Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain Model in Tamarin and Formal Analysis of Hash Time Lock Contract

Formal analysis and veriﬁcation methods can aid the design and validation of security properties in blockchain based protocols. However, to generate a reasonable and correct veriﬁcation, a proper model for the blockchain is needed. In this paper, we give a blockchain model in Tamarin. Based on our model we analyze and give a formal veriﬁcation for the hash time lock contract, an atomic cross chain trading protocol. The result shows that our model is able to identify an underlying assumption for the hash time lock contract and that the model is useful for analyzing blockchain based protocols
SCPP

A collection of individuals is represented by point patterns. Each individual is a finite set of geographical locations representing their visiting pattern to places in a region. We present SCPP, an algorithm for clustering these individuals considering the spatial patterns of their visiting locations. We adopted a probabilistic framework based on the theory of point processes that allows us to derive a non-obvious distance metric between each individual point pattern and the underlying, unobserved continuous intensity function. This metric is the Kullback-Leibler divergence between the true data-generating point process distribution and the model-generating distribution. We also introduce a theoretically based framework for the cost function to be minimized, a functional T(P) taking as arguments the probability distributions underlying the unknown clusters. We present an extensive experimental analysis to show SCPP’s effectiveness using several synthetic datasets and spatial mobility patterns from geo-tagged social media.
Humans in the Black Box: A New Paradigm for Evaluating the Design of Creative Systems

Modular creative systems employ specialized modules that take on challenging responsibilities. Such modules can be expensive to develop and are often imperfect, leading to uncertainty in whether a system’s poor re-sults are due to module imperfection or larger systemic issues. Computational creativity research would bene-ﬁt from a method for validating the design of creative systems before expending development resources implementing them. We present ideal-module prototyping (IMP), a design validation paradigm that serves these needs by using modules that delegate responsibilities to humans as an ideal against which to compare sys-tem expectations and performance. We relate an ex-periment we conducted in applying IMP to an existing creative system to demonstrate the insights that can be gained from this method. We argue for the widespread adoption of modular creative system design validated by ideal-module prototyping.
Following the Flow: Exploring the Impact of Mobile Technology Environment on User's Virtual Experience and Behavioral Response

Although the increasing mobile technology applies in our daily lives, there remain questions that clearly illustrate the experience inspired by the mobile technology environment. It is theoretically and practically meaningful to systematically reveal how mobile technology generates users’ virtual experience and, subsequently, the behavioral response. To fill the gaps, drawing on the Stimulus-Organism-Response model, this paper regards flow as the core experience based on the features of the mobile users’ experience. The framework is that mobile technology will promote users’ virtual experience, and in turn, affect their behavioral response. This study uses online survey data from 452 respondents. We employ the structural equation model based on the partial least squares method and further mediation analysis by the Sobel test to validate the research hypotheses. Our results show that the mobile technology environment has a significantly positive effect on users’ purchase intention/return mediated by the virtual experience. However, the direct relationship between ubiquity and flow, as well as telepresence and intention to purchase/return, are not significant. Finally, a few theoretical and managerial contributions are also discussed.
Populating the Peephole Optimizer of a Smart Contract Compiler

Developing compiler optimizations, especially for new, rapidly evolving smart contract languages, can be onerous and error-prone, but is especially important for smart contracts, where deployment and execution directly translate to monetary cost and which cannot change once deployed. One common optimization technique is the use of peephole optimizations , replacement rules that are applied using pattern-matching. These rules are normally constructed using human expertise, which is both time-consuming and far from systematic in exploring opportunities for optimization. In this work we propose a pipeline to automatically populate the peephole optimizer of a smart contract compiler. We apply superoptimization to an existing code base to obtain sequences of instructions, which can be replaced by cheaper, observationally equivalent instructions. We then generate peephole optimization rules by extracting the underlying patterns of these optimizations. We provide a case study of our approach and a prototype implementation for bytecode of the Ethereum Virtual Machine, the tool ppltr , which combines the superoptimizer ebso and the rule generator sorg . Then we evaluate our approach by generating and applying nearly 1 k peephole optimization rules extracted from 2 k optimizations obtained from deployed bytecode.
Adsorbed leucaena protein on citrate modified Fe3O4 nanoparticles: synthesis, characterization, and its application as magnetic coagulant

