Reviewerly/exHarmony huggingface dataset:
Jointly optimized echo state network for short-term channel state information prediction of fading channel

Accurately obtaining channel state information (CSI) in wireless systems is significant but challenging. This paper focuses the technique of machine-learning-based channel estimation. In particular, a jointly optimized echo state network (JOESN) is proposed to form a concept of the CSI prediction which is made up of two interacting aspects of output weight regularization and initial parameter optimization. First, in order to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) are learned by a linear-weighted particle swarm optimization (LW-PSO) for further improve the prediction accuracy and reliability. The experiments about computational complexity and three evaluating metrics are carried out on two chaotic benchmarks and one real-world dataset. The analyzed results indicate that the JOESN performs promisingly on multivariate chaotic time series prediction.
Benchmarking Pocket-Scale Databases

Embedded database libraries provide developers with a common and convenient data persistence layer. They are a key component of major mobile operating systems, and are used extensively on interactive devices like smartphones. Database performance affects the response times and resource consumption of millions of smartphone apps and billions of smartphone users. Given their wide use and impact, it is critical that we understand how embedded databases operate in realistic mobile settings, and how they interact with mobile environments. We argue that traditional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance only at saturation. To rectify this, we present PocketData, a new benchmark for mobile device database evaluation that uses typical workloads to produce representative performance results. We explain the performance measurement methodology behind PocketData, and address specific challenges. We analyze the results obtained, and show how different classes of workload interact with database performance. Notably, our study of mobile databases at non-saturated levels uncovers significant latency and energy variation in database workloads resulting from CPU frequency scaling policies called governors—variation that we show is hidden by typical benchmark measurement techniques.
Navigation and source localization based on single pseudo-ranges

This paper presents a novel estimation solution for the problems of navigation and source localization based on pseudo-range measurements to a single pinger. In particular, the distance measurements are assumed to be corrupted by an unknown constant bias, which is explicitly taken into consideration in the design. First, the equivalence between the problems of navigation and source localization is established, as well as cooperative navigation of two vehicles in tandem. Then, an augmented system is derived and its observability is carefully studied. The analysis is constructive, in the sense that the means to design an observer for the new system dynamics with globally exponentially stable error dynamics are readily available, resorting to linear systems theory. Moreover, the new augmented system is shown to be equivalent to the original. Finally, simulations results are presented and discussed to assess the performance of the proposed solution in the presence of sensor noise.
Privacy-preserving HE-based clustering for load profiling over encrypted smart meter data

Load profiling is to cluster power consumption data to generate load patterns showing typical behaviors of consumers, and thus it has enormous potential applications in smart grid. However, short-interval readings would generate massive smart meter data. Although cloud computing provides an excellent choice to analyze such big data, it also brings significant privacy concerns since the cloud is not fully trustworthy. In this paper, based on a modified vector homomorphic encryption (VHE), we propose a privacy-preserving and outsourced k-means clustering scheme (PPOk M) for secure load profiling over encrypted meter data. In particular, we design a similarity-measuring method that effectively and non-interactively performs encrypted distance metrics. Besides, we present an integrity verification technique to detect the sloppy cloud server, which intends to stop iterations early to save computational cost. In addition, extensive experiments and analysis show that PPOk M achieves high accuracy and performance while preserving convergence and privacy.
Cretaceous succession of the Cambambe basin, Chapada dos Guimarães, Mato Grosso, Brazil

This paper aims to show the filling and stratigraphy of the units that constitute the Cambambe Basin, that is located at the mid-southern portion of the State of Mato Grosso, Brazil. The rocks of this vulcano-sedimentary unit have been deposited during Upper Cretaceous, between the Santonian and Maastrichtian Stages. The scientific literature has been associated these units to different basins, such as the Paraná Basin, the Parecis Basin and the Bauru Basin and also to other different lithostratigraphic units. Such confusion was the motivation to carry out this study in which the description and interpretation of facies associations was the main tool to clarify some of the aspects related to the Cambambe basin evolution. It has been observed five facies: Polymytic Conglomerate (PCF), Oligomitic Conglomerate (OCF), Siltstone and Mudstone (SMF), Litharenite (LF), Quartzarenite Facies (QF). This facies points to an alluvial fan system, with facies associations interpreted as corresponding to three portions: proximal, immature plains and mature plains. Such facies associations corresponds to the lithostratigrphic units that constitutes the Cambambe basin: the lower portion is the Ribeirão Boiadeiro Group, consisting of the interfingering Quilombinho and Cachoeira do Bom Jardim formations and the upper portion is the Cambambe Formation. An erosive unconfomity separates both portions. The sedimentary record exceeds 400 m in thickness and demonstrates a long-term deposition marked by numerous non-deposition surfaces, including paleosols.
Waypoint-based Topology Inference

