Reviewerly/exHarmony huggingface dataset:
Virtual Reality Based Application for Safety Training at Shipyards

Abstract The implementation of safety training is conducted to provide knowledge on the importance of safety for its employees. However, the implementation of safety training requires many consumable things such as a lot of Portable Fire Extinguishers (PFE) that increase the operational cost and trigger the environmental pollution. This research aims to design a safety simulation based on VR (virtual reality) to provide an immersive experience for safety training and reduce the training costs. It was obtained that the application of VR technology requires two main devices namely HMD (Head-Mounted Display) and controller. Observations were conducted in the training of conventional safety and health at shipyards and training institutes. This research only simulates training on the use of PFE, fire drill, and risk observation on confined space from the ten topics of safety training based on observations that have been undertaken. Software such as Inventor, Microsoft Visual Studio, and Unity were used. After safety simulations was conducted, the result showed that safety training used VR can improve the work safety theory knowledge by 14.05%. Total cost of investment required for the development and equipment was IDR 24,043,667. The reduced cost of the safety training using VR was about IDR 193,889,154.88 or 68.13%.
ROWBACK: RObust Watermarking for neural networks using BACKdoors

Claiming ownership of trained neural networks is critical towards stakeholders investing heavily in high performance neural networks. There is an associated cost for the entire pipeline starting from data curation to high performance computing infrastructure for neural architecture search and training the model. Watermarking neural networks is a potential solution to the problem, but standard techniques suffer from vulnerabilities demonstrated by attackers. In this paper, we propose a robust watermarking mechanism for neural architectures. Our proposed method ROWBACK turns two properties of neural networks, the presence of adversarial examples and the ability to trap backdoors in the network while training, into a scheme that guarantees strong proofs of ownership. We redesign the Trigger Set for watermarking using adversarial examples of the model which needs to be watermarked, and assign specific labels based on adversarial behaviour. We also mark every layer separately, during training, in order to ensure that removing watermarks requires complete retraining. We have tested ROWBACK for satisfying key indicative properties expected of a reliable watermarking scheme (generates accuracies within 1 - 2% of actual model, and a complete 100% match on the Trigger Set for verification), whilst being robust against state-of-the-art watermark removal attacks [1] (requires re-training of all layers with at least 60% samples and for at least more than 45% of epochs of actual training).
TUpper: A top level ontology within standards1

Upper ontologies have traditionally arisen from the approach in which concepts that are common across a set of domains can be axiomatized at a general level. The rationale is that reuse across domains is to be supported through specialization of the general concepts from an upper ontology. Similarly, semantic integration between ontologies is to be achieved through the general concepts they specialize. The TUpper Ontology follows an alternative approach (referred to as the sideways approach) to the conventional upper ontology paradigm. Rather than think of an upper ontology as a monolithic axiomatization centred on a taxonomy, the sideways approach considers an upper ontology to be a modular ontology composed of generic ontologies that cover concepts including those related to time, process, and space. TUpper is therefore composed of a set of generic ontologies, and each generic ontology axiomatizes a particular set of generic concepts (e.g., the classes and relations relevant for time, process, and space). The TUpper Ontology is designed as a top-level ontology that contains modules from the ontologies within existing international standards.
An Analysis of Amazon Echo's Network Behavior

With over 20 million units sold since 2015, Amazon Echo, the Alexa-enabled smart speaker developed by Amazon, is probably one of the most widely deployed Internet of Things consumer devices. Despite the very large installed base, surprisingly little is known about the device&#x0027;s network behavior. We modify a first generation Echo device, decrypt its communication with Amazon cloud, and analyze the device pairing, Alexa Voice Service, and drop-in calling protocols. We also describe our methodology and the experimental setup. We find a minor shortcoming in the device pairing protocol and learn that drop-in calls are end-to-end encrypted and based on modern open standards. Overall, we find the Echo to be a well-designed device from the network communication perspective.
Detecting Anomalies in National Bridge Inventory Databases Using Machine Learning Methods

National Bridge Inventory (NBI) data is regularly collected for 617,000+ national bridges in the U.S. These data, which consist of 100+ fields related to bridges and culverts, have been shown to contain errors. These errors could reduce the effectiveness of the decisions made based on this data, and cause safety issues. For this reason, an anomaly detection platform is developed to identify data anomalies in NBI datasets more effectively than existing rule-based error-check tools can. First, the user provides groups of correlated NBI fields as input to the platform. Then, for each group, it utilizes two tools to detect anomalous data and determine errors. The first tool uses three machine learning algorithms to identify anomalous data points and categorizes them based on their degree of anomaly. The second tool visualizes the distributions of the NBI fields in the group with histograms, scatter plots, and so forth. These plots are used to analyze the data points that are identified from the first tool as anomalies. The results of these two tools, together with expert knowledge about the data fields, are then used to distinguish data errors from outliers. The proposed platform is applied to a state’s NBI dataset that was submitted to the Federal Highway Administration (FHWA) in 2020. For this dataset, two groups of correlated fields are considered. The results showed the platform could effectively pinpoint anomalous values of NBI fields that individually, or in conjunction with other fields, do not follow the patterns that characterize most of the data, prompting the identification of potential inconsistencies and errors.
Learning Shape-Appearance Based Attributes Representation for Facial Attribute Recognition with Limited Labeled Data

