Reviewerly/exHarmony huggingface dataset:
A Serious Game to Improve Phishing Awareness

This paper presents an experimental study in the form of an online serious game to increase IT security awareness regarding phishing. Prior studies have indicated the effectiveness of serious games concerning certain aspects of phishing attacks. This paper combines various aspects of social engineering attacks, existing prevention concepts, and gamification methods. A survey and interviews with 61 participants from different companies were conducted to measure the effectiveness. The findings suggest that using a serious game in context with phishing emails can be used beneficially and effectively.
Visual Tracking Based Deep Learning and Control Design Onboard Small-Sized Quadrotor UAV

It is known that unmanned aerial vehicle (UAV) with computer vision based deep neural network (DNN) capabilities, are extremely promising for applications such as surveillance, path planning and navigation. Unfortunately, DNN's computational and power requirements are still higher than the budget for small-sized UAV due to onboard memory constraints. In this paper, we present the design and implementation of a visual control steering in closed-loop end-to-end target tracking based DNN and directly utilizing the onboard hardware resources of the small-sized quadrotor UAV. This is archived, by adopting convolutional neural network (CNN) models such as, MobileNet combine with single shot multibox detection (SSD) algorithm to solve the problem of real-time object tracking onboard. The network is compressed (optimized) to run in a low powered ARM architecture by employing quantization and cortex microcontroller software interface standard (CMSIS-NN). Finally, a color feature extraction (blob-like-features) is introduced to improve tracking and control accuracy. All the image processing is done real-time onboard, the flexible navigation system can span a wide performance range, making it a feasible solution for real-time tracking-control onboard a small-sized UAV.
File Encryption: PGP

Pretty Good Privacy (PGP) is well-known in the civil rights community – its author Phil Zimmerman was sued for violating US export controls, and Edward Snowden used PGP to secure his email communication with Glenn Greenwald. This chapter will concentrate on OpenPGP as a universal cryptographic data format. It can be used to encrypt files in an operating system, sign software updates, or communicate securely via email. Like any other cryptographic data format, it has been subject to attacks. Attacks on the data format itself are covered in this chapter, and attacks on OpenPGP-based email encryption are covered in chapter 18 .
A Study on Improving the Value of Ubiquitous Data on the Internet

Abstract: In the IoT era where all "things" are connected to the Internet, a lot of information is overflowing on the Internet. Each piece of data on the Internet is extremely valuable, but its effectiveness is limited by itself. In order to increase the value of information, it is necessary to coordinate the information and extract useful information. However, most of the obtained data is unformatted data that can be freely posted and has no type, and it is difficult to recognize and process it by a computer as it is. Therefore, when using data on a computer, it is necessary to unify data formats and values. In this paper, we report specific examples and results of collecting data on the Internet using cooking recipes in Japan as a theme, automatically unifying the structure and values, and increasing the information value.
‘You Are Big, S/he Is Small’ Detecting Body Shaming in Online User Content

Body shaming, a criticism based on the body’s shape, size, or appearance, has become a dangerous act on social media. With a rise in the reporting of body shaming experiences on the web, automated monitoring of body shaming posts will help rescue individuals, especially adolescents, from the emotional anguish they experience. To the best of our knowledge, this is the first work on body shaming detection, and we contribute the dataset in which the posts are tagged as body shaming or non-body shaming. We use transformer-based language models to detect body shaming posts. Further, we leverage unlabeled data in a semi-supervised manner using the GAN-BERT model, as it was developed for tasks where labeled data is scarce and unlabeled data is abundant. The findings of the experiments reveal that the algorithm learns valuable knowledge from the unlabeled dataset and outperforms many deep learning and conventional machine learning baselines.
Position Paper: Towards a Hybrid Approach to Protect Against Memory Safety Vulnerabilities

Memory corruption bugs continue to plague low-level systems software, generally written in unsafe programming languages. In order to detect and protect against such exploits, many pre- and post-deployment techniques exist. In this position paper, we propose and motivate the need for a hybrid approach for the protection against memory safety vulnerabilities, com-bining techniques that can identify the presence (and absence) of vulnerabilities pre-deployment with those that can detect and mitigate such vulnerabilities post-deployment. Our proposed hy-brid approach involves three layers: hardware runtime protection provided by capability hardware, software runtime protection provided by compiler instrumentation, and static analysis pro-vided by bounded model checking and symbolic execution. The key aspect of the proposed hybrid approach is that the protection offered is greater than the sum of its parts - the expense of post-deployment runtime checks is potentially reduced via information obtained during pre-deployment analysis. During pre-deployment analysis, static checking can be guided by runtime information.
Persona-Scenarios in Game Development

