Reviewerly/exHarmony huggingface dataset:
Reusable Two-Round MPC from DDH

We present a reusable two-round multi-party computation (MPC) protocol from the Decisional Diffie Hellman assumption (DDH). In particular, we show how to upgrade any secure two-round MPC protocol to allow reusability of its first message across multiple computations, using Homomorphic Secret Sharing (HSS) and pseudorandom functions in $$NC^1$$ — each of which can be instantiated from DDH. In our construction, if the underlying two-round MPC protocol is secure against semi-honest adversaries (in the plain model) then so is our reusable two-round MPC protocol. Similarly, if the underlying two-round MPC protocol is secure against malicious adversaries (in the common random/reference string model) then so is our reusable two-round MPC protocol. Previously, such reusable two-round MPC protocols were only known under assumptions on lattices. At a technical level, we show how to upgrade any two-round MPC protocol to a first message succinct two-round MPC protocol, where the first message of the protocol is generated independently of the computed circuit (though it is not reusable). This step uses homomorphic secret sharing (HSS) and low-depth pseudorandom functions. Next, we show a generic transformation that upgrades any first message succinct two-round MPC to allow for reusability of its first message.
A Selective Model Aggregation Approach in Federated Learning for Online Anomaly Detection

Cloud computing has established a convenient approach for computing offloading, where the data produced by edge devices is gathered and processed in a centralized server. However, it results in critical issues related to latency. Recently, a neural network-based on-device learning approach is proposed, which offers a solution to the latency problem by relocating processing data to edge devices; even so, a single edge device may face insufficient training data to train a high-quality model, because of its limited available processing capabilities and energy resources. To address this issue, we extend the work to a federated learning system which enables the edge devices to exchange their trained parameters and update local models. However, in federated learning for anomaly detection, the reliability of local models would be different. For example, a number of trained models are likely to contain the features of anomalous data because of noise corruption or anomaly detection failure. Besides, as the communication protocol amongst edges could be exploited by attackers, the training data or model weights may have potential risks of being poisoned. Therefore, when we design a federated training algorithm, we should carefully select the local models that participate in model aggregation. In this work, we leverage an observed dataset to compute prediction errors, so that the unsatisfying local models can be excluded from federated training. Experimental results show that the federated learning approach improves anomaly detection accuracy. Besides, the proposed model aggregation solution achieves obvious improvement compared with the popular Federated Averaging method.
Two 3D spatial phase unwrapping algorithms for dynamic 3D shape measurement

Phase unwrapping is a key step of three-dimensional (3D) shape measurement in fringe projection profilometry (FPP). In this paper, 3D quality-guided (3DQG) phase unwrapping algorithm and 3D fast phase unwrapping (3DFPU) algorithm are designed, programmed and applied in the 3D shape measurement of dynamic scenes. Simulated and actual experiments are conducted to compare the performance between the 3D algorithms for 3D wrapped phase in whole and their corresponding two-dimensional (2D) algorithms for 2D wrapped phase frame by frame. The results show that 3D spatial phase unwrapping (SPU) algorithms have higher unwrapping success rate in severely noisy regions and can overcome some discontinuous problems which is truly benefit for the accurate reconstruction of dynamic 3D.
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads

With diverse IoT workloads, placing compute and analytics close to where data is collected is becoming increasingly important. We seek to understand what is the performance and the cost implication of running analytics on IoT data at the various available platforms. These workloads can be compute-light, such as outlier detection on sensor data, or compute-intensive, such as object detection from video feeds obtained from drones. In our paper, JANUS, we profile the performance/$ and the compute versus communication cost for a compute-light IoT workload and a compute-intensive IoT workload. In addition, we also look at the pros and cons of some of the proprietary deep-learning object detection packages, such as Amazon Rekognition, Google Vision, and Azure Cognitive Services, to contrast with open-source and tunable solutions, such as Faster R-CNN (FRCNN). We find that AWS IoT Greengrass delivers at least 2X lower latency and 1.25X lower cost compared to all other cloud platforms for the compute-light outlier detection workload. For the compute-intensive streaming video analytics task, an opensource solution to object detection running on cloud VMs saves on dollar costs compared to proprietary solutions provided by Amazon, Microsoft, and Google, but loses out on latency (up to 6X). If it runs on a low-powered edge device, the latency is up to 49X lower.
Design and Implementation of LSTM Accelerator Based on FPGA

