Reviewerly/exHarmony huggingface dataset:
A continuous vocoder for statistical parametric speech synthesis and its evaluation using an audio-visual phonetically annotated Arabic corpus

In this paper, we present an extension of a novel continuous residual-based vocoder for statistical parametric speech synthesis by addressing two objectives. First, because the noise component is often not accurately modelled in modern vocoders (e.g. STRAIGHT), a new technique for modelling unvoiced sounds is proposed by adding time domain envelope to the unvoiced segments to avoid any residual buzziness. Four time-domain envelopes (Amplitude, Hilbert, Triangular and True) are investigated, enhanced, and then applied to the noise component of the excitation in our continuous vocoder, i.e. of which all parameters are continuous. With the future aim of producing high-quality Arabic speech synthesis, we secondly apply this vocoder on a modern standard Arabic audio-visual corpus which is annotated both phonetically and visually, and dedicated to emotional speech processing studies. In an objective experiment, we investigated the Phase Distortion Deviation, whereas a MUSHRA type subjective listening test was conducted comparing natural and vocoded speech samples. As a result, both experiments based on the proposed noise modelling have shown satisfactory results in terms of naturalness and intelligibility, while outperforming STRAIGHT and other earlier residual-based approaches.
Toward a Data-Driven Society: A Technological Perspective on the Development of Cybersecurity and Data-Protection Policies

A data-driven society requires a common regulatory umbrella to provide a harmonized vision of cybersecurity. We describe cybersecurity policies and joint initiatives in the European Union and give insights about the need to align ongoing technological advances with such regulatory efforts.
Detection of Diversified Stego Sources with CNNs

The goal of this article is construction of steganalyzers capable of detecting a variety of embedding algorithms and possibly identifying the steganographic method. Since deep learning today can achieve markedly better performance than other machine learning tools, our detectors are deep residual convolutional neural networks. We explore binary classifiers trained as cover versus all stego, multi-class detectors, and bucket detectors in a feature space obtained as a concatenation of features extracted by networks trained on individual stego algorithms. The accuracy of the detector to identify steganography is compared with dedicated detectors trained for a specific embedding algorithm. While the loss of detection accuracy w.r.t. increasing number of steganographic algorithms increases only slightly as long as the embedding schemes are known, the ability of the detector to generalize to previously unseen steganography remains a challenging task.
Vibration Test and Vibration Reduction Design of UAV Load Radar

Lidar has entered a practical stage in military fields such as obstacle avoidance, chemical and biological warfare agent detection and underwater target detection of low-altitude flying UAV. Due to the influence of vibration and airflow fluctuation caused by aircraft flying, the imaging is prone to blur and even damage the lidar. In order to overcome this phenomenon, this paper firstly tests the vibration of lidar in the position of UAV installation and UAV engine under the condition of UAV flying in the air. After testing and analysis, if no vibration reduction measures are taken, the lidar may work abnormally or even be damaged. The acceleration response at the location of lidar installation is used as the input condition of the shaking table to process the lidar. To achieve the simulated load, the vibration reduction measures of the T-type shock absorber are selected and the response of the load is tested under the input condition of the shaking table; the T-type shock absorber is applied to the UAV, and the response of the UAV lidar simulated load is tested under the UAV flight condition; through the vibration test and the UAV flight condition test, the vibration reduction effect of the T-type shock absorber is finally verified, and the method of vibration reduction is verified. It is of great significance to realize seamless fusion of lidar measurement and photography for UAV.
A Novel Task Language for Natural Interaction in Human-Robot Systems for Warehouse Logistics

This paper introduces a novel task description language for human-robot interaction in warehouse logistics to let human workers interact with robots in a natural way. Especially, in decentralized control systems with heterogeneous entities, humans have to retain control over the overall system. This work offers a simple way to build up teams consisting of humans and robots to efficiently use the capabilities of humans as well as robots. With the proposed system it is also possible to provide multi vendor-capabilities and allow an easy integration of robots. We are providing a solution, starting from the user input, converting it into a domain specific language and showing also the execution in human-robot collaboration.
ON-OFF Privacy with Correlated Requests

We introduce the ON-OFF privacy problem. At each time, the user is interested in the latest message of one of N online sources chosen at random, and his privacy status can be ON or OFF for each request. Only when privacy is ON the user wants to hide the source he is interested in. The problem is to design ON-OFF privacy schemes with maximum download rate that allow the user to obtain privately his requested messages. In many realistic scenarios, the user's requests are correlated since they depend on his personal attributes such as age, gender, political views, or geographical location. Hence, even when privacy is OFF, he cannot simply reveal his request since this will leak information about his requests when privacy was ON. We study the case when the users's requests can be modeled by a Markov chain and N = 2 sources. In this case, we propose an ON-OFF privacy scheme and prove its optimality.
Virtual Assistant Prototype for Managing Medication Using Messaging Platforms

