Reviewerly/exHarmony huggingface dataset:
Ultra-Fast Modular Multiplication Implementation for Isogeny-Based Post-Quantum Cryptography

Supersingular isogeny key encapsulation (SIKE) protocol delivers promising public and secret key sizes over other post-quantum candidates. However, the huge computations form the bottleneck and limit its practical applications. The modular multiplication operation, which is one of the most computationally demanding operations in the fundamental arithmetics, takes up a large part of the computations in the protocol. In this paper, we propose an improved unconventional-radix finite-field multiplication (IFFM) algorithm which reduces the computational complexity by about 20% compared to previous algorithms. We then devise a new high-speed modular multiplier architecture based on the IFFM. It is shown that the proposed architecture can be extensively pipelined to achieve a very high clock speed due to its complete feedforward scheme, which demonstrates significant advantages over conventional designs. The FPGA implementation results show the proposed multiplier has about 67 times faster throughput than the state-of-the-art designs and more than 12 times better area efficiency than previous works. Therefore, we think that these achievements will greatly contribute to the practicability of this protocol.
Robust Incremental State Estimation Through Covariance Adaptation

Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this letter, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.
Passive UHF RFID-based User Interface on a Wooden Surface

We present a passive ultra-high frequency (UHF) radio frequency identification (RFID)-based human-technology interface platform. The platform comprises of two dipole antennas and three integrated circuits (IC), each with a unique ID. The platform, which is fixed on a wooden table by cutting the antennas and antenna-IC interconnections from copper tape, can be used for push button and swipe controlling. Each IC can be activated, i.e., connected to the antennas, by touching with finger. As the RFID reader can be connected to any application through WIFI, these ICs can act as wireless input points integrated into furniture, items, and textiles, where they can be used as inputs to desired digital actions. The platform allows all connected devices to be controlled accurately and effortlessly, which will take the convenience of implementation and utilization of these systems to a new level. As a preliminary trial, the platform was tested by two people giving 200 random inputs and 98% and 99% success rates were achieved. Based on these results, this type of passive RFID-based solutions could be used for administrating interfaces that would administer wide variety of interaction modalities, such as touch or tangible interaction on flat surfaces (e.g., tabletop surfaces, walls, doors).
Face Recognition of Identical Twins Based On Support Vector Machine Classifier

Face recognition and biometrics are based on a specific and unique person identification. This procedure is completely dependent on matching the image and other individual image for generating a recognizable proof. This is the one of the best approaches to perform the correlation by choosing facial attributes from the image and from a facial database. In facial recognition calculations ought to have the option to recognize the comparative looking people or ready to isolate the identical twins utilizing face recognition with precision classification. The extract attributes were classified utilizing SVM classifier. The input image of an individual is first identified to be twins or not founded on classification techniques. SVM could be used for both regression and classification challenges. It is commonly used in taxonomy problems. SVM is less computationally intensive and generalizable. Therefore, it would be extensively studied and rapidly developed in recent years.
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation

Convolutional neural networks are state- of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time. To overcome this issue, we introduce a network structure for volumetric data without 3D convolutional layers. The main idea is to include maximum intensity projections from different directions to transform the volumetric data to a sequence of images, where each image contains information of the full data. We then apply 2D convolutions to these projection images and lift them again to volumetric data using a trainable reconstruction algorithm. The proposed network architecture has less storage requirements than network structures using 3D convolutions. For a tested binary segmentation task, it even shows better performance than the 3D U-net and can be trained much faster.
Surface features can deeply affect artificial grammar learning

Three experiments explored the extent to which surface features explain discrimination between grammatical and non-grammatical strings in artificial grammar learning (AGL). Experiment 1 replicated Knowlton and Squire’s (1996) paradigm using either letter strings as in the original study, or an analogous set of color strings to further explore if learning was affected by type of stimuli. Learning arose only with letter strings, but the results were mostly due to the discrimination of non-grammatical strings containing highly salient illegal features. Experiments 2 and 3 tested a new grammar devised to control for those features. Experiment 2 showed reduced grammar learning effects, and again only for letter materials. Experiment 3 explored the effect of additional practice with letter stimuli, and found increased learning only in the spaced practice condition, though additional practice also produced more explicit knowledge. These findings call for further research on the boundary conditions of learning in AGL paradigms.
Light field reconstruction using hierarchical features fusion

