Miracle-dz/UniFAR-FTUs huggingface dataset:
ConStory: Automatic story investigator of public perception on the mega urban infrastructure project: poster abstract

We evaluate the North Houston Highway Improvement Project (NHHIP) in Texas by analyzing social media data (Twitter) to determine the public perception on a series of issues in the project. We analyze the pertinent tweets since the project announcement (2008 to 2021). Our initial analysis is based on three distinct periods in which the volume of tweets has changed according to notable events: 1) release of the alternative design by the agency and 2) project pause, using topic modeling. Our results show a long-term public opinion shift from project itself to community and demolition. Although 'Neighborhood and Homes'-related tweets were inconsiderable in 2015, they became a critical discussion issue when the project was paused. We suggest that public opinion mining should be implemented through different information sources to overcome challenges that exist in identifying the interaction between citizens and the mega urban infrastructure projects.
Improving post-occupancy evaluation engagement using social robots

Post-occupancy evaluation (POE) is essential to evaluate and enhance indoor environmental quality (IEQ) that supports human health and productivity in building environments. Several POE methodical settings have been developed, such as interviews, paper-based questionnaires, and web-based surveys, for collecting sufficient and valid responses from occupants. However, these methods either need extensive preparation of human experts for interviews or have difficulties in engaging participants to get reliable responses and collect detailed feedback from open-ended questions. Considering the recent achievements of social robots in naturally interacting and communicating with humans by following social behaviors, this study introduced a new human-robot interaction (HRI) approach for collecting POE responses from building occupants. Two common types of human-robot interactions-verbal communication and haptic perception were developed based on the Misty Robot prototypes. The experimental results from a within-subject user study (N = 18) showed that participants expressed 61% (p < 0.05) more content to the descriptive open-ended questions using social robots. They also provided 87% (p < 0.01) (mean word counts) more detailed IEQ-related suggestions using social robots compared to traditional web-based questionnaires. The post-study satisfaction results also indicate that almost 90% of the participants preferred HRI-based POE over web-based POE. These findings demonstrate the potential of human-robot interaction approaches for improving POE engagement and reveal the need for further systematic characterization of social robots-based POE designs and implementations.
Crowd crush detection in large mass gatherings via federated learning across multicamera environment: poster abstract

Crowd crush in mass gatherings during large events are more frequent nowadays. It may get threatening quickly and even cause the death of many. Single camera surveillance is not efficient in large events spreading over wide areas. In multicamera surveillance environment, the centralized decision may not be favourable due to solitude and safety concerns. Moreover, existing neural networks need a large amount of training data for accurate detection. This paper proposes a low-cost system that uses a federated learning setup with light-weight model to reduce overheads during training and protect crowd privacy. We introduce a new loss function, denoted by contrastive focal loss, to reduce false positives and organize overcrowded regions in minimal time. We collect crowd crush and stampede videos to create a new annotated dataset, named as CrowdStampede. We achieve good results under different data distribution settings.
Development of a two-dimensional zonally averaged statistical-dynamical model. II - The role of eddy momentum fluxes in the general circulation and their parameterization

Abstract The effect of eddy momentum fluxes on the general circulation is investigated with the aid of perpetual January simulations with a two-dimensional, zonally averaged model. Sensitivity experiments with this model show that the vertical eddy flux has a negligible effect on the general circulation, while the meridional eddy flux has a substantial effect. The experiments on the effect of the mefidional eddy flux essentially confirm the resultsfound by Schneider in a similar (but not identical) set of sensitivity experiments, and, in addition, show that the vertical structure of the mefidional eddy flux has a relatively small effect on the general circulation. In order to parameterize the vertically integrated mefidional eddy momentum flux, we take Green's parameterization of this quantity and generalize it to allow for the effects of condensation. In order to do this, it is necessary to use Leovy's approximation for the eddy fluctuations in specific humidity. With this approximation the equivalent po.
Improved finger-knuckle-print authentication based on orientation enhancement

Presented is a new approach to person verification using finger-knuckle-prints (FKPs). It applies a Gabor filter to enhance the FKP information and a scale invariant feature transform (SIFT) to extract the features. Experiments with the most representative FKP public database confirm that the SIFT features obtained after Gabor enhancement of the principal finger knuckle lines improve the performance of the person identifier.
A centre manifold description of containment dispersion in channels with varying flow properties

