Reviewerly/exHarmony huggingface dataset:
Time is money! Evaluating Augmented Reality Instructions for Time-Critical Assembly Tasks

Manual assembly tasks require workers to precisely assemble parts in 3D space. Often additional time pressure increases the complexity of these tasks even further (e.g., adhesive bonding processes). Therefore, we investigate how Augmented Reality (AR) can improve workers’ performance in time and spatial dependent process steps. In a user study, we compare three conditions: instructions presented on (a) paper, (b) a camera-based see-through tablet, and (c) a head-mounted AR device. For instructions we used selected work steps from a standardized adhesive bonding process as a representative for common time-critical assembly tasks. We found that instructions in AR can improve the performance and understanding of time and spatial factors. The tablet instruction condition showed the best subjective results among the participants, which can increase motivation, particularly among less-experienced workers.
Null-Sampling for Interpretable and Fair Representations

We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations w.r.t. class label annotations, and a robustness to irrelevant correlations with protected characteristics such as race or gender. We introduce a non-trivial setup in which the training set exhibits a strong bias such that class label annotations are irrelevant and spurious correlations cannot be distinguished. To address this problem, we introduce an adversarially trained model with a null-sampling procedure to produce invariant representations in the data domain. To enable disentanglement, a partially-labelled representative set is used. By placing the representations into the data domain, the changes made by the model are easily examinable by human auditors. We show the effectiveness of our method on both image and tabular datasets: Coloured MNIST, the CelebA and the Adult dataset. (The code can be found at https://github.com/predictive-analytics-lab/nifr ).
Tolerance angle concept and formula for practical optimal orientation of photovoltaic panels

Optimization of photovoltaic (PV) panel orientation is a significant part of economic evaluation. However, designing a PV system according to only one theoretical optimal orientation is challenging. Installing a PV panel at a specific orientation can cause low economic efficiency, depending on the features of the area. Therefore, in this study, a new term—the tolerance angle—is introduced for quantitatively analyzing the tolerance of the optimal orientation. This term provides a range for the optimal panel orientation according to the target loss and can be used in decision making for practical optimization of PV panel orientation. The validation of the assumption in the definition and calculation model for the tolerance angle is presented and analyzed. Additionally, a simple calculation formula for the theoretical optimal orientation and tolerance angle is derived. The formula can be used in practical applications without complex calculations or considerable data. The tolerance angle is useful in many practical applications, including sites with restrictions. The results of this study are expected to enhance the practicality of panel orientation optimization and thereby improve actual PV panel installation.
PerfNet

The technology of deep learning has grown rapidly and been widely used in the industry. In addition to the accuracy of the deep learning (DL) models, system developers are also interested in comprehending their performance aspects to make sure that the hardware design and the systems deployed to meet the application demands. However, developing a performance model to serve the aforementioned purpose needs to take many issues into account, e.g. the DL model, the runtime software, and the system architecture, which is quite complex. In this work, we propose a multi-layer regression network, called PerfNet, to predict the performance of DL models on heterogeneous systems. To train the PerfNet, we develop a tool to collect the performance features and characteristics of DL models on a set of heterogeneous systems, including key hyper-parameters such as loss functions, network shapes, and dataset size, as well as the hardware specifications. Our experiments show that the results of our approach are more accurate than previously published methods. In the case of VGG16 on GTX1080Ti, PerfNet yields a mean absolute percentage error of 20%, while the referenced work constantly overestimates with errors larger than 200%.
New passive and active attacks on deep neural networks in medical applications

Security of deep neural network (DNN) inference engines, i.e., trained DNN models on various platforms, has become one of the biggest challenges in deploying artificial intelligence in domains where privacy, safety, and reliability are of paramount importance, such as in medical applications. In addition to classic software attacks such as model inversion and evasion attacks, recently a new attack surface---implementation attacks which include both passive side-channel attacks and active fault injection and adversarial attacks---is arising, targeting implementation peculiarities of DNN to breach their confidentiality and integrity. This paper presents several novel passive and active attacks on DNN we have developed and tested over medical datasets. Our new attacks reveal a largely under-explored attack surface of DNN inference engines. Insights gained during attack exploration will provide valuable guidance for effectively protecting DNN execution against reverse-engineering and integrity violations.
A Blockchain Framework for Secure Electronic Health Records in Healthcare Industry

