Reviewerly/exHarmony huggingface dataset:
Real-Time Fall Detection Using Uncalibrated Fisheye Cameras

In this article, we describe an approach for the problem of fall detection among the several other everyday activities in indoor environment, using three uncalibrated fisheye cameras. The proposed methodology requires the input segmented silhouettes from the three simultaneously acquired frames, and it is capable of detecting fall events in every location of the imaged environment. The presented algorithm uses the model of fisheye image formation that is based on the spherical projection followed by central projection. Under this model, vertical structures are imaged as straight lines passing through the center of field of view, by a camera with approximately vertical optical axis. The main advantages of this article are the simplicity of the detecting rule, the speed of execution, and the utilization of heterogeneous omnidirectional cameras that allows simultaneous imaging along any direction. The proposed algorithm is designed and parameterized using an extensive data set of synthetic frames. The results from the real videos are presented using the frame statistics and the event-based statistics. The proposed algorithm correctly detects the fall events within standing or walking, as well as other nonfalling activities.
On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem

Meta-heuristics perform differently depending on the problem instance they are solving, meaning that manually choosing an algorithm is not trivial and an automatic selection is desirable. This task can be addressed using a meta-learning approach, which relates the characteristics of the problem instances to the performance of a set of solving algorithms. Therefore, the success of such approach is based on the quality of the extracted set of features. Some studies have proposed the use of features based on Fitness Landscape Analysis (FLA) to characterize optimization problems. However, extracting measures based on FLA usually requires a high computational effort. In a previous work, we have employed meta-heuristic selection on the Quadratic Assignment Problem (QAP) using some FLA measures. This research extends our study by including additional FLA meta-features, by using a less costly extraction method, and by considering more QAP instances. In total, we built five multi-label datasets, each composed of meta-features that were extracted by different sampling sizes, and then we used them to train Random Forest classifiers. Besides presenting satisfactory classification performance, and a decrease in time consumption in relation to our previous work, this selection approach was able to achieve better solution costs over the set of QAP instances if compared to running the meta-heuristics individually.
Supply Chain Management World

In the very near future, we anticipate that more and more artificially intelligent agents will be deployed to represent individuals and institutions. Automated negotiation environments are a mechanism by which to coordinate the behavior of such agents. Most existing work on automated negotiation assumes a context that is predefined, and hence, static. This paper focuses on the dynamic case, which we call situated negotiation, where agents need to decide not only how to negotiate, but with whom, and about what. We describe a common benchmark simulation environment for evaluating situated negotiation strategies, and evaluate several baseline strategies in the proposed environment.
Migrating Deprecated API to Documented Replacement

Deprecation is commonly leveraged to preserve the backwards compatibility in API clients during API evolution. Client developers then usually encounter migration tasks when relying on a new version of API. Manually migrating a deprecated API to its replacement may be tedious and time-consuming. Existing research has focused on the requirements of automated migration however has limitations on the consistency and granularity of the generated migration pathway. In this paper, we propose an approach for migrating deprecated APIs to their replacements which are explicitly documented in the code documentation. A migration pattern including the migration type and the migration strategy is first introduced to describe the difference and the code change from a deprecated API to its replacement. An automated process is then designed and implemented to construct the pattern for a specific deprecated API by reviewing the code documentation and analyzing the API source code. Based on the patterns, a tool named DAAMT is developed to locate deprecated APIs and to provide migration suggestions for API clients. An experimental study on open-source Java projects and a user study is performing utilizing the approach. The results show that three-fourth of the deprecated APIs with documented replacements involved in the projects can be automatically migrated. In addition, the DAAMT tool can improve the efficiency and accuracy of the tasks compared with the manual migration.
Fast Video Quality Enhancement using GANs

