Reviewerly/exHarmony huggingface dataset:
KEHKey

For kinetic-powered body area networks, we explore the feasibility of converting energy harvesting patterns for device authentication and symmetric secret keys generation continuously. The intuition is that at any given time, multiple wearable devices harvest kinetic energy from the same user activity, such as walking, which allows them to independently observe a common secret energy harvesting pattern not accessible to outside devices. Such continuous KEH-based authentication and key generation is expected to be highly power efficient as it obviates the need to employ any extra sensors, such as accelerometer, to precisely track the walking patterns. Unfortunately, lack of precise activity tracking introduces bit mismatches between the independently generated keys, which makes KEH-based authentication and symmetric key generation a challenging problem. We propose KEHKey, a KEH-based authentication and key generation system that employs a compressive sensing-based information reconciliation protocol for wearable devices to effectively correct any mismatches in generated keys. We implement KEHKey using off-the-shelf piezoelectric energy harvesting products and evaluate its performance with data collected from 24 subjects wearing the devices on different body locations including head, torso and hands. Our results show that KEHKey is able to generate the same key for two KEH-embedded devices at a speed of 12.57 bps while reducing energy consumption by 59% compared to accelerometer-based methods, which makes it suitable for continuous operation. Finally, we demonstrate that KEHKey can successfully defend against typical adversarial attacks. In particular, KEHKey is found to be more resilient to video side channel attacks than its accelerometer-based counterparts.
Visual analytics of taxi trajectory data via topical sub-trajectories

GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectory data. However, previous methods generally ignore trajectory directions that are important in the analysis of movement patterns. In this paper, we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories. We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories. The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.
Entanglement production and convergence properties of the variational quantum eigensolver

We perform a systematic investigation of variational forms (wave-function Ans\"atze), to determine the ground-state energies and properties of two-dimensional model fermionic systems on triangular lattices (with and without periodic boundary conditions), using the variational quantum eigensolver (VQE) algorithm. In particular, we focus on the nature of the entangler blocks which provide the most efficient convergence to the system ground state inasmuch as they use the minimal number of gate operations, which is key for the implementation of this algorithm in noisy intermediate-scale quantum computers. Using the concurrence measure, the amount of entanglement of the register qubits is monitored during the entire optimization process, illuminating its role in determining the efficiency of the convergence. Finally, we investigate the scaling of the VQE circuit depth as a function of the desired energy accuracy. We show that the number of gates required to reach a solution within an error $\ensuremath{\varepsilon}$ follows the Solovay-Kitaev scaling, $O[{log}_{10}^{c}(1/\ensuremath{\varepsilon})]$, with an exponent $c=1.31\ifmmode\pm\else\textpm\fi{}0.13$.
An efficient and high quality medical CT image enhancement algorithm

Abstract Aiming at the process of medical diagnosis, many problems such as unclear images and low contrast are often caused by noise and interference in the process of medical image acquisition and transmission. This article proposes a new image enhancement method that combines the wavelet domain with the spatial domain. First, we input two identical images (Both of the identical images are original images.) in which the first image is enhanced by histogram equalization. Then, the two images are divided into four sub‐images by a two‐dimensional wavelet transform. The average of the low‐frequency coefficients of the low‐frequency sub‐images of the two images is taken as the low‐frequency coefficients of the final reconstruction. Second, aiming at the problem that the contrast may be too low, the fourth high‐frequency sub‐image is blurred (sharpened) twice. The fourth high‐frequency sub‐image after blurring is denoised by median filtering. Finally, the four sub‐images are fused to obtain the enhanced image. The experimental results show that the peak signal‐to‐noise ratio, structural similarity, and processing time of the proposed algorithm are better than those of other contrast algorithms, especially the processing time. These objective indicators show that the proposed algorithm can not only effectively suppress noise but also significantly enhance the contrast. Subjectively, compared with other algorithms, the proposed algorithm achieves a better visual effect and greatly reduces the processing time.
Importance-weighted conditional adversarial network for unsupervised domain adaptation