Long Short-Term Memory (LSTM) shows excellent performance in the fields of speech recognition, sentiment analysis, machine translation, etc. In this paper, we designed and implemented a LSTM accelerator based on FPGA. First, in order to reduce data movement and power consumption, a multi-level storage strategy is proposed. Next, we arrange multiple Processing Engine (PE) to realize the parallel operation of the matrix-vector multiplication module, and use the pipeline structure to improve performance. In the Element-wise module, time-division multiplexing is proposed to reduce hardware consumption. Finally, we use the Internet Movie Data Base (IMDB) dataset to train a small-scale LSTM neural network, and implemented our accelerator on Virtex-7 VC707 FPGA at 200MHz. Compared with existing works, the peak performance of accelerator can reach 10.9 GOP/s, which means our accelerator performance is better than existing work.
Towards Sailing supported by Augmented Reality: Motivation, Methodology and Perspectives

Sailing is a multidisciplinary activity that requires years to master. Recently this sustainable sport is becoming even harder due to the increasing number of onboard sensors, automation, artificial intelligence, and the high performances obtainable with modern vessels and sail designs. Augmented Reality technology (AR) has the potential to assist sailors of all ages and experience level and improve confidence, accessibility, situation awareness, and safety. This work presents our ongoing research and methodology for developing AR assisted sailing. We started with the problem definition followed by a state of the art using a systematic review. Secondly, we elicited the main task and variables using an online questionnaire with experts. Third, we extracted the main variables and conceptualized some visual interfaces using 3 different approaches. As final phase, we designed and implemented a user test platform using a VR headset to simulate AR in different marine scenarios. For a real deployment, we witness the lack of available AR devices, so we are developing one specific headset dedicated to this task. We also envision the possible redesign of the entire boat as a consequence of the introduction of AR technology.
Statistical Ineffective Fault Analysis of GIMLI

Statistical Ineffective Fault Analysis (SIFA) was introduced as a new approach to attack block ciphers at CHES 2018. Since then, SIFA has proven to be a powerful attack, with an easy to achieve fault model. One of the main benefits of SIFA is to overcome detection-based and infection-based countermeasures. In this paper we explain how the principles of SIFA can be applied to GiMLi, an authenticated encryption scheme participating the NIST-LWC competition. We identified two possible rounds during the intialization phase of GIMLI to mount our attack. If we attack the first location we are able to recover 3 bits of the key uniquely and the parity of 8 key-bits organized in 3 sums using 180 ineffective faults per biased single intermediate bit. If we attack the second location we are able to recover 15 bits of the key uniquely and the parity of 22 keybits organized in 7 sums using 340 ineffective faults per biased intermediate bit. Furthermore, we investigated the influence of the fault model on the rate of ineffective faults in GIMLI. Finally, we verify the efficiency of our attacks by means of simulation.
The Machine Learning based Optimized Prediction Method for Breast Cancer Detection

Breast Cancer is the most prevalent form of cancer and significant reason for high mortality rates among women. Manual diagnosis of this disease requires long hours & specialists. Therefore an Automated breast cancer diagnosis has been developed to reduce the time taken for diagnosis and decreases the spread of cancer. This paper presents a comparative study of four machine learning algorithms namely Logistic Regression, SVM, KNN and Naive Bayes by calculating their classification accuracy, sensitivity, specificity and other parameters. The different hyper-parameters used for different ML algorithms were manually assigned. Among all algorithms, SVM performed better with the accuracy of about 98.24%.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.
Condensing the solution of support vector machines via radius-margin bound

The paper deals with the problem of how to condense efficiently the used basis vectors in the decision function of support vector machines (SVM). Most existing methods learn the basis vectors and the corresponding coefficients by maximizing the margin between different classes. They ignore the key fact that condensing the solution of SVM is equivalent to constructing the SVM model in the transformation space determined by the basis vectors. Thus, the radius-margin bound, which is related with the generalization ability of SVM, changes with them. In the paper, we propose a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to learn the condensed solution for SVM. The key characteristic of RMB-SSVM is that it employs a criterion that exploits integratedly the margin and the radius of the minimum hypersphere enclosing the data to guide the selecting of the basis vectors and the learning of the corresponding coefficients. Thus, the learning criterion of RMB-SSVM is directly related with the generalization ability of SVM and so it can yield better performance. We develop an effective method to handle the proposed optimization model, and propose a heuristic scheme to select the basis vectors used in the decision function. Further, we explore how to use the maximum pairwise distance over the pairs of data points to approximate the radius in our methodology. This can simplify the proposed model and reduce the training time while not drastically impairing the performance. Finally, we conduct the comprehensive experiments to demonstrate the effectiveness and superiority of the proposed methods by comparing them with the counterparts.
The introduction of solar system using augmented reality technology