High-order asymptotic approximations to the equation governing the longitudinal dispersion of a passive contaminant in Poiseuille channel flow are derived, and their validity discussed. The derivation uses centre manifold theory, which provides a systematic and near rigorous approach to calculating each successive approximation. It also enables the derivation of the correct initial conditions for the Taylor model of shear dispersion. Approximations that are valid when the channel is of a varying cross section are systematically derived via this approach, and the effects of time-dependent flow and variable diffusivity are also investigated. The resultant modifications to the effective advection velocity and the effective dispersion coefficient are calculated and some general trends indicated.
A neutron diffraction study of the deuterium site occupancy in Nd/sub 5/Fe/sub 17/D/sub 15.5/

The intermetallic compound Nd/sub 5/Fe/sub 17/ has potentially important magnetic properties and is being investigated as a possible permanent magnetic material. As with many other rare-earth transition metal compounds, this phase can absorb significant quantities of W(D) and lesser amounts of N. Gravimetric measurements indicate that /spl sim/14-16 H(D) atoms can be absorbed per formula unit leading to roughly a 20% increase in saturation magnetization and Curie temperature. We have carried out powder neutron diffraction studies to determine the location of D atoms in a sample of Nd/sub 5/Fe/sub 17/ containing approximately 15.5D/f.u. The large, complex unit cell of Nd/sub 5/Fe/sub 17/, with 21 crystallographically distinct sites per unit cell makes this a daunting task, but the diffraction diagram shows dramatic changes with D uptake, suggesting that such a determination is possible. We have identified about 15 sites fully or partially occupied by D. The majority of these are tetrahedral sites, as expected. Among remaining sites, one is an octahedral site and others show unusual environments with 5 metal neighbors. A few D atoms show anomalously short bonds to neighboring metal atoms suggesting that their positions may not be entirely correct. However, elimination of these atoms from the model, one by one, leads to relaxation of other positions, and in some cases, other bonds become unacceptably short. In addition, some D atoms show very short bonds to neighboring D sites. In these cases the sites are found to be only partially occupied, suggesting that the adjacent sites cannot be simultaneously occupied.
Characterizing Cryptocurrency-themed Malicious Browser Extensions

Due to the surging popularity of various cryptocurrencies in recent years, a large number of browser extensions have been developed as portals to access relevant services, such as cryptocurrency exchanges and wallets. This has stimulated a wild growth of cryptocurrency-themed malicious extensions that cause heavy financial losses to the users and legitimate service providers. They have shown their capability of evading the stringent vetting processes of the extension stores, highlighting a lack of understanding of this emerging type of malware in our community. In this work, we conduct the first systematic study to identify and characterize cryptocurrency-themed malicious extensions. We monitor seven official and third-party extension distribution venues for 18 months (December 2020 to June 2022) and have collected around 3600 unique cryptocurrency-themed extensions. Leveraging a hybrid analysis, we have identified 186 malicious extensions that belong to five categories. We then characterize those extensions from various perspectives including their distribution channels, life cycles, developers, illicit behaviors, and illegal gains. Our work unveils the status quo of the cryptocurrency-themed malicious extensions and reveals their disguises and programmatic features on which detection techniques can be based. Our work serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures. To facilitate future research in this area, we release our dataset of the identified malicious extensions and open-source our analyzer.
Flexible biofilm monitoring device

Biofilms and their analysis are increasingly attracting the attention of the scientific community due to the immense importance and impact of biofilms in various natural, technical and medical fields. For these purposes, an optimized and extended antibiofilm assay system based on the Calgary Biofilm Device (MBEC Assay® system) consisting of microtiter plate and PCR tubes was established. Its implementation was used to study the growth characteristics of the sessile phenotype of Pseudomonas fluorescens exposed to antimicrobial peptides. Inhibitory effects of an antimicrobial peptide on P. fluorescens biofilm formation could be determined at a concentration of 250 μg/ml (biofilm prevention concentration (BPC)) using the modified biofilm assay. Similarly, the biofilm bactericidal concentration (BBC) at 125 μg/ml and the minimum biofilm elimination concentration to remove 90% of the total biofilm mass (MBEC90) were measured at a concentration range of 15.625–1.95 μg/ml. In conclusion, this optimized system provides a highly variable, simple, and cost‐effective alternative to high‐throughput screening based on the Calgary Biofilm Device (CBD).
Artificial intelligence in breast cancer histopathology

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists—namely, molecular and hormonal analysis, grading, mitotic figure counting, ki‐67 indexing, tumour‐infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open‐source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.
Measuring Surface Roughness on Base of the Circular Polarization Coherence as an Input for a Simple Inversion of the IEM Model

