Reviewerly/exHarmony huggingface dataset:
A 3DES Double–Layer Based Message Security Scheme

Nowadays, there are hundreds of applications that need to secure their transmission, where a lot of techniques were implemented and applied for this reason. Attackers are always breaking these secure techniques to penetrate the communication channel. Steganography and cryptography are two approaches that provide the channel with data security. Steganography hides the data by inserting it into digital media e.g. images, videos, and audios. Cryptography encrypts the data in a way that makes it unreadable for anyone other than the receiver. In this work, a well-secured algorithm is implemented through integrating the two layers, the cryptography layer and the steganography layer. In cryptography we applied the 3-DES technique. In steganography we embedded the secret message in an image using an enhanced least signicant bit (LSB) technique. We used the Peak Signal-to-Noise Ratio (PSNR), the mean squared error (MSE) and structure similarity index measure (SSIM) to evaluate the quality of the image after the embedding. In order to investigate the performance we simulated our proposed algorithm on different images and the results showed that the PSNR reaches up to 75 dB and MSE of 0.0018. Moreover, the obtained results were compared with some of the popular LSB steganographic methods. The comparison made shows that the proposed algorithm outperforms the common LSB steganographic methods in terms of the PSNR and the MSE, while maintaining an excellent SSIM.
Deep Learning for Scene Understanding

With the progress in the field of computer vision, we are moving closer and closer towards the ultimate aim of human like vision for machines. Scene understanding is an essential part of this research. It seeks the goal that any image should be as understandable and decipherable for computers as it is for humans. The stall in the progress of the different components of scene understanding, due to the limitations of the traditional algorithms, has now been broken by the induction of neural networks for computer vision tasks. The advancements in parallel computational hardware has made it possible to train very deep and complex neural network architectures. This has vastly improved the performances of algorithms for all the different components of scene understanding. This chapter analyses these contributions of deep learning and also presents the advancements of high level scene understanding tasks, such as caption generation for images. It also sheds light on the need to combine these individual components into an integrated system.
StarZIP: Streaming Graph Compression Technique for Data Archiving

The size of a streaming graph is possibly unbounded, and it is updated by a continuous sequence of edges over time. Due to numerous types of real-world interactions, the nature of edge arrival in a streaming graph is dynamic and holds different types of temporal subgraphs, such as stars, bipartite forms, cliques, and chains. The most current techniques find such subgraphs in each snapshot of a dynamic graph and use a dictionary or hash-based summary to compress the graph before applying a stitching technique to demonstrate its temporal behavior. However, it remains difficult to discover those subgraph structures from the continuous stream of edges found in large and rapidly changing dynamic graphs. In this paper, we propose a streaming graph compression algorithm, StarZIP, that uses a new encoding scheme. Our motivational factor is real-world graphs that contain an overwhelmingly large number of stars and a few other structures. We have observed that all subgraph structures can be represented as star-shaped subgraphs. Moreover, the star-shaped representation can easily be arranged in the form of an inverted index, which enables the application of different inverted list encoding techniques for compression. Therefore, we shatter a graph into a uniform representation of stars to compress it. The evaluation of StarZIP on real-world datasets shows that our proposed system reduced the size of a highly dense graph to 60 times less than its original size. Moreover, the experimental results indicate that StarZIP compression is 4 times better than the state-of-the-art techniques.
Multifeature Named Entity Recognition in Information Security Based on Adversarial Learning

In order to obtain high quality and large-scale labelled data for information security research, we propose a new approach that combines a generative adversarial network with the BiLSTM-Attention-CRF model to obtain labelled data from crowd annotations. We use the generative adversarial network to find common features in crowd annotations and then consider them in conjunction with the domain dictionary feature and sentence dependency feature as additional features to be introduced into the BiLSTM-Attention-CRF model, which is then used to carry out named entity recognition in crowdsourcing. Finally, we create a dataset to evaluate our models using information security data. The experimental results show that our model has better performance than the other baseline models.
"Identifying novel privacy issues of online users on social media platforms" by Ghazaleh Beigi and Huan Liu with Martin Vesely as coordinator

Online users generate tremendous amounts of data. To better serve users, it is required to share the user-related data with advertisers and application developers. Socia media user-related data might make users susceptible to unintended user privacy breach. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of users. This article introduces our recent research on online users privacy in social media. In particular, we review an approach to identifying novel privacy issues via an adversarial attack specialized for social media data. Our work sheds light on the study of new privacy risks in social media data arising from the innate heterogeneity of user-generated data.
Browsing the Web of Things in Mobile Augmented Reality