Abstract Current technological developments have had a huge impact on education, one of which is Augmented Reality technology. In this study, innovative and alternative media learning was made to understand the types of planets in the solar system. The solar system is one of the materials in natural science subjects in elementary schools. This media learning combines picture cards and virtual reality. Markers found on the card will be captured by the camera of the mobile device, processed and animated pieces will appear on the mobile screen in real-time. By using the concept of combining the real world, real images on the card and virtual, the application can stimulate imagination and a sense of desire and motivation to learn more and more. Planet estimates are made using the 3D Blender application, and the Augmented Reader process is created using Unity and Vuforia Software Development Kit. Based on the results of beta testing using the Likert scale, 86% of the 30 respondents stated that the application developed was effective as a medium for the introduction of the planet.
Spatio-Temporal Convolutional Networks and N-Ary Ontologies for Human Activity-Aware Robotic System

Endowing a companion robot with cognitive abilities to recognize human daily activities, in particular from body skeletons information, is a significant challenge, which needs complex and novel approaches. Recently, most of the proposed approaches exploit the hand-crafted features or the predefined traversal rules techniques to recognize human daily activities from skeleton information, which often lead to the deficit of robustness and generalization. In this work, a novel hybrid framework for human activity-aware robotic system is proposed. In the low-level, a novel Spatio-Temporal Joint based Convolutional Neural Network (STJ-CNN) is proposed to recognize human daily activities in the ambient environments. In the high-level, novel representation and inference services based on Narrative Knowledge Representation Language (NKRL) are proposed to represent and combine the detected human activities with the ambient events, and to infer the semantic context of the detected activity. Empirical experiments on real-world datasets have been conducted, besides an online demonstration created to validate the proposed approach. The final results show that the proposed approach outperforms the baseline models with a significant improvement up to 24% in terms of $F$-score on DAHLIA dataset.
Drones tracking based on robust Cubature Kalman-TBD-Multi-Bernoulli filter

The problem of nonlinear tracking and detection of small unmanned aerial vehicles and micro-drone targets is very challenging and has received great attention recently. Recently, the Cubature Kalman-multi-Bernoulli filter which employs a third-degree spherical-radical cubature rule has been presented to handle the nonlinear models. The Cubature Kalman filter is more principled and accurate in mathematical terms. In addition, a recent multi-Bernoulli filter based on variational Bayesian approximation has been presented with the estimation the fluctuation of variances of measurement. However, Cubature Kalman and variational Bayesian-Multi-Bernoulli filters are unsuitable for tracking the micro-drones because of the unknown probability of detection. As we known, the track-before-detect (TBD) schemes was an effective method for tracking the small objects. In this work, a novel robust Cubature Kalman-Multi-Bernoulli filter with variational Bayesian-TBD is proposed jointing with estimate the fluctuated variances of measurement. The improved filter is an effective method to solve the problem of detection profile estimation for micro-drones. A novel implementation with a non-linear Cubature Kalman Gaussian mixture and Inverse Gamma approximation is presented to estimate a hybrid kinematic state of micro-drones. The simulation results confirm the effectiveness and robustness of the proposed algorithm.
Mis-spending on information security measures: Theory and experimental evidence

Information resources are becoming increasingly important to individuals and organizations, and ensuring their security is a major concern. While research in information security has adopted primarily a quantitative method to determine how and how much to invest in security, most decision makers rely on non-quantitative methods for this purpose, thereby introducing a considerable amount of as yet unexplained subjective judgment to the problem. We use a behavioral decision making approach to investigate factors causing possible inefficiencies of security spending decisions. Decision makers in our experiment performed a series of economic games featuring the key characteristics of a typical security problem. We found several biases in investment decisions. For budgeting their investment between major classes of security measures, decision makers demonstrated a strong bias toward investing in preventive measures rather than in detection and response measures, even though the task was designed to yield the same return on investment for both classes of measures. We term this phenomenon the "Prevention Bias." Decision makers also reacted to security threats when the risk was so small that no investment was economically justified. For higher levels of risk that warranted some security investment, decision makers showed a strong tendency to overinvest. Theoretical and practical implications of the findings are discussed.
Machine Learning in Tourism

