Reviewerly/exHarmony huggingface dataset:
An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

IFTS)Nth quantile discretization approach (NQDA) Forecasting A B S T R A C TTraditional time series methods can predict seasonal problems, but not problems with transferred linguistic data.Thus, a forecasting method for such problems is required.However, existing intuitionistic fuzzy time series forecasting methods lack persuasiveness in determining the degree of hesitation and the lengths of intervals.Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series.This paper proposes the weighted intuitionistic fuzzy time series model based on the Nth quantile discretization approach (NQDA).The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data.In the experimental section, the forecasts of several data sets are made for model validation.Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.
Building an Arabic Flight Booking Dialogue System Using a Hybrid Rule-Based and Data Driven Approach

Approaches for developing Dialogue Systems (DSs) are typically categorized into rule-based and data-driven. Data-driven DSs require a massive quantity of training data, while rule-based DSs rely on a predefined set of rules and keywords that are to be detected in the user's utterances. The data-driven approaches show more promising results, but owing to a lack of available training data for Arabic task-oriented DSs, development of an Arabic task-oriented DS has typically been conducted using the rule-based approach. In this article, we propose a hybrid rule-based and data-driven approach in a text-based flight booking DS capable of handling customer's utterances. The proposed DS was built through utilizing the Wit.ai natural language interface. The conversation flow was configured using the Wizard of Oz technique, and the DS intents and entities were developed through the use of crowdsourcing training examples. The evaluation results show that the system developed was able to understand user utterances and to self-feed efficiently.
Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography.Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal.From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC).The parity-check matrix is the key to the encoding and extraction process of STC.In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix.Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position.The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height.To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover.The experimental results show that the proposed method outperforms the state-ofthe-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis

Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a complete solution for ABSA. We construct two machine reading comprehension (MRC) problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing. We conduct experiments on these subtasks, and results on several benchmark datasets demonstrate the effectiveness of our proposed framework, which significantly outperforms existing state-of-the-art methods.
Still going strong

In “Must ...stay ...strong!” (von Fintel and Gillies in Nat Lang Semant 18:351–383, 2010. https://doi.org/10.1007/s11050-010-9058-2 ), we set out to slay a dragon, or rather what we called The Mantra: that epistemic must has a modal force weaker than expected from standard modal logic, that it doesn’t entail its prejacent, and that the best explanation for the evidential feel of must is a pragmatic explanation. We argued that all three sub-mantras are wrong and offered an explanation according to which must is strong, entailing, and the felt indirectness is the product of an evidential presupposition carried by epistemic modals. Mantras being what they are, it is no surprise that each of the sub-mantras have been given new defenses. Here we offer them new problems and update our picture, concluding that must is (still) strong.
Model Reverse-Engineering Attack against Systolic-Array-Based DNN Accelerator Using Correlation Power Analysis

A model extraction attack is a security issue in deep neural networks (DNNs). Information on a trained DNN model is an attractive target for an adversary not only in terms of intellectual property but also of security. Thus, an adversary tries to reveal the sensitive information contained in the trained DNN model from machine-learning services. Previous studies on model extraction attacks assumed that the victim provides a machine-learning cloud service and the adversary accesses the service through formal queries. However, when a DNN model is implemented on an edge device, adversaries can physically access the device and try to reveal the sensitive information contained in the implemented DNN model. We call these physical model extraction attacks model reverse-engineering (MRE) attacks to distinguish them from attacks on cloud services. Power side-channel analyses are often used in MRE attacks to reveal the internal operation from power consumption or electromagnetic leakage. Previous studies, including ours, evaluated MRE attacks against several types of DNN processors with power side-channel analyses. In this paper, information leakage from a systolic array which is used for the matrix multiplication unit in the DNN processors is evaluated. We utilized correlation power analysis (CPA) for the MRE attack and reveal weight parameters of a DNN model from the systolic array. Two types of the systolic array were implemented on field-programmable gate array (FPGA) to demonstrate that CPA reveals weight parameters from those systolic arrays. In addition, we applied an extended analysis approach called "chain CPA" for robust CPA analysis against the systolic arrays. Our experimental results indicate that an adversary can reveal trained model parameters from a DNN accelerator even if the DNN model parameters in the off-chip bus are protected with data encryption. Countermeasures against side-channel leaks will be important for implementing a DNN accelerator on a FPGA or application-specific integrated circuit (ASIC).
The birth of a science

