Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

Mathematical modeling of blanched and unblanched solar dried ginger rhizome varieties.

This research examines the mathematical modelling of blanched and unblanched solar dried ginger rhizome varieties. The Umudike ginger I and II (UG I and UG II) were blanched with an Electric water bath in the Soil and Water Laboratory, Agricultural and Bioresources Engineering Department, Michael Okpara University of Agriculture Umudike, Abia State. The samples UG I and UG II, were blanched for 3, 6, and 9 minutes at 50℃ respectively. Each samples with the treatment were subjected to active solar drying in sequence. Also, blanched and unblanched UG I and UG II were subjected to active solar drying. The treatment was carried out at 10mm thickness for UG I and UG II rhizome. There were ten different mathematical drying models compared based on the correlation coefficient, mean bias error, root mean square error and reduced chi-square method. The various models used are efficient thin layer drying models and its best fitted model varies due to the blanched and unblanched treatments of UG I and UG II. It was also used to validate and predict equations for all the treatments. The Henderson and Pabis model was recommended for predicting the drying characteristics of blanched and unblanched UG I and UG II ginger rhizomes

Electron-pion separation in the ATLAS Tile hadron calorimeter

The ATLAS hadron Tile Calorimeter performance has been extensively studied during the test beam periods at the CERN SPS accelerator. The SPS beams contain the mixtures of the electrons, muons and pions, but for the physics studies it is important to deal with the pure beam species. Several methods of electron - pion separation were comparatively studied in this note, using available test beam data and detailed Monte Carlo simulation

Determinants of physicians’ intention to collect data exhaustively in registries: an exploratory study in Bamako’s community health centres

Background: The incomplete collection of health datais a prevalent problem in healthcare systems around theworld, especially in developing countries. Missing datahinders progress in population health and perpetuatesinefficiencies in healthcare systems.Objective: This study aims to identify the factors that predict the intention of physicians practicing in community health centres of Bamako, Mali, to collect data exhaustively in medical registries.Design: A cross sectional studyMethod: In January and February 2011, we conducted a study with a random sample of thirty two physicians practicing in community health centres of Bamako, using a questionnaire. Data was analyzed by using descriptive statistics, correlations and linear regression.Main outcomes measures: Trained investigators administered a questionnaire measuring physicians’ sociodemographic and professional characteristics as well as constructs from the Theory of Planned Behaviour.Results: Our results showed that physicians’ intention to collect data exhaustively is influenced by subjective norms and by the physician’s number of years in practice.Conclusions: the results of this study could be used as a guide for health workers and decision makers to improve the quality of health information collected in community health centers.Keywords: Physicians’ intention, exhaustive data collection, Bamako, Community Health Centre, Missing dat

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

Bose-Einstein Condensation of Helium and Hydrogen inside Bundles of Carbon Nanotubes

Helium atoms or hydrogen molecules are believed to be strongly bound within the interstitial channels (between three carbon nanotubes) within a bundle of many nanotubes. The effects on adsorption of a nonuniform distribution of tubes are evaluated. The energy of a single particle state is the sum of a discrete transverse energy Et (that depends on the radii of neighboring tubes) and a quasicontinuous energy Ez of relatively free motion parallel to the axis of the tubes. At low temperature, the particles occupy the lowest energy states, the focus of this study. The transverse energy attains a global minimum value (Et=Emin) for radii near Rmin=9.95 Ang. for H2 and 8.48 Ang.for He-4. The density of states N(E) near the lowest energy is found to vary linearly above this threshold value, i.e. N(E) is proportional to (E-Emin). As a result, there occurs a Bose-Einstein condensation of the molecules into the channel with the lowest transverse energy. The transition is characterized approximately as that of a four dimensional gas, neglecting the interactions between the adsorbed particles. The phenomenon is observable, in principle, from a singular heat capacity. The existence of this transition depends on the sample having a relatively broad distribution of radii values that include some near Rmin.Comment: 21 pages, 9 figure