Light field imagery has attracted increasing attention for its capacity of simultaneously capturing intensity values of light rays from multiple directions. Such imagery technique has become widely accessible with the emergence of consumer-grade devices, e.g. Lytro, and the Virtual Reality (VR) / Augmented Reality (AR) areas. Light field reconstruction is a critical topic to mitigate the trade-off problem between the spatial and angular resolutions. Learning-based methods have attained outstanding performance among the recently proposed methods, however, the state-of-the-art methods still suffer from heavy artifacts in the case of occlusion. This is likely to be a consequence of failure in capturing the semantic information from the limited spatial receptive field during training. It is crucial for light field reconstruction to learn semantic features and understand a wider context in both the angular and spatial dimensions. To address this issue, we introduce a novel end-to-end U-Net with SAS network (U-SAS-Net) to extract and fuse hierarchical features, both local and semantic, from a relatively large receptive field while establishing the relation of the correlated sub-aperture images. Experimental results on extensive light field datasets demonstrate that our method produces a state-of-the-art performance that exceeds the previous works by more than 0.6 dB PSNR with the fused hierarchical features, especially the semantic features for handling scenes with occlusion and the local features for recovering the rich details. Meanwhile, our method is at a substantially lower cost which takes 48% parameters and less than 10% computation of the previous state-of-the-art method.
Quantum Boltzmann machine algorithm with dimension-expanded equivalent Hamiltonian

Quantum machine learning explores the devising and implementing of quantum software to enable the speedup for a classical machine learning counterpart, which has attracted extensive attention. The quantum Boltzmann machine is a promising quantum machine learning model and can be executed on near-term quantum devices to demonstrate quantum supremacy. We investigate an efficient algorithm for training a quantum Boltzmann machine based on a continuous-time quantum cluster Monte Carlo method realized with a dimension-expanded equivalent Hamiltonian. The proposed algorithm is capable of efficiently training various types of quantum Boltzmann machines, including a two-dimensional random transverse Ising Hamiltonian and other stochastic $\ensuremath{\kappa}$-local $(\ensuremath{\kappa}\ensuremath{\ge}2)$ Hamiltonians. We further apply a quantum mean-field method to speed up the training process through discarding the quantum fluctuations of visible-hidden interactions. The experiment results show that our algorithm-trained quantum Boltzmann machine is more efficient on a small and large data set in terms of network convergence and time complexity.
A Fast and Accurate Method for Calculating the Center of Gravity of Polygonal Interval Type-2 Fuzzy Sets

Defuzzification plays an important role in the applications of interval type-2 fuzzy sets (IT2FSs). However, computational complexity of existing defuzzification methods has turned this procedure into an important bottleneck toward the use of IT2FSs. It has been proved that the Nie-Tan method is an accurate discretizing-based method for calculating the center of gravity (COG) of IT2FSs. In this article, we propose a fast and accurate method for calculating the COG-Nie-Tan defuzzification-of polygonal IT2FSs on discrete and continuous domains without the need of discretization. The proposed method calculates the accurate COG for trapezoidal and triangular IT2FSs as special cases of polygonal IT2FSs. Moreover, any IT2FS can be approximated by a polygonal IT2FS-the higher the order of polygonal IT2FS, the better the approximation. Hence, using the proposed method on an IT2FS that is approximated by an appropriate polygonal IT2FS results in a more accurate COG.
Secure data deduplication for Internet-of-things sensor networks based on threshold dynamic adjustment

Large amount of data are being produce by Internet-of-things sensor networks and applications. Secure and efficient deduplication of Internet-of-things data in the cloud is vital to the prevalence of Internet-of-things applications. In order to ensure data security for deduplication, different data should be assigned with different privacy levels. We propose a deduplication scheme based on threshold dynamic adjustment to ensure the security of data uploading and related operations. The concept of the ideal threshold is introduced for the first time, which can be used to eliminate the drawbacks of the fixed threshold in traditional schemes. The item response theory is adopted to determine the sensitivity of different data and their privacy score, which ensures the applicability of data privacy score. It can solve the problem that some users care little about the privacy issue. We propose a privacy score query and response mechanism based on data encryption. On this basis, the dynamic adjustment method of the popularity threshold is designed for data uploading. Experiment results and analysis show that the proposed scheme based on threshold dynamic adjustment has decent scalability and practicability.
Adaptive single image dehazing method based on support vector machine

