Reviewerly/exHarmony huggingface dataset:
Further analysis of bent functions from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e20" altimg="si29.svg"><mml:mi mathvariant="script">C</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e25" altimg="si30.svg"><mml:mi mathvariant="script">D</mml:mi></mml:math> which are provably outside or inside <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e30" altimg="si31.svg"><mml:…

In early nineties Carlet (1994) introduced two new classes of bent functions, both derived from the Maiorana–McFarland (M) class, and named them C and D class, respectively. Apart from a subclass of D, denoted by D0 by Carlet, which is provably outside two main (completed) primary classes of bent functions, little is known about their efficient constructions. More importantly, both classes may easily remain in the underlying M class which has already been remarked in Mandal et al. (2016). Assuming the possibility of specifying a bent function f that belongs to one of these two classes (apart from D0), the most important issue is then to determine whether f is still contained in the known primary classes or lies outside their completed versions. In this article, we further elaborate on the analysis of the set of sufficient conditions given in Zhang et al. (2017) concerning the specification of bent functions in C and D which are provably outside M. It is shown that these conditions, related to bent functions in class D, can be relaxed so that even those permutations whose component functions admit linear structures still can be used in the design. It is also shown that monomial permutations of the form x2r+1 have inverses which are never quadratic for n>4, which gives rise to an infinite class of bent functions in C but outside M. Similarly, using a relaxed set of sufficient conditions for bent functions in D and outside M, one explicit infinite class of such bent functions is identified. We also extend the inclusion property of certain subclasses of bent functions in C and D, as addressed initially in Carlet (1994), Mandal et al. (2016) that are ultimately within the completed M class. Most notably, we specify other generic and explicit subclasses of D, denoted by Dk⋆ for k∈{1,…,n−2}, whose members are bent functions provably outside the completed M class.
Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems

Signa1 modulation recognition (SMR) is an essential and challenging topic in orthogonal frequency-division multiplexing (OFDM) systems, and also it is the fundamental technique for signal detection and recovery. However, traditional feature extraction based SMR methods cannot effectively acquire the characteristics of the OFDM signals. Hence, the modulated OFD-M signal cannot be reliably identified. In this paper, we propose a deep learning (DL) based SMR method for recognizing OFDM signals, which is combined with a convolutional neural network (CNN) trained on in-phase and quadrature (IQ) samples. In the network model, the batch normalization (BN) layer and dropout layer are used to speed up model training and prevent overfitting, respectively. Three convolution layers with different convolution kernels perform well than traditional feature extraction methods in obtaining intrinsic properties of OFDM signals. The same number of multiple modulated signals are mixed and sent to the trained model for identification. Experiments are conducted to show that the method we proposed performs better than the traditional methods, mainly reflected in a higher probability of correct classification (PCC) and better consistency.
Whitening for Self-Supervised Representation Learning

Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance (positives) are contrasted with instances extracted from other images (negatives). For the learning to be effective, many negatives should be compared with a positive pair, which is computationally demanding. In this paper, we propose a different direction and a new loss function for SSL, which is based on the whitening of the latent-space features. The whitening operation has a scattering effect on the batch samples, avoiding degenerate solutions where all the sample representations collapse to a single point. Our solution does not require asymmetric networks and it is conceptually simple. Moreover, since negatives are not needed, we can extract multiple positive pairs from the same image instance. The source code of the method and of all the experiments is available at: this https URL.
A Cross-cultural Perspective for Personalizing Picture Passwords