In the construction of expert and intelligent systems, annotating and curating large datasets is very expensive; hence, there is a need to transfer the knowledge from existing annotated datasets to unlabeled data. However, data that are relevant for a specific application usually differ from publicly available datasets because they are sampled from a different domain. Domain adaptation (DA) has emerged as an efficient technique to compensate for such a domain shift. Recent studies have suggested that deep adversarial networks can achieve promising results for DA problems. However, existing adversarial DA methods assign equal importance to different examples and ignore the effect of difference in source domain samples or noise on adversarial performance. Moreover, most DA methods only focus on reducing the distribution difference, but not to learn a good target domain model. To address these issues, we propose an importance-weighted conditional adversarial (IWCA) network for unsupervised DA. In this study, an importance criterion based on domain similarity and prediction certainty is proposed to assign weights to different samples, which can reduce the harmful effects of difficult-to-transfer samples when reducing their cross-domain class conditional distribution differences. Furthermore, a sample selection criterion derived from the perspective of transfer cross validation is used to progressively select appropriate pseudo-labeled target samples to fine-tune the target model. These two criteria work in an EM-like manner that alternating align class conditional distribution for weighted samples and progressively select certain pseudo-labeled target samples to fine-tune the joint model. In this manner, the network will gradually generate features that approximate the actual conditional distribution of the target domain. The results of extensive experiments conducted on four datasets show that IWCA outperforms several state-of-the-art deep DA methods.
The Structure of Cross-Linguistic Differences: Meaning and Context of ‘Readability’ and its Russian Equivalent ‘Chitabelnost’

The article presents the results of an original study aimed at finding (1) frequency fluctuations of the term ‘readability’ in American discourse and its Russian equivalent ‘chitabelnost’ in Russian discourse over the period from 1920s to the present; and (2) semantic similarities and differences between the English term ‘readability’ and its Russian equivalent ‘chitabelnost’ over the same period of time. A contrastive analysis of the words testified to inconsiderable differences in the semantic structures of the terms in the period under study: the term ‘readability’ has been used with the following meanings: (1) ‘the quality of being legible or decipherable’ and (2) ‘the quality of being easy or enjoyable to read’. The Russian equivalent ‘chitabelnost’ has two contemporary meanings similar to the aforementioned English meanings as well as the obsolete ‘library book checkouts’. With the help of the Google NgramViewer, we identified the 1980s frequency peak of both terms when the modern notion of the concepts was formed. The research into the topical context of readability as ‘the quality of being easy or enjoyable to read’ demonstrated empiricist tendencies in American studies focused on two types of parameters, i.e. the ‘objective’ parameters of texts, i.e. sentence length, word counts, number of high/low frequency words, ratio of high/low frequency words to total words, sentence complexity, etc. and ‘individual’ variables affecting a potential reader, such as ‘word familiarity’, cognitive and linguistic abilities, cultural and topic knowledge, etc. The Russian school’s view, until the 1970s, had traditionally been more holistic and ‘biased’ towards an individuals’ factors. The results of the study have the potential to contribute to cross-linguistic research in the area of text readability assessment, semantics, and scientific literature searches.
Digitalization of the educational environment as a factor of personal and professional self-determination of students

О. Н. Томюк, М
“Helping Nemo!”—Using Augmented Reality and Alternate Reality Games in the Context of Universal Design for Learning

This article reports on the main experiences gained from a teaching intervention which utilised the alternate reality game ‘Helping Nemo’ in an augmented reality environment for formative assessment. The purpose of the study was to explore the ways in which the affordances arising from the combination of alternate reality games and augmented reality, situated in the context of Universal Design for Learning, might facilitate students’ learning amongst the aspects of engagement, participation, and response to students’ variability. The study took place in a public primary school located in a rural area of Cyprus. A second-grade class consisting of 24 students aged 7–8 years old was selected to comprise the sample. A qualitative research approach was adopted. The data collection methods included classroom observations and focus groups with the students. Findings gained from the teaching intervention suggest that the creation of a multimodal environment that draws on the principles of Universal Design for Learning and combines the affordances of alternate reality games and augmented reality for formative assessment contributes towards higher levels of engagement and participation in learning of all students, including bilingual students, students with learning disabilities, and students who are currently disengaged.
Handling of advanced persistent threats and complex incidents in healthcare, transportation and energy ICT infrastructures

Abstract In recent years, the use of information technologies in Critical Infrastructures is gradually increasing. Although this brings benefits, it also increases the possibility of security attacks. Despite the availability of various advanced incident handling techniques and tools, there is still no easy, structured, standardized and trusted way to manage and forecast interrelated cybersecurity incidents. This paper introduces CyberSANE, a novel dynamic and collaborative, warning and response system, which supports security officers and operators to recognize, identify, dynamically analyse, forecast, treat and respond to security threats and risks and and it guides them to handle effectively cyber incidents. The components of CyberSANE are described along with a description of the CyberSANE data flow. The main novelty of the CyberSANE system is the fact that it enables the combination of active incident handling approaches with reactive approaches to support incidents of compound, highly dependent Critical Information Infrastructures. The benefits and added value of using CyberSANE is described with the aid of a set of cyber-attack scenarios.
NEWLSTM: An Optimized Long Short-Term Memory Language Model for Sequence Prediction