The Facial Attribute Recognition (FAR) is a challenging task especially when there exists limited labeled data, which may lead the mainstream fully-supervised FAR methods to be no longer in force. To tackle this problem, we propose a novel unsupervised learning framework named Shape-Appearance Based Attributes Representation Learning (SABAL) by leveraging large-scale unlabeled face data. Considering face attributes are mainly determined by 3D shape and facial appearance, we decouple a face image into 3D shape and appearance features by two branch networks, i.e., 3D Shape Branch and Facial Appearance Branch. 3D Shape Branch and Facial Appearance Branch are jointly trained with orthogonal loss and 2D face reconstruction loss to obtain robust facial representations containing 3D-geometry and texture information, which are beneficial for attributes recognition. Finally, the unsupervised learnt features are transferred to the FAR task by fine-tuning on limited labeled data from CelebA. Extensive experiments show that we achieve comparable results to state-of-the-art methods.
Attention Detection using Electro-oculography Signals in E-learning Environment

Recently, on account of the Covid-19 pandemic online learning has become a main strategy of learning in any educational environment. The main problem is the lack of focus or attention during a student watching an online lecture or video due to ways of distraction that can occur and that's what interrupts the students and make them lose their focus. In this research paper, we propose the attention detection system using an Electro-oculogram (EOG) signal generated by eye movement. The first goal of this study was detecting the un-focusing periods in the video. The proposed process of recognizing the un-focusing period depends on recognizing the eye movement direction. 6 classes of eye movements were classified in this study: left, right, up, down, blinking, and no movement. The process started by collecting our benchmark dataset from 50 subjects, which was considered the largest dataset at all for eye movements, using a special hardware device. The pre-processing step was down to filter the signals from any noise that may be caused by the surrounding environment or involuntary movements using the band-pass filter. Finally, the classification step was down. Three models of deep learning were examined for the best classifier: Convolution Neural Network (CNN), Inception Network, and VGG Network. Our experiments achieved the best results with the Inception model, reaching an average accuracy of 93.63%. The second goal of this study was to recognize the Attention-deficit hyperactivity disorder (ADHD) pattern in the student’s recording. The model succeeded to recognize this disordered pattern.
Human-in-the-Loop XAI-enabled Vulnerability Detection, Investigation, and Mitigation

The need for cyber resilience is increasingly important in our technology-dependent society, where computing systems, devices and data will continue to be the target of cyber attackers. Hence, we propose a conceptual framework called ‘Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation’ (HXAI-VDIM). Specifically, instead of resolving complex scenario of security vulnerabilities as an output of an AI/ML model, we integrate the security analyst or forensic investigator into the man-machine loop and leverage explainable AI (XAI) to combine both AI and Intelligence Assistant (IA) to amplify human intelligence in both proactive and reactive processes. Our goal is that HXAI-VDIM integrates human and machine in an interactive and iterative loop with security visualization that utilizes human intelligence to guide the XAI-enabled system and generate refined solutions.
ONTOCONNECT: Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling

The ontology alignment task aims at linking two or more different ontologies from the same domain or different domains. Over the years, many techniques have been proposed for ontology instance alignment, schema alignment, and link discovery. Most of the available approaches require human intervention or work within a specific domain and follow a rule-based and logic-based approach. In this paper, we present an ontology alignment approach using graph embedding with negative sampling that is independent of the domain and does not require any human intervention.
Data Labeling Impact on Deep Learning Models in Digital Pathology: a Breast Cancer Case Study

Image data labeling is a vital step for deep learning model training. Studies on data labeling have not considered its impact on model performance and only focused on problems such as the curse of big data labeling or labeling tools. Furthermore, it seems clear that errors in labeling have a significant impact and should be fixed. However, in the medical domain, it is hard to ensure proper data labeling. In general, trained engineers are asked to annotate histology images, which causes errors in labeling. The aim of this study is to highlight the impact of data labeling on deep learning models. For that purpose, deep learning models are trained on two different annotations with different levels of expertise. Results show the importance of including expertise in deep learning model development. The impact of data labeling is shown through a case study on the proliferation of biomarker Ki-67 labeling index scoring.
An end-to-end face parsing model using channel and spatial attentions