Natural coagulants from plants resources have gained a lot of attention as it is renewable, biodegradable, non-hazardous, lower cost, and less sludge generated compared to chemical coagulants. However there are still some drawbacks, namely long settling time and possible increase of dissolved organic carbon in the treated water. In this paper we tried to address these drawbacks by utilizing citrate modified Fe 3 O 4 to adsorb protein from Leucaena leucocephala as the active coagulating agent. The effect of trisodium citrate concentration and protein adsorption pH to the adsorbed protein was investigated. It was found that the trisodium citrate concentration of 0.5 M and pH 4.0 gave the highest protein adsorption. The obtained magnetic coagulant was furthermore characterized using Scanning Electron Microscopy, X-ray Diffraction, Fourier Transform Infrared Spectroscopy, and Transmission Electron Microscopy to observe the characteristics before and after protein adsorption. Furthermore, the effect of pH (2 to 10) and coagulant dosage (60 to 600 mg L − 1 ) to the removal of synthetic Congo red wastewater and sludge volume formation was investigated. It was found that pH 3 was the best pH for coagulation due to charge neutralization mechanism of leucaena protein. Furthermore the highest removal was obtained at dosage 420 mg L − 1 with 80% removal. This result was comparable with crude extract of leucaena with half settling time (20 min) and lower increase of permanganate value, indicating lower increase of dissolved organics in the treated water.
Synthesis and Characterization of Superparamagnetic Polyaniline Nanocomposites as Conductive Shields

In situ polymerization of aniline was carried out of superparamagnetic (Fe3O4) nanoparticles to synthesize the middle molecular weight HCl-doped polyaniline/polyanitroaniline nanocomposites. The HCl-polyaniline/ polyanitroaniline/Fe3O4 nanocomposites have been synthesized with certain composition of Fe3O4 in the composite. Thus, the synthesized composites have been characterized by infrared spectroscopy and X-ray diffraction. The morphology of these nanocomposites was studied by scanning electron microscopy. Further, the conductivity properties have been investigated in the frequency dependence of conductivity. The presence of polarons and bipolarons is made responsible for frequency dependence of conductivity. Cole—Cole plots indicate clear shift in the distribution of relaxation times as the wt% of Fe3O4 in the polymeric matrix changes. These nanocomposites show almost symmetric semicircles of Cole—Cole plots indicating the Debye-type relaxation in their polarization response.
FDA-VeD: A Future-Demand-Aware Vehicle Dispatching Service

In this paper, we develop a Future-Demand-Aware Vehicle Dispatching Service (FDA-VeD) by considering the relocation of idle vehicles based on the predicted future demands in order to achieve high passenger serving ratio. We evaluate the performance of our system on New York taxi dataset. We demonstrate that our approach achieves a significantly higher serving ratio with a low operating cost increase in comparison with existing methods.
Joint Estimation of Pose and Singular Points of Fingerprints

Fingerprint pose estimation is a challenging problem since the pose is not defined by salient anatomical features and fingerprint images usually suffer from noise and small area. In this article, we proposed a method for joint estimation of pose and singular points of fingerprints, with the expectation that the pose and singular points can improve each other. By virtue of that singular points can be located accurately, we hope to improve the accuracy of pose estimation. Meanwhile, the robustness of pose estimation can improve the anti-noise performance of singular point detection. To achieve this, we propose a multi-task deep neural network, which contains a feature extraction body and two estimation heads for singular point and pose respectively. The proposed network can deal with various types of fingerprints, including plain, rolled and latent fingerprints. Experiments on four databases (NIST SD4, SD14, SD27 and FVC2004 DB1A) show that (1) the estimated poses and detected singular points are close to manual annotations despite of different image qualities; (2) the estimated poses for mated fingerprint pairs are consistent; and (3) the proposed pose estimation method outperforms state-of-the-art methods while utilized as pose constraint for a fingerprint indexing algorithm.
Moving object detection for camera pose estimation in dynamic environments

The reported simultaneous localization and mapping (SLAM) methods show satisfactory object detection results in static environments. However, most of them cannot be directly applied in the moving object detection in various environments due to high detection errors. This work is focused on developing a new method to solve the problem of moving object detection under dynamic environment. To this end, a three-stage program is proposed based on the RGB-D image acquisition. Firstly, target objects are detected by the YOLOv3 model from the RGB images. Secondly, the object features are clustered using the k-means algorithm by fusing the information of the depth images. Finally, the moving objects are identified under dynamic environment based on the multi-view geometry theory. Experimental results show that the localization accuracy of SLAM in dynamic environment can be improved obviously by using the moving object detection method in this paper.
Refined Electrical Cardioversion Outcome Prediction with Bipolar Surface Standard Leads for Patients in Persistent Atrial Fibrillation