Video compression algorithms result in a reduction of image quality, because of their lossy approach to reduce the required bandwidth. This affects commercial streaming services such as Netflix, or Amazon Prime Video, but affects also video conferencing and video surveillance systems. In all these cases it is possible to improve the video quality, both for human view and for automatic video analysis, without changing the compression pipeline, through a post-processing that eliminates the visual artifacts created by the compression algorithms. Generative Adversarial Networks have obtained extremely high quality results in image enhancement tasks; however, to obtain such results large generators are usually employed, resulting in high computational costs and processing time. In this work we present an architecture that can be used to reduce the computational cost and that has been implemented on mobile devices. A possible application is to improve video conferencing, or live streaming. In these cases there is no original uncompressed video stream available. Therefore, we report results using no-reference video quality metric showing high naturalness and quality even for efficient networks.
An Efficient and Scene-Adaptive Algorithm for Vehicle Detection in Aerial Images Using an Improved YOLOv3 Framework

Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene-adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context-aware-based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length–width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.
The Problem of Online Manipulation

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Action Recognition Based on Spatial Temporal Graph Convolutional Networks

Compared with the achievements of convolutional neural networks in image classification, human action recognition for video is not ideal in terms of accuracy and practicability. A major method in action recognition is based on the human skeleton, which is an important information for characterizing human motion in video. In this paper, the human skeleton in video is extracted by OpenPose, and the spatial and temporal graph of skeleton is constructed. The spatial and temporal graph convolution network (ST-GCN) is used to extract the spatial and temporal features of the human skeleton on consecutive video frames, and the features is used for video classification. In order to verify the action recognition performance based on the ST-GCN, a 50.53% top-1 and 81.58% top-5 accuracy is obtained on the UCF-101 dataset. A specific UCF-31 dataset is constructed manually and a 68.73% top-1 and 94.43% top-5 accuracy is obtained, verifying that the identification accuracy of ST-GCN model would also be improved when the accuracy of skeleton acquisition was improved.
Relation Understanding in Videos

ACM Multimedia 2019 Video Relation Understanding Challenge is the first grand challenge aiming at pushing video content analysis at the relational and structural level. This year, the challenge asks the participants to explore and develop innovative algorithms to detect object entities and their relations based on a large-scale user-generated video dataset. The tasks will advance the foundation of future visual systems that are able to perform complex inferences. This paper presents an overview of the grand challenge, including background, detailed descriptions of the three proposed tasks, the corresponding datasets for training, validation and testing, and the evaluation process.
An Analysis of Longitudinal Trends in Consumer Thoughts on Presence and Simulator Sickness in VR Games

Since the release of the Oculus Rift CV1 in 2016, millions of VR headsets have found their way into consumer homes. In this paper, we sought to understand what shifts have taken place within the two years since consumer VR became available. In this paper, we consider what can be learned about long-term use of consumer VR through an analysis of discussions in online forums devoted to VR. We gathered posts made on the /r/Vive subreddit from the first two years after the HTC Vive's release. We present the results from an in-depth qualitative analysis concerning immersion, presence, and simulator sickness. Over time, as users moved from passive to active, their attitudes and expectations towards immersion and simulator sickness matured. Major trends of interest found were game design implementation and locomotion techniques.
The Design Space of Nonvisual Word Completion

Word completion interfaces are ubiquitously available in mobile virtual keyboards; however, there is no prior research on how to design these interfaces for screen reader users. In addressing this, we propose a design space for nonvisual representation of word completions. The design space covers seven categories aiming to identify challenges and opportunities for interaction design in an unexplored research topic. It is intended to guide the design of novel interaction techniques, serving as a framework for researchers and practitioners working on nonvisual word completion. To demonstrate its potential, we engaged blind users in an exploration of the design space, to create their own bespoke word completion solutions. Through this study we found that users create alternative interfaces that extended current screen readers' capabilities. Resulting interfaces are less conservative than mainstream solutions on notification frequency and cardinality. Customization decisions were based on perceived benefits/costs and varied depending on multiple factors such as users' perceived prediction accuracy, potential keystroke gains, and situational restrictions.
Improved Lattice-Based Signcryption in the Standard Model