Traditional network topology inference aims at reconstructing the routing trees rooted at each probing source from end-to-end measurements. However, due to emerging technologies such as network function virtualization, software defined networking, and segment routing, many modern networks are capable of supporting generalized forwarding that can create complex routing topologies different from routing trees. In this work, we take a first step towards closing this gap by proposing methods to infer the routing topology (referred to as 1-1-N topology) from a single source to multiple destinations, where routes may be required to traverse a given waypoint. We first thoroughly study the special case of 1-1-2 topologies, showing that even this seemingly simple case is highly nontrivial with 36 possibilities. We then demonstrate how the solution to the special case can be used as building blocks to infer 1-1-N topologies. The inferred topology is proved to be equivalent to the ground truth up to splitting/combining edges in the same category.
Unveil the Time Delay Signature in Delayed Chaotic Communication System via CNN

With the rapid development and popularization of IoT devices in our daily lives, secure data transmission has become a critical and challenging issue in resource-constrained devices. Simple chaotic circuits can provide highly efficient data encryption and transmission simultaneously. Time delay feedback system is considered as a promising solution to meet the demand for generating highly complex chaotic signal. The time delay is an important parameter that significantly influences the security of such systems. Although there exist many methods that can extract this parameter by adopting the leaked chaotic time series and certain information of the system model, they more or less face some restrictions, especially when the system nonlinearity is quite strong. To break through these limitations, we propose a novel method that can extract the key (i.e., time delay signature, TDS) of time delay chaotic systems by machine learning strategy. The 1D time series transmitted from a time delay chaotic system is embedded in 2D planes to generate training images. A convolutional neural network (CNN) is explored to learn from these images to identify common and different features for TDS extraction. Simulation results verify the feasibility and efficiency of the proposed scheme.
Traceable Revocable Anonymous Registration Scheme with Zero-knowledge Proof on Blockchain

User registration is the beginning of the life cycle of an account for most information systems. Some registration servers have special requirements for the validity of the registrant's identity and attributes, and thus demand his real-life identity and raw attributes for verification, which poses a great threat to privacy. However, the exposure of the registrant's real-life identity and raw attributes is not necessary, as long as he can prove to the server that he is associated with a real-life identity (RId) that fulfills the requirements. In this paper, we propose the concept of anonymous registration, with which a user can register an anonymous identity (AId) without privacy leakage. Anonymous registration has two basic features, i.e. privacy and verifiability, and two extended features, i.e. traceability and revocability. We design and implement an anonymous registration scheme with the above features, named Traceable Revocable Anonymous Registration Scheme (TRARS) based on Blockchain. In the proposed registration process, the user can calculate a zero-knowledge proof for a self-generated AId, which can prove to the registration server that the AId is associated with a valid RId and the RId's attributes meet the server's requirements, without leaking any privacy. We also introduce an identity revocation coordinator, with which evil identities can be tracked and outdated AIds can be revoked timely.
DDNB—Doubly Decentralized Network Blockchain Architecture for Application Services

Decentralization and immutability characteristics of blockchain technology has attracted numerous blockchain-based systems and applications to be proposed. However, technical shortcomings such as low transaction speed, complexity, scalability, and vulnerability to certain attacks have been identified, making it challenging to use the technology on general consumer applications and services. To address the problem, we propose a new application service platform architecture called DDNB (Doubly Decentralized Network Blockchain). DDNB divides the system into multiple layers in order to take advantage of permissioned blockchain for its processing speed and security, while allowing permissionless open use of the system to application developers. To allow any node to freely participate in application services, DDNB adopts a novel periodic node self-verification process and query chaining mechanism to authenticate newly joining nodes and validate transactions effectively and efficiently. The proposed architecture is evaluated in terms of its processing speed and security on a real proof-of-concept prototype system.
Implementation of a New Strongly-Asymmetric Algorithm and Its Optimization