RESUME In this paper a simple inversion approach is applied at the integral equation method model for single scattering (IEM). The inversion of the radar backscatter into soil moisture was accomplished with an iterative method. To date most inversion approaches with use of the IEM try to determine both the surface roughness and the soil moisture on base of two copolarized radar channels. Because there are a number of possible combinations of roughness and moisture parameters to reach the same values for the radar backscatter, these approaches always contain a fragment of uncertainty and inaccurateness. Therefore this work follows the idea of arising the number of input variables to reduce the inaccurateness and ambiguity of the inversion results. For this reason the surface roughness parameters are declared as input variables and were calculated externally of the IEM. The base for computing the rms-height values (s) is the circular polarization coherence of the copolarized channels. The real part of this coherence was found to be a good measure for this surface roughness parameter. For agricultural surfaces the correlation length of the autocorrelation function was found to correlate with the rms-heights. Because of the fact, that the autocorrelation length is not the most deciding parameter, this contiguity was used to calculate the autocorrelation length from the s. The final result in the form of a soil moisture map seems quite promising. Good correlations between measured and modelled values were realised. The arrangement of the soil moisture outside the test areas is rather realistic, which means higher values in depressed areas and lower values on hilltops.
Can resting heart rate explain the heart rate and parasympathetic responses during rest, exercise, and recovery?

The effect of resting heart rate (RHR) on the heart rate (HR) dynamics and parasympathetic modulation at rest, exercise, and recovery remain to be clarified. This study compares HR and parasympathetic responses at rest, during submaximal exercise testing and recovery in young, physically active men with different RHR average values. HR and parasympathetic responses were compared between two groups: bradycardia group–RHR < 60 bpm (BG, n = 20) and normocardia group–RHR ≥ 60 ≤ 100 bpm (NG, n = 20). A Polar RS800® was used to record the RR-interval series and HR at rest in the supine position, following the postural change (from supine to orthostatic position) and in the orthostatic position for 5 minutes, as well as during and after a submaximal exercise testing. Statistical analysis employed the MANOVA, Mann-Whitney, and Simple Linear regression test with a two-tailed p-value set at ≤ 0.05. BG at rest showed lower HR in the orthostatic position, higher parasympathetic activity in the supine and orthostatic positions, and higher parasympathetic reactivity than NG (p ≤ 0.01) after the postural change. BG before exercise showed lower HR and higher values of the chronotropic reserve and parasympathetic withdrawal than NG (p ≤ 0.01) throughout the exercise. Following the exercise, BG showed higher values of HR recovery (HRR) and parasympathetic reactivation in the 3rd and 5th minutes of recovery than NG (p ≤ 0.01). Lastly, the RHR can explain the variance of the HR at rest, during exercise testing, and recovery from 11 to 48%. We concluded that BG (RHR < 60 bpm) showed higher chronotropic and parasympathetic modulation at rest, higher chronotropic reserve, parasympathetic withdrawal during the submaximal exercise test, and faster HRR and parasympathetic reactivation after effort in young physically active men.
Chiral-induced spin selectivity augments quantum coherence in avian compass

This work investigates the effect of chiral-induced spin selectivity (CISS) on quantum spin coherence in the radical pair mechanism of avian magnetoreception. Additionally, we examine the utilitarian role of coherence for the avian compass by analyzing its correlation with the yield of the signaling state. We find that both the relative entropy of global coherence and local coherence in the radical pair increases with CISS. However, only global coherence exhibit the utilitarian role for the avian compass. We also analyze the interplay of dipolar interaction with the CISS and their effect on the coherence of the radical pair. Further, we analyze the effect of environmental decoherence along with CISS. We conclude that a high CISS results in a high correlation of global coherence with signaling state yield. It confirms that CISS plays an important role both for compass sensitivity and coherence in the avian compass.
Shadow Removal by High-Quality Shadow Synthesis

