Reviewerly/exHarmony huggingface dataset:
Deep learning model based multimedia retrieval and its optimization in augmented reality applications

With the uproar of touchless technology, the Virtual Continuum has seen some spark in the upcoming products. Today numerous gadgets support the use of Mixed Reality / Augmented Reality (AR)/ Virtual Reality. The Head Mounted Displays (HMDs) like that of Hololens, Google Lens, Jio Glass manifested reality into virtuality. Other than the HMDs many organizations tend to develop mobile AR applications to support umpteen number of industries like medicine, education, construction. Currently, the major issue lies in the performance parameters of these applications, while deploying for mobile application's graphics performance, latency, and CPU functioning. Many industries pose real-time computation requirements in AR but do not implement an efficient algorithm in their frameworks. Offloading the computation of deep learning models involved in the application to the cloud servers will highly affect the processing parameters. For our use case, we will be using Multi-Task Cascaded Convolutional Neural Network (MTCNN) which is a modern tool for face detection, using a 3-stage neural network detector. Therefore, the optimization of communication between local application and cloud computing frameworks needs to be optimized. The proposed framework defines how the parameters involving the complete deployment of a mobile AR application can be optimized in terms of retrieval of multimedia, its processing, and augmentation of graphics, eventually enhancing the performance. To implement the proposed algorithm a mobile application is created in Unity3D. The mobile application virtually augments a 3D model of a skeleton on a target face. After the mentioned experimentation, it is found that average Media Retrieval Time (1.1471 μ s) and Client Time (1.1207 μ s) in the local application are extremely low than the average API process time (288.934ms). The highest time latency is achieved at the frame rate higher than 80fps.
Reliable Data Collection Methodology for Face Recognition in Preschool Children

Most face datasets target adults who can make their own decisions. In the case of children, consent from parents or guardians is necessary to collect biometric information, thus making it very difficult. As a result, the amount of data on children is quite small and inevitably private. In this work, we built a database by collecting face data of 74 children aged 2-7 years in daycare facilities. In addition, we conducted an experiment to determine the best location to perform face recognition on children by installing cameras in various locations. This study presents the points and methods to be considered to build a children's face dataset and also studies the optimal camera installation setups for the face recognition of children.
Scape: Scalable Collaborative Analytics System on Private Database with Malicious Security

Many data applications can be facilitated or even spawned by joint analysis on databases held by different owners, but privacy concerns are currently the biggest hindrance. Though a practical privacy-preserving collaborative database analytics system is strongly desired, existing approaches do not support efficient queries for several essential SQL operators such as the general join, especially on large databases. In this paper, we propose, analyze, and implement Scape, a Scalable Collaborative Analytics system on Private databasE with malicious security. In Scape, databases from different parties are secretly shared to three non-colluding computing parties. Users can perform various SQL queries (including fully functional Join, Group by, Aggregation, etc.) on shared databases, and all entities learn nothing beyond their priori knowledge during the whole execution even when they deviate from protocols. At the heart of Scape lies several asymptotically efficient SQL protocols. Particularly, our general join protocol has O (n log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n + m) communication/computation cost when joining two tables with o (n) rows to a table with 0 (m) rows, significantly outperforming the state-of-the-art approach with O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) cost. The benchmark results confirm the advantages of Scape, which is up to 25 x faster than the baseline.
A Comparative Study of in-Database Inference Approaches

In Alibaba's IoT platform, we face the challenge of processing analytical queries involving both structured and unstructured data. Normally, collaborative queries need deep learning (DL) models and relational algebras to work intertwined to produce sophisticated analytical answers. To be able to support collaborative queries, a variety of approaches have been proposed. In this paper, we present the three most representative ones and study their advantages and limitations. The first one translates the collaborative query into a series of database and DL sub-queries and then maintains the dependence of the intermediate results of two sub-systems and computes the final results on the fly. The second one transforms a DL model to a database built-in User Defined Function(UDF) implemented in C++. The whole collaborative query is then processed by the database system independently. The third one is our novel solution proposed in the paper, DL2SQL, where neural operators underneath DL models are rewritten as SQL queries, and collaborative queries are processed using native SQL syntax. A cost model for our SQL-native neural operators is designed to leverage the database's optimizer to generate an efficient query plan. All three approaches are implemented on the ClickHouse. Finally, we use the real-world workloads on Alibaba's IoT platform as our benchmark and deploy various approaches on both an embedded device and a Cloud server to compare their performance. Results show that DL2SQL outperforms others in most scenarios and is more extensible.
Using machine learning algorithms to predict individuals’ tendency to be victim of social engineering attacks

