nils-herrmann/clean_scirepeval huggingface dataset:
Briskola: BRISK optimized for low-power ARM architectures

Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or Visual Sensor Networks (VSN). In this work, we dissect and analyze BRISK, a state-of-the-art low-power visual feature extractor, in order to evaluate the impact of its individual building blocks on the overall energy consumption. For each building block, we propose a solution to limit the energy consumption without affecting the overall analysis performance. The resulting BRISKOLA (BRISK Optimized for Low-power ARM architectures) feature extractor exhibits energy savings up to 30% with respect to the original implementation.
Crowdsourcing for Top-K Query Processing over Uncertain Data

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top-K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set.
Low bitrate coding schemes for local image descriptors

Efficient coding of local image descriptors is of paramount importance when they need to be transmitted to a remote destination on bandwidth constrained networks. This is a case that arises, e.g., in mobile visual search and visual wireless sensor networks. In this work we consider SURF, a popular descriptor suitable for low-complexity devices, and we provide a comparative study of lossy coding schemes operating at low bitrate (e.g., less than 128 bits / descriptor). Our investigation covers schemes that address both intra- and inter-descriptor redundancy, including methods that have not been tested before in this context, e.g., sparse coding, lifting-based coding on trees, and hybrid intra and inter-descriptor coding. The experimental evaluation is carried out on two publicly available datasets, in terms of both rate-distortion and rate-accuracy, for the specific task of object recognition. Our results show that a rate saving of 15-30% can be achieved by exploiting intra-descriptor redundancy. On the other side, addressing inter-descriptor redundancy does not lead to substantial gains when applied alone, whereas it leads to marginal gains (up to 3%) when used in hybrid schemes jointly with intra-descriptor coding.
EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

We present a complete, open-source framework for rapid experimentation of visual sensor network (VSN) solutions. From the software point of view, we base our architecture on open-source and widely known C++ libraries to provide the basic image processing and networking primitives. The resulting system can be leveraged to create different types of VSNs, characterized by the presence of multiple cameras, relays and cooperator nodes, and can be run on any Linux-based hardware platform, such as the BeagleBone Black. To demonstrate the flexibility of the proposed framework, we describe two different application scenarios typical of VSNs, namely object recognition and parking monitoring. The framework is then used to evaluate the benefits of two complementary paradigms for networked visual analysis recently discussed in the literature. In the traditional compress-then-analyze (CTA) paradigm, compressed images are transmitted from camera nodes to a central controller, where they are analyzed. In the novel analyze-then-compress (ATC) paradigm, camera nodes extract and compress local features from the acquired images. Such features are transmitted to the central controller and used to perform visual analysis. We show that the ATC paradigm outperforms CTA from the consumed energy point of view, at the same target analysis accuracy in both the application scenarios.
Distributed object recognition in Visual Sensor Networks

This work focuses on Visual Sensor Networks (VSNs) which perform visual analysis tasks such as object recognition. There, the goal is to find the image in a reference database which is the closest match to the image captured by camera sensor nodes. Recognition is performed by relying on visual features extracted from the acquired image, which are matched against a database of labeled features in order to find the closest image match. The matching functionalities are often implemented at a central controller outside the VSN. In contrast, we study the performance trade-offs involved in distributing the matching functionalities inside the VSN by letting sensor nodes performing parts of the matching process. We propose an optimization framework to optimally distribute the matching task to in-network sensor nodes with the goal of minimizing the overall completion time of the recognition task. The proposed optimization framework is then used to assess the performance of distributed matching, comparing it to a traditional, centralized approach in realistic VSN scenarios.
A visual sensor network for parking lot occupancy detection in Smart Cities

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, real-time parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energy-accuracy performance.
HistoGraph -- A Visualization Tool for Collaborative Analysis of Networks from Historical Social Multimedia Collections

This paper describes the design and development of histoGraph, an interactive tool for explorative visualization and collaborative investigation of historical social networks from multimedia collections. Developed in an interdisciplinary collaboration of computer scientists, historians, HCI researchers and interface designers, the tool aims at supporting historians in the discovery and historical analysis of relationships between people, places and events. A special focus is on the identification and interactive visualization of social relations from historical photo collections through a combination of automatic analysis and expert-based crowd sourcing. The tool design bridges the gap between established network analysis and visualization techniques and traditional hermeneutic research methods in historical research. It integrates visual exploration with hybrid social graph construction, hypothesis formulation and the consultation of digitized primary sources. A formative evaluation of the current prototype, developed as a domain-specific application for historians in the field of European integration points to opportunities and critical factors in applying this approach to support and further current research practices in digital humanities.
Crowdsourcing for Top-K Query Processing over Uncertain Data

Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top- $K$ queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set.
Secure image databases through distributed source coding of SIFT descriptors

The adoption of distributed databases calls for storing data at two or more sites, in order to address application-specific requirements including, e.g., redundancy, data locality, and so on. When visual data (including images, videos and their corresponding descriptors) need to be stored, synchronization across different sites might require significant bandwidth resources. In this paper we explore the use of distributed source coding to encode local SIFT descriptors extracted from static images. The key tenet is to exploit, at the decoder side, the correlation between matching pairs of descriptors extracted, respectively, from the out-of-date and up-to-date image. Preliminary results show that a coding efficiency gain up to 1 bit/descriptor can be achieved in the case of ideal lossless coding. In the case of distributed source coding with LDPC codes, a practical average gain of 0.19 bit/descriptor is observed.
Snow phenomena modeling through online public media

We propose a method for the environmental monitoring through the publicly available media User Generated Content (UGC). In particular we address the problem of the snow cover and level estimation by analyzing the social media data such as geotagged photographs and public webcams installed in mountain regions. The entire pipeline of the process is presented to the audience: from the data crawling and automatic relevance classification (does or does not the photograph contain a significant mountain profile) to the image content analysis and environmental models (identification of the snow covered area on the photograph). Each presented component is self-contained and can be inspected individually, the connections between the components however are strongly highlighted allowing the viewer to understand intuitively the entire pipeline structure.
Compress-then-Analyze versus Analyze-then-Compress: What Is Best in Visual Sensor Networks?

Visual sensor networks (VSNs) have attracted the interest of researchers worldwide in the last few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT). When used to perform visual analysis tasks, VSNs may be operated according to two different paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed and transmitted for further analysis. Conversely, in the analyze-then-compress paradigm, image features are extracted by visual sensor nodes, encoded and then delivered to a remote destination where analysis is performed. The question this paper aims to answer is What is the best visual analysis paradigm in VSNs?To do this, first we empirically characterize the rate-energy-accuracy performance of the two aforementioned paradigms. Then, we leverage such models to formulate a resource allocation problem for VSNs. The problem optimally allocates the specific paradigm used by each camera node in the network and the related transmission source rate, with the objective of optimizing the accuracy of the visual analysis task and the VSN coverage. Experimental results over several VSNs instances demonstrate that there is no “winning” paradigm, but the best performance are obtained by allowing the coexistence of the two and by properly optimizing their utilization.
Energy Consumption of Visual Sensor Networks: Impact of Spatio-Temporal Coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PANs) under recently established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by a number of camera sensors deployed (spatial coverage), as well as a number of captured video frames of which each node processes and transmits data (temporal coverage). In this paper we explore this aspect for uniformly formed VSNs, that is, networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatio-temporal coverage parameters of such VSNs under a priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results are derived from a deployment of TelosB motes and reveal that our analytic results are always within 7% of the energy consumption measurements for a wide range of settings. In addition, results obtained via motion JPEG encoding and feature extraction on a multimedia subsystem (BeagleBone Linux Computer) show that the optimal spatio-temporal settings derived by our framework allow for substantial reduction of energy consumption in comparison with ad hoc settings.
Experimental evaluation of a video streaming system for Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks (WMSNs) are recently emerging as an extension to traditional scalar wireless sensor networks, with the distinctive feature of supporting the acquisition and delivery of multimedia content such as audio, images and video. In this paper, a complete framework is proposed and developed for streaming video flows in WMSNs. Such framework is designed in a cross-layer fashion with three main building blocks: (i) a hybrid DPCM/DCT encoder; (ii) a congestion control mechanism and (iii) a selective priority automatic request mechanism at the MAC layer. The system has been implemented on the IntelMote2 platform operated by TinyOS and thoroughly evaluated through testbed experiments on multi-hop WMSNs. The source code of the whole system is publicly available to enable reproducible research.
Geometric calibration of distributed microphone arrays