Most shadow removal methods rely on the invasion of training images associated with laborious and lavish shadow region annotations, leading to the increasing popularity of shadow image synthesis. However, the poor performance also stems from these synthesized images since they are often shadow-inauthentic and details-impaired. In this paper, we present a novel generation framework, referred to as HQSS, for high-quality pseudo shadow image synthesis. The given image is first decoupled into a shadow region identity and a non-shadow region identity. HQSS employs a shadow feature encoder and a generator to synthesize pseudo images. Specifically, the encoder extracts the shadow feature of a region identity which is then paired with another region identity to serve as the generator input to synthesize a pseudo image. The pseudo image is expected to have the shadow feature as its input shadow feature and as well as a real-like image detail as its input region identity. To fulfill this goal, we design three learning objectives. When the shadow feature and input region identity are from the same region identity, we propose a self-reconstruction loss that guides the generator to reconstruct an identical pseudo image as its input. When the shadow feature and input region identity are from different identities, we introduce an inter-reconstruction loss and a cycle-reconstruction loss to make sure that shadow characteristics and detail information can be well retained in the synthesized images. Our HQSS is observed to outperform the state-of-the-art methods on ISTD dataset, Video Shadow Removal dataset, and SRD dataset. The code is available at https://github.com/zysxmu/HQSS.
PKDGA: A Partial Knowledge-Based Domain Generation Algorithm for Botnets

Domain generation algorithms (DGAs) can be categorized into three types: zero-knowledge, partial-knowledge, and full-knowledge. While prior research merely focused on zero-knowledge and full-knowledge types, we characterize their anti-detection ability and practicality and find that zero-knowledge DGAs present low anti-detection ability against detectors, and full-knowledge DGAs suffer from low practicality due to the strong assumption that they are fully detector-aware. Given these observations, we propose PKDGA, a partial knowledge-based domain generation algorithm with high anti-detection ability and high practicality. PKDGA employs the reinforcement learning architecture, which makes it evolve automatically based only on the easily-observable feedback from detectors. We evaluate PKDGA using a comprehensive set of real-world datasets, and the results demonstrate that it reduces the detection performance of existing detectors from 91.7% to 52.5%. We further apply PKDGA to the Mirai malware, and the evaluations show that the proposed method is quite lightweight and time-efficient.
Simulation of Attacker Defender Interaction in a Noisy Security Game

In the cybersecurity setting, defenders are often at the mercy of their detection technologies and subject to the information and experiences that individual analysts have. In order to give defenders an advantage, it is important to understand an attacker's motivation and their likely next best action. As a first step in modeling this behavior, we introduce a security game framework that simulates interplay between attackers and defenders in a noisy environment, focusing on the factors that drive decision making for attackers and defenders in the variants of the game with full knowledge and observability, knowledge of the parameters but no observability of the state (``partial knowledge''), and zero knowledge or observability (``zero knowledge''). We demonstrate the importance of making the right assumptions about attackers, given significant differences in outcomes. Furthermore, there is a measurable trade-off between false-positives and true-positives in terms of attacker outcomes, suggesting that a more false-positive prone environment may be acceptable under conditions where true-positives are also higher.
Progressive Multi-resolution Loss for Crowd Counting

Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been proposed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure the density map at multiple resolutions. By maximizing the posterior probability in such a setting, we obtain a log-formed multi-resolution L2-difference loss, where the traditional single-resolution L2 loss is its particular case. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on four crowd counting datasets, ShanghaiTech A&B, UCF-QNRF, and JHU-Crowd++.
Momentum Calibration for Text Generation

The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually trained by maximizing the likelihood the output text sequence and assumes the input sequence and all gold preceding tokens are given during training, while during inference the model suffers from the exposure bias problem (i.e., it only has access to its previously predicted tokens rather gold tokens during beam search). In this paper, we propose MoCa ({\bf Mo}mentum {\bf Ca}libration) for text generation. MoCa is an online method that dynamically generates slowly evolving (but consistent) samples using a momentum moving average generator with beam search and MoCa learns to align its model scores of these samples with their actual qualities. Experiments on four text generation datasets (i.e., CNN/DailyMail, XSum, SAMSum and Gigaword) show MoCa consistently improves strong pre-trained transformers using vanilla fine-tuning and we achieve the state-of-the-art results on CNN/DailyMail and SAMSum datasets.
Montague semantics and modifier consistency measurement in neural language models

In recent years, distributional language representation models have demonstrated great practical success. At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities. Crucially, distributional models are often inconsistent when dealing with compositional phenomena in natural language, which has significant implications for their safety and fairness. Despite this, most current research on compositionality is directed towards improving their performance on similarity tasks only. This work takes a different approach, and proposes a methodology for measuring compositional behavior in contemporary language models. Specifically, we focus on adjectival modifier phenomena in adjective-noun phrases. We introduce three novel tests of compositional behavior inspired by Montague semantics. Our experimental results indicate that current neural language models behave according to the expected linguistic theories to a limited extent only. This raises the question of whether these language models are not able to capture the semantic properties we evaluated, or whether linguistic theories from Montagovian tradition would not match the expected capabilities of distributional models.