Reviewerly/exHarmony huggingface dataset:
eyeSay: Make Eyes Speak for ALS Patients with Deep Transfer Learning-empowered Wearable

Eye dynamics, a typical expression of brain activities, is an emerging modality for emerging and promising smart health applications. Electrooculogram (EOG) – a natural bio-electric signal generated during eye movements, if decoded, is of great potential to reveal the user’s mind and enable voice-free communication for patients with amyotrophic lateral sclerosis (ALS). ALS patients usually lose physical movement abilities including speech and handwriting but fortunately can move their eyes. In this study, we propose a novel deep transfer learning-empowered system, called "eyeSay", which leverages both deep learning and transfer learning for intelligent eye EOG-to-speech translation. More specifically, we have designed a multi-stage convolutional neural network (CNN) to analyze the eye-written words, named as CNN-word. Moreover, to reveal fundamental patterns of eye movements, we build a transferable feature extractor, CNN-stroke, upon eye strokes that are building components of an eye word. Then, we transfer the CNN-stroke model to the eye word learning task in an innovative way, that is, use CNN-stroke as an additional branch of CNN-word to generate a stroke probability map. The achieved boostCNN-word model, enhanced by the transferable feature extractor, has greatly improved the eye word decoding performance. This novel study will directly contribute to voice-free communications for ALS patients, and greatly advance the ubiquitous eye EOG-based smart health area.
MonoEF: Extrinsic Parameter Free Monocular 3D Object Detection

Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters. The perturbation of objects is very popular in most autonomous driving cases for industrial products. To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation. Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space. By doing so, our 3D detector works independent of the extrinsic parameter variations and produces accurate results in realistic cases, e.g., potholed and uneven roads, where almost all existing monocular detectors fail to handle. Experiments demonstrate our method yields the best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets.
Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream

Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme

The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples. One of the leading class of models are graph convolutional networks (GCNs), which suffer from numerous performance issues. In this paper we present Cleora: a purely unsupervised and highly scalable graph embedding scheme. Cleora can be likened to a GCN stripped down to its most effective core operation - the repeated neighborhood aggregation. Cleora does not require the application of a GPU and can embed massive graphs on CPU only, beating other state-of-the-art CPU algorithms in terms of speed and quality as measured on downstream tasks. Cleora has been applied in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We open-source Cleora under the MIT license allowing commercial use under https://github.com/Synerise/cleora.
Upcycling Systems Design, Developing a Methodology through Design

Design has an important role in shaping the modes of production, consumption and disposal. Decisions made early in the product, service and system development influence the majority of the environmental impact and social consequences. With sustainability emerging as the major challenge of our times, the creation of novel methodologies, economic models and innovative materials is critical. In this paper, we put forward a new methodology that aims to bridge the ecomodernist business-focused circular economy models with the expressive material driven design (MDD) approach. The ‘design out waste methodology’ (DOWM) bridges existing concepts, methods and practices, creating an innovative design and production process that redefines waste and sets it up as a subject of creative study. The purpose of this process is to help designers understand the importance of evaluating the entire life cycle of a product; it also enables local ‘degrowth’ by shifting our modes of production towards a human scale with local makers exchanging knowledge and expressing themselves through upcycled materials, while simultaneously eradicating the very concept of waste. The methodology has been developed in an iterative research-through-design process that combines experiential and tacit knowledge from local case studies with desk research of emerging case studies in MDD.
Deep RED Unfolding Network for Image Restoration

The deep unfolding network (DUN) provides an efficient framework for image restoration. It consists of a regularization module and a data fitting module. In existing DUN models, it is common to directly use a deep convolution neural network (DCNN) as the regularization module, and perform data fitting before regularization in each iteration/stage. In this work, we present a DUN by incorporating a new regularization module, and putting the regularization module before the data fitting module. The proposed regularization model is deducted by using the regularization by denoing (RED) and plugging in it a newly designed DCNN. For the data fitting module, we use the closed-form solution with Faster Fourier Transform (FFT). The resulted DRED-DUN model has some major advantages. First, the regularization model inherits the flexibility of learned image-adaptive and interpretability of RED. Second, the DRED-DUN model is an end-to-end trainable DUN, which learns the regularization network and other parameters jointly, thus leads to better restoration performance than the plug-and-play framework. Third, extensive experiments show that, our proposed model significantly outperforms the-state-of-the-art model-based methods and learning based methods in terms of PSNR indexes as well as the visual effects. In particular, our method has much better capability in recovering salient image components such as edges and small scale textures.
Design and Research of UAV For Campus Express Delivery

This paper discusses the feasibility of using Unmanned Aerial Vehicle (UAV) in in the delivery of university campus, and carries out innovative design on the modeling of UAV express. In the first place, investigate and analyze the problems of express delivery on campus. In the second place, the overall framework of campus distribution platform system is proposed. Finally, through the system division of labor, the function of express UAV is clarified. Combined with the SET factor analysis method, the opportunity gap in the product design of delivery UAV is found. Meanwhile, the mind mapping method is used to analyze and study the modeling, function, color and material of UAV, so as to clarify the feasibility direction of design. According to this, innovative design can be carried out on the shape of university campus UAV. The application of Express UAV in university, it can achieve intelligent, rapid and safe delivery process. It means that not only the pressure of delivery is relieved in university and the efficiency is improved, but also it can enrich the fun of campus life of students, and provide a certain reference for the popularization and promotion of the application of UAV.
Query Response Time in Blockchain Using Big Query Optimization

