Reviewerly/exHarmony huggingface dataset:
Securing Data and Reducing the Time Traffic Using AES Encryption with Dual Cloud

Cloud security is a developing part of computer devices and network security. Cloud platform usage is for third-person information model. In Here we talk about how to provide protection for the information, from the illegal abuser and offer probity to the client. It requires an extremely high level of confidentiality and verification. One of the examples for cloud platform as a service is Heroku. The Heroku is based on a fully administered structure, which includes high data services and a great system, for implementing and operating current applications. The leading concern in cloud computing is data protection, so it can be used to handle the cryptographic methods. A probabilistic method to encrypt the information using AES. At AES algorithm is not only for protection it can be also used in huge speed. AES provides well-built security from third party. In this proposed work, we implemented Heroku is a cloud platform, and then we apply the AES method for data protection in Heroku cloud. AES cryptography is able to use for data security in cloud platform. And also using a dual cloud if one active or both active. If anyone cloud is active then the data should be more efficient in uploading and downloading operation perform in the cloud. Moreover, calculation delay in information to the encryption shows to better amount of information increase and the information time lag for encrypting information.
Ranking Video Salient Object Detection

Video salient object detection has been attracting more and more research interests recently. However, the definition of salient objects in videos has been controversial all the time, which has become a critical bottleneck in video salient object detection. Specifically, the sequential information contained in videos results in a fact that objects have a relative saliency ranking between each other rather than specific saliency. This implies that simply distinguishing objects into salient or not-salient as usual could not represent the information about saliency comprehensively. To address this issue, 1) in this paper we propose a completely new definition for the salient objects in videos---ranking salient objects, which considers relative saliency ranking assisted with eye fixation points. 2) Based on this definition, a ranking video salient object dataset(RVSOD) is built. 3) Leveraging our RVSOD, a novel neural network called Synthesized Video Saliency Network (SVSNet) is constructed to detect both traditional salient objects and human eye movements in videos. Finally, a ranking saliency module (RSM) takes the results of SVSNet as input to generate the ranking saliency maps. We hope our approach will serve as a baseline and lead to a conceptually new research in the field of video saliency.
Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).
Visual Relation Detection with Multi-Level Attention

Visual relations, which describe various types of interactions between two objects in the image, can provide critical information for comprehensive semantic understanding of the image. Multiple cues related to the objects can contribute to visual relation detection, which mainly include appearances, spacial locations and semantic meanings. It is of great importance to represent different cues and combine them effectively for visual relation detection. However, in previous works, the appearance representation is simply realized by global visual representation based on the bounding boxes of objects, which may not capture salient regions of the interaction between two objects, and the different cue representations are equally concatenated without considering their different contributions for different relations. In this work, we propose a multi-level attention visual relation detection model (MLA-VRD), which generates salient appearance representation via a multi-stage appearance attention strategy and adaptively combine different cues with different importance weighting via a multi-cue attention strategy. Extensive experiment results on two widely used visual relation detection datasets, VRD and Visual Genome, demonstrate the effectiveness of our proposed model which significantly outperforms the previous state-of-the-arts. Our proposed model also achieves superior performance under the zero-shot learning condition, which is an important ordeal for testing the generalization ability of visual relation detection models.
Big Data Analysis and Services: Visualization on Smart Data to Support Healthcare Analytics

In this era of big data, high volumes of a wide variety of valuable data can be easily generated or collected at a high velocity. As such, big data analysis is in demand in various real-life applications and services (e.g., healthcare) as it helps to find useful information and valuable knowledge that are embedded in the big data. The resulting information and knowledge is usually in textual or tabular forms. Given that "a picture is worth a thousand words", visualization and visual analytics helps. In this paper, we present a system for visualizing smart data-as well as their related information and knowledge-from health data, which in turn supports healthcare analytics.
Evaluating China’s Automated Essay Scoring System iWrite

