Reviewerly/exHarmony huggingface dataset:
Real-Time Spatio-Temporal Action Localization via Learning Motion Representation

Most state-of-the-art spatio-temporal (S-T) action localization methods explicitly use optical flow as auxiliary motion information. Although the combination of optical flow and RGB significantly improves the performance, optical flow estimation brings a large amount of computational cost and the whole network is not end-to-end trainable. These shortcomings hinder the interactive fusion between motion information and RGB information, and greatly limit its real-world applications. In this paper, we exploit better ways to use motion information in a unified end-to-end trainable network architecture. First, we use knowledge distillation to enable the 3D-Convolutional branch to learn motion information from RGB inputs. Second, we propose a novel motion cue called short-range-motion (SRM) module to enhance the 2D-Convolutional branch to learn RGB information and dynamic motion information. In this strategy, flow computation at test time is avoided. Finally, we apply our methods to learn powerful RGB-motion representations for action classification and localization. Experimental results show that our method significantly outperforms the state-of-the-arts on dataset benchmarks J-HMDB-21 and UCF101-24 with an impressive improvement of \(\sim \)8% and \(\sim \)3%.
Intrinsic Evaluation of Word Embeddings for Turkish

Word embeddings are evaluated through intrinsic and extrinsic tests. Similarity and analogy test are mainly preferred for intrinsic evaluation and natural language processing tasks such as named entity recognition and question answering are prefferred for extrinsic evaluation. Although there are various intrinsic evaluation datasets for English, the datasets for Turkish are very limited and measuring the degree of similarity and relatedness between words without specifying the type of semantic relation. In this paper, we propose an intrinsic evaluation dataset for evaluating different semantic relations other than a synonym, antonym, hypernym, and meronym as well as morphological relations of individual Turkish words. Moreover, we benchmark three publicly available word-embedding models on the proposed dataset and discuss agglutinative characteristics of the Turkish language for language modeling.
Parameter tuning of a genetic algorithm for finding central vertices in graphs

Abstract This paper studies a genetic algorithm for finding the central vertices in graphs. The algorithm uses a different approach to the solution method presentation and describes a new insight in the crossover process. Studies are conducted to find the optimal parameters of the genetic algorithm such us crossover probability, mutation probability and population size. Based on the results, it can be claimed that with the right parameters, our algorithm shows good running time results with high accuracy of the correct answers.
Unsupervised Disentangling of Viewpoint and Residues Variations by Substituting Representations for Robust Face Recognition

It is well-known that identity-unrelated variations (e.g., viewpoint or illumination) degrade the performances of face recognition methods. In order to handle this challenge, a robust method for disentangling the identity and view representations has drawn an attention in the machine learning area. However, existing methods learn discriminative features which require a manual supervision of such factors of variations. In this paper, we propose a novel disentangling framework through modeling three representations of identity, viewpoint, and residues (i.e., identity and pose unrelated) which do not require supervision of the variations. By jointly modeling the three representations, we enhance the disentanglement of each representation and achieve robust face recognition performance. Further, the learned viewpoint representation can be utilized for pose estimation or editing of a posed facial image. Extensive quantitative and qualitative evaluations verify the effectiveness of our proposed method which disentangles identity, viewpoint, and residues of facial images.
Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture

Risk-based security is a concept introduced in order to provide security checks without inconveniencing travelers that are being checked with unqualified scrutiny checks while maintaining the same level of security with current check point practices without compromising security standards. Furthermore, risk-based security, as a means of improving travelers’ experience at check points is expected to reduce queueing and waiting times while improving at the same travelers’ experience during checks. A number of projects have been funded by the European Commission to investigate the concept of risk-based security and develop the means and technology required to implement it. The author is the Coordinator of two of the flagship projects funded by EC on risk-based security: FLYSEC and TRESSPASS. This chapter discusses and analyses the concept of risk-based security, the inherent competing mechanism between risk assessment, screening time and level of security, and means to implement risk-based security based on anomaly detection using deep learning and artificial intelligence (AI) methods.
Human Interaction Behavior Recognition based on Local Spatial - Temporal and Global Feature