Signcryption is a basic cryptographic primitive that simultaneously captures the functions of encryption and signature. To realize comprehensive information security against quantum computing attacks, lattice-based signcryption schemes have been successively proposed. However, the performance of signcryption schemes should be improved in the lattice setting. An efficient lattice-based signcryption scheme in the standard model is proposed in this paper. Under the ring learning with errors (RLWE) assumption and the ideal short integer solution (ISIS) assumption, the proposed signcryption scheme achieves indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability under an adaptive chosen-message attack (EUF-ACMA). Our scheme not only reduces the communication and computational overhead but also realizes a new design that combines the partitioning technique with the idea of tag-based key encapsulation. The performance analysis results show that our scheme is more efficient than previous lattice-based signcryption schemes in the standard model.
Attention-Based Response Generation Using Parallel Double Q-Learning for Dialog Policy Decision in a Conversational System

This article proposes an approach to response generation using a Parallel Double Q-learning algorithm for dialog policy decision in a conversational system. First, a new semantic representation of the user's input sentence is presented by using the CKIP parser to derive the semantic dependency sequence of the input sentence. Then, a Gated Recurrent Unit-based Autoencoder is used to obtain the user's turn representation as well as context representation. A Parallel Double Q-learning algorithm with a Deep Neural Network (PD-DQN), combining two Double DQNs in parallel for the contextual and semantic information in the user's message, respectively, are proposed to determine the dialog act. Finally, the user's input and the determined dialog act are fed to an attention-based Transformer model to generate the response template. With the generated response template, the semantic slots are filled with their corresponding values to obtain the final sentence response. This article collects a multi-turn conversation database consisting of 4186 turns in the travel domain and 447 chitchat question-answer pairs as the evaluation corpus. Five-fold cross validation is employed for performance evaluation. Experimental results show that the proposed approach based on semantic dependency for intent detection increases the accuracy by 4.3%. For dialog policy decision, the PD-DQN achieves 87.57% task success rate, which is 13.9% higher than the baseline Double DQN (73.67%). Finally, using the attention-based Transformer for response template generation obtains a Bleu score of 13.6, improved by 1.5 compared to the Sequence-to-Sequence model. In subjective evaluation, both the dialog policy and sentence generation model achieve a higher appropriateness and grammatical correctness scores than the baseline system.
A Comprehensive Review of Convolutional Neural Network based Image Enhancement Techniques

Image enhancement is the most important field in image research and processing. The main aim here is to artificially enhance the photo quality and visual appearance. Many photos end up with unusually high amount of noise and grain due to poor sensor quality and control, or with low sharpness. It is expensive and sometimes impossible to increase the resolution of the sensors (even counter intuitive at times) inside the cameras. Convolutional Neural Networks (CNN) rely on image structure and the texture to serve as differentiating features. In this paper, we do a literature review of CNN based techniques for Image denoising, Image super resolution, contrast enhancement, improving images in low light conditions, and then propose a method utilizing the above mechanisms, and our own devised system which uses nearest neighbor classification to rebuild images, along with an HDR photography approach similar to Google RAISR's.
IIRNet: A lightweight deep neural network using intensely inverted residuals for image recognition

Deep neural networks have achieved great success in many tasks of pattern recognition. However, large model size and high cost in computation limit their applications in resource-limited systems. In this paper, our focus is to design a lightweight and efficient convolutional neural network architecture by directly training the compact network for image recognition. To achieve a good balance among classification accuracy, model size, and computation complexity, we propose a lightweight convolutional neural network architecture named IIRNet for resource-limited systems. The new architecture is built based on Intensely Inverted Residual block (IIR block) to decrease the redundancy of the convolutional blocks. By utilizing two new operations, intensely inverted residual and multi-scale low-redundancy convolutions, IIR block greatly reduces its model size and computational costs while matches the classification accuracy of the state-of-the-art networks. Experiments on CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate the superior performance of IIRNet on the trade-offs among classification accuracy, computation complexity, and model size, compared to the mainstream compact network architectures.
EECDH to prevent MITM attack in cloud computing

