Reviewerly/exHarmony huggingface dataset:
Privacy-preserved data hiding using compressive sensing and fuzzy C-means clustering

Nowadays, digital images are confronted with notable privacy and security issues, and many research works have been accomplished to countermeasure these risks. In this article, a novel scheme for data hiding in encrypted domain is proposed using fuzzy C-means clustering and compressive sensing technologies to protect privacy of the host image. The original image is preprocessed first to generate multiple highly correlated classes with fuzzy C-means clustering algorithm. Then, all classes are further divided into two parts according to proper threshold. One is encrypted by stream cipher, and the other is encrypted and compressed simultaneously with compressive sensing technology for easy data embedding by information hider. The receiver can extract additional data and recover the original image with data-hiding key and encryption key. Experiments and analysis demonstrate that the proposed scheme can achieve higher embedding rate about additional data and better visual quality of recovered image than other state-of-the-art schemes.
Statistical and combinatorial analysis of the TOR routing protocol: structural weaknesses identified in the TOR network

In this paper, we present the results of a deep TOR routing protocol analysis from a statistical and combinatorial point of view. We have modelled all possible routes of this famous anonymity network exhaustively while taking different parameters into account with the data provided by the TOR foundation only. We have then confronted our theoretical model with the reality on the ground. To do this, we have generated thousands of roads on the actual TOR network and compared the results obtained with those predicted by the theory. A last step of combinatorial analysis has enabled us to identify critical subsets of Onion routers (ORs) which 33%, 50%, 66% and 75% of the TOR traffic respectively depends on. We have also managed to extract most of the TOR relay bridges which are non public nodes managed by the TOR foundation. The same results as for the ORs have been observed.
OPAL: High performance platform for large-scale privacy-preserving location data analytics

Mobile phones and other ubiquitous technologies are generating vast amounts of high-resolution location data. This data has been shown to have a great potential for the public good, e.g. to monitor human migration during crises or to predict the spread of epidemic diseases. Location data is, however, considered one of the most sensitive types of data, and a large body of research has shown the limits of traditional data anonymization methods for big data. Privacy concerns have so far strongly limited the use of location data collected by telcos, especially in developing countries.In this paper, we introduce OPAL (for OPen ALgorithms), an open-source, scalable, and privacy-preserving platform for location data. At its core, OPAL relies on an open algorithm to extract key aggregated statistics from location data for a wide range of potential use cases. We first discuss how we designed the OPAL platform, building a modular and resilient framework for efficient location analytics. We then describe the layered mechanisms we have put in place to protect privacy and discuss the example of a population density algorithm. We finally evaluate the scalability and extensibility of the platform and discuss related work.The code will be open-sourced on GitHub upon publication.
Pelee-Text: A Tiny Convolutional Neural Network for Multi-oriented Scene Text Detection

Nowadays, scene text detection has received a lot of attention due to its complexity given variations in terms of orientations, font size, aspect ratio, and natural backgrounds. In this vein, several deep neural networks have been proposed to deal with this challenging problem. However, such networks produce “heavy” models, hampering their use in applications running in devices with computational constraints. Additionally, few works are focused on the detection of multi-oriented and/or multi-lingual text. Herein, we propose an end-to-end tiny convolutional neural network for multi-oriented multi-lingual scene text called Pelee- Text. Experimental results show that Pelee-Text is at least 3 times smaller than its counterparts with a speed of 2.93 and 18.64 frames per second for its multi-scale and 768-scale versions, respectively. Moreover, in terms of F-measure, our method achieved competitive results on four well-known datasets, i.e., ICDAR'2011 (90.96%), ICDAR'2013 (85.24%), ICDAR'2015 (80.08%), and MSRA-TD500 (80.90%).
What Independent Game Developers Expect from Recommender Systems

Digital distribution and electronic recommender systems have transformed the digital games industry. They serve as the enabler of new business models that allow independent game developers to publish their projects without the mediation of third party publishers and decrease costs of game production.
Multi-Dimension Projection for Non-Linear Data Via Spearman Correlation Analysis (MD-SCA)