Picture passwords, which require users to draw selections on images as their secret password, typically provide globalized solutions without taking into consideration that people across diverse cultures exhibit differences within interactive systems. Aiming to shed light on the effects of culture towards users' interactions within picture password schemes, we conducted a between-subjects cross-cultural (Eastern vs. Western) study (n=67). Users created a password on a picture illustrating content highly related to their daily-life experiences (culture-internal) vs. a picture illustrating the same daily-life experiences, but in a different cultural context (culture-external). Results revealed that people across cultures exhibited differences in visual processing, comprehension, and exploration of the picture content prior to making their password selections. The observed differences can be accounted by considering sociocultural theories highlighting the holistic preference of Eastern populations compared to the analytic preference of Western populations. Qualitative data also triangulate the findings by exposing the likeability and users' engagement towards the picture content familiar to individual's culture. Findings underpin the necessity to consider cultural differences in the design of personalized picture passwords.
A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

A contactless method based on infrared image matching is presented for zero-value insulator detection on outdoor porcelain insulator string. An improved SIFT (scale-invariant feature transformation) method is developed to extract features and accomplish the pre-matching between the insulator string to be detected and the standard string in the image library. An improved RANSAC (random sampling consistency) algorithm is developed to remove mismatching points and achieve more accurate and faster detection. Firstly, an adaptive circle window is designed to improve the sensitivity of the SIFT operator on arc features extraction from insulators. Then, the dimension of the feature is decreased from128 to 32 by dividing the circle into 4 fan-shaped regions and descript the feature on 8 directions to achieve the lower computation burden and high accuracy. For the SIFT method and RANSAC method uses Euclidean distance to measure the similarity between features extracted from the string to be detected and the string in the standard library, mismatching may be caused. Spatial geometric features of the neighborhood around the feature point are used to construct the error function to improve the RANSAC method and remove the mismatching points. Faster matching is obtained by a threshold control for decreasing the number of data checking for the consensus set data models. Testing results show that the presented method is effective in the detection of zero-value resistances for outdoor insulator string. Compare with the SIFT method and SIFT + RANSAC method, the presented method has higher accuracy and faster detection speed.
Optimized Digital Transformation in Government Services with Blockchain

The blockchain concept relies on cryptography that links the blocks and each block holds the cryptographic hash of a previous block and timestamp along with data to be transferred. Blockchain is an emerging technology which has gained interest from various sources like energy industries, startups, financial institutions, supply firms, national and international governments, and so on. These sources are aiming, from different backgrounds such as voting, taxation and land registration, to identify blockchain, which has potential drives to bring substantial welfares and innovation. The blockchain helps people to keep data secure by allowing health record data to be moved whenever they need without any illegal activities like forgery, theft and malice. The chapter provides information on the latest digital payments. It focuses on the operative mechanism of Bitcoin, blockchain technology and describes the scope of this application. Bitcoins are transactions stored in encrypted form with certain conditions for financial transactions.
Blockchain-based IoT platform for autonomous drone operations management

The growing number of unmanned aerial vehicles (UAVs), typically referred to as drones, poses new challenges on how to manage their operations in various internet of things (IoT) use cases such as surveillance and monitoring, weather prediction, agriculture, etc. The latter includes a massive number of devices that sometimes produce invalid messages due to lack of energy or system shutdown and needs to be autonomously monitored with drones in rural areas. In this paper, we develop a blockchain-based platform for managing drone IoT operations while maintaining trust and security. The test-bed consists of IoT devices, a drone and blockchain-enabled gateways through which drones are controlled to replace malfunctioning devices. The latter are detected using Z-score observation algorithm which launches a smart contract and sends the drone with clear operation order. The results obtained in realistic agriculture use case highlight the utility of our proposition in decreasing signaling and operation time, improving the percentage of successful maintenance operations and providing trust and security when managing drones in an autonomous manner.
Users' Knowledge Use and Change during Information Searching Process: A Perspective of Vocabulary Usage