Abstract At present, the human interaction behavior algorithm has received great attention, but how to effectively extract the interactive characteristics of human interaction and establish the complex model between multiple targets is still a difficult problem. Aiming at this problem, we proposed a human interaction recognition method based on local spatial - temporal and global feature. This method treats people who are interacting as a whole. Firstly, foreground human object is extracted by the inter-frame difference. Secondly, the optical flow and space-time points of interest are extracted. Considering the large and high dimension of the feature, the characteristics of each frame image are described by the gradient direction histogram HOG and the optical flow histogram HOF. Then, BP neural network is used to classify each feature of each frame. Considering the limitations of different features for interactive behavior recognition, BP neural network combined with classification fusion is used to obtain the final interaction behavior category. The algorithm tested in the UT interaction dataset, and the experimental results show that this method can recognize the interaction behaviors of hitting, hugging, kicking, pushing and handshaking. The proposed method can be applied to intelligent video surveillance.
Research on Fire Information Based on Computer Network Technology

Abstract In the era of rapid development of information technology, the openness and sharing characteristics of network technology have changed people’s life patterns. Computer information technology not only changes people’s life patterns, but also lays a foundation for the construction of urbanized fire information. Taking the information application of computer network technology as the research object, this paper analyzes the important value and significance of the development of information-based fire protection, as well as the contemporary commonly used technologies, hoping to provide reference for the corresponding units.
A Study on Lightweight Cryptography Algorithm for Internet of Things

Recently IoT is an emerging technology in all aspect of social and economical growth including research and technology. Starting from smart city to smart home, from industry monitoring to product evolution, from smart watch to smart health monitoring system, there are enormous aspect where IoT make a significant. But it is not possible to ensure that any system is fully safe and free from vulnerabilities. These vulnerabilities are used by attackers to access into devices and use them for malicious practice that can harm to serious extent. This review paper focuses on strategy adopted and scenarios in how many ways can IoT be attacked. Threats can be related to hardware devices, access level, software etc. This study mainly objected to layer wise security threat alone with possible counter measure and further scope of research.
Modality-Buffet for Real-Time Object Detection

Real-time object detection in videos using lightweight hardware is a crucial component of many robotic tasks. Detectors using different modalities and with varying computational complexities offer different trade-offs. One option is to have a very lightweight model that can predict from all modalities at once for each frame. However, in some situations (e.g., in static scenes) it might be better to have a more complex but more accurate model and to extrapolate from previous predictions for the frames coming in at processing time. We formulate this task as a sequential decision making problem and use reinforcement learning (RL) to generate a policy that decides from the RGB input which detector out of a portfolio of different object detectors to take for the next prediction. The objective of the RL agent is to maximize the accuracy of the predictions per image. We evaluate the approach on the Waymo Open Dataset and show that it exceeds the performance of each single detector.
Diffie-hellman Protocol on Raspberry pi

Abstract Cryptography is a technique that applies the number theory to maintain information security. Securing data often means encrypting it. Each of the known cryptosystems has advantages and disadvantages. The most popular key exchange protocol is the Diffie-Hellman protocol. The paper presents the obtained results of the Diffie-Hellman protocol’s implementation on portable devices, namely on raspberry boards.
Detection of Reckless Driving using Deep Learning

Reckless driving has always been a major concern for people. Every year it results in collisions and casualties. It is important to develop an automated system that detects reckless driving on highways. In order to overcome this issue, a new Deep Leaning system is designed that detects rash driving and notifies the concerned authority to take necessary action. The proposed system has two main modules: (i) Extracting the trajectory and (ii) Detecting the dents in the vehicle body. Our proposed model demonstrates the promising results in detecting the rash driving. We validated our results using YouTube videos with promising accuracy.
Adversarial retraining attack of asynchronous advantage actor‐critic based pathfinding

Pathfinding becomes an important component in many real-world scenarios, such as popular warehouse systems and autonomous aircraft towing vehicles. With the development of reinforcement learning (RL) especially in the context of asynchronous advantage actor-critic (A3C), pathfinding is undergoing a revolution in terms of efficient parallel learning. Similar to other artificial intelligence-based applications, A3C-based pathfinding is also threatened by the adversarial attack. In this paper, we are the first to study the adversarial attack to A3C, that can unexpectedly wake up longtime retraining mechanism until successful pathfinding. We also discover an attack example generation to launch the attack based on gradient band, in which only one baffle of extremely few unit lengths can successfully perform the attack. Experiments with detailed analysis are conducted to show a high attack success rate of 95% with an average baffle length of 2.95. We also discuss defense suggestions leveraging the insights from our analysis.
Efficient partially policy-hidden with multi-authority for access control scheme in Internet of Things