Blockchain is the core of computer technology. It is cryptographically endangered distributed and parallelized database (DB) knowledge for storing and transmitting facts. Each tuple in the DB broadly called a block and covers such as the query timestamp and a link to the previous block. Blockchain and Big data are two expertises in full swing then again they are also two complementary expertise. We can study the how the Blockchain executes big query analysis. Many pursuits query only require the traditional approach of ranking a set of matching results. However, some queries require a more complex approach, either because they are broad or ambiguous. It is important for a DB search engine to identify such queries along with to differentiate broad queries from ambiguous ones. In this chapter, we propose that there is a diversity of indications that the Hadoop-based DB search engine is used. This allows the Hadoop DB search engine to explore and improve the query as correct relevant. The experiment result is measured by the query response time (RT) for optimized queries and broad queries.
Emotions During Covid-19: LSTM Models for Emotion Detection in Tweets

Deep learning techniques for emotion detection in micro-blogs are a relatively less explored area of research. This paper investigates the performance of Long Short-Term Memory (LSTM) networks in detecting emotions from English tweets that relate to the Covid-19 pandemic. The two proposed LSTM models viz. Simple LSTM and EmoLex Boost LSTM use a corpus of streaming tweets and train the networks to detect emotions in tweets. Simple LSTM architecture comprises two hidden layers and a fully connected layer with softmax activation. EmoLex Boost LSTM uses the NRC emotion lexicon to enhance the Simple LSTM architecture. Emotion classification experiments were conducted to test both LSTM models. While the Simple LSTM model shows an accuracy of 60.57% when trained for 30 epochs, the EmoLex Boost model shows an enhanced accuracy of 61.75% when trained for 30 epochs, and 63.09% when trained for 50 epochs. Both deep learning models identify emotions in tweets but do not compute their valence. Since a tweet can convey multiple emotions, the annotated emotion labels in the training set tend to be subjective or fuzzy. This adversely impacts the performance scores of models. The results of our experiments, however, are promising and motivate further research in deep learning models that compute the valence of emotion(s).
Quantum partial least squares regression algorithm for multiple correlation problem

Partial least squares (PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares (QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory (QRAM) during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n , the dependent variable dimension w , and the number of variables m . This algorithm achieves exponential speed-ups over the PLS regression on n , m , and w . In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.
Differential Privacy and the 2020 US Census

In 2017 the US Census Bureau announced that it would update the statistical methods it used to meet its statutory obligation to protect the confidentiality of information furnished to the agency.The new system would be based on an approach called "differential privacy" that was invented in 2006 in the context of cryptography.Unlike the Census Bureau's previous system-a technique based on swapping responses from easy-to-identify residential units-the confidentiality protection afforded by differential privacy involves adding carefully structured random values (called "noise") to every intermediate computation, and then implementing "postprocessing" algorithms to make the noise-infused data resemble the legacy data produced by the previous Census Bureau methods. 1 Although for decades the Census Bureau intentionally added error to the decennial census tables and publicly released microdata to protect the privacy of respondents, it had never revealed how much error was added nor the exact mechanism.For 2020, the Census Bureau decided to engage stakeholders directly and tell data users how much error would be introduced.As the Census Bureau raced against the clock to implement the new system, thousands of stakeholders became alarmed that differential privacy might introduce so much randomness into the published data that they would be unusable for many purposes, including drawing new districts for the US House of Representatives as well as academic research in demography and economics.In the interest of transparency, the Census Bureau produced multiple Demonstration Data Products (DDP) using the 2010 Census data.Still, the bureau's Disclosure Avoidance System (DAS) team made significant changes to the system between the release of the final DDP and the official redistricting statistics for the 2020 Census.The Census Bureau never released information about how it had decided on the "privacy loss budget" for the 2020 Census Redistricting File.
Study different quantum teleportation amounts by solving Lindblad master equation

Abstract A realizable model based on the interaction between an excited two-level atom and a radiation field inside two quantum electrodynamics cavities is proposed. It consists of sending the excited atom through two serial cavities which contain the radiation field. Thus, the Lindblad master equations which describe the evolution of the reduced density matrix regarding the radiation field generated from the excited atom inside the cavities are solved in Markovian and non-Markovian regimes. Thereby, the rate of entanglement inherent in the total field-field system is evaluated using various witnesses by calculating analytically the concurrence and quantum discord, where we illustrate quantitatively the advantage of using an initial EPR and NOON states in the presence of radiation field losses. As an application, a scheme of quantum teleportation using two partial entangled channels is investigated. Finally, a comparative study between fidelity and the different levels of entanglement of the teleported state in the two regimes is also given.
Quantum true random number generation on IBM’s cloud platform

