nils-herrmann/clean_scirepeval huggingface dataset:
Partial Copy Detection in Videos: A Benchmark and an Evaluation of Popular Methods
Venue: IEEE Transactions on Big Data

The goal of partial video copy detection is to find one or more segments of a query video which have (transformed) copies in a large dataset. Previous related research in this field used either small scale datasets or large datasets with simulated partial copies by imposing several pre defined transformations (e.g. photometric changes) due to the extremely time consuming annotation of real copies. It is still unknown how well the techniques developed on simulated datasets perform on real copies, which are much more challenging and too complex to be simulated. In this paper, we introduce a large scale video copy database (VCDB) with over 100,000 videos, and more than 9,000 copy pairs found by manual annotation. A state of the art system of video copy detection is evaluated on VCDB to show the limitations of existing techniques. We also evaluate deep learning features learned by two neural networks: one is independently trained on a different dataset and the other is tailored to deal with the copy detection task. Our evaluation suggests that all the existing techniques, including the deep learning features, are far from satisfactory in detecting complex real copies.
Weber Binarized Statistical Image Features (WBSIF) based video copy detection
Venue: J. Vis. Commun. Image Represent.

Display Omitted A novel Video Copy Detection (VCD) is proposed.The VCD is based on the Weber Binarized Statistical Image Features descriptor (WBSIF).The matching process is performed by using of the 2 test.Results outline the robustness and effectiveness with high accuracy.Proposed method shows a noteworthy performance against currently existing techniques. This paper presents a new video copy detection system based on testing similarities between textural feature vectors which are extracted from videos. Herein, the proposed method is based on Weber Binarized Statistical Image Features (WBSIF) which is an interior improvement of Weber Local Descriptor (WLD) Actually, the orientation gradient in WLD is substituted by a recent Binarized Statistical Image Features (BSIF) as a local textural descriptor. The WBSIF approach is tested on three databases and evaluated through several attacks. Moreover, the proposed method is compared to the recent existing approaches, especially those mostly used in the literature, which are based on the binary pattern descriptors. The obtained results outline the robustness and the effectiveness of the proposed video copy detection system in terms of precision, recall, Fscore, accuracy and collision test. This study shows clearly a noteworthy performance of the proposed scheme against currently existing techniques.
Shearlet Based Video Fingerprint for Content Based Copy Detection

Content based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the "natural parts" (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content based video copy detection applications. The proposed Shearlet based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion) geometry attacks (letter box and rotation) and temporal attacks (dropping frame, time shifting) The experimental results demonstrate that the proposed TIRI SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.
Towards Camera LIDAR Fusion Based Terrain Modelling for Planetary Surfaces: Review and Analysis
Venue: Sensors

In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state of the art method and can achieve short distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR) LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera LIDAR fusion in terms of range, fidelity, accuracy and computation.
Supporting agent oriented requirement analysis with ontologies
Venue: Int. J. Hum. Comput. Stud.

Requirements analysis activities underpin the success of the software development lifecycle. Subsequent errors in the requirements models can propagate to models in later phases and become much costlier to fix. Errors in requirement analysis are more likely in developing complex systems. Particularly, errors due to miscommunication and misinterpretation of a client's intentions are common. Ontologies relying on formal descriptions of semantics have often been used in multi agent systems (MAS) development to support various activities and generally improve the complex systems produced. However, their use during requirements analysis to validate match with the client's conceptualisation is largely unexplored. This article presents an ontology driven validation process to support requirement analysis of MAS models. This process is underpinned by an agent based metamodel that describes commonly used informal agent requirement models. The process concurrently and incrementally validates the informal MAS requirement models produced. The synthesis of the process is first justified and illustrated in a manual tracing of the process. The paper then describes an interactive support tool to harness the formal semantics of ontologies and by pass the costly manual effort. The validation process is evaluated and illustrated using three case studies. There is need for high quality requirement models in any iterative process.This is acute in informal modelling settings e.g. agent oriented analysis.A tool harnesses domain knowledge to support analysis.We validate highlighting the methodology independence of the overall approach.We validate also highlighting the domain independence of the overall approach.
RF system requirement analysis and simulation methods towards 5G radios using massive MIMO
Venue: 2016 46th European Microwave Conference (EuMC)

Methods of performing RF system requirement analysis towards 5th generation massive multiple input multiple output system are described. For the system analysis, digital signal processing is abstracted to minimum SNR requirement definitions and RF system SNR is described based on realistic but highly simplified transmitter and receiver performance related to output and input power levels, respectively. In addition, a simple link adaptation model is created for dynamic simulation tasks. As the contribution of antenna array is seen to become necessity in cm and mm wave bands for 5G systems, we show ways to include antenna modelling in system analysis. By using these analysis methods, we have generated a link level simulator that can be used for system analysis using different abstractions of antennas. Finally, it is shown that even simple RF models need to have certain implementation related details in order to provide good accuracy in link budget and operation range analysis.
Improving IS development teams' performance during requirement analysis in project The perspectives from shared mental model and emotional intelligence