A dehazing method often only shows good results when processing the image for a certain haze concentration. So an adaptive hazy image dehazing method based on SVM is proposed. The innovation points are as follows: Firstly, combining the characteristics of the degraded images of haze weather, the dark channel histogram and texture features of the input images are extracted to form the feature vectors. These are trained by supervised learning through SVM algorithm to realize automatic binary classification of images; Secondly, the defined dehazing methods are called to process the classified result as a hazy image and the same quality evaluation indexes are used to evaluate each image output by different dehazing methods. Then, it outputs the highest evaluation image after haze removal. Finally, the output image is classified again by SVM until the image reaches the clearest it can be. The experimental results show that the proposed algorithm exhibits good contrast, brightness and color saturation from the visual effect. Also the scene adaptability and robustness of the algorithm are improved.
A survey on quantum positioning system

The quantum positioning system (QPS) takes advantage of the entanglement property of the quantum system itself, and it is possible to breakthrough the limitation of the positioning accuracy due to signal bandwidth and power limitation in classical radio-based global positioning system (GPS), and improve the positioning performance. In this paper, a comprehensive survey of the research status of QPS and its several key components are presented, including the quantum entanglement states preparation, the quantum satellite technique, the optical signal acquisition, tracking and pointing (ATP) system, and the quantum positioning and clock synchronization technique. We also discuss the current challenges and future research directions at the end of the paper.
ATT-BM-SOM: A Framework of Effectively Choosing Image Information and Optimizing Syntax for Image Captioning

The current challenges of image captioning technology are how to make the generated captions closely relate to the image information, and generated captions are highly syntactically readable. Therefore, we focus on two problems: 1) how to correctly choose semantic and visual information in an image, 2) how to optimize the syntactic structure of captions, and improve the readability of the syntax. However, the existing work lacks attention to these issues. To solve these problems, we propose an image captioning framework based on the attention balance mechanism and the syntax optimization module, namely ATT-BM-SOM. This model realizes effective fusion of image information and generates high-quality captions, which makes up for the lack of image information selection and syntax readability. Experiments show that our model achieves excellent performance on the MS COCO dataset. Compared with the baseline models, our model performed the best results of 78.1, 58.4, 119.2, and 24.7 on BLUE-1, ROUGE-L, CIDER, and SPICE, respectively.
IoT Enabled Prescription Reading Smart Medicine Dispenser Implementing Maximally Stable Extremal Regions and OCR

The concept of the Internet of Things (IoT) integrated with embedded system and new technologies in the healthcare sector has opened a new era. The natural decrease in physical condition of senior citizens with aging prompts and expansion in frequencies of different diseases, for that reason they need to take medicine on time to improve their health conditions. In this research work we are focusing on the circumstances of senior citizens, we have proposed an IoT enabled smart medicine box equipped with camera for scanning the prescription. After the system scans the prescription through camera, a number of preprocessing techniques are applied on the prescription for better extraction of information. Following that, we applied Maximally Stable Extremal Regions (MSER). Later, string manipulation is done on the extracted text and relevant information is uploaded in the database. Afterwards, our medicine box uses the information to notify the patient using buzzer and shows the medication information on the LCD display. A patient has to verify his identity using fingerprint to take the medicine. Finally, the medicine box dispenses specific medicines and updates the consumption time in the database. Our system will also notify the patient if the dispenser is getting low on medicine using the display by showing message.
Towards a Blockchain-Based Decentralized Reputation System for Public Fog Nodes

