Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Motion Segmentation for Urban Street View Scenes

Though a long-studied problem, motion segmentation has yet to migrate into practical applications. We argue that a vital step towards that goal lies in addressing motion segmentation for the specific setting of interest. To this end, this paper presents a new approach for image-based motion segmentation in the case of vehicles navigating inside an urban environment. We exploit two application-specific factors – the restricted camera movement and the known type of moving objects – to deal with the two major limiting factors – missing data and strong perspective effects – that affect most previous “generic” motion segmentation algorithms. By constraining the geometry and exploiting known semantic classes in the scene, we achieve much higher accuracy than previous approaches. In addition to the novel algorithm, we contribute a more realistic motion segmentation benchmark dataset for moving platforms by annotating real video sequences from the KITTI dataset. Experiments on this dataset and other synthetic data confirm the effectiveness of the proposed approach.
A Bayesian Framework to Optimize Double Band Spectra Spatial Filters for Motor Imagery Classification

The ability to discriminate and classify different tasks is a crucial requirement for any Electroencephalogram (EEG) based Brain computer Interface (BCI). However, the intra and inter subject variability in the brain signal patterns is a bottleneck for developing general BCI systems and needs to be tackled. To address this issue, recently filter banks are deployed to extract frequency specific features, which are then fused at the classification step. On the other hand, some works deploy optimization techniques to design (extract) subject-specific filters (features). While both approaches have reached compromising results, there is still a huge gap between the performance of the techniques and that of humans. In this regard, we propose a Bayesian framework to simultaneously optimize a number of filter banks and spatial filters according to the patterns of brain activity for each subject. Referred to as the Bayesian double band spectro-spatial filter optimization (B2B-SSFO), the proposed method aims at combining the advantages of the two aforementioned approaches, and consists of two bandpass filters providing frequency specific features for each subject. The proposed framework is evaluated on dataset 2b from BCI Competition IV. The proposed B2B-SSFO approach outperforms its counterparts and introduces a robust framework for motor imagery studies.
The Learned Inexact Project Gradient Descent Algorithm

Accelerating iterative algorithms for solving inverse problems using neural networks have become a very popular strategy in the recent years. In this work, we propose a theoretical analysis that may provide an explanation for its success. Our theory relies on the usage of inexact projections with the projected gradient descent (PGD) method. It is demonstrated in various problems including image super-resolution.
Inordinate Voice Based Examination System

To abates the use of papers, to alleviate human efforts and to ameliorate examination system we have ascertain this system. As traditional system has attenuated there was a need of developing compound system to cope with technological advancements, so the solution is to build such an inordinate system. Gamut of systems do exist in the market some use OCR to evaluate the answers, some use machine learning to analyze answers, some even make use of short text matching algorithms. Roster of the techniques in improving inordinate voice based examination system are having some performance issues to achieve mere accuracy, so this paper presents a novel technique for inordinate voice based examination system.
Identifying safety properties guaranteed in changed environment at runtime

Safety properties for systems are guaranteed under assumptions to an environment. If the assumptions are broken at runtime, the safety properties are no longer guaranteed. The system should adapt to the changes in order to guarantee the safety properties or relaxed safety properties. Our purpose is establishing techniques to identify the maximum level of safety properties that can be guaranteed in a changed environment. The technique should be efficient so that it is applicable to runtime usage. In this paper, we propose an efficient algorithm that identifies the maximum level of safety properties. Our idea is analyzing availability of each safety property guarantee at a time and restricting analysis only in changed part of the previous result, instead of analysis from the scratch. We extend an existing analysis algorithm based on two-player game to realize the difference analysis. We evaluate our algorithm in terms of (1) level of safety properties and (2) computational time through two case studies.
The role of 17β-estradiol-induced upregulation of Piwi-like 4 in modulating gene expression and motility in breast cancer cells

A majority of breast cancer cases are positive for the estrogen receptor (ER), which means that they can respond to the estrogen hormone to achieve growth. Hence, the ER signaling pathway has been extensively targeted in pharmaceutical research and development in order to suppress tumor growth. However, prevalent hormone therapy and targeted therapy often become ineffective as cancer cells ultimately develop resistance, suggesting that there could be unidentified signaling molecules and events that regulate breast cancer growth. Notably, recent studies have uncovered that Piwi-like (Piwil) proteins, which were initially found in germline cells, are expressed in a wide spectrum of human cancers, including breast cancers. Although Piwil proteins have been well established to silence retrotransposons and to promote heterochromatin formation in germline cells, their somatic functions in cancer cells remain largely unknown. In the present study, we profiled the expression of four Piwi homologs in an ER-positive breast cancer cell line, MCF-7, and found that only Piwil4 was upregulated by 17β-estradiol treatment. Notably, Piwil4 upregulation was not observed in an ER-positive but non-tumorigenic breast cancer cell line, MCF-12A. In addition, the induced expression of Piwil4 was dependent on estrogen/ERα signaling. To explore the biological significance of Piwil4 in breast cancer growth, we knocked down Piwil4 with multiple siRNAs and observed the suppressed expression of some canonical targets of ER. The knockdown of Piwil4 expression also decreased the migration and invasion capabilities of MCF-7 cells. Furthermore, the loss-of-function of Piwil4 reduced the motility of MCF-7 cells in wound-healing assays, which could be associated to decreased expression of vimentin and N-cadherin. Collectively, these findings revealed that Piwil4 is a novel regulator of ER signaling that could be targeted to inhibit breast cancer growth and migration.
Cross-Modal Message Passing for Two-Stream Fusion