In terms of access, data processing, monitoring and healthcare, the world's health systems experience significant transformations. The advances in data capturing and connected technology are expected to produce about 2314 exabytes of health care data for 2020 [1]. Cyber criminals put a lot of effort for gaining access of healthcare knowledge. This challenge is expected to carry the cybersecurity market to about 27.1 billion dollars in the year 2026 [2]. The blockchain technology will help to form a centralized repository for data collection in clinical experiments. This article suggests a secure system using blockchain to make sure the protection of electronic healthcare records (EHR). The framework includes sensors, Interne of things, databases and other computing resources. This framework for securing EHR will improve the security, privacy of EHR as compared to traditional healthcare system.
3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images for seven different classes, including eyes, brows, nose, hair, mouth, skin, and back. We extract features from gray scale images by using DCNNs. We train a classifier using the extracted features. We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class. We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase. We assess the performance of our newly proposed model on four standard head pose datasets, including Pointing’04, Annotated Facial Landmarks in the Wild (AFLW), Boston University (BU), and ICT-3DHP, obtaining superior results as compared to previous results.
Ponzi Contracts Detection Based on Improved Convolutional Neural Network

As one of the leading blockchain systems in operation, Ethereum has numerous smart contracts deployed to implement a variety of functions. Unfortunately, speculators introduce scams such as Ponzi scheme in the traditional financial sector into some of these smart contracts, causing millions of dollars of losses to investors. At present, there are a few of quantitative identification methods for new fraud modes under the background of Internet finance, and detection methods for the Ponzi scheme contracts on Ethereum are even less. In this paper, we propose an improved convolutional neural network as a detection model for Ponzi schemes in smart contracts. We use real smart contracts to evaluate the feasibility and usefulness of our mode. Results show that our improved convolutional neural network can overcome difficulties in training caused by different length of smart contracts' bytecodes. Compared with the state-of-the-art methods, the precision and recall rate of our model for Ponzi scheme detection are improved by 3.2% and 24.8% respectively.
Gait Recovery System for Parkinson’s Disease using Machine Learning on Embedded Platforms

Freezing of Gait (FoG) is a common gait deficit among patients diagnosed with Parkinson's Disease (PD). In order to help these patients recover from FoG episodes, Rhythmic Auditory Stimulation (RAS) is needed. The authors propose a ubiquitous embedded system that detects FOG events with a Machine Learning (ML) subsystem from accelerometer signals. By making inferences on-device, we avoid issues prevalent in cloud-based systems such as latency and network connection dependency. The resource-efficient classifier used, reduces the model size requirements by approximately 400 times compared to the best performing standard ML systems, with a trade-off of a mere 1.3% in best classification accuracy. The aforementioned trade-off facilitates deployability in a wide range of embedded devices including microcontroller based systems. The research also explores the optimization procedure to deploy the model on an ATMega2560 microcontroller with a minimum system latency of 44.5 ms. The smallest model size of the proposed resource efficient ML model was 1.4 KB with an average recall score of 93.58%.
VibLive: A Continuous Liveness Detection for Secure Voice User Interface in IoT Environment

The voice user interface (VUI) has been progressively used to authenticate users to numerous devices and applications. Such massive adoption of VUIs in IoT environments like individual homes and businesses arises extensive privacy and security concerns. Latest VUIs adopting traditional voice authentication methods are vulnerable to spoofing attacks, where a malicious party spoofs the VUIs with pre-recorded or synthesized voice commands of the genuine user. In this paper, we design VibLive, a continuous liveness detection system for secure VUIs in IoT environments. The underlying principle of VibLive is to catch the dissimilarities between bone-conducted vibrations and air-conducted voices when human speaks for liveness detection. VibLive is a text-independent system that verifies live users and detects spoofing attacks without requiring users to enroll specific passphrases. Moreover, VibLive is practical and transparent as it requires neither additional operations nor extra hardwares, other than a loudspeaker and a microphone that are commonly equipped on VUIs. Our evaluation with 25 participants under different IoT intended experiment settings shows that VibLive is highly effective with over 97% detection accuracy. Results also show that VibLive is robust to various use scenarios.
Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