Facial image parsing requires accurate extraction of facial components and features, and image segmentation can be used. Recently, various attention mechanisms showed excellent performance in segmentation by extracting features based on spatial and channel relationships for input images. In this paper, we propose a new face parsing technique using an attention block that combines the spatial attention block and the channel attention block to effectively utilize their functions. In this process, we improve the structure of the two blocks to compensate for their weaknesses. The attention block extracts features related to the shape of facial components from spatial relationships and concentrates on more important channels from correlation among channels. We built several segmentation models using the proposed block and compared their performance with well-known segmentation models. Experimental results showed that our combined block-based model can improve the segmentation accuracy by more than 5% in F1 score compared to other models.
ARCN: A Real-time Attention-based Network for Crowd Counting from Drone Images

Crowd counting is the process of counting or estimating the number of individuals in a crowd. There has been a rapid surge in the amount of Unmanned Aerial Vehicles (UAV) images over the last few years. However, efficient crowd counting techniques from UAV images have hardly come into the focus of the research community. Crowd counting from UAV images has its unique challenges compared to crowd counting from images in natural scenes. Moreover, solving the problem in real-time makes the task even harder.In this paper, we introduce an attention-based encoder-decoder model called Attention-based Real-time CrowdNet (ARCN). ARCN is a computationally efficient density estimation-based crowd counting model. It can perform crowd-counting from UAV images in real-time with high accuracy. Ours is the first work that proposes a real-time density map estimation and crowd counting model from drone-based images. The key idea of our work is to add “Convolution Block Attention Module” (CBAM) blocks in-between the bottleneck layers of the MobileCount architecture. The proposed ARCN model achieves an MAE of 19.9 and MSE of 27.7 on the DroneCrowd dataset. Also, on NVIDIA GTX 2080 Ti GPU, ARCN has a processing speed of 48 FPS, making it a real-time technique. The pre-trained model is available at https://bit.ly/3na7LUy
Capturing Injustice: The Screenshot as a Tool for Sousveillance

Framing the screenshot as a tiny-but-mighty tool for digital sousveillance, this case study explores the use of screenshots in online social justice movements, specifically those dedicated to identifying and eradicating systemic racism. Using one antiracism-focused Twitter account as a single case for analysis, we examined how social justice advocates may use the screenshot to "watch from below." Grounded theory qualitative analysis of 228 screenshots posted to the Racism Watchdog account (@RacismDog) between June 2019 and June 2020 revealed that screenshots offer a means to capture and call attention to injustices in online spaces. Equipping users with the ability to select and frame content appearing onscreen, screenshots also allow users to emphasize portions of racist comments, incorporate information to support an argument, juxtapose bits of information to highlight rhetorical contradictions, and occasionally to bring levity to those engaged in online activism. We discuss the screenshot as an effective, visually persuasive tool in online sousveillance.
Features of Professional Activity Reflection of the Olympiad “I Am a Professional” Participants

The article presents the results of a study about the features of pre-service teachers’ professional reflection. The average statistical results of the pre-service teachers who were the participants of the Olympiad “I am a professional” are considered in the context of students’ professional pedagogical reflection development. The research is based on the concept of three reflection functions: research, criticism and redesign, three types of reflection: prospective, situational, retrospective, the universal competences of a teacher: cognition skills, interaction skills, self skills. The study about the features of students’ professional reflection was carried out in 2021 during the period of the Olympiad “I am a professional” in the framework of three stages: planning/design, self-assessment/reflection, delayed group reflection. The sample included 261 students from universities in Moscow and other Russian regions, and 78 independent experts among acting school teachers. The methods of questioning (oral and written), content analysis, case studies, focus groups, video analysis, observation were used. As a result, the following features of students’ reflection were revealed, such as: the contradictory nature of goal-setting and the determination of the necessary conditions and means when planning a lesson; the tendency to overestimate their professional strengths and not notice insufficiently formed competencies; the tendency to give exaggerated importance to the technical aspects; misunderstanding of the professional pedagogical language against the background of an intuitively adequate understanding of many pedagogical and psychological phenomena, etc. From a practical point of view, the results obtained make it possible to identify the area of professional deficiencies of pre-service teachers and set the focus for a closer examination and more thorough extensive study of evaluative, critical and other complex thinking skills, which provide a thorough professional reflection. In this regard, reflective-analytical and reflective-design work formats become significant both in the educational process and in the practical training of pre-service teachers.
Voice privacy using CycleGAN and time-scale modification