Cloud computing has reached the peak of Gartner hype cycle, and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment. But data privacy and communication issues will occur with the increment of the cloud data storage. The key privacy concern for scalability is caused by the dynamic membership allocation and multi-owner data sharing. This paper addresses the issues faced by multiple owners through a mutual authentication mechanism using the Enhanced Elliptic Curve Diffie - Hellman (EECDH) key exchange protocol along with the Elliptic Curve Digital Signature Algorithm (ECDSA). The proposed EECDH scheme is used to exchange the secured shared key among multiple owners and also to eliminate the Man-In-The-Middle (MITM) attacks with less computational complexity. By leveraging these algorithms, the integrity of data sharing among multiple owners is ensured. The EECDH improves the level of security only slightly increasing the time taken to encrypt and decrypt the data, and it is secured against the MITM attacks, which is experimented using the AVISPA tool.
Adaptive Activation Functions Using Fractional Calculus

We introduce a generalization methodology for the automatic selection of the activation functions inside a neural network, taking advantage of concepts defined in fractional calculus. This methodology enables the neural network to define and optimize its own activation functions during the training process, by defining the fractional order of the derivative of a given primitive activation function, tuned as an additional training hyper-parameter. By following this approach, the neurons inside the network can adjust their activation functions, e.g. from MLP to RBF networks, to best fit the input data, and reduce the output error. The result show the benefits of using this technique implemented on a ResNet18 topology by outperforming the accuracy of a ResNet100 trained with CIFAR10 reported in the literature.
LiveSense

Live streaming has become a new form of entertainment, which attracts hundreds of millions of users worldwide. The huge amount of multimedia data in live streaming platforms creates tremendous opportunities for online advertising. However, existing state-of-the-art video advertising strategies (e.g., pre-roll and contextual mid-roll advertising) that rely on analyzing the whole video, are not applicable to live streaming videos. This paper describes a novel monetization framework, named LiveSense, for live streaming videos, which is able to display a contextually relevant ad at a suitable timestamp in a non-intrusive way. Specifically, given a live streaming video, we first employ a deep neural network to determine whether the current moment is appropriate for displaying an ad using the historical streaming data. Then, we detect a set of candidate ad insertion areas by incorporating image saliency, background map, and location priorities, so that the ad is displayed over the non-important area. We introduce three types of relevance metrics including textual relevance, global visual relevance and local visual relevance to select the contextually relevant ad. To minimize user intrusiveness, we initially display the ad at a non-important area. If the user is interested in the ad, we will show the ad in an overlaid window with a translucent background. Empirical evaluation on a real-world dataset demonstrates that our proposed framework is able to effectively display ads in live streaming videos while maintaining users' online experience.
ENF Based Video Forgery Detection Algorithm

The electric network frequency (ENF) is recorded in the videos taken under the lights powered by grid and can be used for digital forensics. However, due to the lack of data caused by the low frame rate of the video, the ENF-based forensics methods always need a reference signal extracted from the grid, which limits the practical application of these methods. In this article, a new ENF-based time domain video forgery detection algorithm is proposed to solve the problem of data lack. The cubic spline interpolation is used to generate suitable data points of the ENF signal, and the detection sequence generated based on the correlation coefficient between data points in adjacent periods is used to catch the phase continuity interruption of the ENF signal and detect the exact position of forgery. The proposed algorithm can be used independently without any reference signals. The experimental results show that the proposed algorithm has good performance in detecting forgery videos with varying degrees of deletion, duplication and insertion of frames.
Human-as-a-security-sensor for harvesting threat intelligence

Abstract Humans are commonly seen as the weakest link in corporate information security. This led to a lot of effort being put into security training and awareness campaigns, which resulted in employees being less likely the target of successful attacks. Existing approaches, however, do not tap the full potential that can be gained through these campaigns. On the one hand, human perception offers an additional source of contextual information for detected incidents, on the other hand it serves as information source for incidents that may not be detectable by automated procedures. These approaches only allow a text-based reporting of basic incident information. A structured recording of human delivered information that also provides compatibility with existing SIEM systems is still missing. In this work, we propose an approach, which allows humans to systematically report perceived anomalies or incidents in a structured way. Our approach furthermore supports the integration of such reports into analytics systems. Thereby, we identify connecting points to SIEM systems, develop a taxonomy for structuring elements reportable by humans acting as a security sensor and develop a structured data format to record data delivered by humans. A prototypical human-as-a-security-sensor wizard applied to a real-world use-case shows our proof of concept.