfully made middle-aged and older users understand desired behaviors, but they were often observed to need a longer time to take in the instructions. The study introduces use cases incorporating these findings in an actual experience-based system.
Virtual Reality (VR) and Augmented Reality (AR) Based 360 Degree Panoramic Environment Generator for Virtual Tour

Abstract: We are entering an era of digital wonders and some of the most fascinating fields to have emerged from the technological revolution are virtual-reality and other related technologies, which are playing an increasingly important role in connecting the whole world like never before. The proposed platform will enable the rapid origination of virtual interactive environments using panoramic photos, videos with 360-degree orientation and audio along with the capability to integrate live audio and video in the virtual environment. A prototype for this platform has been designed by using the open-source Marzipano tool that uses the Apache 2.0 license and is hosted on an Amazon Elastic Compute Cloud-server. To create a virtual model, environment suitable panoramic images with 360-degree orientation are supplied and then interconnected together to form the virtual experience. In the present day and age, the need for such a platform has become increasingly vital as such a platform can be implemented to avoid social interactions as and when necessary, viz., in a situation where physically visiting a place is not possible due to the enactment of lockdown measures. The platform can also be used to very rapidly design and implement virtual-tours and campaigns. This brings with it the promise to ensure that people don’t feel isolated in case a physical visit to a location is not possible due to unforeseen circumstances such as pandemics and it can also be used by organizations and businesses for product marketing purposes by quickly creating interactive virtual environments showcasing their products to their prospective clients or by creating interactive virtual models which will enable their clients to gain an idea about their services in a manner similar to that of kinaesthetic-learning. Keywords: virtual-reality, Marzipano, Cloud-server, virtual-tours, pandemics, kinaesthetic-learning etc.
Real-Time Arial Targets Tracking for Anti-UAV Systems Using OpenCV

Recently, Unmanned Aerial Vehicles (UAVs) have been widely used in numerous applications nevertheless in military operations. Therefore, there is a crucial need to develop various mechanisms to confront these UAVs. For this reason, the counter system must first determine targets’ locations then track them. Thus, Accurate target tracking is an important demand in the drone defense system. Because of the significant advancement in computers and software processors, it is now possible to employ these current programs in the operations of positioning and tracking. In this paper, an Open-Source Computer Vision (OpenCV) based drone tracking system is implemented as a tracking system. The performance of the proposed technique is investigated and analyzed through a comparative analysis using a small data set and its ground-truth generated by the annotation tool. The evaluation parameters presented in this study are precision percentage and time analysis. The simulation results present a tradeoff between the accuracy and execution time for the used techniques.
June 2022 Continuing Education

To submit your answers to this test and earn 3 hours of CE credit, go to www.aaoinfo.org and log in as a member. Select the Education tab, then AJO-DO Tests. Purchase the current test, or the test for any issue published in the preceding 12 months. The fee for each test is $20. You will take the test online but can download and print a PDF version of the test. Results are tabulated immediately. To earn 3 hours of CE credit, you must answer 75% of the questions correctly. If you do not receive a passing score the first time, you can take the test again, free of charge, until you pass. Upon successful completion of the test, your CE credits will be added to the AAO's online CE Credit Manager, and you can use the CE Credit Manager to print a certificate.
Design and development of digital information product and service using colour QR codes for smartphone user's of academic libraries

Purpose The objectives of the study include the following: (1) to design and develop digital information products by using Quick Response (QR) codes (2) to make an awareness of QR codes in library users; (3) to study user preferences for black and green QR codes in the library services of management institutes and (4) to raise awareness of the product layer among the library profession. Design/methodology/approach This study uses a participatory action research approach. The researcher implemented QR codes in the library, and participants scanned QR codes through their smartphones. All data analyses were performed using IBM SPSS and Microsoft Excel 2016. Findings The researcher found that both QR codes will be popular in the future; however, users will prefer to scan the green QR code multiple times due to the code's increased benefits and advantages (which have been discussed in this report). Research limitations/implications This study implemented QR codes in MBA libraries affiliated with Savitribai Phule Pune University in the Pune and Pimpri Chinchwad areas. Originality/value The current study implemented a QR code in the library service with green and black colours. This study examined which QR code will be popular in the future owing to QR code's advanced features and specifications. This, in turn, will assist the library in designing such a collection and developing more QR codes that will be recommended to users. This will assist in the creation of good digital information products for smartphone users.
Real-time Magnetic Actuated Control and Virtual Interaction for Remote Interventional Surgery

