Reviewerly/exHarmony huggingface dataset:
Multilayer Self-defense System to Protect Enterprise Cloud

A data breach can seriously impact organizational intellectual property, resources, time, and product value.The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols.As TCP relies on IP addresses, an attacker may easily trace the IP address of the organization.Given that many organizations run the risk of data breach and cyber-attacks at a certain point, a repeatable and well-developed incident response framework is critical to shield them.Enterprise cloud possesses the challenges of security, lack of transparency, trust and loss of controls.Technology eases quickens the processing of information but holds numerous risks including hacking and confidentiality problems.The risk increases when the organization outsources the cloud storage services through the vendor and suffers from security breaches and need to create security systems to prevent data networks from being compromised.The business model also leads to insecurity issues which derail its popularity.An attack mitigation system is the best solution to protect online services from emerging cyber-attacks.This research focuses on cloud computing security, cyber threats, machine learning-based attack detection, and mitigation system.The proposed SDN-based multilayer machine learning-based self-defense system effectively detects and mitigates the cyber-attack and protects cloud-based enterprise solutions.The results show the accuracy of the proposed machine learning techniques and the effectiveness of attack detection and the mitigation system.
Negativity-mutual information conversion and coherence in two-coupled harmonic oscillators

Quantum information is a common topic of research in many areas of quantum physics, such as quantum communication and quantum computation, as well as quantum thermodynamics. It can be encoded in discrete or continuous variable systems, with the appropriated formalism to treat it generally depending on the quantum system to be chosen. For continuous variable systems, it is convenient to employ a quasi-probability function to represent quantum states and non-classical signatures. The Wigner function is a special quasi-probability function because it allows to describe a quantum system in the phase space very similar to the classical one. In this work we consider some informational aspects of two-mode continuous variables systems. In particular we illustrate how the mutual information may be useful to transfer the non-Gaussianity property between two states. Moreover, the coherence of a Gaussian state is studied when the system is coupled to a thermal reservoir but also with an extra single-mode Gaussian state. Our results may be, in principle, investigated in trapped ions setups, where the two-mode system can be encoded in the vibrational modes of the ion.
A New Steganography Without Embedding Based on Adversarial Training

Steganography is an art to hide information in the carriers to prevent from being detected, while steganalysis is the opposite art to detect the presence of the hidden information. With the development of deep learning, several state-of-the-art steganography and steganalysis based on deep learning techniques have been proposed to improve hiding or detection capabilities. Generative Adversarial Networks (GANs) based steganography directly uses the minimax game between the generator and discriminator, to automatically generate steganography algorithms resisting being detected by powerful steganalysis. The steganography without embedding (SWE) based on GANs, where the generated cover images themselves are stego ones carrying secret information has shown its state-of-the-art steganography performance. However, SWE based on GANs has serious weaknesses, such as low information recovery accuracy, low steganography capacity and poor natural showing. To solve these problems, this paper proposes a new SWE based on adversarial training, with carefully designed generator, discriminator and extractor, as well as their loss functions and optimized training mode. The proposed method can achieve a very high information recovery accuracy (100% in some cases), and at the same time improve the steganography capacity and image quality.
XML navigation and transformation by tree-walking automata and transducers with visible and invisible pebbles

The pebble tree automaton and the pebble tree transducer are enhanced by additionally allowing an unbounded number of “invisible” pebbles (as opposed to the usual “visible” ones). The resulting pebble tree automata recognize the regular tree languages (i.e., can validate all generalized DTD's) and hence can find all matches of MSO definable patterns. Moreover, when viewed as a navigational device, they lead to an XPath-like formalism that has a path expression for every MSO definable binary pattern. The resulting pebble tree transducers can apply arbitrary MSO definable tests to (the observable part of) their configurations, they (still) have a decidable typechecking problem, and they can model the recursion mechanism of XSLT. The time complexity of the typechecking problem for conjunctive queries that use MSO definable patterns can often be reduced through the use of invisible pebbles.
Dual-View Semantic Inference Network for image-text matching