For persistent atrial fibrillation (AF), a low-cost, simple and effective rhythm control strategy is electrical cardioversion (ECV). This procedure presents high mid- and long-term AF recurrence rates, so anticipation of its outcome could avoid patients with high likelihood of relapsing to AF. To this end and considering the proximity of lead V1 to the right atrium, different parameters assessing fibrillatory (f-) waves reflected on this lead have been recently proposed. However, information from other leads have been mostly discarded. This study hence evaluates the effectiveness of several common parameters computed over the whole set of 12 standard ECG leads to predict ECV outcome with respect to just V1. The results for the amplitude of the f-waves, their dominant frequency and their regularity have shown better predictive abilities from limb lead II than from V1, thus providing improvements between 5 and 12% in the classification rates between patients who relapsed to AF and maintained SR during a follow-up of 4 weeks. Consequently, accessible limb lead II seems to be the best choice to anticipate ECV outcome from the ECG.
Remote Sensing Scene Classification Using Heterogeneous Feature Extraction and Multi-Level Fusion

Understanding the scenes provided by remote sensing (RS) images has drawn increasing attention in the last decade. It is to automatically classify a RS scene image by feature extraction and label assignment. Although much effort has been dedicated to developing discriminative feature extraction as well as automatic classification techniques, it is still very challenging owing to the complex distributions of the ground objects in high spatial resolution scenes. To enhance the ability to represent the RS scenes, an integration of multiple types of features for remote sensing scenes is considered as an effective way. Nevertheless, different kinds of features possess different characteristics, and how to fuse them together is a critical problem. In this paper, to fuse three different but complementary types of features that are extracted to characterize global attributes of scenes together, a discriminant correlation analysis based feature-level fusion approach is proposed, which maximizes the correlation of corresponding features across the two feature sets and in addition de-correlates features that belong to different classes within each feature set. In addition, to further improve the scene classification performance, another kind of information fusion form, called decision-level fusion is adopted in the classification stage. In our proposed decision-level fusion technique, the final results of multiple classifiers are combined via majority voting. Extensive experiments results conducted on the well-known SIRI-WHU dataset, the WHU-RS dataset and the UC Merced Land Use dataset have demonstrated that the proposed remote sensing scene classification method based on heterogeneous feature extraction and multi-level fusion is superior to many state-of-the-art scene classification algorithms.
Petrel: Heterogeneity-Aware Distributed Deep Learning Via Hybrid Synchronization

The parameter server (PS) paradigm has achieved great success in deploying large-scale distributed Deep Learning (DL) systems. However, these systems implicitly assume that the cluster is homogeneous and this assumption does not hold in many real-world cases. Although the previous efforts are paid to address heterogeneity, they mainly prioritize the contribution of fast workers and reduce the involvement of slow workers, resulting in the limitations of workload imbalance and computation inefficiency. We reveal that grouping workers into communities, an abstraction proposed by us, and handling parameter synchronization at the community level can conquer these limitations and accelerate the training convergence progress. The inspiration of community comes from our exploration of prior knowledge about the similarity between workers, which is often neglected by previous work. These observations motivate us to propose a new synchronization mechanism named <italic>Community-aware Synchronous Parallel</italic> (CASP), which uses the <italic>Asynchronous Advantage Actor-Critic</italic> (A3C)-based algorithm to intelligently determine community configuration and fully improve the synchronization performance. The whole idea has been implemented in a prototype system called <inline-formula><tex-math notation="LaTeX">${\sf Petrel}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Petrel</mml:mi></mml:math><inline-graphic xlink:href="zhou-ieq1-3040601.gif"/></alternatives></inline-formula> that achieves a good balance between convergence efficiency and communication overhead. The evaluation under various benchmarks with multiple metrics and baseline comparison demonstrates the effectiveness of <inline-formula><tex-math notation="LaTeX">${\sf Petrel}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Petrel</mml:mi></mml:math><inline-graphic xlink:href="zhou-ieq2-3040601.gif"/></alternatives></inline-formula>. Specifically, <inline-formula><tex-math notation="LaTeX">${\sf Petrel}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Petrel</mml:mi></mml:math><inline-graphic xlink:href="zhou-ieq3-3040601.gif"/></alternatives></inline-formula> accelerates the training convergence speed by up to 1.87 × faster and reduces communication traffic by up to 26.85 percent, on average, over the non-community synchronization mechanisms.
Lightweight Configurable Ring Oscillator PUF Based on RRAM/CMOS Hybrid Circuits