Machine Learning is a subset of Artificial Intelligence, which is a process of learning from different types of data to make accurate predictions. Data in tourism is various such as Statistics, Photos, Maps, and Texts. Also, each tourism cycle has different stages: Pre, During, and After Trip. In this paper application of machine learning in tourism related data and trip stages are introduced in detailed.
U-Net versus Pix2Pix: a comparative study on degraded document image binarization

Document image binarization is the process in which pixels in a document image are classified into two groups—foreground and background. This process becomes challenging when it deals with various degradation and noise present in the images. In the recent past, it has been observed that researchers are relying on deep learning-based approaches to solve the problem of document image binarization. Of these, a group of methods considers the segmentation as a pixel-level classification problem, whereas another group considers it as an image-to-image translation problem. We have explored two popular deep learning-based architectures, one from each group, namely, U-Net and Pix2Pix, and presented a comparative assessment of their performances when applied for degraded document image binarization. In this study, no preprocessing or postprocessing methods are applied, which helps us to realize the actual strength of these architectures for the said purpose. For the performance evaluation and comparative assessment, six publicly available standard datasets, namely, document image binarization competition 2013 (DIBCO 2013), H-DIBCO 2014, H-DIBCO 2016, DIBCO 2017, H-DIBCO 2018, and DIBCO 2019, are considered. The performances of these architectures are compared with the best performing methods of the respective binarization competitions, some state-of-the-art nondeep learning-based methods, and some recently published deep learning-based methods separately. The obtained results confirm that in most of the cases U-Net outperforms the Pix2Pix model.
Combining VGG16, Mask R-CNN and Inception V3 to identify the benign and malignant of breast microcalcification clusters

Breast cancer has the highest incidence among women in Taiwan, which is mainly screened by mammography. When the doctor observes the mammogram and initially judges that the patient has malignant microcalcification (MC) clusters, the patient must undergo needle localization surgical biopsy. However, needle localization surgical biopsy makes the patient painful, and the color of breast tissue and MCs are all white, which makes it difficult for doctors to judge where MCs are clustered immediately. Thus, we use VGG16 to find out breast MC clusters from the image. Moreover, we use Mask RCNN to find MCs from the clusters to remove the noise from the background. Finally, we use Inception V3 to identify the benign and malignant of MC clusters. The accuracy of the cluster classification, MCs labeling and benign and malignant analysis are 93%, 95% and 91%. Furthermore, the precision, specificity and sensitivity of our proposed methods are about 87%, 89% and 90%, respectively. It proved that our system can effectively assist doctors in diagnosing and reduce the burden on patients and medical personnel.
Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things

The significant growth of the Internet of Things (IoT) takes a key and active role in healthcare, smart homes, smart manufacturing, and wearable gadgets. Due to complexness and difficulty in processing multimedia data, the IoT based scheme, namely Internet of Multimedia Things (IoMT) exists that is specialized for services and applications based on multimedia data. However, IoMT generated data are facing major processing and privacy issues. Therefore, tensor-based deep computation models proved a better platform to process IoMT generated data. A differentially private deep computation method working in the tensor space can attest to its efficacy for IoMT. Nevertheless, the deep computation model comprises a multitude of parameters; thus, it requires large units of memory and expensive computing units with higher performance levels, which hinders its performance for IoMT. Motivated by this, therefore, the paper proposes a deep private tensor train autoencoder (dPTTAE) technique to deal with IoMT generated data. Notably, the compression of weight tensors to manageable tensor train format is achieved through Tensor Train (TT) network. Moreover, TT format parameters are trained through higher-order back-propagation and gradient descent. We applied dPTTAE on three representative datasets. Comprehensive experimental evaluations and theoretical analysis show that dPTTAE enhances training time efficiency, and greatly improve memory utilization efficiency, attesting its potential for IoMT.