Recently, image-text matching based on local region-word semantic alignment has attracted considerable research attention. The fine-grained interplay can be achieved by aggregating the similarities of the region-word pairs. However, the similarities of aligned region-word pairs are treated equally in most cross-modal matching literatures, without considering their respective importance. Moreover, the local alignment methods are prone to bring about a global semantic drift due to the ignorance of thematic considerations for the image-text pairs. In this paper, a novel Dual-View Semantic Inference (DVSI) network is proposed to leverage both local and global semantic matching in a holistic deep framework. For the local view, a region enhancement module is proposed to mine the priorities for different regions in the image, which provides differentiate abilities to discover the latent region-word relationships. For the global view, the overall semantics of image is summarized for global semantic matching to avoid global semantic drift. The two views are unified together for final image-text matching. Extensive experiments conducted on MSCOCO and Flicr30K demonstrate the effectiveness of the proposed DVSI.
Using a Set of Triangle Inequalities to Accelerate K-means Clustering

The k-means clustering is a well-known problem in data mining and machine learning. However, the de facto standard, i.e., Lloyd's k-mean algorithm, suffers from a large amount of time on the distance calculations. Elkan's k-means algorithm as one prominent approach exploits triangle inequality to greatly reduce such distance calculations between points and centers, while achieving the exactly same clustering results with significant speed improvement, especially on high-dimensional datasets. In this paper, we propose a set of triangle inequalities to enhance the filtering step of Elkan's k-means algorithm. With our new filtering bounds, a filtering-based Elkan (FB-Elkan) is proposed, which preserves the same results as Lloyd's k-means algorithm and additionally prunes unnecessary distance calculations. In addition, a memory-optimized Elkan (MO-Elkan) is provided, where the space complexity is greatly reduced by trading-off the maintenance of lower bounds and the run-time efficiency. Throughout evaluations with real-world datasets, FB-Elkan in general accelerates the original Elkan's k-means algorithm for high-dimensional datasets (up to 1.69x), whereas MO-Elkan outperforms the others for low-dimensional datasets (up to 2.48x). Specifically, when the datasets have a large number of points, i.e., \(n\ge 5\)M, MO-Elkan still can derive the exact clustering results, while the original Elkan's k-means algorithm is not applicable due to memory limitation.
Mining discriminative patches for script identification in natural scene images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.
Bypassing the load balancer without regrets

Load balancers are a ubiquitous component of cloud deployments and the cornerstone of workload elasticity. Load balancers can significantly affect the end-to-end application latency with their load balancing decisions, and constitute a significant portion of cloud tenant expenses.
Towards Rapid Prototyping of Foldable Graphical User Interfaces with Flecto

Whilst new patents and announcements advertise the technical availability of foldable displays, which are capable to be folded to some extent, there is still a lack of fundamental and applied understanding of how to model, to design, and to prototype graphical user interfaces for these devices before actually implementing them. Without waiting for their off-the-shelf availability and without being tied to any physical foldable mechanism, Flecto defines a model, an associated notation, and a supporting software for prototyping graphical user interfaces running on foldable displays, such as foldable smartphone or assemblies of foldable surfaces. For this purpose, we use an extended notation of the Yoshizawa-Randlett diagramming system, used to describe the folds of origami models, to characterize a foldable display and define possible interactive actions based on its folding operations. A guiding method for rapidly prototyping foldable user interfaces is devised and supported by Flecto, a design environment where foldable user interfaces are simulated in 3D environment instead of in physical reality. We report on a case study to demonstrate Flecto in action and we gather the feedback from users on Flecto, using Microsoft Product Reaction Cards.
GARNet: Graph Attention Residual Networks Based on Adversarial Learning for 3D Human Pose Estimation

Recent studies have shown that, with the help of complex network architecture, great progress has been made in estimating the pose and shape of a 3D human from a single image. However, existing methods fail to produce accurate and natural results for different environments. In this paper, we proposed a novel adversarial learning approach and studied the problem of learning graph attention network for regression. Graph Attention Residual Networks (GARNet), which processes regression tasks with graphic-structured data, learns to capture semantic information, such as local and global node relationships, through end-to-end training without additional supervision. The adversarial learning module is implemented by a novel multi-source discriminator network to learn the mapping from 2D pose distribution to 3D pose distribution. We conducted a comprehensive study to verify the effectiveness of our method. Experiments show that the performance of our method is superior to that of most existing techniques.
From Agents to Blockchain: Stairway to Integration