Healthcare chatbots (a.k.a. virtual assistants) offer services, tools, and interactions to the users, providing advice, help, support, and management to different diseases. The users of this type of chatbots can be, for example, patients, caregivers, and healthcare professionals, with different needs and requirements. Sometimes, patients with chronic diseases needing to take different medications several times per day, can have difficulties to remember their medication intakes. This situation can lead to negative effects on treatment effectiveness. In this paper, we present a virtual assistant prototype, aimed to help and manage patient’s medication through the use of Signal, a secure messaging platform that uses end-to-end encryption for better user privacy. The objective of this work is to create a handy tool to remind medication intakes through the creation of a supportive community where patients, caregivers, and healthcare professionals interact with helpful tools and services offered by the virtual assistant. Moreover, the developed prototype makes use of medical standards for data management and Artificial Intelligence Markup Language (AIML) for conversation support.
Order-preserving encryption using approximate common divisors

Order-preservation is a highly desirable property for encrypted databases as it allows range queries over ciphertexts. Order-preserving encryption (OPE) is used in the encrypted database systems CryptDB and Cipherbase. The former has been adopted by several commercial organisations and the latter was developed as an extension of Microsoftâs SQLServer. We present two novel, but simple, randomised OPE schemes based on the general approximate common divisor problem (GACDP) and decisional polynomial approximate common divisor problem (DPolyACDP) respectively. These appear to be the first OPE schemes to be based on a computational hardness primitive, rather than a security game. Our GACDP based scheme is very efficient, requiring only O(1) arithmetic operations for encryption and decryption. Our DPolyACDP based scheme is similarly efficient. We show that these schemes have near optimal information leakage. We demonstrate how our OPE schemes can be integrated into a secure distributed computing system which computes over encrypted data. We report on an extensive evaluation of our GACDP-based algorithms in such a scenario, a MapReduce computation over encrypted data. The results clearly demonstrate extremely favourable execution times in comparison with existing OPE schemes.
A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking

A forward-backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.
Local-hidden-state models for Bell diagonal states and beyond

For a bipartite entangled state shared by two observers, Alice and Bob, Alice can affect the post-measured states left to Bob by choosing different measurements on her half. Alice can convince Bob that she has such an ability if and only if the unnormalized postmeasured states cannot be described by a local-hidden-state (LHS) model. In this case, the state is termed steerable from Alice to Bob. By converting the problem to construct LHS models for two-qubit Bell diagonal states to the one for Werner states, we obtain the optimal models given by Jevtic \textit{et al.} [J. Opt. Soc. Am. B 32, A40 (2015)], which are developed by using the steering ellipsoid formalism. Such conversion also enables us to derive a sufficient criterion for unsteerability of any two-qubit state.
The Role of Robots, Artificial Intelligence, and Service Automation in Events

Originality/value: This chapter provides a new perspective in focusing on the potential applicability of RAISA in event management practice.
Authorship Attribution in Russian in Real-World Forensics Scenario

Recent demands in authorship attribution, specifically, cross-topic authorship attribution with small numbers of training samples and very short texts, impose new challenges on corpora design, feature and algorithm development. In the current work we address these challenges by performing authorship attribution on a specifically designed dataset in Russian. We present a dataset of short written texts in Russian, where both authorship and topic are controlled. We propose a pairwise classification design closely resembling a real-world forensic task. Semantic coherence features are introduced to supplement well-established n-gram features in challenging cross-topic settings. Distance-based measures are compared with machine learning algorithms. The experiment results support the intuition that for very small datasets, distance-based measures perform better than machine learning techniques. Moreover, pairwise classification results show that in difficult cross-topic cases, content-independent features, i.e., part-of-speech n-grams and semantic coherence, are promising. The results are supported by feature significance analysis for the proposed dataset.
An Attribute-Based Availability Model for Large Scale IaaS Clouds with CARMA

