Reviewerly/exHarmony huggingface dataset:
Testing imaging confocal microscopy, laser scanning confocal microscopy, and focus variation microscopy for microscale measurement of edge cross-sections and calculation of edge curvature on stone tools: Preliminary results

The application of micro- and nanotechnology, adopted from engineering and materials sciences, is proving valuable in studying stone tool surfaces. Measurement systems for surface characterization have been coupled with parameters that allow for the mathematical characterization of stone tool surfaces. For this project, a Sensofar S neox imaging confocal microscope (ICM)/focus variation microscope (FVM) and an Olympus LEXT OLS4100 laser scanning confocal microscope (LSCM) are used to mathematically document the edge cross-sections on five chipped stone tools made from basalt, chert, obsidian, and quartz. Based on the surface documentation of edge cross-sections, a new algorithm for the calculation of edge curvature at multiple scales is tested. Results indicate the confocal systems experience difficulty documenting edge cross-sections. However, focus variation microscopy can mathematically document edge cross-sections on stone tools made from basalt and chert, thus permitting the calculation of edge curvature over multiple scales of measurement. Microscale documentation of edge cross-sections and calculation of edge curvature will be helpful in understanding the relationships between tool edges, edge sharpness, edge attrition, and microwear. Comparisons between these three measurement systems (ICM, LSCM, FVM) provide information necessary for further method development in edge cross-section measurement and the potential to generate more robust edge analysis protocols.
On Detecting Hidden Wireless Cameras: A Traffic Pattern-based Approach

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose DeWiCam, a lightweight and effective detection mechanism using smartphones. The basic idea of DeWiCam is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, DeWiCam is more challenging because it cannot access the encrypted information in the data packets. Yet, DeWiCam overcomes the difficulty and can detect nearby wireless cameras reliably. To further identify whether a camera is in an interested room, we propose a human-assisted identification model. Extension functions of DeWiCam further enable the video resolution and audio channel inference to provide extra protection. We implemented DeWiCam on the Android platform and evaluated it with extensive experiments on 20 cameras. The evaluation results show that DeWiCam can detect cameras with an accuracy of 99 percent within 2:7 s.
Perfect state transfer on Cayley graphs over dihedral groups

Recently, there are extensive studies on perfect state transfer on graphs due to their significant applications in quantum information processing and quantum computations. However, most of the graphs previously investigated are abelian Cayley graphs. In this paper, we study perfect state transfer on Cayley graphs over dihedral groups. Using the representations of the dihedral group , we present some necessary and sufficient conditions for the Cayley graph to have a perfect state transfer between two distinct vertices for some connection set S. Based on these conditions, we show that cannot have PST if n is odd and S is conjugation-closed. For some even integers n, it is possible for to have PST, some concrete constructions are provided.
Observer-based control of positive polynomial fuzzy systems with unknown time delay

This paper presents a methodology to investigate the observer-based control for positive nonlinear systems with unknown time delay through analyzing the stability and positivity of positive polynomial fuzzy-model-based (PPFMB) observer-control systems with unknown time delay and unmeasurable premise variables. In order to widen the application of research results and relax the research results, the polynomial fuzzy observer-controller employs a membership function depending on estimated premise variables and the observer system matrix is treated as decision variable. This scenario will lead to non-convex stability and positivity conditions when the stability and positivity are analyzed based on the Lyapunov stability theory. To overcome this issue, some transformation techniques and matrix decoupling techniques (MDT) are adopted to turn the non-convex stability/positivity conditions into convex ones so that they can be handled by convex programming techniques. Finally, two simulation examples are presented to demonstrate the feasibility and validity of the analysis results.
A Design and Implementation of Montgomery Modular Multiplier