One of the key questions in studies of Search as Learning is how to represent and measure the intangible and invisible learning processes that occur during the search process. In this study, participants are presented with two tasks and asked to represent their current relevant knowledge in a mind map. Participants then perform the search tasks and modify the mind maps as they search. In this paper we report on the use of measurement of vocabulary that is added to or removed from the mind map as a proxy for knowledge change in the search process. Mind maps represent user's knowledge with content in the tree structure. We examined the effect of users' prior knowledge upon their query behaviors by comparing users' pre-search mind map vocabulary with their query terms; and investigate how the content pages users read during search affected their knowledge change during search by comparing pre- and post-search mind maps. Our results demonstrated that users' prior knowledge played an important role in their query formulation. More than 50% of query terms came from users' prior knowledge, which accounted for nearly 40% of pre-search mind map vocabulary. As the search process proceeded, users added new vocabulary to their mind maps, and about 1/3 of new vocabulary was present in and copied directly from content pages. This study shows how prior knowledge and search results contribute to users' learning during information searching, and can help us better understand how learning occurred in the information searching process.
Automatic Dialogic Instruction Detection for K-12 Online One-on-One Classes

Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.
A Dynamic Deep Neural Network for Multimodal Clinical Data Analysis

Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that (1) are robust with respect to missing and wrong values, and (2) can deal with highly variable-sized lists and long-term dependencies of individual diagnosis, procedures, measurements and medication prescriptions. In this work, we elaborate limitations of fully-connected neural networks and classical machine learning methods in this context and propose AdaptiveNet, a novel recurrent neural network architecture, which can deal with multiple lists of different events, alleviating the aforementioned limitations. We employ the architecture to the problem of disease progression prediction in rheumatoid arthritis using the Swiss Clinical Quality Management registry, which contains over 10.000 patients and more than 65.000 patient visits. Our proposed approach leads to more compact representations and outperforms the classical baselines.
An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% – 29.3%), on minimum resources (3.9% – 25.2% less) while majority were meeting their deadlines (93.21% – 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.
Batch Reading and Interrupted Interpretation of Digital Screening Mammograms Without and With Tomosynthesis

Objective To compare batch reading and interrupted interpretation for modern screening mammography. Methods We retrospectively reviewed digital mammograms without and with tomosynthesis that were originally interpreted with batch reading or interrupted interpretation between January 2015 and June 2017. The following performance metrics were compared: recall rate (per 100 examinations), cancer detection rate (per 1,000 examinations), and positive predictive values for recall and biopsy. Results In all, 9,832 digital mammograms were batch read, yielding a recall rate of 9.98%, cancer detection rate of 4.27, and positive predictive values for recall and biopsy of 4.40% and 35.5%, respectively. There were 49,496 digital mammograms that were read with interrupted interpretation, yielding a recall rate of 11.3%, cancer detection rate of 4.44, and positive predictive values for recall and biopsy of 3.92% and 30.1%, respectively. Of the digital mammograms with tomosynthesis, 7,075 were batch read, yielding a recall rate of 6.98%, cancer detection rate of 5.37, and positive predictive values for recall and biopsy of 7.69% and 38.0%, respectively. Of the digital mammograms with tomosynthesis, 24,380 were read with interrupted interpretation, yielding a recall rate of 8.30%, cancer detection rate of 5.41, and positive predictive values for recall and biopsy of 6.52% and 33.3%, respectively. For both digital mammograms without and with tomosynthesis, recall rates improved with batch reading compared with interrupted interpretation (P < .001), but no significant differences were seen for other metrics. Discussion Batch reading digital mammograms without and with tomosynthesis improves recall rates while maintaining cancer detection rates and positive predictive values compared with interrupted interpretation.
Vehicle detection in thermal images with an improved yolov3-tiny

In driver assistance systems, thermal cameras are often used because they can provide compensation information for other sensors in the case of darkness or glare. For many existing thermal image vehicle detection algorithms, they can get a good detection accuracy in some occasions but the detection speed is relatively slow so they can't meet the real-time requirements. In order to address this problem, we proposed a real-time thermal image vehicle detection algorithm based on yolov3-tiny. We have made two major improvements to the original yolov3-tiny. The first is to recalculate the anchor box priors by running k-means clustering algorithm on the bounding boxes of training dataset to make the network easier to learn. The second is to deepen the network structure of the original yolov3-tiny, so that it can better extract the characteristics of the vehicle in thermal images, so as to improve the vehicle detection accuracy. Our experimental results show that the mean Average Precision (mAP) of our proposed method is 6% higher than the origin yolov3-tiny while maintaining the detection speed comparable to the original yolov3-tiny.
Simultaneous measurement of double surfaces of transparent lenses with phase measuring deflectometry