The 3D Tune-In project developed serious/leisure game applications to educate hearing aid (HA) users about how HA functionalities could improve hearing in different sound environments. The application development team had little prior experience catering for end-users with hearing loss. HA users and their communication partner were consulted regarding their communication difficulties and communication strategies in different environments. Participants reported many hearing problems, affective issues, problems with their HA, tensions in their relationship caused by hearing issues, and they noted a need for training in how best to use HAs. Persona-scenarios were created outlining user needs and goals and a user-requirements table detailed how end-users might interact with proposed applications, both of which were presented to developers during the initial application design period. Game developers identified that these resources positively influenced the development of their application. They were able to produce a useful and useable application for their new target user.
Radian: Leveraging PKI for Long-Term Validation Enabled Digital Academic Testimonials - A Case-Study

Portable Document Formats (PDFs) are widely used throughout the world for exchanging documents. The main reason of their popularity is that this format maintains the structure of the original document without taking into consideration the software that was used to draft the document. PDFs also provide authenticity and integrity of documents by using and verifying signatures. Since this all happens digitally the security notion of the verification has to be stronger than in analog world. Nowadays digital certificates and digital contracts have equivalent importance as that of physical certificates and contracts. Digital signatures using cryptographic algorithms provide a way to verify a PDF's authenticity and integrity. In this paper, we show how Public-Key Infrastructure (PKI) can be leveraged to seamlessly deploy a solution that can revolutionize the way academic testimonials are distributed. The solution not only uses digital signatures using PKCS11 hardware tokens to provide authenticity to documents but also used an interesting feature in PDF ISO standard that allows embedding of revocation information in the document itself for allowing Long-Term Validation (LTV). All this is combined to develop Radian, a software conceived and designed in IIT Bhilai to provide digitally signed academic testimonials. These testimonials hold an immediate advantage over physical counterparts as they are tamper proof and self-contained in terms of their verification. Finally, this work showcases an interesting combination of PKI mechanism with advances in PDF technology to present a high impact use-case that can change the way academic testimonials will be issued in the future.
A Benchmark on Masked Face Recognition

With the COVID-19 pandemic’s emergency, using facial masks and contactless biometric systems became even more relevant to reduce the risk of contamination. Several direct and indirect problems gained relevance with the pandemic. Among them, masked face recognition (MFR) aims to recognize a person even when the person is wearing a face mask. Some state-of-the-art algorithms that work well for unmasked faces have suffered a severe performance drop when receiving masked faces as input. In this sense, the scientific community proposed approaches and competitions related to this topic. In this paper, we introduce a comparative study of four prominent solutions pipelines that use different techniques to tackle the masked face recognition problem, proposed by Huber et al. [1], Neto et al. [2], Boutros et al. [3], and Hsu et al. [4]. The performance evaluation was conducted on a real masked face database (MFR2 [5]), and using synthetic masks in three mainstream databases (LFW, AgeDB30, and CFP-FP). We report results regarding unmasked-masked (U-M) and masked-masked (M-M) face verification performance. The unmasked-unmasked (U-U) scenario was also reported as a baseline to evaluate the drop of the selected models on non-occluded face verification. We further analyze the obtained results, generating a comprehensive comparative study of the selected approaches.
Development of Calculating Machine Intelligent Proofreading System Based on Phrase Translator Model

At present, there are various types of machine translation tools, but the translation accuracy is low. However, the current research mainly focuses on improving translation accuracy, lightweight translation products, good user experience, etc., but lacks some attention to translation quality control. Due to the influence of translators' translation ability and workload, the translated text contains a certain number of misspellings and inconsistencies with the original text. The evaluation of translation quality is mainly based on some subjective translation standards with different opinions, and is done manually by experts. With the development of society, this manual process has gradually become a major obstacle to translation activities, translation teaching and even translation research. There may be several consecutive sentence fragments in the corpus, which have short vocabularies and the same length. In this case, the alignment algorithm based on vocabulary is prone to errors. Based on the phrase translator model, this paper constructs a computer intelligent proofreading system to realize machine intelligent proofreading of computer translation.
Leveraging Convolutional Neural Networks for Face Mask Detection