Random numbers are the lifeline of any cryptographic operation in modern computing. Quantum True Random Number Generator (QTRNG) can yield real random data to replace random-looking periodic sequences. Here, we present the fastest QTRNG on factual Quantum Computational Devices (QCD) locally on our device to save time. To construct a random number generator, the IBM Q Experience called Qiskit is used. The ease of our source Hadamard gate pooled with its verifiably sole randomness is imperative for attaining quantum random number generators at no cost. The inner matrix product of the square of the Hadamard gate is proved to be identified in the quantum lab. Applying the H gate twice on the quantum register cancelled out the H gate pair. It acted as a single Hadamard gate that was practically visualised using the IMB Q experience. The 24-qubit random number is experimented with and investigated in this work. The min-entropy of such generated random numbers is 0.0007244 with the worst-case entropy value of 0.999507. Besides, subsequent verification of genuine randomness was confirmed by steering restart experimentation, & the statistical properties of TRNG were evaluated over autocorrelation study, National Institute of Standards and Technology (NIST) SP 800-90B & 800-22 tests.
Lévy flight and chaos theory-based gravitational search algorithm for mechanical and structural engineering design optimization

Abstract The main aim of this article is to explore the real-life problem-solving potential of the proposed Lévy flight-based chaotic gravitational search algorithm (LCGSA) for the minimization of engineering design variables of speed reducer design (SRD), three bar truss design (TBTD), and hydrodynamic thrust bearing design (HTBD) problems. In LCGSA, the diversification of the search space is carried out by Lévy flight distribution. Simultaneously, chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Moreover, the penalty function method has been used to deal with the non-linear and fractional design constraints. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plot analysis. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, eleven heuristic algorithms were employed for comparative analysis of the simulation results. The simulation outcomes clearly show that LCGSA provides better values for TBTD and HTBD benchmarks than standard GSA and most of the competing algorithms. Besides, all the participating algorithms, including LCGSA, have the same results for the SRD problem. On the qualitative side, LCGSA has successfully resolved entrapment in local minima and convergence issues of standard GSA.
Conflict-Driven Inductive Logic Programming

Abstract The goal of inductive logic programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns answer set programs (ASP). Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. Early versions of ILASP can be considered meta-level ILP approaches, which encode a learning task as a logic program and delegate the search to an ASP solver. More recently, ILASP has shifted towards a new method, inspired by conflict-driven SAT and ASP solvers. The fundamental idea of the approach, called Conflict-driven ILP (CDILP), is to iteratively interleave the search for a hypothesis with the generation of constraints which explain why the current hypothesis does not cover a particular example. These coverage constraints allow ILASP to rule out not just the current hypothesis, but an entire class of hypotheses that do not satisfy the coverage constraint. This article formalises the CDILP approach and presents the ILASP3 and ILASP4 systems for CDILP, which are demonstrated to be more scalable than previous ILASP systems, particularly in the presence of noise.
Patent data access control and protection using blockchain technology

Abstract The purposes are to develop the patent data profoundly, control the data access process effectively, and protect the patent information and content. The traditional patent review systems are analyzed. For the present patent data security and privacy protection technologies and algorithms, the patent information data are stored on different block nodes after data fragmentation using blockchain technology. Then the data are shared using the data encryption algorism. In this way, data access control can be restricted to particular users. Finally, a patent data protection scheme based on privacy protection is proposed. The security of the scheme and the model performance are verified through simulation experiments. The time required to encrypt 10 MB files with 64-bit and 128-bit data is 35 ms and 105 ms, respectively. The proposed re-encryption algorithm only needs 1 s to decrypt 64 KB data, and only 1% of the data needs asymmetric encryption. This greatly reduces the computational overhead of encryption. Results demonstrate that the system can effectively control the access methods of users, efficiently protect the personal privacy and patent content of patent applicants, and reduce the patent office cloud computing overhead using the local resources of branches. The distributed storage methods can reduce the cloud system interaction of the patent office, thereby greatly improving the speed of encryption and ensuring data security. Compared with the state of the art methods, the proposed patent data access and protection system based on blockchain technology have greater advantages in data security and model performance. The research results can provide a research foundation and practical value for the protection and review systems of patent data.
A Confidential QR Code Approach with Higher Information Privacy

In present times, barcode decoders on mobile phones can extract the data content of QR codes. However, this convenience raises concerns about security issues when using QR codes to transmit confidential information, such as e-tickets, coupons, and other private data. Moreover, current secret hiding techniques are unsuitable for QR code applications since QR codes are module-oriented, which is different from the pixel-oriented hiding manner. In this article, we propose an algorithm to conceal confidential information by changing the modules of the QR Code. This new scheme designs the triple module groups based on the concept of the error correction capability. Additionally, this manner can conceal two secret bits by changing only one module, and the amount of hidden confidential information can be twice the original amount. As a result, the ordinary data content (such as URL) can be extracted correctly from the generated QR code by any barcode decoders, which does not affect the readability of scanning. Furthermore, only authorized users with the secret key can further extract the concealed confidential information. This designed scheme can provide secure and reliable applications for the QR system.