This paper introduces an algorithm of multidimensional informative projection or view of multiple variable and more than two random variables via Spearman correlation analysis (SCA). The proposed algorithm is an extension of Spearman correlation analysis to extract linear or nonlinear information of projections through pairwise correlation analysis. These multi-dimensional informative projections used as common patterns in pattern recognition application. The proposed algorithm extends SCA through linear algebraic solution for the optimization problem, the problem of dual representation of high multi-dimensional data, and structural dilemma issues along with deep learning model. Additionally, the proposed method decreases the quadratic algorithm complexity among linear and non-linear data through Spearman rank ability. The demonstration of proposed approached performs on two-bench mark data set: Face96 and Yale Face Database.
A Light Weight Background Subtraction Algorithm for Motion Detection in Fog Computing

A novel light weight background subtraction algorithm proposed in this letter addresses the issue of executing real time motion detection in fog computing, which is a resource constrained environment. The proposed algorithm compresses the original high dimensional video frame into low dimensional space using random projection (RP) and the motion detection is identified using the structural similarity (SSIM) index. The objective of the proposed algorithm is to select the frames with motion and discard the remaining frame in order to reduce the storage and computational cost for the cloud users. The proposed algorithm is evaluated using CDNET 2014 video data and the results are verified in terms of selected frames per video(SFPV), percentage of reduction (POR) and running time. From the experiment, it is evident that the proposed algorithm reduces nearly 67 percent of storage cost with trifling computational expense.
Literature Review of Generative models for Image-to-Image translation problems

In recent years, data-driven (image based) methodologies like deep learning and computer vision have made computers immensely accurate in terms of identifying features inside images. Research in this area has given way to a relatively new set of deep learning models known as Generative models which generate images alongside identifying features inside them. These models, particularly conditional generative adversarial networks (CGANs), conditional variational autoencoders (CVAE) and generative stochastic networks (GSN) have become popular as they are able to translate images from one setting to another while keeping the structure of generated images aligned with the input images. In this paper, we review the work that has been done using these models to the area of web design automation which needs to be considered during the development phase. We also try to identify the benefits of implementing these models based on architectural features while keeping in view the different problem scenarios. Finally, some key challenges in solving such image-to-image translation problems has been mentioned.
Two-stage metric learning for cross-modality person re-identification

Cross-modality person re-identification faces big challenges as the different characteristics of images collected by visible and thermal cameras. The existing deep learning methods always use metric learning to learn the discriminative features. However, the existing metric learning is executed based on batch examples, the solution is local optimal. In order to learn a global solution, we propose a two-stage metric learning (TML) method, which uses local and global metric learning successively. In the first stage, a local metric learning is used based on mini-batch images via triplet loss. A new mixed-modality triplet loss is proposed to train more valid triplet examples. It supervises to learn more efficient features for the next stage. In the second stage, a global metric learning is adopted based on the features of all training images. Experiments are conducted on the public SYSU-MM01 dataset. The TML achieved 39.75% in Rank-1 and 42.73% in mAP, which surpass the state-of-the-art performance.
New Algorithms of Feature Selection and Big Data Assignment for CBR System Integrated by Bayesian Network

Under big data, the integrated system of case-based reasoning and Bayesian network has exhibited great advantage in implementing the intelligence of engineering application in many domains. To further improve the performance of the hybrid system, this article proposes Probability Change Measurement of Solution Parameters (PCMSP)–Half-Division-Cross (HDC) method, which includes two algorithms, namely PCMSP and HDC algorithm. PCMSP algorithm can select principal problem features according to their effects upon all solution features measured by calculating the weighted relative probability (RP) change of all solution features caused by each problem feature. PCMSP algorithm can perfectly work under big data no matter how complex the data types are and how huge the data size is. HDC algorithm is used to assign the computation task of big data to enhance the efficiency of the integrated system. HDC algorithm assigns big data by grouping all the problem parameters into many small sub-groups and then distributing the data which covers the same sub-group of problem parameters to a slave node. HDC algorithm can guarantee enough efficiency of the integrated system under big data no matter how large the number of problem parameters is. Finally, lots of experiments are executed to validate the proposed method.
Games for Cybersecurity Decision-Making

Decision-makers are often confronted with cybersecurity challenges, which they may not fully comprehend but nonetheless need to critically address. Efficient preparation through cybersecurity games has become an invaluable tool to better prepare strategy and response to cyber incidents. Such games offer the potential for capacity building of decision-makers through a controlled environment, often presenting hypothetical scenarios that are designed to invoke discussion, while decision-making skills are put to the test. While games are acknowledged to be an effective method for such situations, many rely on technical capabilities to address these challenges. However, a key challenge is to understand the factors that influence cybersecurity decision-making. Further, game effectiveness for developing these skills is often not validated. This paper surveys cybersecurity games and compiles a data-set of 46 games to investigate how effective cybersecurity games are for assessing decision-making skills, and determines the state-of-the-art game. Through critical review and analysis of the data-set, a criteria to assess games for decision-making skills is presented. Furthermore, the criteria is applied to ten games, which determined Cyber 9/12 to be the state-of-the-art cybersecurity game for decision-making. The paper concludes with insights into how the assessment criteria can support the development of better decision-making skills through games.
PROTECT – An Easy Configurable Serious Game to Train Employees Against Social Engineering Attacks