A new public key agreement (PKA) algorithm, called the strongly-asymmetric algorithm (SAA-5), was introduced by Accardi et al. The main differences from the usual PKA algorithms are that Bob has some independent public keys and Alice produces her public key by using some part of the public keys from Bob. Then, the preparation and calculation processes are essentially asymmetric. This algorithms has several free parameters more than the usual symmetric PKA algorithms and the velocity of calculation is largely dependent on the parameters chosen; however, the performance of it has not yet been tested. The purpose of our study was to discuss efficient parameters to share the key with high speeds in SAA-5 and to optimize SAA-5 in terms of calculation speed. To find efficient parameters of SAA-5, we compared the calculation speed with Diffie–Hellman (D-H) while varying values of some parameters under the circumstance where the length of the secret shared key (SSK) was fixed. For optimization, we discuss a more general framework of SAA-5 to find more efficient operations. By fixing the parameters of the framework properly, a new PKA algorithm with the same security level as SAA-5 was produced. The result shows that the calculation speed of the proposed PKA algorithm is faster than D-H, especially for large key lengths. The calculation speed of the proposed PKA algorithm increases linearly as the SSK length increases, whereas D-H increases exponentially.
Rule-based velocity selection for mobile robots under uncertainties

The velocity selection for mobile robots, that results a collision-free motion between moving and static obstacles, is a challenging task of planning algorithms. In this paper, a novel concept is introduced that uses a grid for the investigation of the possible velocity vectors of the agent. Next to the rule-based velocity selection strategies, which can be applied both in right-hand and left-hand traffic, the uncertainties of the measured data of positions and velocity vectors of the obstacles are also considered. Using a cost function, an appropriate solution can be calculated that ensures a feasible motion for the agent. As an assumption, during a time interval the velocities of the obstacles will are unchanged. The algorithm is tested in simulation environment.
Consent-based Privacy-preserving Decision Tree Evaluation

Decision trees are prevalent machine learning models used for data classification. Cloud servers can build their decision tree models and provide users with many classification services, such as remote medical diagnosis. Moreover, users would also like to share the classification results with third-party applications for customized services. For example, the medical diagnosis results can be further utilized by a nutrition application to provide users with dietary recommendations. However, as stringent privacy regulations of personal data, such as GDPR, takes effect, the decision tree evaluation must comply with the following requirements. First, the model parameters and user data (input and output) should be protected from public disclosure. Second, different applications should obtain the classification results with users' consent in the context of user-customised services. In this paper, we propose a consent-based privacy-preserving decision tree evaluation scheme, named CPDE. Specifically, to achieve model parameter privacy and user data privacy, the original decision tree evaluation is conducted in a private manner in CPDE. As a result, all operations can be performed in the encrypted domain using an additively homomorphic encryption primitive and a secure comparison protocol. In addition, by integrating a proxy re-encryption technique, CDPE enables user-authorized applications to obtain the user's classification results even if the user is offline. The security analysis shows that CPDE achieves the desirable security properties and performance evaluation demonstrates CPDE is efficient and is suitable for real-world implementations.
Adaptive Minimum-Bit-Error Rate PDNP Detection for Magnetic Recording

The granular nature of the recording medium in magnetic recording leads to a type of noise known as media noise; it arises because each magnetic grain can take on only one of two polarities, which causes the boundary of each written bit to be random in shape, coinciding with the boundaries of the randomly sized grains. A key feature of media noise is that it depends on the data being written, and in particular is more pronounced in the vicinity of bit transitions. A widely used strategy for mitigating media noise in a trellis-based detector is pattern-dependent noise prediction (PDNP); in this approach, each bit pattern (which determines a trellis branch) will have its own set of branch metric parameters (including the signal levels, noise predictor coefficients, and residual variances). Traditionally these detector parameters are chosen according to some form of a minimum-mean-squared-error (MMSE) criterion. In this paper, we propose the adaptive minimum-bit-error rate (AMBER) algorithm for adapting these pattern-dependent parameters with the aim of minimizing BER. The AMBER algorithm updates the parameters whenever the add-compare-select operation in the Viterbi detector selects an incorrect path instead of the correct path; the parameters are then updated so as to increase the metric of the incorrect path, and reduce the metric of the correct path. Numerical results based on a set of quasi-micromagnetic simulated channel waveforms show that the AMBER PDNP detector provides at least a 20% BER decrease and an 8% increase in areal density over a traditional MMSE PDNP detector.
Vehicular Edge Computing Meets Cache: An Access Control Scheme for Content Delivery

Vehicular Edge Computing (VEC) is an integration of Mobile Edge Computing with traditional vehicular networks, which aims to shift computing, communication, and storage resources to the edge of networks and is more close to vehicles. Due to the high mobility of vehicles, connection interruption and network changes may frequently occur. To tackle this issue, cache-based content delivery is regarded as a promising solution to achieve efficient data sharing in VEC. However, privacy-preserving access control and fair incentive distribution are rarely taken into account in prior VEC-oriented studies. In this paper, we propose an efficient and secure access control scheme for providing cache-based content delivery in VEC. Specifically, we construct two layer access control to enable flexible access control and fair incentive distribution with the assistance of edge nodes. Moreover, we construct a group signature-based scheme to achieve anonymous authentication and conditional revocation. The performance analysis shows that our secure scheme has an acceptable effect on network performance.
Proxy Re-Encryption Scheme For Complicated Access Control Factors Description in Hybrid Cloud