Large integer multiplication is the critical operation to design a modular multiplier. Karatsuba algorithm(KO algorithm) to split the operands into two parts is generally used to design a large integer multiplier. In this paper, we firstly propose a design of 258-bit multiplier based on KO-3 algorithm deduced by KO algorithm, with which hardware resources can be reduced than KO algorithm. Then we construct a 256-bit four-stage pipelined Montgomery modular multiplier on the base of proposed multiplier. Finally, we implement the design of modular multiplier on Virtex-6 FPGA platform. This design can run at the clock rate of 68 MHz with 187.9k LUTs approximately. In addition, our design can obtain the result of Montgomery modular multiplier for every clock. Compared with other designs on FPGA, our design shows a better performance in term of area-time product.
A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration

We propose a novel Bayesian decision theoretic deep-neural-network (DNN) framework for image segmentation, enabling us to define a principled measure of uncertainty associated with label probabilities. Our framework estimates uncertainty analytically at test time, unlike the state of the art that relies on approximate and expensive algorithms using sampling or multiple passes. Moreover, our framework leads to a novel Bayesian interpretation of the softmax layer. We propose a novel method to improve DNN calibration. Results on three large datasets show that our framework improves segmentation quality and calibration, and provides more realistic uncertainty estimates, over existing methods.
Hexagonal Volume Local Binary Pattern (H-VLBP) with deep stacked autoencoder for Human Action Recognition

Human action recognition plays a significant role in a number of computer vision applications. This work is based on three processing stages. In the first stage, discriminative frames are selected as representative frames per action to minimize the computational cost and time. In the second stage, novel neighbourhood selection approaches based on geometric shapes including triangle, quadrilateral, pentagon, hexagon, octagon and heptagon are used in Volumetric Local Binary Pattern (VLBP) to extract the features from frame sequences based on motion and appearance information. Hexagonal Volume Local Binary Pattern (H-VLBP) descriptor has been found to produce better results among all other novel geometric shape based neighbourhood selection approaches for human action recognition. However, the dimensionality of extracted feature from H-VLBP is too large. Therefore, the deep stacked autoencoder is used for dimensionality reduction with the decoder layer replaced by softmax layer for performing multi-class recognition. The developed approach is applied to four publicly available benchmark datasets, namely KTH, Weizmann, UCF11 dataset and IXMAS dataset for human action recognition. The results obtained show that the proposed approach outperforms the state-of-art techniques. Moreover, the approach has been tested with a synthetic dataset and better results have been obtained. This illustrates the effectiveness of the approach in real time environment.
A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.
Shouting into the Void: A Database of the Alternative Social Media Platform Gab

As social media platforms have increased their role as content moderators, alternative social media platforms have emerged with fewer rules and policies on moderating content. One such platform is Gab, a social media platform similar to Twitter that champions free speech with minimal standards on content. Early research has linked this platform with alt-right, hate speech, conspiracy theories, and other alternative content that is sometimes marginalized in mainstream social media platforms like Twitter and Facebook. In an effort to provide a means for researchers to study this platform, we introduce a database of 37,012,061 posts (with additional edit histories), 24,551,804 comments, and 819,957 user profiles webscraped from Gab between August 2016 and December 2018. In this paper, we outline our data collection process, describe the data, consider the ethics of our data collection process, and provide suggested avenues of inquiry for researchers interested in analyzing our database.
Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes

With the objective of producing high-resolution and high-accuracy maps of mean annual irradiance at country scale, this contribution exploits the complementary properties of two distinct sources of solar irradiance data: gridded modeled data derived from satellite observations, and station-specific typical meteorological year (TMY) data. A data fusion procedure based on Gaussian process modeling is used to optimally combine the two sources of data and derive solar resource maps. Gridded physical solar model version 3 (PSM3) satellite-derived data at 4-km resolution and TMY3 data from 67 stations in California are used to produce a map of mean annual global horizontal irradiance at 2-km resolution and exemplify the procedure. It is shown that by integrating the PSM3 data with TMY3 data, the original 5.2% mean error in the PSM3 map is reduced to 1.6%. The demonstrated approach is suitable for a variety of regional-scale applications for which both high-resolution data of low accuracy and low-resolution measurements of high accuracy are available.
Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets.
Weaklier Supervised Semantic Segmentation With Only One Image Level Annotation per Category