The omnipresence of Internet of Things (IoT) devices nowadays has resulted in a large amount of data transferred regularly to the cloud. This continuous data transfer degrades the application performance in terms of latency, bandwidth consumption, connectivity, security, privacy, user experiences, and energy efficiency. Fog computing brings cloud services closer to the IoT devices. In public settings, variety of handheld and IoT devices need to connect to public fog nodes in order to access localized compute, storage, and networking capabilities. Therefore, the reputations of publicly available fog nodes become critical. Maintaining a reputation score is one of the popular techniques to ensure trust for fog nodes. This paper introduces a blockchain-based solution to establish trust in public fog nodes that provides services for IoT devices in a decentralized manner. Our proposed solution exploits blockchain smart contracts to compute the reputation in a decentralized manner by capturing and analyzing its past interactions with IoT devices. The solution also penalizes IoT devices that may collude to provide dishonest reputation scores.
CPSX: Using AI-Machine Learning for Mapping Human-Human Interaction and Measurement of CPS Teamwork Skills

The objective of this work is to present a machine learning (ML) -based framework to identify evidence about collaborative problem solving (CPS) cognitive (teamwork) and social-emotional learning (SEL) skills from the dyadic (human-human-HH) interactions. This work extends our previous work (Chopade et al. IEEE HST 2018, LAK2019) [1], [2]. Explicitly, we are interested in how teamwork skills and team dynamics are demonstrated as verbal and nonverbal behaviors, and how these behaviors can be captured and analyzed via passive data collection. For this work we use a two-player cooperative CPS game, Crisis in Space (CIS) from LRNG (Previously GlassLab Inc). During the summer of 2018, we implemented this CIS game for interns as a group study. A total of 34 participants played the game and provided study and survey data. During the study, we collected participants' game play data, such as audio, video and eye tracking data streams. This research involves analyzing CIS multimodal game data, and developing skill models, and machine learning techniques for CPS skills measurement. In this paper, we present our ML framework for the analysis of audio data along with preliminary results from a pilot study. The analysis of audio data uses natural language processing (NLP) techniques, such as bag-of-words and sentence embedding. Our preliminary results show that various NLP features can be used to describe successful and unsuccessful CPS performances. The ML based framework supports the development of evidence centered design for teamwork skills-mapping and aims to help teams operate effectively in a complex situation. Potential applications of this work include support for the Department of Homeland Security (DHS), and the US Army for the development of learner and team centric training, cohort, and team behavioral skill-mapping.
You Sank My Battleship! A Case Study to Evaluate State Channels as a Scaling Solution for Cryptocurrencies

Off-chain protocols (or so-called Layer 2) are heralded as a scaling solution for cryptocurrencies. One prominent approach, state channels, allows a group of parties to transact amongst themselves and the global blockchain is only used as a last resort to self-enforce any disputed transactions. To evaluate state channels as a scaling solution, we provide a proof of concept implementation for a two-player battleship game. It fits a category of applications that are not considered reasonable to execute on the blockchain, but it is widely perceived as an ideal application for off-chain protocols. We explore the minimal modifications required to deploy the battleship game as a state channel and propose a new state channel construction, Kitsune, which combines features from existing constructions. While in the optimistic case we demonstrate the battleship game can be played efficiently in a state channel, the requirement for unanimous off-chain agreement introduces new economic and time-based attacks that can render the game as unreasonable to play.
The TrieJax Architecture

Graph pattern matching (e.g., finding all cycles and cliques) has become an important component in domains such as social networks, biology and cyber-security. In recent years, the database community has shown that graph pattern matching problems can be mapped to an efficient new class of relational join algorithms.
A Survey on Breast Cancer Diagnosis Methods and Modalities

Breast cancer is one of the most prevalent cancer types existing in women. The probability of saving a breast cancer patient is majorly dependant on the early stage detection and initiation of treatment. The unawareness about the importance of early detection of symptoms of the heterogeneous varieties of breast cancers and the lack of training in breast cancer symptom identification are the major reasons for the peak mortality rate due to breast cancer. To address these challenges, this paper is intended to provide a glimpse about the most common breast cancer varieties along with its staging and the available detection methodologies and modalities. This survey provides the details of each of the breast cancer detection methodologies and modalities, comparison of its cost versus accuracy and the insights into effectiveness and usefulness of each of the breast cancer detection methodologies and modalities with respect to the breast cancer type and staging. This survey comprehends the state of the art in breast cancer detection, and provides valuable insights into the future direction of research and technology development.