Physics, biology, chemistry, for example, do not already only study programs in many universities but are at the forefront of the development of science, and that is science itself. The development of the basic foundation becomes the mother of science, such as mathematics, as a suspicion of the birth of new sciences. The interests related to the life and welfare of humans always invites the possibility of the birth of science in concepts, but mathematics confirms it. In certain cases, as an application, or it gives birth to answers in technology form, which is distorted according to the social demands of society. Every science has its methodology, which is based on the logic that helps to reason. A natural model is an approach to generating methods, as well as the principles that develop in artificial intelligence based on mathematics. This principle not only changes the way of perceiving data, but it also establishes new definitions of data, and changes statistics, optimization, and other fields. Each science also has implications for other sciences but also has applications that are also related to other sciences. Every science supports technology to improve social welfare. Historically, century by century, many new scientific fields were born, and it has evidence from the publication of scientific works to books as scientific standards. Then, the teaching curricula supported its initial dissemination and its development through research with evidence of scientific publications either through papers in proceedings of scientific meetings or articles in the journals. A study program about science in the related faculty is mandatory, and it is as the spearhead in further scientific and technological development. So everything starts and ends at one point, namely the birth of new science, as the birth of data science. This paper describes the birth of science by involving historical traces. The reflection of scientific development is starting from the foundation to become a scientific field, namely data science. It also illustrates the roadmap of a scientific indirectly.
Security analysis and improvement of source independent quantum random number generators with imperfect devices

Abstract A quantum random number generator (QRNG) as a genuine source of randomness is essential in many applications, such as number simulation and cryptography. Recently, a source-independent quantum random number generator (SI-QRNG), which can generate secure random numbers with untrusted sources, has been realized. However, the measurement loopholes of the trusted but imperfect devices used in SI-QRNGs have not yet been fully explored, which will cause security problems, especially in high-speed systems. Here, we point out and evaluate the security loopholes of practical imperfect measurement devices in SI-QRNGs. We also provide corresponding countermeasures to prevent these information leakages by recalculating the conditional minimum entropy and adding a monitor. Furthermore, by taking into account the finite-size effect, we show that the influence of the afterpulse can exceed that of the finite-size effect with the large number of sampled rounds. Our protocol is simple and effective, and it promotes the security of SI-QRNG in practice as well as the compatibility with high-speed measurement devices, thus paving the way for constructing ultrafast and security-certified commercial SI-QRNG systems.
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos

Taking advantage of human pose data for understanding human activities has attracted much attention these days. However, state-of-the-art pose estimators struggle in obtaining high-quality 2D or 3D pose data due to occlusion, truncation and low-resolution in real-world un-annotated videos. Hence, in this work, we propose 1) a Selective Spatio-Temporal Aggregation mechanism, named SST-A, that refines and smooths the keypoint locations extracted by multiple expert pose estimators, 2) an effective weakly-supervised self-training framework which leverages the aggregated poses as pseudo ground-truth in-stead of handcrafted annotations for real-world pose estimation. Extensive experiments are conducted for evaluating not only the upstream pose refinement but also the downstream action recognition performance on four datasets, Toyota Smarthome, NTU-RGB+D, Charades, and Kinetics-50. We demonstrate that the skeleton data refined by our Pose-Refinement system (SSTA-PRS) is effective at boosting various existing action recognition models, which achieves competitive or state-of-the-art performance.
Blockchain in eCommerce

As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions. As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions.
Use of SHDM in commutative watermarking encryption

Abstract SHDM stands for Sphere-Hardening Dither Modulation and is a watermarking algorithm based on quantizing the norm of a vector extracted from the cover work. We show how SHDM can be integrated into a fully commutative watermarking-encryption scheme and investigate implementations in the spatial, DCT, and DWT domain with respect to their fidelity, robustness, capacity, and security of encryption. The watermarking scheme, when applied in the DCT or DWT domain, proves to be very robust against JPEG/JPEG2000 compression. On the other hand, the spatial domain-based approach offers a large capacity. The increased robustness of the watermarking schemes, however, comes at the cost of rather weak encryption primitives, making the proposed CWE scheme suited for low to medium security applications with high robustness requirements.
Semantic-SCA: Semantic Structure Image Inpainting With the Spatial-Channel Attention