Image semantic segmentation tasks and methods based on weakly supervised conditions have been proposed and achieve better and better performance in recent years. However, the purpose of these tasks is mainly to simplify the labeling work. In this paper, we establish a new and more challenging task condition: weaklier supervision with one image level annotation per category, which only provides prior knowledge that humans need to recognize new objects, and aims to achieve pixel-level object semantic understanding. In order to solve this problem, a three-stage semantic segmentation framework is put forward, which realizes image level, pixel level, and object common features learning from coarse to fine grade, and finally obtains semantic segmentation results with accurate and complete object regions. Researches on PASCAL VOC 2012 dataset demonstrates the effectiveness of the proposed method, which makes an obvious improvement compared to baselines. Based on fewer supervised information, the method also provides satisfactory performance compared to weakly supervised learning-based methods with complete image-level annotations.
Passive Haptic Menus for Desk-Based and HMD-Projected Virtual Reality

In this work we evaluate the impact of passive haptic feedback on touch-based menus, given the constraints and possibilities of a seated, desk-based scenario in VR. Therefore, we compare a menu that once is placed on the surface of a desk and once mid-air on a surface in front of the user. The study design is completed by two conditions without passive haptic feedback. In the conducted user study (n = 33) we found effects of passive haptics (present vs- non-present) and menu alignment (desk vs. mid-air) on the task performance and subjective look & feel, however the race between the conditions was close. An overall winner was the mid-air menu with passive haptic feedback, which however raises hardware requirements.
Neural Topic Model with Reinforcement Learning

In recent years, advances in neural variational inference have achieved many successes in text processing.Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors.However, minimising reconstruction errors does not necessarily lead to high quality topics.In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model.Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models.Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models.
An Effective Background Estimation Method for Shadows Removal of Document Images

Shadows of document images bring about difficulties for digitization application and uncomfortable perception in vision. This paper proposes an effective method to remove shadows from the single document images, which contains two stages: shadow detection and shadow removal. For the shadow detection, an iterative neighboring information-based approach is designed to estimate pixel-wise local background color image, which can be used to generate a shadow map. For the shadow removal, a global reference background color is obtained from the local background color image. The shadow scale, which is applied to relight shadowed regions, can be estimated by local and global background color. Moreover, a tone fine-tuning process is designed to make the output colors be normal values. Experiments on document images indicate that the proposed method can produce high-quality unshad-owed document images with a comparable efficiency.
UNDERSTANDING QUANTUM ALGORITHMS VIA QUERY COMPLEXITY

Query complexity is a model of computation in which we have to compute a function $f(x_1, \ldots, x_N)$ of variables $x_i$ which can be accessed via queries. The complexity of an algorithm is measured by the number of queries that it makes. Query complexity is widely used for studying quantum algorithms, for two reasons. First, it includes many of the known quantum algorithms (including Grover's quantum search and a key subroutine of Shor's factoring algorithm). Second, one can prove lower bounds on the query complexity, bounding the possible quantum advantage. In the last few years, there have been major advances on several longstanding problems in the query complexity. In this talk, we survey these results and related work, including: - the biggest quantum-vs-classical gap for partial functions (a problem solvable with 1 query quantumly but requiring $\Omega(\sqrt{N})$ queries classically); - the biggest quantum-vs-determistic and quantum-vs-probabilistic gaps for total functions (for example, a problem solvable with $M$ queries quantumly but requiring $\tilde{\Omega}(M^{2.5})$ queries probabilistically); - the biggest probabilistic-vs-deterministic gap for total functions (a problem solvable with $M$ queries probabilistically but requiring $\tilde{\Omega}(M^{2})$ queries deterministically); - the bounds on the gap that can be achieved for subclasses of functions (for example, symmetric functions); - the connections between query algorithms and approximations by low-degree polynomials.
Efficient Cryptography on the RISC-V Architecture