Processing and fusing information among multi-modal is a very useful technique to achieving high performance in many computer vision problem. In order to tackle multi-modal information more effectively, we introduce a novel framework for multi-modal fusion: Cross-modal Message Passing (CMMP). Specifically, we propose a cross-modal message passing mechanism to fuse two-stream network for action recognition, which composes of an appearance modal network (RGB image) and a motion modal (optical flow image) network. The objectives of individual networks in this framework are two-fold: a standard classification objective and a competing objective. The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one. We quantitatively show that the proposed CMMP fuse the traditional two-stream network more effectively, and outperforms all existing two-stream fusion method on UCF-101 and HMDB-51 datasets.
Learning Explicit Shape and Motion Evolution Maps for Skeleton-Based Human Action Recognition

Human action recognition based on skeleton sequences has wide applications in human-computer interaction and intelligent surveillance. Although previous methods have successfully applied Long Short-Term Memory(LSTM) networks to model shape evolution of human actions, it still remains a problem to efficiently recognize actions, especially for similar actions from sequential data due to the lack of the details of motion. To solve this problem, this paper presents an improved LSTM-based network to jointly learn explicit long-term shape evolution maps (SEM) and motion evolution maps (MEM). Firstly, human actions are represented as compact SEM and MEM, which mutually compensate. Secondly, these maps are jointly learned by deep LSTM networks to explore high-level temporal dependencies. Then, a weighted aggregate layer (WAL) is designed to aggregate outputs of L-STM networks cross different temporal stages. Finally, deep features of shape and motion are combined by decision level fusion. Experimental results on the currently largest NTU RGB+D dataset and public SmartHome dataset verify that our method significantly outperforms the state-of-the-arts.
User Experience of Web Browsing - The Relationship of Usability and Quality of Experience

The experience of web services depends on multiple factors such as the usability of the service itself as well as influences on Quality of Experience (QoE) like waiting times. Existing studies so far have investigated both experimental factors (usability and QoE) in isolation, thus it is not possible to investigate dependencies between the two. In this study, we manipulated the usability and QoE of a news web page in the same experiment. Participants had to solve 9 tasks (three usability level and three QoE level, all combinations). Data (N=44) from two different sessions with different QoE manipulations were recorded: Simulation of initial page display delay via 1) a complete overlay and 2) a semi-transparent overlay. Our preliminary results show that the usability manipulation resulted in significant effects along multiple dimensions (QoE, aesthetic, and usability). Furthermore, also the implementation of the QoE manipulation had an effect on, e.g., performance parameters. Surprisingly, our results reveal that participants seem to be unable to rate the constructs usability and QoE independently from each other. Even after adding pronounced explanations of the constructs to the introductory phase of the second session of the experiment, the results still show a strong correlation between the two constructs. We thus recommend for similar future experiments to check beforehand whether Quality of Experience and Usability can be reliably judged and distinguished by naive participants.
A New Soft Force Sensor using Blended Silicone-Magnetic powder

In this paper, a new soft sensor is introduced in order to measure normal forces for use in soft robotics applications. Soft sensors and actuators usually have high flexible structures and therefore require elements which are compatible with this flexibility. We introduce a new approach to achieve more desired softness and flexibility specifically for soft magnetic force sensors. For this purpose, magnetic powder is used instead of a solid piece of magnet. Therefore, the Hall Effect sensor detects magnetic flux density induced by magnetic powder blended with soft silicone rubber. Using magnetic powder provides flexibility and soft behavior to the sensor. In addition, compared with solid magnet designs, the effective working rang is expanded. Three different structural parameters i.e. powder density, magnetic part diameter and height of the sensor are studied in order to choose the most proper design to get the best performance depending on the expected characteristics behavior for the sensor. It is shown by experimental results that different desired measurement range as well as sensitivity can be achieved by appropriate selection of these three parameters, which makes the sensor usable for many low-cost soft robotics applications.
A Hybrid Method to Detect and Verify Vehicle Crash with Haar-Like Features and SVM Over the Web