The blockchain concept and technology are impacting many different research and application fields; hence, many are looking at the blockchain as a chance to solve long-standing problems or gain novel benefits. In the agent community several authors are proposing their own combination of agent-oriented technology and blockchain to address both old and new challenges. In this paper we aim at clarifying which are the opportunities, the dimensions to consider, and the alternative approaches available for integrating agents and blockchain, by proposing a roadmap and illustrating the issues yet to be addressed. Then, as both validation of our roadmap and grounds for future development, we discuss the case of Tenderfone, a custom blockchain integrating concepts borrowed from agent-oriented programming.
Evolving System Families in Space and Time

Managing the evolution of system families in space and time, i.e., system variants and their revisions is still an open challenge. The software product line (SPL) approach can support the management of product variants in space by reusing a common set of features. However, feature changes over time are often necessary due to adaptations and/or bug fixes, leading to different product versions. Such changes are commonly tracked in version control systems (VCSs). However, VCSs only deal with the change history of source code, and, even though their branching mechanisms allow to develop features in isolation, VCS does not allow propagating changes across variants. Variation control systems have been developed to support more fine-grained management of variants and to allow tracking of changes at the level of files or features. However, these systems are also limited regarding the types and granularity of artifacts. Also, they are cognitively very demanding with increasing numbers of revisions and variants. Furthermore, propagating specific changes over variants of a system is still a complex task that also depends on the variability-aware change impacts. Based on these existing limitations, the goal of this doctoral work is to investigate and define a flexible and unified approach to allow an easy and scalable evolution of SPLs in space and time. The expected contributions will aid the management of SPL products and support engineers to reason about the potential impact of changes during SPL evolution. To evaluate the approach, we plan to conduct case studies with real-world SPLs.
Become a Scrum Master: Immersive Virtual Reality Training to Learn Scrum Framework

Serious games are digital games that have an additional goal beyond entertainment, e.g., educational games can be a motivational tool for teaching or training players. Among other things, educational games can also be used in IT companies to convey approaches of agile software development. Such learning environments can improve the player’s skills in software development processes to reduce risks of schedule delays or incurred cost overruns. In particular, educational games should provide an effective and motivating learning environment for the players and should include hands-on activities to reinforce learning. To this end, we have developed an immersive virtual reality educational game to teach the Scrum framework. This game aims to efficiently train complex processes and improve the player’s skills in agile process management. In the virtual environment, players can go through a software development process to discover different activities, such as daily Scrum and sprint planning. The presented paper introduces a game design, results of initial user testings, and subsequent re-design of the early state educational game. The evaluation results of a user study show that the developed game can indeed teach the basics of the Scrum framework to players with little knowledge. However, the game scenario needs to be improved to make it more engaging and immersive for experienced players.
A Spatial and Sequential Combined Method for Web Service Classification

With the growing prosperity of the Web service ecosystem, high-quality service classification has become an essential requirement. Web service description documents are semantic definitions of services, which is edited by service developers to include not only usage scenarios and functions of services but also a lot of prior knowledge and jargons. However, at present, existing deep learning models cannot fully extract the heterogeneous features of service description documents, resulting in unsatisfactory service classification results. In this paper, we propose a novel deep neural network which integrates the Graph Convolutional Network (GCN) with Bidirectional Long Short-Term Memory (Bi-LSTM) network to automatically extract the features of function description documents for Web services. Specifically, we first utilize a two-layer GCN to extract global spatial structure features of Web services, which serves as a pre-training word embedding process. Afterwards, the sequential features of Web services learned from the Bi-LSTM model are integrated for joint training of parameters. Experimental results demonstrate that our proposed method outperforms various state-of-the-art methods in classification performance.
Opportunities to Share Collective Human Hearing

Worldwide, people are hearing various natural and artificial sounds, but it is hard to imagine how hearing the sounds from people collectively can be effectively used in our everyday lives if those sounds become sharable. The paper presents a digital platform to share collective human hearing named CollectiveEars, and shows potential opportunities and pitfalls of the CollectiveEars prototype based on a preliminary user study.
The Blockchain Evolution and Revolution of Accounting