The long short-term memory (LSTM) model trained on the universal language modeling task overcomes the bottleneck of vanishing gradients in the traditional recurrent neural network (RNN) and shows excellent performance in processing multiple tasks generated by natural language processing.Although LSTM effectively alleviates the vanishing gradient problem in the RNN, the information will be greatly lost in the long distance transmission, and there are still some limitations in its practical use.In this paper, we propose a new model called NEWLSTM, which improves the LSTM model, and alleviates the defects of too many parameters in LSTM and the vanishing gradient.The NEWLSTM model directly correlates the cell state information with current information.The traditional LSTM's input gate and forget gate are integrated, some components are deleted, the problems of too many LSTM parameters and complicated calculations are solved, and the iteration time is effectively reduced.In this paper, a neural network model is used to identify the relationship between input information sequences to predict the language sequence.The experimental results show that the improved new model is simpler than traditional LSTM models and LSTM variants on multiple test sets.NEWLSTM has better overall stability and can better solve the sparse words problem.
Fixed poin sets in digital topology, 1

&lt;p&gt;In this paper, we examine some properties of the fixed point set of a digitally continuous function. The digital setting requires new methods that are not analogous to those of classical topological fixed point theory, and we obtain results that often differ greatly from standard results in classical topology.&lt;/p&gt;&lt;p&gt;We introduce several measures related to fixed points for continuous self-maps on digital images, and study their properties. Perhaps the most important of these is the fixed point spectrum F(X) of a digital image: that is, the set of all numbers that can appear as the number of fixed points for some continuous self-map. We give a complete computation of F(C&lt;sub&gt;n&lt;/sub&gt;) where C&lt;sub&gt;n&lt;/sub&gt; is the digital cycle of n points. For other digital images, we show that, if X has at least 4 points, then F(X) always contains the numbers 0, 1, 2, 3, and the cardinality of X. We give several examples, including C&lt;sub&gt;n&lt;/sub&gt;, in which F(X) does not equal {0, 1, . . . , #X}.&lt;/p&gt;&lt;p&gt;We examine how fixed point sets are affected by rigidity, retraction, deformation retraction, and the formation of wedges and Cartesian products. We also study how fixed point sets in digital images can be arranged; e.g., for some digital images the fixed point set is always connected.&lt;/p&gt;
Feedback Recurrent Autoencoder

In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be learned. We demonstrate its effectiveness in speech spectrogram compression. Specifically, we show that the FRAE, paired with a powerful neural vocoder, can produce high-quality speech waveforms at a low, fixed bitrate. We further show that by adding a learned prior for the latent space and using an entropy coder, we can achieve an even lower variable bitrate.
R-Dedup: Secure client-side deduplication for encrypted data without involving a third-party entity

Deduplication technology is used extensively in applications such as cloud computing services to optimize their storage performance (e.g., cloud service providers store only one copy of identical data). However, due to the use of encryption (to ensure the confidentiality of data stored in the cloud), it can be challenging for cloud service providers to perform deduplication (e.g., due to the use of different keys on the same content). In this paper, we propose a randomized, secure, cross-user deduplication scheme (R-Dedup). The scheme does not involve any third-party entity (e.g., an additional cloud server) or require assistance from other users. In R-Dedup, the randomness feature means that users with identical copies of a file share the same random value via ElGamal encryption. The security analysis and experimental results demonstrate that R-Dedup is lightweight, and achieves both data privacy and data integrity.
Cellular automaton decoders for topological quantum codes with noisy measurements and beyond

Abstract We propose an error correction procedure based on a cellular automaton, the sweep rule, which is applicable to a broad range of codes beyond topological quantum codes. For simplicity, however, we focus on the three-dimensional toric code on the rhombic dodecahedral lattice with boundaries and prove that the resulting local decoder has a non-zero error threshold. We also numerically benchmark the performance of the decoder in the setting with measurement errors using various noise models. We find that this error correction procedure is remarkably robust against measurement errors and is also essentially insensitive to the details of the lattice and noise model. Our work constitutes a step towards finding simple and high-performance decoding strategies for a wide range of quantum low-density parity-check codes.
An Efficient and Scalable Platform for Java Source Code Analysis Using Overlaid Graph Representations