In this paper, a multi-level vehicle crash detection is proposed. The first step is to detect and track the movement of the vehicles in a scene and based on the stop condition of any vehicle, we trigger the first level which is a possible crash detected. Next we scan the object with a pre-trained cascade detector looking for possible defects in it. The areas detected are passed into a verification function which is based on a pre-trained SVM to classify their HOG if they represent area of damage in a vehicle or not. We tested static images of few cars and we were able to detect and verify the defect in their body. Then we built a webcam simulator and a web application to test the solution in a real-time environment and over the web. The test succeeded and we were able to detect a real accident successfully.
Coverage and Cell-Edge Sum-Rate Analysis of mmWave Massive MIMO Systems With ORP Schemes and MMSE Receivers

In this study, we consider the downlink of millimeter-wave massive multiple-input multiple-output systems that employ orthogonal random precoding (ORP) and minimum-mean-square-error (MMSE) receivers. In the ORP scheme, a precoding matrix consisting of orthogonal vectors is utilized at the transmitter, which provides beamforming gains to enhance the maximum signal-to-interference-plus-noise ratio (SINR) at the receivers. In this study, the ability of the ORP scheme with MMSE receivers to extend the cell coverage and its sum-rate performance for cell-edge users are investigated. In particular, we first derive the approximate distribution of the maximum SINR at the output of the MMSE receiver. Then, to analyze and optimize the ORP scheme with MMSE receivers in terms of coverage and sum-rate performance, the analytical expressions for the downlink coverage probability and sum rate are derived. It is shown that there is a tradeoff between coverage and sum-rate performance in the ORP scheme. In particular, a smaller number of precoding vectors provides larger coverage; however, it leads to a lower sum rate. To achieve optimal tradeoff between coverage and sum rate for the ORP scheme, we propose using multiple random precoder groups over multiple time slots. It is shown that the proposed scheme is capable of significantly enhancing coverage performance while preserving an acceptable sum rate for cell-edge users. The simulation results validate the analytical findings.
Continuously Controllable Series Clutches for Efficient Robot Actuation

This paper investigates the energy efficiency potential of continuously controllable clutches between the motors and links of robot joints. Inspired by biological muscles, the clutch enables free, purely gravity driven robot link motion phases gradually disengaged from gear friction, not requiring motor effort. The concept combines the energetic benefits of direct drives during unforced motion phases with the high torque density of conventional and mature geared robotic drive technology during forced motion phases. The paper specifies the general functional principle of the clutch for energy saving independent of any particular clutch implementation. The feasible energy saving of up to 60 % is investigated for harmonic link motions with varying frequencies and with respect to different ratios of link weight to friction torque. The outcomes of the theoretical investigations are supported by first experimental results.
Towards Understanding Object-Directed Actions: A Generative Model for Grounding Syntactic Categories of Speech Through Visual Perception

Creating successful human-robot collaboration requires robots to have high-level cognitive functions that could allow them to understand human language and actions in space. To meet this target, an elusive challenge that we address in this paper is to understand object-directed actions through grounding language based on visual cues representing the dynamics of human actions on objects, object characteristics (color and geometry), and spatial relationships between objects in a tabletop scene. The proposed probabilistic framework investigates unsupervised Part-of-Speech (POS) tagging to determine syntactic categories of words so as to infer grammatical structure of language. The dynamics of object-directed actions are characterized through the locations of the human arm joints - modeled on a Hidden Markov Model (HMM) - while manipulating objects, in addition to those of objects represented in 3D point clouds. These corresponding point clouds to segmented objects encode geometric features and spatial semantics of referents and landmarks in the environment. The proposed Bayesian learning model is successfully evaluated through interaction experiments between a human user and Toyota HSR robot in space.
Divergent Biological Response to Neoadjuvant Chemotherapy in Muscle-invasive Bladder Cancer

Purpose: After cisplatin-based neoadjuvant chemotherapy (NAC), 60% of patients with muscle-invasive bladder cancer (MIBC) still have residual invasive disease at radical cystectomy. The NAC-induced biological alterations in these cisplatin-resistant tumors remain largely unstudied. Experimental Design: Radical cystectomy samples were available for gene expression analysis from 133 patients with residual invasive disease after cisplatin-based NAC, of whom 116 had matched pre-NAC samples. Unsupervised consensus clustering (CC) was performed and the consensus clusters were investigated for their biological and clinical characteristics. Hematoxylin & Eosin and IHC on tissue microarrays were used to confirm tissue sampling and gene expression analysis. Results: Established molecular subtyping models proved to be inconsistent in their classification of the post-NAC samples. Unsupervised CC revealed four distinct consensus clusters. The CC1-Basal and CC2-Luminal subtypes expressed genes consistent with a basal and a luminal phenotype, respectively, and were similar to the corresponding established pretreatment molecular subtypes. The CC3-Immune subtype had the highest immune activity, including T-cell infiltration and checkpoint molecule expression, but lacked both basal and luminal markers. The CC4-Scar–like subtype expressed genes associated with wound healing/scarring, although the proportion of tumor cell content in this subtype did not differ from the other subtypes. Patients with CC4-Scar–like tumors had the most favorable prognosis. Conclusions: This study expands our knowledge on MIBC not responding to cisplatin by suggesting molecular subtypes to understand the biology of these tumors. Although these molecular subtypes imply consequences for adjuvant treatments, this ultimately needs to be tested in clinical trials.
Vulnerability Assessment and Penetration Testing of Web Application