High availability is one of the core properties of Infrastructure as a Service (IaaS) and ensures that users have anytime access to on-demand cloud services. However, significant variations of workflow and the presence of super-tasks, mean that heterogeneous workload can severely impact the availability of IaaS clouds. Although previous work has investigated global queues, VM deployment, and failure of PMs, two aspects are yet to be fully explored: one is the impact of task size and the other is the differing features across PMs such as the variable execution rate and capacity. To address these challenges we propose an attribute-based availability model of large scale IaaS developed in the formal modeling language CARMA. The size of tasks in our model can be a fixed integer value or follow the normal, uniform or log-normal distribution. Additionally, our model also provides an easy approach to investigating how to arrange the slack and normal resources in order to achieve availability levels. The two goals of our work are providing an analysis of the availability of IaaS and showing that the use of CARMA allows us to easily model complex phenomena that were not readily captured by other existing approaches.
Shape-optimizing mesh warping method for stereoscopic panorama stitching

In this paper, we propose a novel shape-optimizing mesh warping method for stereoscopic panorama stitching, which aims to resolve shape distortion and unnatural rotation of traditional stitching methods, simultaneously coping with the challenges, misalignment, and stereoscopic inconsistency. Specifically, based on the grid mesh analysis of projective warping, we propose a differential warping method by gradually changing the inclination angle of each mesh line in non-overlapping regions of the image to reduce shape distortion and unnatural rotation. Furthermore, an extended moving direct linear transformation method is proposed to effectively and robustly improve alignment accuracy and maintain stereoscopic consistency in multiple stereoscopic images. Finally, a consistent seam based on the matched feature points in the left- and right- view images of a stereoscopic image is designed to blend images and generate a stereoscopic panorama image. Experiments demonstrate that the proposed method has a superior performance compared to previous methods.
A Multimodal Dialogue Framework for Cloud-Based Companion Systems

Companion systems are cooperative, cognitiveKraus, Matthias systems aiming at assisting a user in everyday situations. Therefore, theseSchiller, Marvin systems require a high level of availability. One option to meetBehnke, Gregor thisBercher, Pascal requirement is to use a web-deployable architecture. In this demo paper, we present a multimodal cloud-basedBiundo, Susanne dialogue framework for the development of a distributed, web-based companionGlimm, Birte system. The proposed framework is intended to provide an efficient, easily extensible, andMinker, Wolfgang scalable approach for these kinds of systems and will be demonstrated in a do-it-yourself assistance scenario.
Feedback control loop design for workload change detection in self-tuning NoSQL wide column stores

Database management systems are the main part of information systems that the size and complexity of these systems are increased in recent years. Due to the growing complexity of DBMSs, database administrators (DBAs) face increasingly more problems and challenges, and so managing these systems are difficult and laborious. More over the main part of the total cost of ownership includes the cost of expert database administrator who can manage these large and complicated systems. Autonomic database, by providing self-management functionality, leads to a reduction in the total cost of ownership for the database system. Self-management decisions such as automated schema database tuning are dependent on the database workload. Therefore, one of the important issues in realizing the database automated tuning is workload monitoring and analysis for changes detection and schema re-tuning with this changes. In this paper, a feedback control loop is designed for continuous monitoring and light-weight workload analysis in NoSQL wide column stores. This loop describes a design pattern for the self-tuning feature and it is used to detect workload changes that are necessary for the automated schema database re-tuning. Our concept is based on workload model construction using reconfigurable colored petri-net model. The results of the experiments show the effectiveness of the proposed approach in discovering significant workload changes.
An Efficient Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks Using Online/Offline Certificateless Aggregate Signature

Vehicular ad hoc networks (VANETs) are fundamental components of building a safe and intelligent transportation system. However, due to its wireless nature, VANETs have serious security and privacy issues that need to be addressed. The conditional privacy-preserving authentication protocol is one important tool to satisfy the security and privacy requirements. Many such schemes employ the certificateless signature, which not only avoids the key management issue of the PKI-based scheme but also solves the key escrow problem of the ID-based signature scheme. However, many schemes have the drawback that the computational expensive bilinear pairing operation or map-to-point hash function are used. In order to enhance the efficiency, certificateless signature schemes for VANETs are usually constructed to support signature aggregation or online/offline computation. In this paper, we propose an efficient conditional privacy-preserving authentication scheme using an online/offline certificateless aggregate signature, which does not require bilinear pairing or map-to-point hash function, to address the security and privacy issues of VANETs. Our proposed scheme is proven to be secure with a rigorous security proof, and it satisfies all the security and privacy requirements with a better performance compared with other related schemes.
Listwise Neural Ranking Models