Computational auditory scene analysis exploits signals acquired by means of microphone arrays. In some circumstances, more than one array is deployed in the same environment. In order to effectively fuse the information gathered by each array, the relative location and pose of the arrays needs to be obtained solving a problem of geometric inter-array calibration. We consider the case where the arrays do not share a synchronous clock, which impairs the use of time-difference of arrival measures across arrays. Conversely, each array produces an acoustic image, which describes the energy of acoustic signals received from different directions. We jointly consider acoustic images acquired by the different arrays and adapt computer vision techniques to solve the calibration problem, thus estimating the location and pose of microphone arrays sensing the same auditory scene. We evaluate the robustness of the calibration process in a simulated environment and we investigate the effect of the various system parameters, namely the number of probing signal locations, the resolution of the acoustic images, the non-ideal intra-array calibration.
Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks

We compare two paradigms for image analysis in visual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the “winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images.
A Draw-and-Guess Game to Segment Images

Human Computation is defined as the integration of human tasks and automated algorithms to achieve superior quality in complex tasks like multimedia content analysis. This paper discusses a scenario in which human computation is used to segment time stamped fashion images for mining trends based on visual features of garments (e.g., color and texture) and attributes of portrayed subjects (e.g., gender and age). State-of-the-art algorithms for body part detection and feature extraction can produce low quality results when parts of the body are occluded and when dealing with complex human poses. In such cases, these algorithms could benefit from the assistance of human agents. In order to jointly leverage the potential of crowds and image analysis algorithms, a game with a purpose (GWAP) is proposed, whereby players can help segment images for which specialized algorithms have failed, so as to improve the extraction of color and texture features of garments and their association with the features of the subject wearing them.
Multi-view coding and routing of local features in Visual Sensor Networks

Visual Sensor Networks (VSNs) have been recently used for implementing automatic visual analysis tasks where local image features, instead of images, are compressed and transmitted to a central controller. Such features may also be compressed in a multi-view fashion, exploiting the redundancy between overlapping views. In this paper we analyze the problem of multi-view coding and routing of features in VSNs. We empirically analyze the relationship between the bitrate reduction obtained with a practical multi-view local features encoder and several geometry-based, image-based and feature-based predictors. The purpose of this analysis is to identify the most accurate, yet compact predictor of the achievable compression efficiency when jointly encoding correlated streams of local features. Then, we propose a robust optimization framework that exploits the aforementioned predictors. The proposed mathematical problem maximizes the amount of data extracted from the VSN by properly routing the streams of features, subject to capacity, interference and energy constraints, explicitly considering the uncertainty in the compression efficiency estimation. Extensive experiments on simulated VSNs show that multi-view coding maximizes the amount of data extracted from camera nodes, while the robust optimization approach provides significant improvement in uncertain scenarios compared to the optimal solution of a deterministic approach.
Anomaly-free Prediction of Gene Ontology Annotations Using Bayesian Networks

Gene and protein structural and functional annotations expressed through controlled terminologies and ontologies are paramount especially for the aim of inferring new biomedical knowledge through computational analyses. However, the available annotations are incomplete, in particular for recently studied genomes, and only a few of them are highly reliable human curated information. To support and speed up the time-consuming curation process, prioritized lists of computationally predicted annotations are hence extremely useful. In this paper we leverage a previous work on the automatic prediction of Gene Ontology annotations based on the singular value decomposition (SVD) of the gene-to-term annotation matrix, and we propose a novel post-processing method that uses a Bayesian network to eliminate predictions of anomalous annotations. In fact, we observed that the predicted annotation profiles might suggest that a gene shall be annotated to a term, but not to one of its ancestors, thus violating the constraint imposed by the Gene Ontology. To this end, the proposed algorithm processes the annotation profiles predicted by a SVD based method, and produces a ranked list of computationally discovered candidate annotations which is consistent with the Gene Ontology.
Audio tampering detection via microphone classification

In this paper, we present a new approach for audio tampering detection based on microphone classification. The underlying algorithm is based on a blind channel estimation, specifically designed for recordings from mobile devices. It is applied to detect a specific type of tampering, i.e., to detect whether footprints from more than one microphone exist within a given content item. As will be shown, the proposed method achieves an accuracy above 95% for AAC, MP3 and PCM-encoded recordings.
Estimating Snow Cover From Publicly Available Images

In this paper, we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user-generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite, and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, and handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year the fraction of visible area covered by snow, possibly at different elevations. We created a manually labeled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.
Search Computing: Managing Complex Search Queries

Search computing focuses on building answers to complex search queries (for example, "Where can I attend an interesting conference in my field near a sunny beach?") by interacting with a constellation of cooperating search services, and using result ranking and joining as the dominant factors for service composition. The service computing paradigm has so far been neutral to the specific features of search applications and services. To address this weakness, search computing advocates a new approach in which search, join, and ranking are the central aspects for service composition.