Cloud computing as a model, provides an environment for execution of large computationally or data intensive task under some agreed Service Level Agreement (SLA). Today, the rate of cloud adoption by organizations has immensely increased due to its reliability, reduced cost and performance. This increase necessitates the need for efficient resource scheduling technique that ensure Quality of Service (QoS) delivery, optimize resource usage and maintain the agreed SLA. Researchers in the literature have proposed several techniques that maps complex task to cloud resources considering QoS requirements which records significant improvements with the need for more ideal solutions. This paper proposed an efficient SLA violation preventive task scheduling to optimize cloud resources, ensure QoS delivery while maintaining the agreed SLA. Processing capacity, bandwidth, storage, execution time, waiting time and task deadline are considered as QoS parameters for mapping of task to resource. The proposed method was implemented using CloudSim simulation tool and the performance result shows that the method will significantly enhance cloud performance in terms of response time and client QoS expectations.
Early Detection of Smart Ponzi Scheme Contracts Based on Behavior Forest Similarity

Smart contracts empowered by blockchains often manage digital assets in a distributed and decentralized environment. People believe in smart contracts based on these new technologies. Unfortunately, malicious smart contacts, such as smart Ponzi scheme contracts (ponzitracts, for short), pose risk. Existing techniques detect ponzitracts by analyzing the code as well as a large amount of transaction data after time-consuming deployment. However, a conclusion based on transaction data can only be gotten after the damage has been caused. This paper proposes PonziDetector, a ponzitract detection technique that does not rely on transaction data. Behavior forest is introduced into PonziDetector to capture dynamic behaviors of smart contracts during interacting with them, which makes it possible to early detect ponzitracts. The empirical study demonstrates that PonziDetector, without transaction data, can improve the precision and the recall of the state-of-the-art to 94.6% and 93.0% respectively. This means that PonziDetector can avoid potential losses by early detecting ponzitracts.
Double Patterns: A Usable Solution to Increase the Security of Android Unlock Patterns

Android unlock patterns are still commonly used, and roughly 25% of the respondents to our study use a pattern when unlocking their phone. Despite security issues, the design of the patterns have remained unchanged. We propose Double Patterns (DPatts), a natural advancement on Android unlock patterns that maintains the core design but instead of selecting a single pattern, a user selects two patterns entered one-after-the-other super-imposed on the same 3x3 grid. We evaluated DPatts for both security and usability through an online study (n = 634) with three treatments: a control a first pattern entry blocklist, and a blocklist for both patterns. We find that in all settings, user chosen DPatts are more secure than traditional patterns based on standard guessability metrics, more similar to that of 4-/6-digit PINs, and even more difficult to guess for a simulated attacker. Users express positive sentiments in qualitative feedback, particularly those who currently (or previously) used Android unlock patterns, and overall, participants found the DPatts interface quite usable, with high recall retention and comparable entry times to traditional patterns. In particular, current Android pattern users, the target population for DPatts, reported SUS scores in the 80th percentile and high perceptions of security and usability in responses to open- and closed-questions. Based on these findings, we would recommend adding DPatts as an advancement to Android patterns, much like allowing for added PIN length.
Deep learning and multimodal target recognition of complex and ambiguous words in automated English learning system

On the basis of convolution neural network, deep learning algorithm can make the convolution layer convolute the input image to complete the hierarchical expression of feature information, which makes pattern recognition more simple and accurate. Now, in the theory of multimodal discourse analysis, the nonverbal features in communication are studied as a symbol system similar to language. In this paper, the author analyzes the deep learning complexity and multimodal target recognition application in English education system. Multimodal teaching gradually has its practical significance in the process of rich teaching resources. The large-scale application of multimedia technology in college English classroom is conducive to the construction of a real language environment. The simulation results show that the multi-layer and one-dimensional convolution structure of the product neural network can effectively complete many natural language problems, including the tagging of lexical and semantic roles, and thus effectively improve the accuracy of natural language processing. Multimodal teaching mode helps to memorize vocabulary images more deeply. 84% of students think that multi-modal teaching mode is closer to life. Meanwhile, multimedia teaching display is more acceptable. College English teachers should renew their teaching concepts and adapt themselves to the new teaching mode.
An IoT-owned Service for Global IoT Device Discovery, Integration and (Re)use

This paper introduces a novel IoT-owned service for Global IoT Device Discovery and Integration (GIDDI) of existing IoT devices that are owned and managed by different parties who are the IoT devices providers. The GIDDI service promotes the sharing of existing IoT devices and the deployment of new devices via a revenue generating scheme for the IoT device providers. Unlike existing IoT device discovery and integration solutions that are currently owned and/or controlled by specific IoT platform or service providers, the GIDDI service has been specifically designed to manage all the metadata needed for IoT device discovery and integration in a specialized blockchain (we refer to this as GIDDI Blockchain) and via this blockchain-based solution be IoT-owned (i.e., not owned or controlled by any specific provider). In addition to the GIDDI Blockchain, the GIDDI service includes a distributed GIDDI Marketplace that provides the functionality of IoT device discovery, integration and payment. The paper describes a proof-of-concept implementation of the GIDDI blockchain. It also provides an experimental evaluation of the GIDDI blockchain in variety of IoT device registration and query workloads. An evaluation of the proposed GIDDI service concludes the paper.
Augmented Reality Gaming as a Tool for Subjectivizing Visitor Experience at Cultural Heritage Locations—Case Lights On!