In information security context, social engineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which gets benefit of human error to reach private information. This study used machine learning algorithms to predict individuals’ susceptibility to be tricked by social engineering attacks. Simulated scenarios were presented to study participants, and they were asked to identify whether each scenario was a social engineering attack or not. Different kinds of attacks related to various industries were integrated to social engineering simulations. For each participant, different types of social engineering scores were calculated according to their responses. Besides simulations, questionnaires related to demographics, technology usage, and personality traits were filled out by the participants. All of these collected data were used in building predictive classification and regression machine learning models. Through regression and classification models, it was aimed to proactively predict individuals’ social engineering risk levels and classify them into different risk groups in terms of different attack types. This research revealed that it is possible to predetermine the social engineering risk levels of individuals. This important finding means that possible attacks can be prevented by raising awareness before the attack occurs. Within the scope of this study, a social engineering risk detection mobile application has also been developed to give practitioners and policy makers an idea of what kind of systems can be developed in order to determine the risk levels of individuals and then to educate them about various attacks. The ones who need to take action against social engineering attacks will get benefit from findings of this research.
Improving the robustness of machine reading comprehension via contrastive learning

Abstract Pre-trained language models achieve high performance on machine reading comprehension task, but these models lack robustness and are vulnerable to adversarial samples. Most of the current methods for improving model robustness are based on data enrichment. However, these methods do not solve the problem of poor context representation of the machine reading comprehension model. We find that context representation plays a key role in the robustness of the machine reading comprehension model, dense context representation space results in poor model robustness. To deal with this, we propose a Multi-task machine Reading Comprehension learning framework via Contrastive Learning. Its main idea is to improve the context representation space encoded by the machine reading comprehension models through contrastive learning. This special contrastive learning we proposed called Contrastive Learning in Context Representation Space(CLCRS). CLCRS samples sentences containing context information from the context as positive and negative samples, expanding the distance between the answer sentence and other sentences in the context. Therefore, the context representation space of the machine reading comprehension model has been expanded. The model can better distinguish between sentence containing correct answers and misleading sentence. Thus, the robustness of the model is improved. Experiment results on adversarial datasets show that our method exceeds the comparison models and achieves state-of-the-art performance.
Understanding preservice teachers' affective responses to VR-enabled scientific experiments

Preservice teachers' preparedness, perception, and affect toward certain technology systems influence the student acquisition of science knowledge, process skills, teaching innovation, and willingness to use technology in their classroom. The purpose of this study was to explore teachers' affective responses to a virtual reality-enabled scientific experiment (VaSE) system. Fifty-one preservice teachers majoring in educational technology participated in the study. They were divided into two groups, and their reactions were measured separately on two occasions. The first occasion used a standard system following the principle of Donald Norman's affective design model (ADM). The VaSE system was then revised with a strict version of ADM before the participants' reactions were measured for a second time. The positive and negative affect scale (PANAS) was utilized for affective state evaluation. Semi-structured interviews that focused on affective experiences were organized using an ADM framework and conducted immediately after the participants used VaSE. The results indicated that the positive affect experienced by the preservice teachers was significantly enhanced, and the negative affect was significantly weakened. Academic level, gender, and prior experience were important random effect factors that impacted the affective experience. It was also revealed that participants were more likely to be affected by immersion and interactivity in terms of enhancing positive affect and were more likely to be affected by behavioral layer elements in terms of weakening negative affect. A conclusion has been drawn to provide theoretical and practical suggestions for training preservice teachers in ways that improve their ability to integrate technology into the classroom.
Building a neural speech recognizer for quranic recitations