Although source code programs are commonly written as textual information, they enclose syntactic and semantic information that is usually represented as graphs.This information is used for many different purposes, such as static program analysis, advanced code search, coding guideline checking, software metrics computation, and extraction of semantic and syntactic information to create predictive models.Most of the existing systems that provide these kinds of services are designed ad hoc for the particular purpose they are aimed at.For this reason, we created ProgQuery, a platform to allow users to write their own Java program analyses in a declarative fashion, using graph representations.We modify the Java compiler to compute seven syntactic and semantic representations, and store them in a Neo4j graph database.Such representations are overlaid, meaning that syntactic and semantic nodes of the different graphs are interconnected to allow combining different kinds of information in the queries/analyses.We evaluate ProgQuery and compare it to the related systems.Our platform outperforms the other systems in analysis time, and scales better to program sizes and analysis complexity.Moreover, the queries coded show that ProgQuery is more expressive than the other approaches.The additional information stored by ProgQuery increases the database size and associated insertion time, but these increases are significantly lower than the query/analysis performance gains obtained.
A Revisit of Direct and Diffuse Solar Radiation in China Based on Homogeneous Surface Observations: Climatology, Trends, and Their Probable Causes

Abstract Increasing energy and food demands require an understanding of not only the availability and variability of total solar radiation ( R ) but also its partitioning into direct ( R d ) and diffuse ( R f ) components, which are key elements for solar power generation and plant photosynthesis, respectively. Potential inhomogeneities in surface solar radiation observations, however, add uncertainties to the published patterns and trends of R d and R f over China. To exclude inhomogeneous time series and outliers, a data quality control process has been applied to the surface observations of R d and R f . The homogeneous data show that, in response to the global dimming and brightening phenomena in R , decadal changes can also be observed in the R d and R f trends over China with the actual transition occurring in the mid‐2000s. Since the 1960s, R d dimming has mainly distributed over eastern China with intensive anthropogenic activities, with recoveries especially from 2008 onward over regions with high R d energy potential. R f , on the other hand, had further enhanced since the 1960s especially over the high R f proportion areas but has decreased in the recent decade over the northern region. The summer season shows the most significant decadal trends in R d and R f . Under clear‐sky conditions, that is, ruling out cloud effects, the transitions in R d and R f trends become more remarkable, pointing to atmospheric aerosols as the main driving force. In addition, dramatic R d and R f variations are observed in the subsequent years after large volcanic eruptions. The varying R d and R f proportions are in line with recent governmental control on SO 2 emissions over China.
Improving Low-Resource Speech Recognition Based on Improved NN-HMM Structures

The performance of the ASR system is unsatisfactory in a low-resource environment. In this paper, we investigated the effectiveness of three approaches to improve the performance of the acoustic models in low-resource environments. They are Mono-and-triphone Learning, Soft One-hot Label and Feature Combinations. We applied these three methods to the network architecture and compared their results with baselines. Our proposal has achieved remarkable improvement in the task of mandarin speech recognition in the hybrid hidden Markov model - neural network approach on phoneme level. In order to verify the generalization ability of our proposed method, we conducted many comparative experiments on DNN, RNN, LSTM and other network structures. The experimental results show that our method is applicable to almost all currently widely used network structures. Compared to baselines, our proposals achieved an average relative Character Error Rate (CER) reduction of 8.0%. In our experiments, the size of training data is ~10 hours, and we did not use data augmentation or transfer learning methods, which means that we did not use any additional data.
Extended Motion Diffusion-Based Change Detection for Airport Ground Surveillance

Change detection in airport ground is important for airport security. Due to the particularity of ground environment, e.g. haze and camouflage, airport ground change detection is generally incomplete. If an incomplete detection is used as reference for the detection in subsequent frames, it may result in noticeable detection defects across the frames. In this paper, extended motion diffusion (EMD) is proposed to address the problems. The core idea of the EMD is to design a novel model insensitive to incomplete detection. Firstly the one-to-many correspondence in traditional motion diffusion is extended in the prediction step of EMD to build up correspondence from incomplete detection to intact objects. Prior information, e.g. aircraft motion prior and ground structure prior, is employed in the development of the correspondence. Then based on the correspondence a number of new samples are synthesized and filtered in the identification step of the EMD to compensate possible detection defects. Finally, the reserved samples are collected to train a foreground model, which is used in conjunction with another background model for classification. The proposed method is verified based on the Airport Ground Video Surveillance (AGVS) benchmark. Experimental results show effectiveness of the proposed algorithm in dealing with haze and camouflage.