Automated writing scoring can not only provide holistic scores but also instant and corrective feedback on L2 learners’ writing quality. It has been increasing in use throughout China and internationally. Given the advantages, the past several years has witnessed the emergence and growth of writing evaluation products in China. To the best of our knowledge, no previous studies have touched upon the validity of China’s automated essay scoring systems. By drawing on the four major categories of argument for validity framework proposed by Kane—scoring, generalization, extrapolation, and implication, this article aims to evaluate the performance of one of the China’s automated essay scoring systems—iWrite against human scores. The results show that iWrite fails to be a valid tool to assess L2 writings and predict human scores. Therefore, iWrite currently should be restricted to nonconsequential uses and cannot be employed as an alternative to or a substitute for human raters.
Emotion Recognition Based on Double Tree Complex Wavelet Transform and Machine Learning in Internet of Things

Corresponding to the continual development of human-computer interaction technology, the use of emotional computing (EC) is gradually emerging in the Internet of Things (IoT). Emotion recognition is considered a highly valuable aspect of EC. Numerous studies have examined emotion recognition based on electroencephalogram (EEG) signals, but the recognition rate is unreliable. In this paper, a feature extraction method is proposed that is based on double tree complex wavelet transform (DTCWT) and machine learning. The emotions of 16 subjects are induced under video stimulation, and the original signal is acquired using a Neuroscan device. Both EEG and electromyography (EMG) signal are then eliminated by band-pass filtering, and the reconstructed signal of each frequency band is obtained by DTCWT. Finally, support vector machine (SVM) is utilized to classify three kinds of emotions: calm, happy, and sad, obtaining a classification accuracy of 90.61%. Results show that the proposed algorithm can effectively extract the feature vector and improve the problem of low accuracy in multiple class recognition.
Matrix Nets: A New Deep Architecture for Object Detection

We present Matrix Nets (xNets), a new deep architecture for object detection. xNets map objects with different sizes and aspect ratios into layers where the sizes and the aspect ratios of the objects within their layers are nearly uniform. Hence, xNets provide a scale and aspect ratio aware architecture. We leverage xNets to enhance key-points based object detection. Our architecture achieves mAP of 47.8 on MS COCO, which is higher than any other single-shot detector while using half the number of parameters and training 3x faster than the next best architecture.
The cyber-insurance market in Norway

Purpose This paper aims to describe the cyber-insurance market in Norway but offers conclusions that are interesting to a wider audience. Design/methodology/approach The study is based on semi-structured interviews with supply-side actors: six general insurance companies, one marine insurance company and two insurance intermediaries. Findings The Norwegian cyber-insurance market supply-side has grown significantly in the past two years. The General Data Protection Regulation (GDPR) is found to have had a modest effect on the market so far but has been used by the supply-side as an icebreaker to discuss cyber-insurance with customers. The NIS Directive has had little or no impact on the Norwegian cyber-insurance market until now. Informants also indicate that Norway is still the least mature of the four Nordic markets. Practical implications Some policy lessons for different stakeholders are identified. Originality/value Empirical investigation of cyber-insurance is still rare, and the paper offers original insights on market composition and actor motivations, ambiguity of coverage, the NIS Directive and GDPR.
Training Efficient Saliency Prediction Models with Knowledge Distillation

Recently, deep learning-based saliency prediction methods have achieved significant accuracy improvements. However, they are hard to embed in practical multimedia applications due to large memory consumption and running time caused by complicated architectures. In addition, most methods are fine-tuned from pre-trained models for classification tasks, and networks cannot flexibly be transferred for a new task. In this paper, a condensed and randomly initialized student network is employed to achieve higher efficiency by transferring knowledge from complicated and well-trained teacher networks. This is the first use of knowledge distillation for efficient pixel-wise saliency prediction. Instead of directly minimizing Euclidean distance between feature maps, we propose two statistical representations of feature maps (i.e., first-order and second-order statistics) as knowledge. We conduct experiments on three kinds of teacher networks and four benchmark datasets to verify the effectiveness of the proposed method. Compared with the teacher networks, the student networks achieve an acceleration ratio of 4.56-4.73. Compared with state-of-the-art approaches, the proposed model achieves competitive accuracy with faster running speed (up to 4.38 times) and smaller model size (up to 93.27% reduction). We further embedded the proposed saliency prediction model into a video captioning application. The saliency-embedded approaches improve video captioning on all test metrics with a small complexity cost. The student-model embedded approach achieves 25% time saving with similar performance to the teacher embedded one.
Learning Safety Equipment Detection using Virtual Worlds