Numerous studies have investigated factors affecting the project requirement analysis of information system development (ISD) teams from the view of technology, but our research focused on how developers' behaviors affected project team members' requirements analysis work from the emotional intelligence (EI) and shared mental model (SMM) perspectives. Specifically, we separated SMM into task related SMM and member related SMM to examine their impacts on ISD teams' performances during requirement analysis phase. Then we chose four scales of EI to research the relationships between them and SMMs. Using the approach of structural equation model, the results indicated that two aspects of SMM both have significant and positive impact on team performance, and EI could be the antecedents of SMM. The results indicate that SMM could enhance the influences of EI on project team performance, so the choice of individual team members and the team building are both significant to ISD teams for better performance in project requirement analysis.
Automatic Classification of Anuran Sounds Using Convolutional Neural Networks
Venue: C3S2E

Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds.
A Review on Applications of Machine Vision Systems in Industries

This paper presents a review on the applications of machine vision technology in various industries such as food industry, textile industry, PCB industry and tile industry, thereby enhancing the conventional system.  This technique used for inspection, sorting, handling and testing various components by machine vision is given with ample illustrations. Using this as a guide, generic machine vision approaches can be derived and applied to solve many problems.  The machine vision techniques applied in the above mentioned fields are studied. It has been proven from research that automating processes in several industries has led to better results than manual processing. As a scientific discipline, machine vision is concerned with the concept behind artificial systems that incorporates image processing. Machine vision systems are based on extracting the images of the objects which are to be inspected/tested and processing them to retrieve the required data. This review paper provides an insight on how to combine two or more machine vision techniques to successfully incorporate them in more generic applications that may have the potential to perform with greater agility and accuracy.
Machine vision system for food grain quality evaluation: A review

Quality of pre processed food grains is a critical aspect and a major decider of market acceptability, storage stability, processing quality, and overall consumer acceptance. Among various indices of food grain quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the grain. Conventional method of grain quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy. Scope and approach Machine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for quality evaluation of numerous agricultural products. This note provides an up to date review on the major applications of machine vision systems for grain quality evaluation applications in non touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems.  Machine vision systems can provide rapid and accurate information about external quality aspects of food grains. However, it is a task to integrate such systems with those that can explain internal grain quality attributes. In the near future, with ever growing application requirements and research developments, machine vision systems can provide effective solutions for various grain quality evaluation applications.
A Review of Machine Vision Based Structural Health Monitoring: Methodologies and Applications
Venue: J. Sensors

In the past two decades, a significant number of innovative sensing and monitoring systems based on the machine vision based technology have been exploited in the field of structural health monitoring (SHM) This technology has some inherent distinctive advantages such as noncontact, nondestructive, long distance, high precision, immunity to electromagnetic interference, and large range and multiple target monitoring. A lot of machine vision based structural dynamic measurement and structural state inspection methods have been proposed. Real world applications are also carried out to measure the structural physical parameters such as the displacement, strain/stress, rotation, vibration, crack, and spalling. The purpose of this review article is devoted to presenting a summary of the basic theories and practical applications of the machine vision based technology employed in structural monitoring as well as its systematic error sources and integration with other modern sensing techniques.
Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
Venue: J. Mach. Learn. Res.

Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full texts of trial reports is thus a critical yet time consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence level annotations. To this end, we propose a novel method supervised distant supervision (SDS) that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.
Root Endophyte Colletotrichum tofieldiae Confers Plant Fitness Benefits that Are Phosphate Status Dependent
Venue: Cell

A staggering diversity of endophytic fungi associate with healthy plants in nature, but it is usually unclear whether these represent stochastic encounters or provide host fitness benefits. Although most characterized species of the fungal genus Colletotrichum are destructive pathogens, we show here that C. tofieldiae (Ct) is an endemic endophyte in natural Arabidopsis thaliana populations in central Spain. Colonization by Ct initiates in roots but can also spread systemically into shoots. Ct transfers the macronutrient phosphorus to shoots, promotes plant growth, and increases fertility only under phosphorus deficient conditions, a nutrient status that might have facilitated the transition from pathogenic to beneficial lifestyles. The host's phosphate starvation response (PSR) system controls Ct root colonization and is needed for plant growth promotion (PGP) PGP also requires PEN2 dependent indole glucosinolate metabolism, a component of innate immune responses, indicating a functional link between innate immunity and the PSR system during beneficial interactions with Ct.
Evaluation of breviscapine on prevention of experimentally induced abdominal adhesions in rats.
Venue: American journal of surgery