Extensive use of Intelligent Personal Assistants (IPA) and biometrics in our day-to-day life asks for privacy preservation while dealing with personal data. To that effect, efforts have been made to preserve the personally identifiable characteristics from human voice using different speaker anonymization techniques. In this paper, we propose Cycle Consistent Generative Adversarial Network (CycleGAN) to modify (transform) the speaker’s gender as well as the other prosodic aspects using their Mel cepstral coefficients (MCEPs) and fundamental frequency (i.e., F 0 ). For effective anonymization in the context of voice privacy, we propose two-level (i.e., double) anonymization, where first-level anonymization is done using CycleGAN, followed by second-level anonymization using time-scale modification. The speaker anonymization and intelligibility are measured objectively using the automatic speaker verification (ASV) and automatic speech recognition (ASR) experiments, respectively, on development and test sets of Librispeech and VCTK datasets. For CycleGAN-based anonymization, the average % EERs (% WERs) are 40.3% (8.89%) and 40.95% (9.37%) with original enrollments and anonymized trials of the development and test datasets, respectively. The average % EERs (% WERs) for double anonymization are 46.19% (9.95%) and 44.76% (10.34%) with original enrollments and anonymized trials of the development and test datasets, respectively. For the voice privacy evaluation , the performance of ASV system is much important, when the enrollments and trials both are anonymized (called as A-A case), which is also briefly discussed in this work. The average % EERs for A-A case (test set) are 24.29% and 2.81% using CycleGAN-based anonymization and double anonymization, respectively. Objective evaluation for more advanced attack model (i.e., attacker having anonymized data) is also explored in this study. The performance reflected the robustness of proposed anonymization approach towards voice privacy. The subjective tests using 101 listeners and corresponding analysis of variance (ANOVA) and Tukey–Kramer-based Ad-hoc tests are also carried out in order to quote statistical significance of our results. The subjective test show that the CycleGAN and double anonymization approaches give better naturalness, intelligibility, and speaker dissimilarity than the state-of-the-art x-vector-based baseline system.
Toward an End-to-End Voice to Sign Recognition for Dialect Moroccan Language

Automatic Voice to sign recognition language is a challenging task. Building a system able to generate sign language from voice has the potential to help deaf people. However, most of the research on this topic neglects to include less used sign languages like Moroccan sign language (MSL). In this work, we present a holistic approach for building an automated voice to sign system that can be applied to Moroccan Dialect (Darija) based on pose estimation. The system was created using a consolidated dataset, namely Dvoice and newly created Moroccan sign language dataset.
Should You Go Deeper? Optimizing Convolutional Neural Network Architectures without Training

When optimizing convolutional neural networks (CNN) for a specific image-based task, specialists commonly overshoot the number of convolutional layers in their designs. By implication, these CNNs are unnecessarily resource intensive to train and deploy, with diminishing beneficial effects on the predictive performance. The features a convolutional layer can process are strictly limited by its receptive field. By layer-wise analyzing the size of the receptive fields, we can reliably predict sequences of layers that will not contribute qualitatively to the test accuracy in the given CNN architecture. Based on this analysis, we propose design strategies based on a so-called border layer. This layer allows to identify unproductive convolutional layers and hence to resolve these inefficiencies, optimize the explainability and the computational performance of CNNs. Since neither the strategies nor the analysis requires training of the actual model, these insights allow for a very efficient design process of CNN architectures, which might be automated in the future.
<i>NN-Lock</i> : A Lightweight Authorization to Prevent IP Threats of Deep Learning Models

The prevalent usage and unparalleled recent success of Deep Neural Network (DNN) applications have raised the concern of protecting their Intellectual Property (IP) rights in different business models to prevent the theft of trade secrets. In this article, we propose a lightweight, generic, key-based DNN IP protection methodology, NN-Lock , to defend against unauthorized usage of stolen DNN models. NN-Lock utilizes SBox, a cryptographic primitive, with good security properties to encrypt each parameter of a trained DNN model with the secret keys derived from a master key through a key-scheduling algorithm. The method ensures that only an authorized user with a correct master key can accurately use the locked DNN model. Evaluation results of NN-Lock on a Google Coral edge device for various DNN architectures on several datasets show that for an incorrect master key, the accuracy of a locked model is that of a random classifier. The dense network of encrypted parameters makes the method robust against the model fine-tuning attack and a novel approximation attack using the Genetic Algorithm, which achieves reasonable success against another recent IP protection scheme called HPNN Chakraborty et al. 2020 . The security evaluation of NN-Lock against other families of attacks demonstrates its soundness in practical scenarios. NN-Lock does not modify any internal structure of a DNN model, making it scalable for all of the existing DNN implementations without adversely affecting their performance.