Catheterization is a fundamental procedure in interventional surgery. Here, we present a computer-assisted real-time system for remote interventional surgery, with a guide wire having omnidirectional steering capabilities, actuated by a magnetic field generated from an array of coils in which the electric current is controlled. We propose a 5G network slicing architecture and verify an efficient Software Defined Network(SDN) traffic prediction method which can be used in the future for remote interventional surgery to ensure the real-time control and interaction. The experimental result shows that our method meets the requirement of real-time guidance for catheter intervention.
Optimization of English Online Teaching Scheme Based on Cloud Platform Technology

Due to the increasing progress of IT and cloud computing technology, traditional teaching methods and instructional resources can no longer meet the needs of modern education. How to use network technology to optimize classroom teaching and improve the quality and effect of online education is a topic worthy of in-depth discussion. Based on the current situation of English online education, this paper analyzes the theoretical basis and application status of cloud computing and puts forward the optimization strategy of English online education under the network environment, combining with the theories of constructivism and collaborative learning and building an instructional resource sharing management system based on cloud computing. The system can provide a great quantity of high-quality instructional resources for English online education and further promote the optimization of English online education.
The Hidden Language of Vibrators: A Politico-Ontological Reading

As digital artifacts for women's pleasurable experience, vibrators are sophistically designed, embedded with cutting-edge technology, and popular among women. Yet they are still facing social, cultural, and legal obstacles; they are disappointingly understudied by academia as a topic of research. In this pictorial, we use vibrators as our vehicles to speculate future aesthetic, social, and political ways of being related to female sexuality. Our speculation is based on a critical politico-ontological reading of the contemporary design of vibrators by situating it in the historical, cultural, and visual contexts through an art historical analysis. We find that the contemporary design of vibrators carries gender norms and sexual normativity into the futures that they construct. We call on Interaction Design and HCI to tackle prejudice against vibrators to explore the potential of vibrators to improve sexual autonomy, sexual self-determination, and sexual independence of women, especially women with dis/abilities and the elderly.
A Compact Object Detection Architecture with Transformer Enhancing

Abstract With the advancements in rising computer vision processing, Transformer has attracted increasing interesting in this field. However, it is limited because of its unprecedented storage, heavy reliance on data size and intolerable computational power consumption. While lightweight network is in other extreme, pursuing the compact architectures accompanied by performance loss. In this paper, we enhance an architecture as the backbone of object detection networks through combining right-size Transformer, i.e. Vision Transformer module. Specifically, based on GhostNet, a well-known lightweight neural network structure moreover, embed this Vision Transformer module at the end of GhostNet, and use the input data with slicing design to reduce the computational burden of the neural networks. Vision Transformer is taken to enhance the architecture as the backbone of object detection networks, and the well-known YOLOv5 as the baseline. We conduct multi-metric comparison experiments on two medium-scale object detection datasets with large, medium and small scale networks. Results show that without relying on ultra-large dataset and pre-trained models, the proposed Transformer module enhanced architecture achieves comparable or even higher mAP metrics with only half of the model size and floating-point computation of the baseline.
An Image Defogging Method Based on Depth CNN Network

A defogging method to remove the haze from the images of video surveillance system for the high-voltage transmission lines is addressed in this paper. Rain, smog and glare are the leading causes of haze images. To identify the condition of the transmission lines, the video surveillance system need to remove the haze effectively. With CNN network, a haze removal method to defog the image is designed. Firstly, the regression function of CNN network is used to make a difference between positive and negative samples. The foggy image degradation model is chosen for CNN. The foggy images feed into the network, and the network is used for learning and processing. Finally, the CNN network are trained iteratively and learns to transform the fogged negative samples regressively to positive samples in the network. In each iteration, the semantic information of the image between the context and the original image is effectively utilized. Results of experiments show that the network has obtained good processing ability and can be applied to real fogged images of high-voltage transmission lines.