With the current augmented reality and low-power radio technology present on mobile platforms, we can imagine a standard and physically tangible browsing mechanism for objects in the Web of Things. We explore a model for user interaction with IoT devices that makes use of mobile augmented reality to allow users to identify new devices or easily access regularly-used devices in their environment, enables immediate interaction with quickly-obtainable user interfaces from the web, and provides developers a convenient platform to display custom interfaces for their devices. This model represents a step towards software-based interaction that might, one day, feel as intuitive, accessible, and familiar as the physical interfaces we commonly encounter in our daily lives.
An approach to enhance argument-based multi-criteria decision systems with conditional preferences and explainable answers

In our current society, it is very common to have access to a huge amount of information and scenarios in which it is possible to choose among a great number of alternatives. Therefore, systems that automate or assist with the decision-making process are becoming crucial. Those systems should allow users to represent their preferences accurately, even in scenarios where the user has many forms of comparing the available alternatives. In this paper, we propose an argument-based framework for multi-criteria decision-making based on conditional rules, that allows users to encode their preferences among the comparison criteria in terms of requirement and tolerance values. From these rules, arguments that are involved in the comparison process are generated. In order to comply with users' demands for explanations of the given answers, our proposed framework can provide qualitative explanations about the argumentation process, and also an explanatory graph that shows how all the alternatives were ranked and the connections with the user's rules that were used. We also show the results of an empirical analysis of the performance of a developed prototype.
Tractable learning of Bayesian networks from partially observed data

The majority of real-world problems require addressing incomplete data. The use of the structural expectation-maximization algorithm is the most common approach toward learning Bayesian networks from incomplete datasets. However, its main limitation is its demanding computational cost, caused mainly by the need to make an inference at each iteration of the algorithm. In this paper, we propose a new method with the purpose of guaranteeing the efficiency of the learning process while improving the performance of the structural expectation-maximization algorithm. We address the first objective by applying an upper bound to the treewidth of the models to limit the complexity of the inference. To achieve this, we use an efficient heuristic to search the space of the elimination orders. For the second objective, we study the advantages of directly computing the score with respect to the observed data rather than an expectation of the score, and provide a strategy to efficiently perform these computations in the proposed method. We perform exhaustive experiments on synthetic and real-world datasets of varied dimensionalities, including datasets with thousands of variables and hundreds of thousands of instances. The experimental results support our claims empirically.
Intuitive Hand Gestures for the Interaction with Information Visualizations in Virtual Reality

The development of virtual reality provides opportunities for immersive information visualizations and therefore it is expected to facilitate the exploration and understanding of data. Hand gesture control enables intuitive interaction and thus it is suggested to amplify the level of immersion further. This paper conducts and experiment to identify a set of intuitive gestures when interacting with an information visualization. Participants are asked to provide hand gestures to given information seeking tasks in an interactive data visualization application in virtual reality that they did not know in advance. The results are analysed and findings with intuitive gestures are communicated and discussed.
uAssign

We describe uAssign, an assignment system for teaching and assessing command line terminal skills. uAssign allows instructors to create auto-graded terminal assignments that require students to perform a high-level action that can be completed in many ways. Assignments can be randomized so that students can't re-use old solutions. uAssign is implemented via an in-browser terminal emulator that uses WebSockets to connect to a Docker container. Performance testing and its use in a large-enrollment lecture course show that it is efficient enough to handle a large number of concurrent users. A survey of students shows significant improvement in terminal skill confidence after using uAssign and that students have a high level of satisfaction with uAssign assignments.
What data should I protect?

Major breaches of sensitive company data, as for Facebook's 50 million user accounts in 2018 or Equifax's 143 million user accounts in 2017, are showing the limitations of reactive data security technologies. Companies and government organizations are turning to proactive data security technologies that secure sensitive data at source. However, data security analysts still face two fundamental challenges in data protection decisions: 1) the information overload from the growing number of data repositories and protection techniques to consider; 2) the optimization of protection plans given the current goals and available resources in the organization. In this work, we propose an intelligent user interface for security analysts that recommends what data to protect, visualizes simulated protection impact, and helps build protection plans. In a domain with limited access to expert users and practices, we elicited user requirements from security analysts in industry and modeled data risks based on architectural and conceptual attributes. Our preliminary evaluation suggests that the design improves the understanding and trust of the recommended protections and helps convert risk information in protection plans.
Multi-Path Relationship Preserved Social Network Embedding