The widespread application from all walks of life in the Internet of Things (IoT) has improved our daily lives, however, the rapidly increased number of devices and data has brought security attacks frequently. To maximize the protection of data privacy and security, an attribute-based encryption(ABE) scheme based on hidden policy with multi-authority is proposed to achieve the fine-grained access control of the shared data. First, the computation complexity is reduced through outsourcing decryption and authentication to edge nodes to avoid the burden of terminals with limited computing and communication capabilities. Second, the system exploits Multi-Authority, in which users are identified with a hash function to effectively resist collusion attacks caused by joint communication between different authorities. At the same time, partially policy is hidden using the linear secret sharing scheme (LSSS) to preserve the attribute privacy. Theoretical analysis and comparison prove that the proposed scheme is secure to resist the attacks and efficient in computation.
A Novel Dynamic Group Signature with Membership Privacy

Group signature is a cryptography primitive that has been widely researched. It strikes a balance between digital signature and the user's demand for anonymity. A valid member in the group can generate a signature on behalf of the whole group. The public can only know that it was provided by a valid group member and learn nothing about the actual identity of the signer when verifying a group signature. Backes et al pointed out that the existing dynamic group signature schemes implicitly assume that the membership of everyone in the group is open to the public. Thus, they put forward a property called membership privacy for dynamic group signature. In this paper, we design a dynamic group signature scheme with membership privacy on top of Signature Proofs of Knowledge (SPK) and BBS+ signature. Further more, dynamic accumulator mechanism is adopted to revoke a group member's authority to sign. Then, a security analysis demonstrates that the proposed group signature scheme satisfies join-leave privacy. Finally, quantitative analysis and experimental results show that the proposed group signature scheme achieves the fewer signature size and less computation overhead compared with Backes's scheme.
Enhancing Cyber Security Using Audio Techniques: A Public Key Infrastructure for Sound

This paper details the research into using audio signal processing methods to provide authentication and identification services for the purpose of enhancing cyber security in voice applications. Audio is a growing domain for cyber security technology. It is envisaged that over the next decade, the primary interface for issuing commands to consumer internet-enabled devices will be voice. Increasingly, devices such as desktop computers, smart speakers, cars, TV's, phones an Internet of Things (IOT) devices all have built in voice assistants and voice activated features. This research outlines an approach to securely identify and authenticate users of audio and voice operated systems that utilises existing cryptography methods and audio steganography in a method comparable to a PKI for sound, whilst retaining the usability associated with audio and voice driven systems.
Synergy between Semantic Segmentation and Image Denoising via Alternate Boosting

The capability of image semantic segmentation may be deteriorated due to the noisy input image, where image denoising prior to segmentation may help. Both image denoising and semantic segmentation have been developed significantly with the advance of deep learning. In this work, we are interested in the synergy between these two tasks by using a holistic deep model. We observe that not only denoising helps combat the drop of segmentation accuracy due to the noisy input, but also pixel-wise semantic information boosts the capability of denoising. We then propose a boosting network to perform denoising and segmentation alternately. The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates a semantic map and then uses the map to regularize denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that on clean images, and segmentation and denoising are both boosted as the number of SDBs increases. On the Cityscapes dataset, using three SDBs improves the denoising quality to 34.42 dB in PSNR, and the segmentation accuracy to 66.5 in mIoU, when the additive white Gaussian noise level is 50.
Recognizing Generating Subgraphs Revisited

A graph [Formula: see text] is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function [Formula: see text] is defined on its vertices. Then [Formula: see text] is [Formula: see text]well-covered if all maximal independent sets are of the same weight. For every graph [Formula: see text], the set of weight functions [Formula: see text] such that [Formula: see text] is [Formula: see text]-well-covered is a vector space, denoted as WCW(G). Deciding whether an input graph [Formula: see text] is well-covered is co-NP-complete. Therefore, finding WCW(G) is co-NP-hard. A generating subgraph of a graph [Formula: see text] is an induced complete bipartite subgraph [Formula: see text] of [Formula: see text] on vertex sets of bipartition [Formula: see text] and [Formula: see text], such that each of [Formula: see text] and [Formula: see text] is a maximal independent set of [Formula: see text], for some independent set [Formula: see text]. If [Formula: see text] is generating, then [Formula: see text] for every weight function [Formula: see text]. Therefore, generating subgraphs play an important role in finding WCW(G). The decision problem whether a subgraph of an input graph is generating is known to be NP-complete. In this article we prove NP- completeness of the problem for graphs without cycles of length 3 and 5, and for bipartite graphs with girth at least 6. On the other hand, we supply polynomial algorithms for recognizing generating subgraphs and finding WCW(G), when the input graph is bipartite without cycles of length 6. We also present a polynomial algorithm which finds WCW(G) when [Formula: see text] does not contain cycles of lengths 3, 4, 5, and 7.
Method to Perform 3D Localization of Text in Shipboard Point Cloud Data Using Corresponding 2D Image