The phase measuring deflectometry is a promising method for the high precision measurement of optical freeform surfaces. However, it fails to measure the double surfaces of transparent lenses, because the fringes reflected from both the upper and the lower surfaces are superimposed in the captured images. An effective method is proposed to solve this problem by displaying a set of fringe patterns with different frequencies and retrieving the fringe phases associated with both surfaces. The form of the lower surface is reconstructed using a hybrid reflection-refraction model. Experimental results demonstrate that the proposed method can reliably reconstruct both surfaces of a lens, making deflectometry capable of measuring transparent optics with high accuracy.
A novel feature selection approach based on clustering algorithm

Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. In many data mining issues, the dataset contains a large number of fields and, therefore, the identification of the effective fields is an important issue. Appling the proposed algorithm, the important variables of the dataset would be identified. In the proposed method, the dataset is clustered in several stages and in each step the characteristics of the created clusters are examined and the features that transform the structure of clusters are introduced as effective features of the dataset. The proposed method was examined on 4 datasets and the results of this method were compared with other similar work and demonstrated that using this algorithm would eliminate redundant and unrelated features of the dataset and improve classification accuracy.
Clairvoyance

Reentrancy bugs in smart contracts caused a devastating financial loss in 2016, considered as one of the most severe vulnerabilities in smart contracts. Most of the existing general-purpose security tools for smart contracts have claimed to be able to detect reentrancy bugs. In this paper, we present Clairvoyance, a cross-function and cross-contract static analysis by identifying infeasible paths to detect reentrancy vulnerabilities in smart contracts. To reduce FPs, we have summarized five major path protective techniques (PPTs) to support fast yet precise path feasibility checking. We have implemented our approach and compared Clairvoyance with three state-of-the-art tools on 17770 real-worlds contracts. The results show that Clairvoyance yields the best detection accuracy among all the tools.
Musical wisdom teaching strategy under the internet + background

The modernization of our country has opened a new era, and the “Internet +” national strategy has gone deep into various areas of people’s livelihood. Faced with many changes in the new situation, a variety of emerging information technologies continue to integrate into the classroom, such as Baidu teaching, Whiteboard, IPAD, flipping classroom, and learning space and so on, which makes the teacher’s teaching method and organization form have very big changes. There is a certain “flip” in the classroom indeed. This study explores the connotation and characteristics of music intelligent teaching and the main factors that affect the formation of music teachers’ intelligent teaching under the background of “Internet +” education. In this paper, the related strategies of the formation of wisdom teaching are applied through the design of the auxiliary system, in order to optimize the traditional teaching mode by making full use of the network technology, improve the teaching efficiency, and train more and more music talents for the country.
Accelerating Post-Quantum Cryptography using an Energy-Efficient TLS Crypto-Processor

Post-quantum cryptography (PQC) is currently a growing area of research and NIST PQC Round 2/3 schemes are being actively analyzed and optimized for both security and efficiency.In this work, we re-purpose the cryptographic accelerators in an energy-efficient pre-quantum TLS cryptoprocessor to implement post-quantum key encapsulation schemes SIKE, Kyber, Frodo and ThreeBears and signature scheme SPHINCS + through software-hardware co-design.We utilize the modular arithmetic unit inside the elliptic curve cryptography accelerator to implement SIKE, while we use the AES and SHA2 hardware primitives to substitute SHA3 computations and accelerate the other three protocols.We accelerate the most computationally expensive components of these PQC protocols in hardware, thereby achieving up to an order of magnitude improvement in energy-efficiency over software implementations.