Social network embedding, namely, embedding social network nodes into a low-dimensional space, is the foundation of social network analysis, such as node classification and link prediction. Although many existing methods attempt to address this task, most of them only consider the shallow relationship between two nodes in the network, which ignore capturing multiple and semantic-rich social relationships between users. To this end, we define such multiple and semantic-rich relationships as multi-path relationships, and propose a multi-path relationship preserved social network embedding method named MPR-SNE, which is based on the recurrent neural network framework that incorporates both social network structure and node profile information. Specifically, we first utilize random walks to explore the multiple social relationship paths between nodes. Then, a new recurrent unit called bi-directional multi-path relationship unit is proposed to better capture the properties of multi-path relationships. Finally, two objective functions are designed to seamlessly integrate social network structure and node profile information into node representation. The experimental results on two real-world networks show that MPR-SNE outperforms the state-of-the-art baselines on node classification task and link prediction task.
Error analysis and detection procedures for elliptic curve cryptography

In this paper, a fault detection scheme is introduced with the ability to perform with increased protection and reliability of the Elliptic Curve Cryptography (ECC) in realistic environments at a competitive price point. Fall of errors in encrypted/decrypted data delivery with the transmission can create the situation of erroneous data occurring. Without fault detection, in the context of encryption as well as decryption process can create the unprotected system due to random faults, and vulnerable to attack. We are striving to overcome these system weaknesses by introducing the application of nonlinear fault detection codes as it allows safeguarding the elliptic curve point while adding and doubling the operations from the fault attacks. The use of these codes ensures almost perfect error discovery capacity with a reasonably priced upfront cost. The ECC cryptosystem offered a failsafe fault detection scheme using an error detecting code with a proven rate of 99% fault detection coverage.
Automatic sub-word unit discovery and pronunciation lexicon induction for ASR with application to under-resourced languages

We present a method enabling the unsupervised discovery of sub-word units (SWUs) and associated pronunciation lexicons for use in automatic speech recognition (ASR) systems. This includes a novel SWU discovery approach based on self-organising HMM-GMM states that are agglomeratively tied across words as well as a novel pronunciation lexicon induction approach that iteratively reduces pronunciation variation by means of model pruning. Our approach relies only on recorded speech and associated orthographic transcriptions and does not require alphabetic graphemes. We apply our methods to corpora of recorded radio broadcasts in Ugandan English, Luganda and Acholi, of which the latter two are under-resourced. The speech is conversational and contains high levels of background noise, and therefore presents a challenge to automatic lexicon induction. We demonstrate that our proposed method is able to discover lexicons that perform as well as baseline expert systems for Acholi, and close to this level for the other two languages when used to train DNN-HMM ASR systems. This demonstrates the potential of the method to enable and accelerate ASR for under-resourced languages for which a phone inventory and pronunciation lexicon are not available by eliminating the dependence on human expertise this usually requires.
Nonlinear dynamical processes in musical interactions: Investigating the role of nonlinear dynamics in supporting surprise and exploration in interactions with digital musical instruments

Nonlinear dynamical processes play a central role in many acoustic instruments, yet they rarely feature in digital instruments, and are little understood from an interaction design perspective. Such processes exhibit behaviours that are complex, time-dependent, and chaotic, yet in the context of acoustic instruments can facilitate interactions that are explorable, learnable and repeatable. This suggests that these processes merit deeper investigation for digital music interaction design. Two studies are presented which investigate user interaction with nonlinear dynamical musical tools. A lab-based study used four purpose-built digital musical instruments to test interaction designs featuring nonlinear dynamical processes. Evaluations with 28 musicians demonstrated the potential for these processes to provoke creative surprises, and support exploration without a corresponding loss of control. A subsequent ethnographically-informed study with 24 musicians linked these findings to a mode of engagement which we term ‘edge-like interaction’. Edge-like interactions draw on the complex, unpredictable behaviours found in nonlinear dynamical processes close to critical thresholds, facilitating creative exploration. The two complementary studies provide evidence both for the existing importance of nonlinear dynamical processes in musical interactions with acoustic interactions, and their potential for deployment in the development of new creative digital technologies, musical or otherwise.
An Architecture for Distributed Ledger-Based M2M Auditing for Electric Autonomous Vehicles