Deep learning has brought unprecedented progress to image inpainting. However, the existing methods often generate images with blurry textures and distorted structures because they may either fail to maintain semantic consistency or restore fine-grained textures. In this paper, we propose a two-stage adversarial model to further improve the accuracy of the structure and details of image inpainting. Our model splits the inpainting task into two parts: semantic structure reconstructor and texture generator. In the first stage, we first utilize the semantic structure map based on the unsupervised segmentation to train the semantic structure reconstructor, which completes the missing structures of the inputs and maintains consistency between the missing part and the overall image. In the second stage, we introduce the spatial-channel attention (SCA) module to obtain the fine-grained textures. The SCA module strengthens the capability to obtain information from the long-distance pixel and different channels of the model. Furthermore, we propose a spatial-channel loss to stabilize the network training process and improve visual effects. Finally, we evaluate our model over the publicly available datasets CelebA, Places2, and Paris StreetView. When the inpainting tasks involved in large-area defects or heavy structure, the experimental results show that our method has a higher inpainting quality than the existing state-of-the-art approaches.
A subspace code of size 333 in the setting of a binary q-analog of the Fano plane

We show that there is a binary subspace code of constant dimension 3 in ambient dimension 7, having minimum subspace distance 4 and cardinality 333, i.e., \begin{document}$ 333 \le A_2(7, 4;3) $\end{document} , which improves the previous best known lower bound of 329. Moreover, if a code with these parameters has at least 333 elements, its automorphism group is in one of 31 conjugacy classes. This is achieved by a more general technique for an exhaustive search in a finite group that does not depend on the enumeration of all subgroups.
Bottleneck Transformers for Visual Recognition

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision
Generalized Fuzzy <b><i>c</i></b>-Means Clustering and its Property of Fuzzy Classification Function

This study shows that a generalized fuzzy c -means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c -means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits a behavior similar to that of both standard and exponential fuzzy c -means clustering.
Generative adversarial network-based rogue device identification using differential constellation trace figure

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure generation process is initially employed to transform RF fingerprint features from time-domain waveforms to two-dimensional figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A universal software radio peripheral receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.
Customer Centricity in Medical Affairs Needs Human-centric Artificial Intelligence

The evolution of healthcare, together with the changing behaviour of healthcare professionals, means that medical affairs functions of pharmaceutical organisations are constantly reinventing themselves. The emergence of digital ways of working, expedited by the COVID-19 pandemic, means that pharmaceutical–healthcare relationships are evolving to operate in an increasingly virtual world. The value of the pharmaceutical medical affairs function is dependent on understanding customers' needs and providing the right knowledge at the right time to physicians. This requires a human-centric artificial intelligence (AI) approach for medical affairs, which allows the function to query internal and external data sets in a conversational format and receive timely, accurate and concise intelligence on their customers.
Innovative Dual-Binary-Field Architecture for Point Multiplication of Elliptic Curve Cryptography

High-performance Elliptic Curve Cryptography (ECC) implementation in encryption authentication severs has become a challenge due to the explosive growth of e-commerce's demand for speed and security. Point multiplication (PM) is the most common and complex operation in ECC which directly determines the performance of the whole system. This article proposes a 6CC-6CC (clock cycle) dual-field PM architecture and a 6CC-4CC dual-field PM architecture based on maximizing utilization of Karatsuba multipliers and re-ordering schedule strategy in PM. The Montgomery Ladder algorithm used in PM is modified to a 4CC algorithm for better resource utilization and parallel computation. To solve the frequency drop problem while working on large finite field, the PM architectures for high and low field are carefully studied to have universal critical path length and balanced performance. Both of the architectures are implemented over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">571</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">283</sup> ) on Xilinx Virtex-5 and Virtex-7 FPGAs (Field-Programmable Gate Array) for comparison. The 6CC-6CC architecture is shown to have the best performance on GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">571</sup> ), which achieves one PM operation in $17.44~\mu s$ using 81549 LUTs (Look-Up-Table) with the frequency of 197.2 MHz on Virtex-5, and $12.55~\mu s$ using 80970 LUTs with the frequency of 274.1 MHz on Virtex-7. The 6CC-4CC architecture performs better on GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">283</sup> ) with the shortest computation time. It takes only $3.21~\mu s$ to finish one PM operation on Virtex-5 and $2.22~\mu s$ on Virtex-7, which are faster than all the previous designs. The implementation results prove that the proposed architectures have state-of-the-art performance as well as higher versatility for ECC designs.
Design and Implement of Dual Axis Solar Tracker System Based Arduino

The computer control plays important role in the solar cell design and development of dual axis solar tracker for the sun's position. The main goal of this paper is to maximize energy output to reduce panel temperature (cooling), to increase efficiency of the PV panel. Small-scale solar is developed through a complete hardware and software in order to function accurately. The main parts in this work are Arduino Uno R3, kit relay, LDR (Light Dependent Resistor), LM35 (temperature sensor), high-efficiency solar panel and satellite motor. The Protuse software is used with (Arduino Uno) as embedded computer control. The results show the effectiveness of the complete tracking system.