Reviewerly/exHarmony huggingface dataset:
Blockchain Systems, Technologies, and Applications: A Methodology Perspective

In the past decade, blockchain has shown a promising vision to build trust without any powerful third party in a secure, decentralized and scalable manner. However, due to the wide application and future development from cryptocurrency to the Internet of Things, blockchain is an extremely complex system enabling integration with mathematics, computer science, communication and network engineering, etc. By revealing the intrinsic relationship between blockchain and communication, networking and computing from a methodological perspective, it provided a view to the challenge that engineers, experts and researchers hardly fully understand the blockchain process in a systematic view from top to bottom. In this article we first introduce how blockchain works, the research activities and challenges, and illustrate the roadmap involving the classic methodologies with typical blockchain use cases and topics. Second, in blockchain systems, how to adopt stochastic process, game theory, optimization theory, and machine learning to study the blockchain running processes and design the blockchain protocols/algorithms are discussed in details. Moreover, the advantages and limitations using these methods are also summarized as the guide of future work to be further considered. Finally, some remaining problems from technical, commercial and political views are discussed as the open issues. The main findings of this article will provide a survey from a methodological perspective to study theoretical model for blockchain fundamentals understanding, design network service for blockchain-based mechanisms and algorithms, as well as apply blockchain for the Internet of Things, etc.
Quantitative Mineral Mapping of Drill Core Surfaces I: A Method for <i>µ</i>XRF Mineral Calculation and Mapping of Hydrothermally Altered, Fine-Grained Sedimentary Rocks from a Carlin-Type Gold Deposit

Abstract Mineral distributions can be determined in drill core samples from a Carlin-type gold deposit, using micro-X-ray fluorescence (µXRF) raster data. Micro-XRF data were collected using a Bruker Tornado µXRF scanner on split drill core samples (~25 × 8 cm) with data collected at a spatial resolution of ~100 µm. Bruker AMICS software was used to identify mineral species from µXRF raster data, which revealed that many individual sample spots were mineral mixtures due to the fine-grained nature of the samples. In order to estimate the mineral abundances in each pixel, we used a linear programming (LP) approach on quantified µXRF data. Quantification of µXRF spectra was completed using a fundamental parameters (FP) standardless approach. Results of the FP method compared to standardized wavelength dispersive spectrometry (WDS)-XRF of the same samples showed that the FP method for quantification of µXRF spectra was precise (R2 values of 0.98–0.97) although the FP method gave a slight overestimate of Fe and K and an underestimate of Mg abundance. Accuracy of the quantified µXRF chemistry results was further improved by using the WDS-XRF data as a calibration correction before calculating mineralogy using LP. The LP mineral abundance predictions were compared to Rietveld refinement results using X-ray diffraction (XRD) patterns collected from powders of the same drill core samples. The root mean square error (RMSE) for LP-predicted mineralogy compared to quantitative XRD results ranges from 0.91 to 7.15% for quartz, potassium feldspar, pyrite, kaolinite, calcite, dolomite, and illite. The approaches outlined here demonstrates that µXRF maps can be used to determine mineralogy, mineral abundances, and mineralogical textures not visible with the naked eye from fine-grained sedimentary rocks associated with Carlin-type Au deposits. This approach is transferable to any ore deposit, but particularly useful in sedimentary-hosted ore deposits where ore and gangue minerals are often fine grained and difficult to distinguish in hand specimen.
Hierarchical Recurrent Neural Network for Handwritten Strokes Classification

The paper presents an original solution to the online handwritten document processing in a free form, which is aimed at separating multi-class handwritten documents into texts, tables, formulas, drawings, etc. Stroke classification is an important step in automatic document layout analysis (DLA) in handwritten document recognition systems. Major DLA challenges arise due to a wide diversity of handwritten content, various writing styles, a lack of contextual knowledge, and the complicated structure of freeform handwritten documents. In this paper, we propose the hierarchical recurrent neural network (RNN) architecture to address the hierarchical structure inherent to the handwritten document. The novelty of feature aggregation pooling technique for transferring data between hierarchical levels allows achieving higher computational efficiency for using the suggested approach in on-device mobile computing. The presented approach gives an access to new state-of-the-art results in the task of multi-class classification with an accuracy of 97.25% on the IAMonDo dataset. This result can serve as the basis for efficient mobile applications for freeform handwriting document recognition.
UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). These two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].
Reducing Uncertainty and Offering Comfort: Designing Technology for Coping with Interpersonal Racism

Ranging from subtle to overt, unintentional to systemic, navigating racism is additional everyday work for many people. Yet the needs of people who experience racism have been overlooked as a fertile ground for better technology. Through a series of workshops we call Foundational Fiction, we engaged BIPOC (Black, Indigenous, People of Color) in participatory design to identify qualities of technology that can support people coping before, during, and after a racist interaction. Participants developed storyboards for digital tools that offer advice, predict consequences, identify racist remarks and intervene, educate both targets and perpetrators about interpersonal and systemic racism, and more. In the paper we present our workshop method utilizing interactive fiction, participants' design concepts, prevalent themes (reducing uncertainty and offering comfort), and we provide critical analysis of the complexity of technology in these contexts. This work identifies specific opportunities for exploring anti-racist social tools.
Small Local Earthquake Detection Using Low-Cost MEMS Accelerometers: Examples in Northern and Central Italy