Blockchain, the technology behind digital currency, is a decentralized, distributed ledger that records transactions in digital assets. By authenticating and recording immutable transactions, decentralized blockchains perform the same function as many intermediaries in our society that establish trust and maintain integrity between transacting parties. Due to its natural relation to accounting and possible uses in accounting functions, business operations and financial services, it is important that accountants learn about blockchain technology and its opportunities and limitations. This chapter explores applications of blockchain technology in finance, auditing, financial reporting and supply chain. We first discuss the classification, characteristics and issuance of cryptoassets and the evolving regulatory environment. Then, we address potential innovative uses of blockchain in auditing and financial reporting, keeping in mind the limitations of its application. Finally, we explore how blockchain technology can enhance communication and trust between organizations in a supply chain or in contracting relationships.
Incentivizing cooperative relay in UTXO-based blockchain network

Today’s blockchain system provides two incentives for participants: block reward and transaction fee. However, these incentives benefit block miners more than ordinary participants. Besides, the message propagation relies on the volunteer work of these participants. We consider that there could be a third incentive to reward the relay process in the blockchain network. Nodes should be paid for corresponding relay work and incentives to expedite message propagation. We take advantage of the payment nature of blockchain and the characteristics of the UTXO Model, and propose a relay payment scheme to tackle the relay incentive issue. UTXO (unspent transaction output) is an abstract ownership of cryptocurrency and it represents a chain of payment relations between transactions. Any legal transaction should have valid UTXO(s) and generate new UTXO(s) to accomplish payment process. Native token in blockchain provides a straight and favorable payment method, and the UTXO model binds the interest of relay nodes and message originator. Then we model a cooperative, competitive relay game based on our scheme. We analyze the equilibrium results and run massive simulations on different datasets. This incentive scheme is free-rider resilient and could further expand to meet diverse relay requirement via transaction scripts in UTXO model. Our discussion and results help explore the relay incentives in the blockchain network and draw an example for the design of new blockchain systems.
Faster Secure Multiparty Computation of Adaptive Gradient Descent

Most of the secure multi-party computation (MPC) machine learning methods can only afford simple gradient descent (sGD 1) optimizers, and are unable to benefit from the recent progress of adaptive GD optimizers (e.g., Adagrad, Adam and their variants), which include square-root and reciprocal operations that are hard to compute in MPC. To mitigate this issue, we introduce InvertSqrt, an efficient MPC protocol for computing 1/√x. Then we implement the Adam adaptive GD optimizer based on InvertSqrt and use it for training on different datasets. The training costs compare favorably to the sGD ones, indicating that adaptive GD optimizers in MPC have become practical.
Composition of Component Models - A Key to Construct Big Systems

Modern informatics based systems are mostly composed from self-contained components. To be useful for really big systems, composed of many components, proper abstraction mechanisms for components are necessary. Furthermore, when composing many components, composition must be associative, i.e. for components A, B, C must hold: $$(\mathbf{A} \bullet \mathbf{B} ) \bullet \mathbf{C} = \mathbf{A} \bullet (\mathbf{B} \bullet \mathbf{C} )$$ . This paper suggests a general framework for such components and their composition. With examples of systems represented in different formalisms such as Petri nets, BPMN, and UML, we show the high degree of independence of the formal framework from concrete modeling techniques.
Context-Based 3D Grids for Augmented Reality User Interfaces

Accurate 3D registration of real and virtual objects is a crucial step in AR, especially when manipulating those objects in space. Previous work simplifies mid-air 3D manipulations by removing one or more degrees of freedom by constraining motion using automatic algorithms. However, when designing objects, limiting the user's actions can affect their creativity. To solve this problem, we present a new system called Context-based 3D Grids that allows users to do precise mid-air 3D manipulations without constraining their actions. Our system creates 3D grids for each object in the scene that change depending on the object pose. Users can display additional reference frames inside the virtual environment using natural hand gestures that are commonly used when designing an object. Our goal is to help users visualize more clearly the spatial relation and the differences in pose and size of the objects.
Gender Identification based on Speech Recognition using Backpropagation Neural Network

Research in the field of speech recognition is very interesting and challenging. Speech recognition can also be used to identify the gender of the owner. This research proposes the Backpropagation Neural Network (BPNN) method to recognize and classify gender based on sound as input. BPNN was chosen because it has the main characteristics that can learn nonlinear, input, and output complex and can adjust the data used. The experiments were carried out on three types of datasets namely private dataset, Kaggle dataset, and Javanese gender dataset. Based on the test results obtained the highest accuracy, which is 95%. Where out of the 100 voice datasets used, there was one data that failed to be identified and four others were incorrectly recognized.