COVID-19 has made face masks an imperative whenever an individual is going out in public. However, many people are remiss in fulfilling their duty to society. They are deviating from the lockdown norms and violating the regulatory measures set by the government. Such a situation only proliferates the spread of COVID-19 and makes it difficult to control it. In this paper, we use Convolutional Neural Networks (CNNs) to detect whether a person is wearing a face mask. This research uses TensorFlow and Keras to build a CNN which detects face masks with an accuracy of over 98% within 10 epochs. This algorithm will be a boon in places like malls or public areas where automated doors can be shut tight if the prospect trying to enter the store is not wearing a mask. Overall, this paper will help create products that can be used to safely break the COVID-19 chain.
Cognitive Architecture Configuration Model for Performing Dynamic Pervasive Service Composition

With the breakthroughs in capabilities of mobile and embedded devices which allows them to become part of our everyday lives, and the subsequent advent of fields such as Internet of Things, Smart Cities and Ambient Intelligence, the process of exploiting individual device functionalities to create complex services, known as service composition, gains more traction. However, to perform service composition in such dynamic environments, where availability of resources is far from uncertain, there are many challenges that must be overcome. The way the human brain works allows it to configure itself to perform specific tasks, just like a service composition arranges a set of services to achieve a goal, and is capable to adapt to changes in its environment, providing us with a great degree of resilience, something very valuable for dealing with dynamic environments. In this work, we propose a model whereby the various modules of the Cuāyōllōtl Biologically Inspired Cognitive Architecture (BICA), which will be in charge of managing the different devices of a pervasive system, are to be configured to create a workflow that employs only the required modules to achieve an objective, in a similar fashion to how conventional service composition approaches produce composite services.
Visualization and Diagnosis of Medical Images, Using Virtual Reality Tools

The training and diagnostic imaging processes are performed by viewing medical images in digital format, which are presented on medical grade monitors, taking advantage of their resolution, this procedure is performed in an established place in medical centers and in Most of the cases there are several doctors performing the same procedure, the present proposal consists of making the medical diagnosis using virtual reality glasses, we managed to share the images thanks to a connectivity through a wireless WIFI network, between the workstations and the glasses. Thanks to the Virtual Desktop application, which allows duplicating what is observed as well as taking control of the workstation, to be able to use the different options that allow working and processing medical images, this maneuverability is carried out thanks to the controls that have virtual reality glasses, one of the advantages it provides The lenses is to be able to eliminate all types of distracting agent thanks to the fact that the doctor can observe only the image that he is diagnosing as well as provide an environment formed by a background and a music that can be chosen randomly, the results that we present show that the image is displayed without presenting problems in its resolution, we present the visualization of mammography images in Dicom format and a sequence of angiography images in video format, by using the same workstation as a reading agent for medical images. You can use any type of medical image as well as viewers that can be used.
Gesture recognition of wrist motion using low-frequency PPG

This paper evaluated two machine learning techniques using low-frequency photoplethysmography and motion sensor data from wearable devices in gesture segmentation and classification. SVM and random forests were the classifiers selected for testing. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision

Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set. Our key insight is that by seeking correspondence between elements of different sets, we learn strong representations that exclude the inactive factors of variation and isolate the active factors that vary within all sets. As a consequence of focusing on the active factors, our method can leverage a mix of setsupervised and wholly unsupervised data, which can even belong to a different domain. We tackle the challenging problem of synthetic-to-real object pose transfer, without pose annotations on anything, by isolating pose information which generalizes to the category level and across the synthetic/real domain gap. The method can also boost performance in supervised settings, by strengthening intermediate representations, as well as operate in practically attainable scenarios with set-supervised natural images, where quantity is limited and nuisance factors of variation are more plentiful. Accompanying code may be found on github.
Revisiting the Critical Factors of Augmentation-Invariant Representation Learning