This article discusses an augmented reality game as a way of subjectivizing visitor experience at cultural heritage locations. The text describes the starting point, development aims, and some initial user data of the Lights On! game while contextualizing the game's development with relevant discussion concerning the use of ludic and narrative augmented reality elements in enhancing the experience of place at historical sites. The initial user data were collected via an in-app questionnaire and learning diaries. The results indicate that the game was well suited for children, whose imaginations were stirred by gaming on-location at the sites. It seems that with an appropriate implementation, an AR game can offer visitors an alternative way of experiencing cultural heritage attractions that creates significant added value to the user.
Web environment of distance learning

This article examines the use of various Internet resources in education in the context of the global COVID-19 coronavirus pandemic. The relevance of the research topic is determined by new requirements and trends in education in the era of distance learning. That is why recently it has become extremely important to introduce new educational technologies in the educational process, in particular in teaching a foreign language. The aim of the article is to identify the advantages and disadvantages of online learning and the impact of information technology on the learning process. The research was carried out using the analytical method and questionnaires. The article also raises the issue of using Internet resources and multimedia tools, which is the main way to provide distance learning using various communication platforms (Zoom, Hangouts, Teams, Skype, Moodle, and others) and social networks (Viber, Skype, WhatsApp, etc.). The result of the research presented in this article is the conclusion that the use of Internet resources and modern teaching aids of the new generation allows teachers to fill professional educational activities with new creative content and form, actively apply problematic, variable, innovative teaching methods, intensively use educational Web -sites, Internet portals.
Deep Variation Transformation Network for Foreground Detection

In existing literature, the distribution of pixel observations is analyzed with models designed for the video foreground detection task. However, it is possible that the background and foreground share similar observations, causing false detections. We propose a novel foreground detection method called Deep Variation Transformation Network (DVTN), focusing on analyzing the pixel variations instead of distributions. In particular, pixel variations are represented by a sequence of pixel observations, and DVTN is trained to transform the pixel variations into a new space, where the observations can be classified easily. Following this, the output of DVTN is utilized by a linear classifier to label pixels as foreground or background. As a result of the global analysis and the strong learning ability of DVTN, the proposed approach adaptively learns a good transformation from pixel variations to probabilities of labels to improve performance. Comprehensive experiments on several benchmark datasets demonstrate the superiority of our DVTN approach compared to both state-of-the-art deep learning and traditional methods, especially in scenes lacking texture and color information. Code is available at https://github.com/Zhangjunyin/DVTN.
Efficient hardware/software co-design for post-quantum crypto algorithm SIKE on ARM and RISC-V based microcontrollers

Post-quantum cryptography has emerged as a very attractive research topic due to the recent advancements in the development of quantum computers. Among the different available post-quantum public-key algorithms, Supersingular Isogeny Key-Encapsulation (SIKE) has posed a unique design challenge due to its resource intensive arithmetic but is characterized by small key sizes. Existing implementations of SIKE either focus on dedicated accelerators on FPGA platforms or on assembly optimized software implementations on ARM. A full FPGA implementation, though offering low latency and high performance, suffers from the disadvantage of having a large area footprint and a low flexibility. On the other hand, a pure software implementation has lower performance compared to FPGA implementations. In this paper, we propose hardware/software co-design methodologies for SIKE and integrate a redundant number based finite field accelerator into two microcontroller platforms based on ARM and RISC-V. The result shows that our implementation on ARM Cortex-A9 enhanced with a field accelerator offers significant speedup in terms of clock cycles when compared to standalone software implementations on ARM32 and ARM64. Moreover, to show how the communication overhead between processor and accelerator can be mitigated, we integrated the finite field accelerator directly into the core of a RISC-V processor. To the best of our knowledge, this is the first design that applies hardware/software co-design methodologies to implement SIKE on ARM and RISC-V platforms. Our proposed design requires 65500 K clock cycles to execute SIKEp434 on an ARM Cortex-A9 processor. On RISC-V, our proposed design requires only 36900 K clock cycles.