This work is an effort towards building Neural Speech Recognizers system for Quranic recitations that can be effectively used by anyone regardless of their gender and age. Despite having a lot of recitations available online, most of them are recorded by professional male adult reciters, which means that an ASR system trained on such datasets would not work for female/child reciters. We address this gap by adopting a benchmark dataset of audio records of Quranic recitations that consists of recitations by both genders from different ages. Using this dataset, we build several speaker-independent NSR systems based on the DeepSpeech model and use word error rate (WER) for evaluating them. The goal is to show how an NSR system trained and tuned on a dataset of a certain gender would perform on a test set from the other gender. Unfortunately, the number of female recitations in our dataset is rather small while the number of male recitations is much larger. In the first set of experiments, we avoid the imbalance issue between the two genders and down-sample the male part to match the female part. For this small subset of our dataset, the results are interesting with 0.968 WER when the system is trained on male recitations and tested on female recitations. The same system gives 0.406 WER when tested on male recitations. On the other hand, training the system on female recitations and testing it on male recitation gives 0.966 WER while testing it on female recitations gives 0.608 WER.
Mixed Reality-based chemical reactor visualization

A multi-tubular reactor for butadiene synthesis was developed, visualized using Microsoft HoloLens2, and built to allow immersive human interaction. Fluid analysis is particularly important in reactor design, including heat generation, temperature distribution, and mole fraction of reactants and products. Flow analysis is mostly executed with CFD tools like Open FOAM and ANSYS Fluent. Post processing software visualizes the CFD findings. However, the post-processing software is focused on the engineer, which lacks user-friendliness for the user who actually builds the reactor [Matthias Berger and Verina Cristie, 2015]. Contemporary technologies like virtual reality and mixed reality were applied to improve this. Virtual reality is a technique that creates a virtual world in a three-dimensional digital environment that blocks out the actual world. Furthermore, mixed reality technology interacts with virtual things made in the real world, minimizing heterogeneity and creating a smart environment where reality and virtuality are united by maximizing immersion. So, using Unity 3D, a gaming engine, a multi-tube reactor was created in virtual reality. Menus, selection windows, and buttons are also implemented in C# to facilitate interaction. This VR was then deployed to Microsoft HoloLens2 via Visual Studio. The HoloLens2 is an un tethered wearable holographic computer that offers bidirectional video, speech, and MR composite communication [Guy Martin et al., 2019]. An integrated mixed reality system was finally created that instantly reflected mutual conversation. This approach can also be employed in distillation towers, heat exchangers, separators, pumps and other process equipment performance monitoring. Smart manufacturing and a digital twin termed future plant can be accomplished by observing the actual process and implementing an autonomous data interaction system.
Cleaning Quality Control Management of Medical Equipment in Hospital Disinfection Supply Room Based on Smart Medicine

With the continuous improvement of medical level, the demand for medical devices increases year by year, and it is necessary to track the quality and safety of medical devices. Establishing the cleaning quality control management of medical devices can not only reduce medical accidents and inhibit the spread of counterfeit or substandard medical devices but also help enterprises find the source of problems, determine the flow of the same batch of products, and warn relevant enterprises to take measures after the occurrence of problem products, and ultimately ensure the rights and interests of consumers. This study aims to study the cleaning quality control management of medical equipment in the hospital disinfection supply room based on smart medical care and proposes related concepts of smart medical care, as well as related theories of Internet of Things technology and medical equipment tracking system and related concepts of medical equipment cleaning. In this study, by comparing the cleaning compliance rates of the two groups of medical devices through smart medical care, it can be seen that 100% of the research group applying program-based management is significantly higher than 76.6% of the conventional management path. There was statistical significance in the data comparison between groups (P < 0.05). Therefore, programmatic management is more targeted than conventional management paths.
Parallel optical hash function based on the interaction between linearly polarized light and multiple-scattering media

A method is proposed for constructing a cryptographic hash function based on the interaction between linearly polarized light and multiple-scattering media in a parallel fashion. It is well known that an unpredictable noise-like speckle pattern will appear when a light beam passes through a scattering medium. By leveraging this natural optical-encoding mechanism, we developed a paralleled algorithm to construct an optical hash function. It was shown by numerical simulation to have a high security level. Furthermore, in the proposed conceptual optical-digital setup, a strategy for multiplexing linearly polarized light was introduced to accelerate data processing.
Unrolled GAN-based Oversampling of Credit Card Dataset for Fraud Detection