Hybrid cloud has both the strong computing power of public cloud and easy control of private cloud. It provides users with robust services and convenience, meanwhile faces numerous security challenges. How to implement the effective access control is one of them, the purpose of which is deploying policy in private cloud to protect the ciphertext in public cloud. Furthermore, it becomes more and more difficult to describe the access control policy, which is suitable for multi-factor and dynamic updating. Considering the issues above, we propose a proxy re-encryption (PRE) scheme for complicated access control factors description in hybrid cloud. Firstly, we build the system model combining PRE with access control in hybrid cloud. Secondly, we design the algorithm for our scheme including the key construction with multi-factor and its weight, which achieve the target of dynamic updating. Finally, we analyze the security of this scheme by the mathematical method and performance by theory, experiment and comparisons with some other works. Our scheme has made the deployment of access control in hybrid cloud more reliable and scalable.
An Augmented Reality-Based mLearning Approach to Enhance Learning and Teaching: A Case of Study in Guadalajara

Augmented Reality (AR) is one of the most widespread technologies used in education, and many researches have been published proving its effectiveness. In recent times, the use of AR has been fostered through the use of mobile devices (smartphones), giving rise to what is called mLearning (mobile learning), with the aim to enhance the educational experience at all levels. Most of AR-based mLearning systems have been developed from a software development perspective, without the involvement of educational stakeholders to provide the content to show to students. On the other hand, many other researches propose solutions to cover specific educational requirements, but they completely forget about the acceptance of end-users (e.g. students). This paper presents an approach to assist in the development of AR-based mobile learning systems. The approach presents a methodology to guide the procedure to deploy an AR-based system, where different stakeholders are involved. In addition, a contribution of the approach is a survey to validate the user acceptance of AR-based mobile applications. To support our approach, most educational know-how and needs to improve in education through ICT were gathered during this research alongside the Polytechnic School “Ing. Jorge Matute Remus" (Guadalajara, Mexico). For this educational center we have developed cuceAR, an AR-based mLearning prototype application to enhance teaching and learning as an example and a contribution of the approach.
Media Based on Technology to Study Spoken Javanese Politeness

Abstract Being able to speak using unggah-ungguh basa (speech levels) is one of the indicators of students’ speaking skill in Javanese language. The use of IT-based teaching media can be an alternative way to develop students’ speaking skill and stimulate students’ creativity, innovation, and thinking skills as well. This was a quasi experiment carried out at Senior High School in Purworejo, Central Java, Indonesia. The finding wasthe average score of the control group was 74.5 and that of the experimental group was 82.2. T-test showed that t table was 4.87 and t count was 2.000. The research confirms that IT-based teaching media are effective to teach Javanese speaking skill using unggah-ungguh basa (speech levels).
An Information Security Evaluation Model Supporting Measurement Model Adaptation

In view of the difficulty in determining reasonably evaluation indicator system in the information security certification and accreditation work, an information security evaluation framework supporting the adaptation of measurement models is proposed. And the mapping-based information security evaluation indicator construction rule and a measurement model library are established. The optimal measurement model is adapted according to head-to-tail consistency and standard deviation index. Then, the evaluation model relies on the information security index feedback algorithm based on probability iteration to adjust the evaluation indicator system for more reasonableness. This paper provides a model reference for information security certification.
A Privacy Preserving Method for Publishing Set-valued Data and Its Correlative Social Network

Set-valued data and social network provide opportunities to mine useful, yet potentially security-sensitive, information. While there are mechanisms to anonymize data and protect the privacy separately in set-valued data and in social network, the existing approaches in data privacy do not address the privacy issue which emerge when publishing set-valued data and its correlative social network simultaneously. In this paper, we propose a privacy attack model based on linking the set-valued data and the social network topology information and a novel technique to defend against such attack to protect the individual privacy. To improve data utility and the practicality of our scheme, we use local generalization and partial suppression to make set-valued data satisfy the grouped ρ-uncertainty model and to reduce the impact on the community structure of the social network when anonymizing the social network. Experiments on real-life data sets show that our method outperforms the existing mechanisms in data privacy and, more specifically, that it provides greater data utility while having less impact on the community structure of social networks.