Physical unclonable function (PUF) is a lightweight security primitive for energy constrained digital systems. As an enhanced design of conventional ring oscillator (RO) PUFs, configurable ring oscillator (CRO) PUFs improve the uniqueness and reliability compared with the conventional RO PUF designs. In typical CRO PUF designs, multiplexers (MUXs) are utilized as configurable components. In this paper, a hybrid nano-scale CRO (<inline-formula><tex-math notation="LaTeX">$hn$</tex-math></inline-formula>-CRO) PUF is proposed. The configurable components of the proposed <inline-formula><tex-math notation="LaTeX">$hn$</tex-math></inline-formula>-CRO PUF are implemented by RRAMs. The delay elements are based on CMOS inverters. Compared with traditional CRO PUF designs, the proposed <inline-formula><tex-math notation="LaTeX">$hn$</tex-math></inline-formula>-CRO PUF is cost-efficient in terms of circuit density and gate per challenge response pair (CRP) bit. To validate the proposed <inline-formula><tex-math notation="LaTeX">$hn$</tex-math></inline-formula>-CRO PUF, the Monte Carlo simulation results of a compact RRAM model under UMC 65 nm technology are presented. The results show that the proposed <inline-formula><tex-math notation="LaTeX">$hn$</tex-math></inline-formula>-CRO PUF has a good uniqueness and low hardware consumption compared with the previous works.
Sandsbots: Robots That Sync and Swarm

This article presents a multi-robot system that forms emergent space-time patterns. Inspired by the theory of swarmalators, in which synchronization and swarming of agents are mutually coupled, we propose a robot-suitable model for coordination in time and space. The approach is evaluated by simulations and demonstrated as proof of concept using small robots and drones. The novel building blocks comprise a time-discrete swarm aggregation model—which works robustly with low update rates in systems with communication delays—and specific functions that couple this spatial model to a discrete temporal coordination model, resulting in an overall discrete spatio-temporal coordination model.
A Microwave/Millimeter Wave Dual-Band Shared Aperture Patch Antenna Array

This paper describes the design of microwave/millimeter wave dual-band patch antenna array with shared aperture. The antenna array is designed of stack structure of three printed circuit boards. The Ka-band patches are fed directly with microstrip lines of the Ka-band power divider network and the E-band patches are coupling fed by the E-band power divider network through slots cut in the antenna ground. An antenna unit is studied, fabricated and measured firstly to verify the feasibility of the shared aperture structure. Finally, a shared aperture antenna array consists of a <inline-formula> <tex-math notation="LaTeX">$2\times 4$ </tex-math></inline-formula> Ka-band array and an <inline-formula> <tex-math notation="LaTeX">$8\times 16$ </tex-math></inline-formula> E-band array is designed based on the antenna unit. The E-band and the Ka-band feeding network are carefully designed to reduce the reflections, and the Ka-band feeding network combines series feed and parallel feed in order to save space. The shared aperture antenna array is fabricated and measured, and the results are presented. In order to measure the E-band array, the main port of the E-band power divider is connected to a standard WR12 waveguide through an antipodal fin-line waveguide to microstrip transition.
Using Eye-tracking Technologies in Vision Teachers’ Work – a Norwegian Perspective

To support specialists assessing children’s vision is essential. However, it is difficult to determine a common support due to the missing standardized methods and tools they are using for their assessments. This study aims to investigate how affordable eye-tracking (ET) technologies can help vision teachers to determine children’s oculomotor dysfunction (OMD) and to find out how the teachers can integrate this technology in their screening procedures The evaluation is based on an experimental study demonstrating the use of ET-based software measuring OMD and responses from eight vision teachers testing the technology. The results illustrate the benefits and current limitations of technologies and argue for the need for such technologies.
Balancing Unpredictable Load and Intermittent Renewables by Semi-Dispatchable Distributed Generation

The paper presents a principle of a consequent hierarchical power and energy balancing in a power grid including a number of interconnected microgrids. Due to a load and intermittent energy sources output fluctuations, strong power and energy unbalances may arise in microgrids. These unbalances lead to inner and outer power and energy exchange that can be represented as the dispersion components of power and energy profiles. Compliance of the power balance is a part of microgrids’ secondary power regulation aimed at Area control error and power exchange keep given the limited interconnection capacity. Compliance of energy balance is a part of tertiary power regulation. It is of great importance for market settlements. An approach to semi-dispatchable distributed generation utilization for decreasing the power and energy dispersions taken by microgrids’ sources, and interconnecting microgrids power lines is considered. Different strategies of power and energy dispersions elimination applied to distinguishing load compositions are examined. An analysis show that generations’ typical power level and ramping settings corresponding to steady-state equipment operation enable to reduce the dispersion components in a great extent, although not to eliminate them at all. A mechanism of financial incentives for decreasing power flows dispersions is developed. It is based on a discount for technological interconnection due to maximum load decrease or discount for energy exchange price due to power and energy losses decrease.