Nowadays, the possibilities offered by state-of-the-art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high quality training sets, containing a large number of examples (e.g. million), in addition to the the machine learning tools themselves. For several applications, very good training sets can be obtained, for example, crawling (noisily) annotated images from the internet, or by analyzing user interaction (e.g.: on social networks). However, there are several applications for which high quality training sets are not easy to be obtained/created. Consider, as an example, a security scenario where one wants to automatically detect rarely occurring threatening events. In this respect, recently, researchers investigated the possibility of using a visual virtual environment, capable of artificially generating controllable and photo-realistic contents, to create training sets for applications with little available training images. We explored this idea to generate synthetic photo-realistic training sets to train classifiers to recognize the proper use of individual safety equipment (e.g.: worker protection helmets, high-visibility vests, ear protection devices) during risky human activities. Then, we performed domain adaptation to real images by using a very small image data set of real-world photographs. We show that training with the generated synthetic training set and using the domain adaptation step is an effective solution to address applications for which no training sets exist.
Chasing Play Potentials in Food Culture to Inspire Technology Design

We propose a Situated Play Design (SPD) workshop aimed at exploring how culture and traditions can guide playful design. Using food as an accessible starting point, we invite scholars from diverse communities to share, analyze, and make creative use of playful traditions, and prototype new and interesting eating experiences. Through hands-on engagement with traditions, play and technology, we will discuss strategies to make designerly use of forms of play that are embedded in culture. The outcomes of the workshop will be twofold: First, in response to recent calls for increasingly situated and emergent play design methods, we explore strategies to chase culturally-grounded play. Second, we produce an annotated portfolio of "play potentials" to inspire the design of future food-related technologies. The workshop will contribute to enriching the set of tools available for designers interested in play and technologies for everyday-use, in and beyond the food domain.
SmartSenti: A Twitter-Based Sentiment Analysis System for the Smart Tourism in Turkey

Today, it is possible to see smart solutions based on artificial intelligence in almost every area such as smart homes, smart assistants, smart cities, smart campuses as well as smart tourism centers. Smart tourism centers are mainly tourism solutions that targets more visitors. Visitors, before traveling to any tourist center, conduct research on the social media application, and they are generally evaluating previous visitor comments. Considering that there are many social media platforms, evaluating these comments manually requires great effort and time. In this study, we have developed a SmartSenti application that sentiment analysis which was performed on Twitter posts about tourism centers using machine learning and transfer learning methods. We have collected tweets about five touristic place in Turkey. These tweets are classified as positive or negative after pre-processing and feature extracting phase. The classification results are shared visually with the users through the mobile application that we developed.
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Let p be an odd prime number. In this paper, we construct 2(2p−3) classes of codes over the ring R=Fp+uFp,u2=0, which are associated with down sets. We compute the Lee weight distributions of the 2(2p−3) classes of codes when the down sets are generated by a single maximal element. Moreover, by using the Gray map of the linear codes over R, we find out 2(p−1) classes of p-ary distance optimal linear codes. Two of them meet the Griesmer bound.
A Lightweight Collaborative Recognition System with Binary Convolutional Neural Network for Mobile Web Augmented Reality

Lightweight and precise recognition is a key component of web-based augmented reality (Web AR) applications. Although edge-based distributed deep learning approach is now possible to achieve satisfactory recognition for Web AR applications, it puts significant pressure on the computation and energy consumption of the mobile web browser, especially the app-based embedded browser. Thus, reducing the model size and accelerating the inference are regarded as the two fundamental challenges to enable this edge-based collaborative recognition system efficiently. In this paper, we propose a lightweight collaborative recognition system (LCRS) for Web AR applications. LCRS contributes to three aspects: (1) we design a composite deep neural network for reducing the model size and inference latency by introducing binary convolutional neural network; (2) we provide a joint training method to co-train the general branch and the binary branch; (3) we develop a JavaScript library for the mobile web browser to execute and accelerate inference of the binary branch, which also provides a collaborative mechanism between the mobile web browser and the edge server. We have conducted extensive experiments using several well-known networks and datasets. The experimental results have shown that the proposed system outperforms the existing approaches in terms of reducing the model size by about 16x to 29x, and it also reduces end-to-end latency and outpaces the existing state-of-the-art approaches by over 3x to 60x when applying it in practical Web AR cases.
Edge-assisted Adaptive Video Streaming with Deep Learning in Mobile Edge Networks