Several neural networks have been developed for end-to-end training of information retrieval models. These networks differ in many aspects including architecture, training data, data representations, and loss functions. However, only pointwise and pairwise loss functions are employed in training of end-to-end neural ranking models without human-engineered features. These loss functions do not consider the ranks of documents in the estimation of loss over training data. Because of this limitation, conventional learning-to-rank models using pointwise or pairwise loss functions have generally shown lower performance compared to those using listwise loss functions. Following this observation, we propose to employ listwise loss functions for the training of neural ranking models. We empirically demonstrate that a listwise neural ranker outperforms a pairwise neural ranking model. In addition, we achieve further improvements in the performance of the listwise neural ranking models by query-based sampling of training data.
Privacy and Security in Aadhaar

The Aadhaar is a project of the Government of India and is the most ambitious program in the world aimed at issuing unique 12-digit numbers to every Indian as well as recording their biometrics for authentication services. A data leak would be potentially disastrous and would constitute a major breach of privacy as well. Such a leak can take place at the application level, the network level and the storage level. Data pertaining to the number of data breaches in the past decade has been analyzed to emphasize the importance of a secure ecosystem for such an ambitious project. A new methodology has also been studied which will help in heightening the security of the Aadhaar ecosystem and safeguarding the privacy of the people better.
Joint Modeling of Recognizing Macro Chinese Discourse Nuclearity and Relation Based on Structure and Topic Gated Semantic Network

Nowadays, in the Natural Language Processing field, with the object of research gradually shifting from the word to sentence, paragraph and higher semantic units, discourse analysis is one crucial step toward a better understanding of how these articles are structured. Compared with micro-level, this has rarely been investigated in macro Chinese discourse analysis and faces tremendous challenges. First, it is harder to grasp the topic and recognize the relationship between macro discourse units due to their longer length and looser relation between them. Second, how to mine the relationship between nuclearity and relation recognition effectively is another challenge. To address these challenges, we propose a joint model of recognizing macro Chinese discourse nuclearity and relation based on Structure and Topic Gated Semantic Network (STGSN). It makes the semantic representation of a discourse unit can change with its position and the topic by Gated Linear Unit (GLU). Moreover, we analyze the results of our models in nuclearity and relation recognition and explore the potential relationship between them. Conducted experiments show the effectiveness of the proposed approach.
Skull Stripping of Brain MRI Using Mathematical Morphology

Aberrant growth of brain cells leads to a brain abnormality known as brain tumor. Magnetic resonance imaging (MRI) is the most widely used screening technique for tumor, but skull tissue in a form of a ring surrounding the brain region has similar intensity with that of tumor which can lead to false detection of skull as a tumorous tissue. To avoid this problem, skull is removed from the brain MRI prior to segmentation by using Skull Stripping algorithm. In this work, the algorithm is based on mathematical morphology using a combination of dilation and erosion operators. A disk-shaped flat structuring element is used which is rotationally invariant and more suitable for biomedical images.
A Character-Level BiLSTM-CRF Model With Multi-Representations for Chinese Event Detection

Using the word as a basic unit may undermine Chinese event detection model’s performance because of the inaccurate word boundaries generated by segmentation tools. Besides, word embeddings are contextual independent and cannot handle the polysemy of event triggers, which may prevent us from obtaining the desired performance. To address these issues, we propose a BiLSTM-CRF (Bidirectional Long Short-Term Memory Conditional Random Field) model using contextualized representations, which regards event detection task as a character-level sequence labeling problem and uses contextualized representations to disambiguate event triggers. Experiments show that our proposed method sets a new state-of-the-art, which proves Chinese characters could replace words for the Chinese event detection task. Besides, using contextualized representation reduces the false positive case, which verifies that this kind of representation could remedy the weakness of the word embedding technique. Based on the results, we believe that character-level models are worth exploring in the future.
Subjective Annotation and Evaluation of Three Different Chatbots WOCHAT: Shared Task Report

This paper evaluates the performance of threeKong-Vega, Naomi different chatbots: IRIS, TickTock and Joker, that have been made available to the public online. All three retrieval-based dialogue systems are chat-oriented and designedShen, Mingxin to engageWang, Mo theD'Haro, Luis Fernando users into all types of conversations for as long as possible. They employ different approaches to provide relevant and valid responses, and constantly utilize conversational strategies to further automatically improve its own system through machine learning. The analysis of annotations of more than 2000 responses for the three chatbots allowed us to confirm the robustness, scalability and usability of the systems, as well as to detect a few areas in which response accuracy was lacking, and propose future work to further improve the three systems and annotations scheme.