We focus on better understanding the critical factors of augmentation-invariant representation learning. We revisit MoCo v2 and BYOL and try to prove the authenticity of the following assumption: different frameworks bring about representations of different characteristics even with the same pretext task. We establish the first benchmark for fair comparisons between MoCo v2 and BYOL, and observe: (i) sophisticated model configurations enable better adaptation to pre-training dataset; (ii) mismatched optimization strategies of pre-training and fine-tuning hinder model from achieving competitive transfer performances. Given the fair benchmark, we make further investigation and find asymmetry of network structure endows contrastive frameworks to work well under the linear evaluation protocol, while may hurt the transfer performances on long-tailed classification tasks. Moreover, negative samples do not make models more sensible to the choice of data augmentations, nor does the asymmetric network structure. We believe our findings provide useful information for future work.
Web-Based Platform Development for Interactive 3D Visual Exhibition of Painting Artwork

We can use technological capabilities as a solution during a pandemic. We use virtual exhibitions as one of the solutions, as it is not only implementing multimedia capabilities but also for artists and viewers to present artworks with a different experience than conventional exhibitions. In this study, we worked with artists who could not exhibit their works due to social restrictions on community activities. We use the implementation of WebGL on the exhibition platform to provide an interactive experience for users to enjoy the work more freely. We built the platform using the mixed Rapid Application Development (RAD) and Multimedia Development Life Cycle (MDLC) methods by Luther to make the work efficient. The novelty in this research is that the platform not only presents works of art but also becomes a repository for storing work data so that we can update the data on the dashboard to store multimedia data. Tests conducted on Beta Testing of users with the System Usability Scale (SUS) showed that the virtual exhibition in terms of adjective ratings included the Good category and acceptability ranges including Acceptable. We hope that with this platform, the multimedia application will become more dynamic to solve community problems under challenging conditions such as Covid 19.
Hybrid Recommendation Algorithm based on Attribute Knowledge Embedded Collaborative Filtering

Recommendation is increasingly widely used in many areas in which collaborative filtering is one of the most well-known algorithms, but the traditional collaborative filtering (CF) recommendation algorithm suffers from data sparsity and user similarity imprecision problems. To conquer these problems mentioned above, this paper studies user attributes representing and proposes a hybrid recommendation algorithm based on attribute knowledge embedded collaborative filtering. By making use of attribute information, the similarity between users is improved. The hybrid algorithm assembles user attribute and collaborative filtering by a weighted method. Experiments validates its effectiveness.
Guidelines for the Development of Common Ground-Based Relational Agents for Elderly

The elderly population grows worldwide while the size of families decreases. In addition, there is the figure of caregivers of the elderly, who play a crucial role in preserving these older adults’ health and reducing their loneliness. However, in the not-too-distant future, the availability of this professional in the market may become scarce to meet the growing demand arising from the aging of the world population. Therefore, it is necessary to build systems that promote healthy aging. Social agents embedded in devices like Alexa can be a significant weapon in this scenario. However, the development of such agents is still in its infancy, and there are few guidelines to guide new projects. This work investigated the construction of an agent based on the common ground theory as a conversation starter, which proved to be effective in producing meaningful dialogues. The text reports this experience and presents a series of guidelines for future developers.
Supporting Spatial Thinking in Augmented Reality Narrative: A Field Study

Immersive augmented reality (AR) is an exciting medium for locative narrative. Immersive AR experiences are largely custom-made and site-specific, however: we lack generic tools that help authors consider interactions between physical layout, viewer perspective, and story progression, for specific sites or for locations unknown to the author. In this paper we evaluate Story CreatAR, a tool that incorporates spatial analysis techniques used in architecture, planning, and social sciences to help authors construct and deploy immersive AR narratives. We worked with three authors over several months, moving from script writing and story graph creation to deployment using the tool. We conduct a thematic analysis of each author's actions, comments, generated artifacts, and interview responses. Authors faced a steep learning curve, sometimes misinterpreting spatial properties, and found it difficult to consider multi-site deployment. Despite these challenges, Story CreatAR helped authors consider the impact of layout on their stories in ways their scripts and graphs did not, and authors identified several additional areas for spatial analysis support, suggesting that tools like Story CreatAR are a promising direction for producing immersive AR narratives. Reflecting on author experiences, we identify a number of features that such tools should provide.