Adhesion formation, which results from mechanical peritoneal damage, tissue ischemia, or the presence of foreign materials, is a complicated process. The formation of adhesions is associated with inflammatory response and extracellular matrix deposition in response to injury. Although the pathophysiology of adhesion formation is widely understood, an absolute solution to this problem does not exist yet. As a main component of Erigeron breviscapus, breviscapine has exhibited the ability of anti inflammatory and antifibrosis on many diseases. The purpose of this study was to investigate the effect of breviscapine on the development of postoperative intra abdominal adhesions in Wistar rats.  Abdominal adhesions were induced by scraping the cecum in rats. Various dosages of breviscapine drugs were administered for 10 days after surgery. On the 11th day after surgery, the levels of interleukin (IL) 18, IL 6, tumor necrosis factor a in blood serum and transforming growth factor b1 (TGF b1) connective tissue growth factor, tissue plasminogen activator (tPA) plasminogen activator inhibitor 1 (PAI 1) in peritoneal fluid were determined by enzyme linked immunosorbent assay. The expression of Smad7 and TGF b1 in rat cecum tissue was evaluated by Western blot analysis. Grades of intestinal adhesion were ranked by macroscopic observation.  The intraperitoneal administration of breviscapine is effective on the prevention of the formation of postoperative adhesions in rats. Breviscapine decreased the levels of IL 18, IL 6, and tumor necrosis factor a in blood serum and TGF b1, connective tissue growth factor, PAI 1 in peritoneal fluid. But the levels of tPA and the ratio of tPA and PAI 1 in peritoneal fluid were increased. In addition, breviscapine significantly inhibited the expression of TGF b1 and increased the level of Smad7 in the rat cecum tissue.  These results suggested that intraperitoneal administration of breviscapine was effective in preventing intra abdominal adhesion formation in rats. Breviscapine appears to have synergetic effects which could decrease fibrosis by inhibiting inflammation, upregulating peritoneal fibrinolytic activity and regulating the TGF and/or Smad signaling pathway. These data indicated a potential new therapeutic use of breviscapine on adhesion prevention.
Keratinocyte Growth Factor Combined with a Sodium Hyaluronate Gel Inhibits Postoperative Intra Abdominal Adhesions
Venue: International journal of molecular sciences

Postoperative intra abdominal adhesion is a very common complication after abdominal surgery. One clinical problem that remains to be solved is to identify an ideal strategy to prevent abdominal adhesions. Keratinocyte growth factor (KGF) has been proven to improve the proliferation of mesothelial cells, which may enhance fibrinolytic activity to suppress postoperative adhesions. This study investigated whether the combined administration of KGF and a sodium hyaluronate (HA) gel can prevent intra abdominal adhesions by improving the orderly repair of the peritoneal mesothelial cells. The possible prevention mechanism was also explored. The cecum wall and its opposite parietal peritoneum were abraded after laparotomy to induce intra abdominal adhesion formation. Animals were randomly allocated to receive topical application of HA, KGF, KGF HA, or normal saline (Control) On postoperative day 7, the adhesion score was assessed with a visual scoring system. Masson's trichrome staining, picrosirius red staining and hydroxyproline assays were used to assess the magnitude of adhesion and tissue fibrosis. Cytokeratin, a marker of the mesothelial cells, was detected by immunohistochemistry. The levels of tissue plasminogen activator (tPA) interleukin 6 (IL 6) and transforming growth factor b1 (TGF b1) in the abdominal fluid were determined using enzyme linked immunosorbent assays (ELISAs) Western blotting was performed to examine the expression of the TGF b1, fibrinogen and a smooth muscle actin (a SMA) proteins in the rat peritoneal adhesion tissue. The combined administration of KGF and HA significantly reduced intra abdominal adhesion formation and fibrin deposition and improved the orderly repair of the peritoneal mesothelial cells in the rat model. Furthermore, the combined administration of KGF and HA significantly increased the tPA levels but reduced the levels of IL 6, tumor necrosis factor a (TNF a) and TGF b1 in the abdominal fluid. The expression levels of TGF b1, fibrinogen and a SMA protein and mRNA in the rat peritoneum or adhesion tissues were also down regulated following the combined administration of KGF and HA. The combined administration of KGF and HA can significantly prevent postoperative intra abdominal adhesion formation by maintaining the separation of the injured peritoneum and promoting mesothelial cell regeneration. The potential mechanism may be associated with rapid mesothelial cell repair in the injured peritoneum. This study suggests that combined administration of KGF and HA may be a promising pharmacotherapeutic strategy for preventing abdominal adhesions, which is worth further study, and has potential value in clinical applications.