Social engineering is the clever manipulation of human trust. While most security protection focuses on technical aspects, organisations remain vulnerable to social engineers. Approaches employed in social engineering do not differ significantly from the ones used in common fraud. This implies defence mechanisms against the fraud are useful to prevent social engineering, as well. We tackle this problem using and enhancing an existing online serious game to train employees to use defence mechanisms of social psychology. The game has shown promising tendencies towards raising awareness for social engineering in an entertaining way. Training is highly effective when it is adapted to the players context. Our contribution focuses on enhancing the game with highly configurable game settings and content to allow the adaption to the player’s context as well as the integration into training platforms. We discuss the resulting game with practitioners in the field of security awareness to gather some qualitative feedback.
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation

Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance label generator under this weakly supervised setting. Motivated by the observation that an MIL model can handle bags of varying sizes, we propose to repurpose an MIL model originally trained for bag-level classification to produce reliable predictions for single instances. To this end, we introduce a novel regularization strategy based on virtual adversarial training for improving MIL training, and subsequently develop a knowledge distillation technique for repurposing the trained MIL model. Using empirical studies on colon cancer and breast cancer detection from histopathological images, we show that the proposed approach produces high-quality instance-level prediction and significantly outperforms state-of-the MIL methods.
Modelling Online Comment Threads from their Start

The social Web is a widely used platform for online discussion. Across social media, users can start discussions by posting a topical image, url, or message. Upon seeing this initial post, other users may add their own comments to the post, or to another user's comment. The resulting online discourse produces a comment thread, which constitutes an enormous portion of modern online communication. Comment threads are often viewed as trees: nodes represent the post and its comments, while directed edges represent reply-to relationships. The goal of the present work is to predict the size and shape of these comment threads. Existing models do this by observing the first several comments and then fitting a predictive model. However, most comment threads are relatively small, and waiting for data to materialize runs counter to the goal of the prediction task. We therefore introduce the Comment Thread Prediction Model (CTPM) that accurately predicts the size and shape of a comment thread using only the text of the initial post, allowing for the prediction of new posts without observable comments. We find that the CTPM significantly outperforms existing models and competitive baselines on thousands of Reddit discussions from nine varied subreddits, particularly for new posts.
Cryptography and Digital Image Steganography Techniques

Exchange of secure information between the sender and receiver draws the attention of several researchers due to its importance in various fields ranging from national security (Tutun et al. in Procedia Comput Sci 114:132–138, 2017 [73]) to social profile (Gupta and Dhami in J Direct Data Digit Mark Pract 17:43–53, 2015 [29]). Cryptography (Coron in IEEE Secur Priv 4:70–73, 2006 [17]) and Steganography (Cheddad and Condell in Sig Process 90:727–752, 2010 [14], Rabah in Inf Technol J 3:245–269, 2004 [59]) are the two major streams dealing with information security. This chapter focuses on different techniques used for cryptography and steganography. The major emphasis is given on image steganography.
Bi-direction Feature Pyramid Temporal Action Detection Network

Temporal action detection in long-untrimmed videos is still a challenging task in video content analysis. Many existing approaches contain two stages, which firstly generate action proposals and then classify them. The main drawback of these approaches is that there are repeated operations in the proposal extraction and the classification stages. In this paper, we propose a novel Bi-direction Feature Pyramid Temporal Action Detection (BFPTAD) Network based on 1D temporal convolutional and deconvolutional layers to detect action instances directly in long-untrimmed videos. We use the top-down pathway to add semantic information to the shallow feature maps, and then use the bottom-up pathway to add location information to the deep feature maps. We evaluate our network on THUMOS14 and ActivityNet benchmarks. Our approach significantly outperforms other state-of-the-art methods by increasing mAP@IoU = 0.5 from 44.2% to 52.2% on THUMOS14.
NewSQL Through the Looking Glass