RISC-V is a promising free and open-source instruction set architecture. Most of the instruction set has been standardized and several hardware implementations are commercially available. In this paper we highlight features of RISC-V that are interesting for optimizing implementations of cryptographic primitives. We provide the first optimized assembly implementations of table-based AES, bitsliced AES, ChaCha, and the Keccak- $$f$$ [1600] permutation for the RV32I instruction set. With respect to public-key cryptography, we study the performance of arbitrary-precision integer arithmetic without a carry flag. We then estimate the improvement that can be gained by several RISC-V extensions. These performance studies also serve to aid design choices for future RISC-V extensions and implementations.
Deep Learning Based Weighted Feature Fusion Approach for Sentiment Analysis

Deep learning algorithms have achieved remarkable results in the natural language processing(NLP) and computer vision. Hence, a trend still going on to use these algorithms, such as convolution and recurrent neural networks, for text analytic task to extract useful information. Features extraction is one of the important reasons behind the success of these networks. Moreover passing features from one layer to another layer within the network and one network to another network have done. However multilevel and multitype features fusion remains unexplored in sentiment analysis. So, in this paper, we use three datasets to display the advantages of extracting and fusing multilevel as well as multitype features from different neural networks. Multilevel features are from different layers of the same network, and multitype features are from different network architectures. Experiment results demonstrate that the proposed model based on multilevel and multitype weighted features fusion outperforms than many exiting works with an accuracy of 80.2%, 48.2%, and 87.0% on MR, SST1, and SST2 datasets respectively.
Developing scenario-based technology roadmap in the big data era: an utilisation of fuzzy cognitive map and text mining techniques

Scenario-based technology roadmaps (TRM) have been used to analyse future uncertainties in the technology planning phase. However, there is a limitation in the existing related studies that the internal and external factors of the increasing uncertainty of the future are not considered. In response, this study proposes a framework that analyses the textual big data to understand the relationship between factors that may affect future uncertainty and build a TRM through scenarios based on these relationships. To do this, Fuzzy Cognitive Map technique, which analyses causal relationships between various factors in fuzzy graph form, is incorporated into TRM. To verify the effectiveness of the proposed framework, an illustrative case study was conducted using real data for an unmanned aerial vehicle. This study is expected to help integrate and utilise big data effectively in the technology planning stage.
Global digital image correlation up to very high temperatures with grey level corrections

The characterisation of high temperature materials for extreme environment implies experimental challenges. Displacement measurements using digital image correlation (DIC) have proven to be an efficient non-contact method even at high temperatures. This paper uses global DIC and grey level corrections to analyse very high temperature tests in which inversion of the grey level distribution may occur. The benefits of grey level corrections are shown on a coated ceramic matrix composite heated up to 1380 °C. The inversion of the grey level distribution is then accounted for to study various ceramics subjected to temperatures up to 1860 °C.
High accuracy 3D calibration method of phase calculation-based fringe projection system by using LCD screen considering refraction error

Phase calculation-based fringe projection techniques have been an excitement for three-dimensional (3D) shape measurement. The accuracy of a measurement system is mainly hindered by the calibration accuracy. Existing calibration methods are subjected to the low accuracy because planar boards with checker, ring or dots are used to provide only limited number (tens to hundreds) of reference points with lower accuracy (pixel level). In this paper, a novel 3D calibration method is proposed by using a liquid crystal display (LCD) screen to offer high density reference points. A refraction model of the LCD screen is established to reduce phase error introduced by the refractive index and the coating thickness of the screen. Placing the LCD screen at several positions parallel to each other in front of the camera can determine not only the relationship of absolute phase and depth, but pixel position and X, Y coordinates. Experimental results demonstrate that the proposed 3D calibration method achieves the higher measurement accuracy of the shape data compared with the existing state of the art methods.