As Internet usage is rising day by day security has become a vital facet to the Internet world. Security of the website in today's world is very important. Vulnerability Assessment and Penetration Testing are two different vulnerability testing. These tests have different strengths and are frequently combined to get a more complete vulnerability analysis. Penetration Testing and Vulnerability Assessments execute two different tasks, usually with distinctive outcomes, within the same area of application. For any organization, proper working of security arrangement is checked by Vulnerability Assessment and Penetration Testing. Web applications vulnerable to attacks like Session exploitation, Cross-Site Scripting, SQL injection, Cross Site Request Forgery, Buffer over Flows, and Security Misconfiguration etc. are described in Open Web Application Security Project Top 10. The manual penetration test or automatically penetration test can be done, which depends upon vulnerabilities. Comparison is made between these two tests.
Topoisomerases I and III inhibit R-loop formation to prevent unregulated replication in the chromosomal Ter region of Escherichia coli

Type 1A topoisomerases (topos) are the only ubiquitous topos. E. coli has two type 1A topos, topo I (topA) and topo III (topB). Topo I relaxes negative supercoiling in part to inhibit R-loop formation. To grow, topA mutants acquire compensatory mutations, base substitutions in gyrA or gyrB (gyrase) or amplifications of a DNA region including parC and parE (topo IV). topB mutants grow normally and topo III binds tightly to single-stranded DNA. What functions topo I and III share in vivo and how cells lacking these important enzymes can survive is unclear. Previously, a gyrB(Ts) compensatory mutation was used to construct topA topB null mutants. These mutants form very long filaments and accumulate diffuse DNA, phenotypes that appears to be related to replication from R-loops. Here, next generation sequencing and qPCR for marker frequency analysis were used to further define the functions of type 1A topos. The results reveal the presence of a RNase HI-sensitive origin of replication in the terminus (Ter) region of the chromosome that is more active in topA topB cells than in topA and rnhA (RNase HI) null cells. The S9.6 antibodies specific to DNA:RNA hybrids were used in dot-blot experiments to show the accumulation of R-loops in rnhA, topA and topA topB null cells. Moreover topA topB gyrB(Ts) strains, but not a topA gyrB(Ts) strain, were found to carry a parC parE amplification. When a topA gyrB(Ts) mutant carried a plasmid producing topo IV, topB null transductants did not have parC parE amplifications. Altogether, the data indicate that in E. coli type 1A topos are required to inhibit R-loop formation/accumulation mostly to prevent unregulated replication in Ter, and that they are essential to prevent excess negative supercoiling and its detrimental effects on cell growth and survival.
Access to Dermatological Care with an Innovative Online Model for Psoriasis Management: Results from a Randomized Controlled Trial.

BACKGROUND Many patients with chronic skin diseases lack regular access to dermatologists in the United States and suffer poor clinical outcomes. INTRODUCTION We performed a 12-month randomized controlled trial to evaluate the impact of an online, collaborative connected health (CCH) model for psoriasis management on access to specialty care. MATERIALS AND METHODS The 300 enrolled patients were randomized to online or in-person care. We compared distance traveled as well as transportation and in-office waiting time between the two groups and obtained patient and provider perspectives on CCH. RESULTS At baseline, no differences existed between the groups in difficulties obtaining specialty care. Over 12 months, the mean (standard deviation [SD]) distance traveled to and from appointments was 174.8 (±577.4) km/person for the in-person group and 2.2 (±14.2) km/person for the online group (p = 0.0003). The mean (SD) time spent on transportation and in-office waiting for in-person appointments was 4.0 (±4.5) h/person for the in-person group and 0.1 (±0.4) h/person for the online group (p = 0.0001). Patients found CCH to be safe, accessible, equitable, efficient, effective, and patient-centered. Providers found CCH to be useful for providing psoriasis care. DISCUSSION The CCH model resulted in significantly less distance traveled as well as transportation and in-office waiting time compared to in-person care. Both patients and providers were highly satisfied with CCH. CONCLUSIONS The CCH model resulted in increased access to specialty care and enabled patient-centered, safe, and effective management of psoriasis patients.