The excellent performance of most classification algorithms is based on the balance of classes. However, in anomaly detection, the classes are mostly biased. The performance of traditional machine learning algorithms applied to anomaly detection of ten fails to achieve the target effect. Considering the data source, oversampling method addresses class imbalance from data source. In light of the capability of capturing the original sample data distribution, Generative Adversarial Networks offer an inspiring oversampling solution. In this research we demonstrate the applicability of an oversampling method based on Unrolled GAN with credit card data sets. We contrast that method with traditional oversampling methods. Empirical results show the capacity of Unrolled GAN-based oversampling.
A New Cloud-Based Adaptive Mobile Learning System Using Multi-Agent Paradigm

Today, Mobile devices are receiving a wide recognition as a cutting-edge technology to assist teaching and learning strategies that harness individual learners’ context. As mobile technology is being resorted to in education systems, tools that help accessing information have changed. In similar fashion, concepts such as Mobile Learning have emerged. Mobile learning is widespread. Furthermore, students nowadays are able to learn regardless of time and place. This is accomplished by mobile technologies and wireless internet connections. Nevertheless, there are still critical challenges regarding this new learning paradigm. In fact, researchers are meeting a difficulty related to providing accessibility for all students. This paper introduces a New Cloud-based Adaptive Mobile Learning System using a Multi-agent paradigm. The system provides adaptive learning content and personalised to the learner’s style and preferences, to increase learner satisfaction and facilitate the learning process. The system was implemented and will be tested with students in a real educational environment.
Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network

Event-based vision has already revolutionized the perception task for robots by promising faster response, lower energy consumption, and lower bandwidth without introducing motion blur. In this work, a novel deep learning method based on gated recurrent units utilizing sparse convolutions for detecting gates in a race track is proposed using event-based vision for the autonomous drone racing problem. We demonstrate the efficiency and efficacy of the perception pipeline on a real robot platform that can safely navigate a typical autonomous drone racing track in real-time. Throughout the experiments, we show that the event-based vision with the proposed gated recurrent unit and pretrained models on simulated event data significantly improve the gate detection precision. Furthermore, an event-based drone racing dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code and data will be available at https://github.com/open-airlab/neuromorphic_au_drone_racing.git consisting of both simulated and real data sequences is publicly released.
Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines

In this work, we present a vision system particularly suited to the automatic recognition of reels in the field of automatic packaging machines. The output of the vision system is used to guide the autonomous grasping of the reels by a robot for a subsequent manipulation task. The proposed solution is built around three different methods to solve the ellipse-detection problem in an image. Such methods leverage standard image processing and mathematical algorithms, which are tailored to the targeted application. An experimental campaign demonstrates the efficacy of the proposed approach, even in the presence of low computational power and limited hardware resources, as in the use-case at hand.
Detecting human-object interactions in videos by modeling the trajectory of objects and human skeleton

This article focuses on the task of detecting human-object interactions (HOI) in videos, with the goal of identifying objects interacting with humans and predicting human-object interaction classes. Two frameworks are proposed which detect human-object interactions in videos by modeling the trajectory of objects and human skeleton. The first framework (knowledge-based spatial-temporal HOI) treats the entire scene to be a HOI graph made up of the human skeleton and objects. It has fewer parameters and a higher possibility for knowledge embedding. The second framework (hierarchical spatial-temporal HOI) constructs a HOI graph after obtaining the feature of the human skeleton and objects. It outperforms the competition in terms of performance and generalization. Experimental results in CAD-120 dataset and SYSU-HOI dataset show that the proposed frameworks are more advanced than the state-of-the-art methods, with smaller parameters and shorter inference time. Such results confirm that the proposed frameworks effectively reduce parameters and inference time while maintaining detection accuracy in HOI videos.
Robust secret image sharing scheme resistance to maliciously tampered shadows by AMBTC and quantization

For (k, n)-threshold secret image sharing (SIS) scheme, only k or more than k complete parts can recover the secret information, and the correct image cannot be obtained if the count of shadow images is not enough or the shadow images are damaged. The existing schemes are weak in resisting large-area shadow image tampering. In this paper, we propose a robust secret image sharing scheme resisting to maliciously tampered shadow images by Absolute Moment Block Truncation Coding (AMBTC) and quantization (RSIS-AQ). The secret image is successively compressed in two ways: AMBTC and quantization. The sharing shadow images contain the sharing results of both compressed image from different parts, so that even the shadow images are faced with large-scale area of malicious tampering, the secret image can be recovered with acceptable visual quality. Compared with related works, our scheme can resist larger area of tampering and yield better recovered image visual quality. The experimental results prove the effectiveness of our scheme.