Electric Autonomous Vehicles (EAVs) promise to be an effective way to solve transportation issues such as accidents, emissions and congestion, and aim at establishing the foundation of Machine-to-Machine (M2M) economy. For this to be possible, the market should be able to offer appropriate charging services without involving humans. The state-of-the-art mechanisms of charging and billing do not meet this requirement, and often impose service fees for value transactions that may also endanger users and their location privacy. This paper aims at filling this gap and envisions a new charging architecture and a billing framework for EAV which would enable M2M transactions via the use of Distributed Ledger Technology (DLT).
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions

This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional random fields (CRFs) for context modeling in DA classification. We explore the impact of transcriptions generated from different automatic speech recognition systems such as hybrid TDNN/HMM and End-to-End systems on the final performance. Experimental results on two benchmark datasets (MRDA and SwDA) show that the combination CNN and CRF improves consistently the accuracy. Furthermore, they show that although the word error rates are comparable, End-to-End ASR system seems to be more suitable for DA classification.
Gentle Adaboost algorithm based on multi‐feature fusion for face detection

There are few types of Haar-like rectangle features, which leads to the problem that the classifier training time is too long due to the large number of feature quantities required in the description of the face. Local binary patterns (LBP) are used to describe the local texture features of the face image. Considering the inadequacy of basic LBP features in face detection, unified MB-LBP features and unified rotation-invariant LBP features are used to describe local texture features of faces. Considering the shortcomings of MB-LBP feature and rotation-invariant LBP feature on face edge information, the edge azimuth field feature based on Canny operator is combined with the above two features to describe face information. Finally, the Gentle Adaboost classifier was designed to classify all the extracted features. The experimental results show that the unified MB-LBP feature and the unified rotation invariant LBP feature and the edge azimuth field feature based on Canny operator can not only describe the face information from the local but also the whole, which greatly improves the detection rate and detection speed of the face with multiple poses and different rotation modes.
Usability testing on website wadaya based on ISO 9241-11

Wadaya (in Bahasa: Warisan Budaya) is a crowdsourcing information system which is intended for collecting and spreading cultural heritage information in Indonesia. As a crowdsourcing system, Wadaya relies on community participation to provide information, hence it must be easy to use. This research aims to evaluate the usability of Website Wadaya based on ISO 9241-11. The usability attributes consist of effectiveness, efficiency and user satisfaction. The effectiveness attribute is measured by the accuracy and completeness of the users to achieve specified goals. The efficiency attribute is measured by resources spent by user in order to ensure accurate and complete achievement of the goals especially based on expert completes on the scenario. The user satisfaction attribute is measured based on the comfortability and acceptability of use by using Software Usability Test (SUS) questionnaire. This research is divided into three steps, namely: usability evaluation on existing system, development of wireframe as alternative design, and usability evaluation on alternative design. Data collection was done by distributing questionnaire and conducting observation based on the scenario task. The number of participants are 10 users who consist of expert users and first time users. The result of this research shows that there are some usability issues on Wadaya especially when the users attempt to complete contribute cultural object task. The alternative design is developed based on the result of usability testing and Shneiderman's Eight Golden Rules. The result of usability testing on alternative design shows that the usability of Wadaya has been improved.
Practical Subtyping for Curry-Style Languages

We present a new, syntax-directed framework for Curry-style type systems with subtyping. It supports a rich set of features, and allows for a reasonably simple theory and implementation. The system we consider has sum and product types, universal and existential quantifiers, and inductive and coinductive types. The latter two may carry size invariants that can be used to establish the termination of recursive programs. For example, the termination of quicksort can be derived by showing that partitioning a list does not increase its size. The system deals with complex programs involving mixed induction and coinduction, or even mixed polymorphism and (co-)induction. One of the key ideas is to separate the notion of size from recursion. We do not check the termination of programs directly, but rather show that their (circular) typing proofs are well-founded. Termination is then obtained using a standard (semantic) normalisation proof. To demonstrate the practicality of the system, we provide an implementation accepting all the examples discussed in the article.
State-of-the-Art Duolingo Features and Applications

Duolingo is a rapidly growing on-line platform for language learning. In this paper learning theories that are embodied in its design are analyzed and certain shortcomings are identified. In the past two years, Duolingo has expanded its platform with the addition of new applications and features. State-of-the-art updates are reviewed in order to uncover whether they address limitations of Duolingo's original design or provide enhancements to the learning platform.