Most HTTP Adaptive Streaming (HAS) video content delivery solutions today are governed by purely client-based logics. For lack of coordination among clients and awareness of the dynamic Radio Access Network (RAN) conditions, these approaches may lead to suboptimal user experience and underutilization of network resources. Recently, Mobile Edge Computing (MEC) has been studied as a new networking paradigm to play a part in the adaptive video streaming process with lower end-to-end latency and better network awareness. In this paper, we present an edge-assisted adaptive video streaming scheme with deep Q-learning techniques and bandwidth sharing policies, which performs video adaptation according to the real-time radio and network information collected at the network edge. The adaptation scheme is implemented as an edge application hosted on our own deployed MEC server in a real LTE network testbed for experimental evaluation against three popular client-based solutions, namely Buffer-Based Adaptation (BBA), Rate-Based Adaptation (RBA) and adaptation performed by dash.js with its default adaptation logic. To the best of our knowledge, it is one of the few deep Q-learning adaptive video streaming solutions that have been deployed in the MEC framework at network edge in practice. Experiment results show that our proposed scheme outperforms the other three client-based solutions with higher Quality of Experience (QoE) and fairness in the evaluated network environments.
Unified Underwater Structure-from-Motion

This paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstruction techniques for a variety of scenarios such as single static camera and moving refractive interface, single moving camera and static refractive interface, and single moving camera and moving refractive interface. In our basic setup, we assume that the refractive interface is planar, and simultaneously estimate the unknown transformations of the planar interface and the camera, and the unknown target shape using bundle adjustment. We also extend it to relax the planarity assumption, which enables us to use waves of the refractive interface for the reconstruction task. Experiments with real data show the superiority of our method to existing methods.
A Survey on Vision Based Activity Recognition, its Applications and Challenges

Tracking objects is gaining momentum in the field of research due to its widespread applications. Such systems can be enhanced or made more meaningful by allowing the activities of those objects to be recognized. Activity recognition is the ability to identify and recognize the action or goals of the agent. The agent can be any object or entity that performs action, which has end goals. The agents can be a single agent performing the action or group of agents performing the actions or having some interaction. Human activity recognition has gained popularity due to its demands in many practical applications such as entertainment, healthcare, simulations and surveillance systems. Vision based activity recognition is gaining advantage as it does not require any human intervention or physical contact with humans. Moreover, there are networked set of cameras, which makes it both easier to track and recognize the activities. Through this work, approaches involved in activity recognition, advantages and challenges involved in activity recognition are discussed.
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors

We present a simple yet effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in localization information. We excite certain activations in order to help the network learn to better localize (Figure 2). In the later stages of training, we gradually reduce our assisted excitation to zero. We reached a new state-of-the-art in the speed-accuracy trade-off (Figure 1). Our technique improves the mAP of YOLOv2 by 3.8% and mAP of YOLOv3 by 2.2% on MSCOCO dataset. This technique is inspired from curriculum learning. It is simple and effective and it is applicable to most single-stage object detectors.
Sentiment Polarity Detection in Bengali Tweets Using LSTM Recurrent Neural Networks

A vast amount of user generated content in the form of blog posts, tweets and comments etc. are now available online. This huge amount of social media texts can be analyzed for evaluation of public opinions and deriving market intelligence. Sentiment analysis is the research area dealing with analysis of sentiment such as feelings, emotions and opinions expressed in the social media texts by the internet users. Sentiment polarity detection is an important sentiment analysis task. Sentiment polarity detection in tweets can be viewed as a task for classifying an input tweet into one of three categories -positive, negative or neutral. Now-a-days, a large number of Indian language social media texts are also becoming available on the web. In this paper, we present a sentiment polarity detection system for Bengali tweets that uses a special type of recurrent neural networks called LSTM. Our experiments using a Bengali tweet dataset show that our proposed system outperforms some existing systems used for sentiment polarity detection in Bengali tweets.