Abstract This study evaluates the seismicity detection efficiency of a new low-cost triaxial accelerometer prototype based on microelectromechanical systems (MEMS) technology. Networks of MEMS sensors were installed in telecommunication infrastructures to build two small arrays in northern and central Italy. The sensor prototypes recorded major earthquakes as well as nine small seismic events with 2.0&amp;lt;ML&amp;lt;3.0. Where possible, MEMS were compared to the closest high-quality seismic stations belonging to the national accelerometric network. The comparison, in terms of peak ground accelerations and spectral responses, confirms that the signals are in good agreement. The tested inexpensive MEMS sensors were able to detect small local events with epicentral distances as large as 50 km and provided an efficient characterization of the main motion parameters. This confirms that the proposed accelerometer prototypes are promising tools to integrate into traditional networks for local seismicity monitoring.
Cross-View Relation Networks for Mammogram Mass Detection

In medical image analysis, multi-view modeling is crucial for pathology detection when the target lesion is presented in different views, e.g. mass lesions in breasts. Currently mammogram is the most effective imaging modality for mass lesion detection of breast cancer at the early stage. The pathological information from the two paired views (i.e., medio-Iateral oblique and cranio-caudal) are highly relational and complementary, which is crucial for diagnosis in clinical practice. Existing mass detection methods do not consider learning synergistic features from the two relational views. For the first time, we propose a novel mass detection framework to capture the latent relation information from the two paired views of a same mass in mammogram. We evaluate our model on a public mammogram dataset and a large-scale private dataset, demonstrating that the proposed method outperforms existing feature fusion approaches and state-of-the-art mass detection methods. We further analyze the performance gains from the relation modeling. Our quantitative and qualitative results suggest that jointly learning cross- view features boosts the detection performance of existing models, which is a promising avenue for mass detection task in mammogram.
Adaptive Bi-Directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension

Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i.e., passage and question. Previous studies have shown that the representation of source sequence becomes more coarse-grained from fine-grained as the encoding layer increases. It is generally believed that with the growing number of layers in deep neural networks, the encoding process will gather relevant information for each location increasingly, resulting in more coarse-grained representations, which adds the likelihood of similarity to other locations (referring to homogeneity). Such a phenomenon will mislead the model to make wrong judgments so as to degrade the performance. To this end, we propose a novel approach called Adaptive Bidirectional Attention, which adaptively exploits the source representations of different levels to the predictor. Experimental results on the benchmark dataset, SQuAD 2.0 demonstrate the effectiveness of our approach, and the results are better than the previous state-of-the-art model by 2.5% EM and 2.3% F1 scores.
Event-Driven Modulo Sampling

In contrast to Shannon sampling theory, where measurements are recorded at equally-spaced time instants, event-driven sampling records values at non-uniform instants dependent on the input. However, both sampling schemes are subject to input dynamic range constraints. This represents a fundamental bottleneck, which can only be alleviated via adjustments in the encoder architecture. Here we explore an alternative strategy based on the recent work on Unlimited Sampling theory, which uses a modulo non-linearity to guarantee a predefined input amplitude range. We propose a cascade model comprising a modulo non-linearity in series with an integrate-and-fire (IF) event-driven encoder. The modulo component does not act on inputs within the IF dynamic range, thus our model is fully compatible with the existing IF methodology. For inputs outside the IF dynamic range, the modulo output is discontinuous, and it currently cannot be recovered from the IF output with existing methods. We introduce theoretical conditions for which the input of the proposed cascade model can be recovered with arbitrary precision. Through numerical simulations, we show the performance of the reconstruction algorithm. The proposed methodology paves the way for a new generation of event-driven models suitable for a much wider range of applications.
Flashpen: A High-Fidelity and High-Precision Multi-Surface Pen for Virtual Reality

Digital pen interaction has become a first-class input modality for precision tasks such as writing, annotating, drawing, and 2D manipulation. The key enablers of digital inking are the capacitive or resistive sensors that are integrated in contemporary tablet devices. In Virtual Reality (VR), however, users typically provide input across large regions, hence limiting the suitability of using additional tablet devices for accurate pen input. In this paper, we present Flashpen, a digital pen for VR whose sensing principle affords accurately digitizing hand writing and intricate drawing, including small and quick turns. Flashpen re-purposes an inexpensive gaming mouse sensor that digitizes extremely fine grained motions in the micrometer range at over 8 kHz when moving on a surface. We combine Flashpen's high-fidelity relative input with the absolute tracking cues from a VR headset to enable pen interaction across a variety of VR applications. In our two-block evaluation, which consists of a tracing task and a writing task, we compare Flashpen to a professional drawing tablet (Wacom). With this, we demonstrate that Flashpen's fidelity matches the performance of state-of-the-art digitizers and approaches the fidelity of analog pens, while adding the flexibility of supporting a wide range of flat surfaces.
Grover on PIPO