3D object detection and localization have been major focuses for the computer vision community for the past several years. However, extracting information from a 3D point cloud is often a more cumbersome and labor intensive task compared to just 2D images. 2D techniques are more mature and also have far more labelled training data. The contribution of this work is to leverage 2D computer vision techniques on a panorama image and use that to extract information from the 3D point cloud, in the case where there is an existing correspondence between the panorama and the point cloud. Performance of the algorithm will be based on 2D object detection, and 3D position and rotation of the object. The objects of interest are text placards called "bullseyes" that are found throughout US Navy ships. 3D data of this type of environment is limited, impacting the ability of researchers to develop and test their algorithms. Another contribution of this work is making available a large corpus of shipboard LiDAR scan data from the museum ship USS Midway.
Achieving Consensus in Privacy-Preserving Decentralized Learning

Machine learning algorithms have been widely deployed on decentralized systems so that users with private, local data can jointly contribute to a better generalized model. One promising approach is Aggregation of Teacher Ensembles, which transfers knowledge of locally trained models to a global one without releasing any private data. However, previous methods largely focus on privately aggregating the local results without concerning their validity, which easily leads to erroneous aggregation results especially when data is unbalanced across different users. Hence, we propose a private consensus protocol - which reveals nothing else but the label with the highest votes, in the condition that the number of votes exceeds a given threshold. The purpose is to filter out undesired aggregation results that could hurt the aggregator model performance. Our protocol also guarantees differential privacy such that any adversary with auxiliary information cannot gain any additional knowledge from the results. We show that with our protocol, we achieve the same privacy level with an improved accuracy compared to previous works.
Sentinel-2 and SPOT-7 Images in Machine Learning Frameworks for Super-Resolution

Monitoring construction sites from space using high-resolution (HR) imagery enables remote tracking instead of physically traveling to a site. Thus, valuable resources are saved while recording of the construction site progression at anytime and anywhere in the world is feasible. In the present work Sentinel-2 (S2) images at 10 m (m) are spatially super-resolved per factor 4 by means of deep-learning. Initially, the very-deep super-resolution (VDSR) network is trained with matching pairs of S2 and SPOT-7 images at 2.5 m target resolution. Then, the trained VDSR network, named SPOT7-VDSR, becomes able to increase the resolution of S2 images which are completely unknown to the net. Additionally, the VDSR net technique and bicubic interpolation are applied to increase the resolution of S2. Numerical and visual comparisons are carried out on the area of interest Karditsa, Greece. The current study of super-resolving S2 images is novel in the literature and can prove very useful in application cases where only S2 images are available and not the corresponding SPOT-7 higher-resolution ones. During the present super-resolution (SR) experimentations, the proposed net SPOT7-VDSR outperforms the VDSR net up to 8.24decibel in peak signal to noise ratio (PSNR) and bicubic interpolation up to 16.9% in structural similarity index (SSIM).
The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN

The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.
A Multi-view Clustering Approach for the Recommendation of Items in Social Networking Context

The review of the literature shows that the clustering-based approaches suffer from relatively low accuracy and coverage. We propose in this article a multi-views clustering approach for the recommendation of items in social networks. First, users are iteratively clustered from the views of both rating patterns and social information which includes different features, namely: friendship, trust and influence. Different clustering algorithms have been used (Kmedoids and CLARANS algorithms). Then, based on the multi-view clustering, recommendations are generated according to different hybridization. In order to evaluate our approach, experiments have been conducted using the well-known FilmTrust dataset. The results we have obtained show that our approach outperforms the existing related work approaches and baselines.