Several applications require to handle large and heterogeneous data volumes as well as thousands of OLTP transactions per second. Traditional relational databases are not suitable for these requirements. On the other hand, NoSQL databases are able to deal with Big Data, but lacks the support to the traditional ACID properties. NewSQL is a new class of databases that combines the support to OLTP transactions of relational databases with the high availability and scalability of NoSQL databases. However, few works in the literature explore the differences among different NewSQL solutions. In this paper, we discuss the main features of the most prominent NewSQL products, besides we present benchmarking results for analyzing their performance. We believe that both analysis can be useful as a guide to a future choice of NewSQL technologies.
Anomaly Detection in Surveillance Videos

Every public or private area today is preferred to be under surveillance to ensure high levels of security. Since the surveillance happens round the clock, data gathered as a result is huge and requires a lot of manual work to go through every second of the recorded videos. This paper presents a system which can detect anomalous behaviors and alarm the user on the type of anomalous behavior. Since there are a myriad of anomalies, the classification of anomalies had to be narrowed down. There are certain anomalies which are generally seen and have a huge impact on public safety, such as explosions, road accidents, assault, shooting, etc. To narrow down the variations, this system can detect explosion, road accidents, shooting, and fighting and even output the frame of their occurrence. The model has been trained with videos belonging to these classes. The dataset used is UCF Crime dataset. Learning patterns from videos requires the learning of both spatial and temporal features. Convolutional Neural Networks (CNN) extract spatial features and Long Short-Term Memory (LSTM) networks learn the sequences. The classification, using an CNN-LSTM model achieves an accuracy of 85%.
Recording Daily Health Status with Chatbot on Mobile Phone - A Preliminary Study

To support in-home long-term care, we are studying techniques of mind sensing, which externalizes internal states of elderly people as words through conversations with agents or robots. We previously developed the memory-aid service, where a chatbot on a mobile phone autonomously talks to elderly people, to record their conditions, events, and memorandums. During experiments with healthy elders, we found that they regularly talked to the chatbot about health status, such as weight and blood pressure. This motivated us to use the mind sensing as an affordable and practical means to record daily health status. In this paper, we present a method where individual users can declare health metrics of their interests, and record them through the mind sensing. Specifically, for each user-defined metric, the chatbot asks the user the current value of the metric at the designated time. The text conversations are then put in a data mining process to extract time-series values of the metric. The time-series data is finally visualized as a graph, with which the user can review the health status. Our preliminary experiment shows that individual health metrics can be recorded and visualized successfully even without “connected” measuring instruments.
Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos

Video classification is an essential process for analyzing the pervasive semantic information of video content in computer vision. Traditional hand-crafted features are insufficient when classifying complex video information due to the similarity of visual contents with different illumination conditions. Prior studies of video classifications focused on the relationship between the standalone streams themselves. In this paper, by leveraging the effects of deep learning methodologies, we propose a two-stream neural network concept, named state-exchanging long short-term memory (SE-LSTM). With the model of spatial motion state-exchanging, the SE-LSTM can classify dynamic patterns of videos using appearance and motion features. The SE-LSTM extends the general purpose of LSTM by exchanging the information with previous cell states of both appearance and motion stream. We propose a novel two-stream model Dual-CNNSELSTM utilizing the SE-LSTM concept combined with a Convolutional Neural Network, and use various video datasets to validate the proposed architecture. The experimental results demonstrate that the performance of the proposed two-stream Dual-CNNSELSTM architecture significantly outperforms other datasets, achieving accuracies of 81.62%, 79.87%, and 69.86% with hand gestures, fireworks displays, and HMDB51 datasets, respectively. Furthermore, the overall results signify that the proposed model is most suited to static background dynamic patterns classifications.
Enabling Social Information Exchange via Dynamically Robust Annotations

With the emergence of new web paradigms, we currently see a tremendous increase in interest in different applications of the social web. However, this rising interest in social platforms has also led to the rise of numerous new challenges, especially issues like fake-news, filter-bubble, and web-page-decay. Motivated by these issues, we propose a novel DOM-oriented edit distance anchoring approach that enables stable tracking of ephemeral web content. We argue that such a stable anchoring approach could indeed foster the creation of a browser-based crowdsourcing information system that could help us tackle rising issues on the web. Building on this hypothesis, we present a new web annotation tool called Tippanee, that is designed around the proposed anchoring approach; and provides its users with a collaborative environment where web users could help in improving the quality of textual content on the web by annotating, archiving, linking, sharing and semantically describing content on-the-fly.