The emergence of quantum computers is threatening the security of cryptography through various quantum algorithms. Among them, the Grover search algorithm is known to be efficient in accelerating brute force attacks on block cipher algorithms. To utilize the Grover’s algorithm for brute force attacks, block ciphers must be implemented in quantum circuits. In this paper, we present optimized quantum circuits of the SPN (Substitution Permutation Network) structured lightweight block cipher, namely the PIPO block cipher. In particular, the compact design of quantum circuits for the 8-bit Sbox is investigated. These optimization techniques are used to implement other cryptographic operations as quantum circuits. Finally, we evaluate quantum resources of Grover search algorithm for the PIPO block cipher in ProejctQ, a quantum simulator provided by IBM.
CHIP-KNN: A Configurable and High-Performance K-Nearest Neighbors Accelerator on Cloud FPGAs

The k-nearest neighbors (KNN) algorithm is an essential algorithm in many applications, such as similarity search, image classification, and database query. With the rapid growth in the dataset size and the feature dimension of each data point, processing KNN becomes more compute and memory hungry. Most prior studies focus on accelerating the computation of KNN using the abundant parallel resource on FPGAs. However, they often overlook the memory access optimizations on FPGA platforms and only achieve a marginal speedup over a multithread CPU implementation for large datasets. In this paper, we design and implement CHIP-KNN-an HLS-based, configurable, and high-performance KNN accelerator-which optimizes the off-chip memory access on cloud FPGAs with multiple DRAM or HBM (high-bandwidth memory) banks. CHIP-KNN is configurable for all essential parameters used in the algorithm, including the size of the search dataset, the feature dimension of each data point, the distance metric, and the number of nearest neighbors - K. To optimize its performance, we build an analytical performance model to explore the design space and balance the computation and memory access performance. Given a user configuration of the KNN parameters, our tool can automatically generate the optimal accelerator design on the given FPGA platform. Our experimental results on the Nimbix cloud computing platform show that: Compared to a 16-thread CPU implementation, CHIP-KNN on the Xilinx Alveo U200 FPGA board with four DRAM banks and U280 FPGA board with HBM achieves an average of 7.5x and 19.8x performance speedup, and 6.1x and 16.0x performance/dollar improvement.
Path Planning Algorithm Based on the Improved RRT-Connect for Home Service Robot Arms

Path planning is one of the important research fields of service robots. However, each path plan algorithm has its advantages and disadvantage. Path planning for home service robot arms in ROS usually uses bidirectional fast extended random tree(RRT-Connect) algorithm. However, due to the operation principle of the algorithm, there are many redundant path points and the smoothness and security of the path are poor. The idea of the improved algorithm is to delete redundant nodes to simplify the path, the path transition area uses parabolic transition to smooth the path, the obstacles are surrounded by a ball model and the safe distance is calculated to improve the safety of the robot's working process. The simulation results show that the improved algorithm plans a simpler, smoother and safer path for the home service robot arms.
Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.
Data Epics

In this paper, we use fiction as a method to complicate the commonplace narratives of data as intangible and objective, in the particular context of Internet of Things (IoT) in the home. We, a team of two design researchers, partnered with a fiction writer and a single IoT enthusiast, Susan, to create The Data Epics: four short stories based on Susan's monthly home IoT data logs. The Data Epics revealed new imaginaries for data, showing new world-views and lively data, but also surfaced data's entanglement in meshes and hierarchies, and concerns about control and power. Our work also examines the labor of tending to and interpreting data and a particular interest in anomalies. We conclude with discussions of how data imaginaries from fiction might be imperfect, but are uniquely generative, offering a path to get closer to IoT data by trying things on and zooming in and slowing down.
Anomaly Detection in COVID-19 Time-Series Data

Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown.
Interpretable Program Synthesis

Program synthesis, which generates programs based on user-provided specifications, can be obscure and brittle: users have few ways to understand and recover from synthesis failures. We propose interpretable program synthesis, a novel approach that unveils the synthesis process and enables users to monitor and guide a synthesizer. We designed three representations that explain the underlying synthesis process with different levels of fidelity. We implemented an interpretable synthesizer for regular expressions and conducted a within-subjects study with eighteen participants on three challenging regex tasks. With interpretable synthesis, participants were able to reason about synthesis failures and provide strategic feedback, achieving a significantly higher success rate compared with a state-of-the-art synthesizer. In particular, participants with a high engagement tendency (as measured by NCS-6) preferred a deductive representation that shows the synthesis process in a search tree, while participants with a relatively low engagement tendency preferred an inductive representation that renders representative samples of programs enumerated during synthesis.