Reviewerly/exHarmony huggingface dataset:
Reconfigurable chaotic pseudo random number generator based on FPGA

This paper presents an FPGA Pseudo Random Number Generator (PRNG) that is based on the Lorenz and Lü chaotic systems. These two systems are used to generate four different 3D chaotic attractors. One attractor is generated from Lorenz while the other three attractors are generated from Lü. The output attractor of the proposed PRNG can be reconfigured during real time operation using an efficient hardwired shifting and multiplexing scheme. Furthermore, in order to exploit the proposed reconfiguration feature, the proposed PRNG has been embedded in an FPGA cascaded encryption processor that ciphers the input data from one up to four times successively. In each ciphering operation the PRNG is set to a new configuration and is initialized according to a part of the encryption key. The size of the encryption key can be varied according to the number of required ciphering operations. The proposed PRNG has been realized using VHDL, synthesized on Xilinx using the FPGA device XC5VLX50T, and analyzed using MATLAB and the NIST statistical suite. The proposed PRNG has utilized only 1.4% from the FPGA’s slices, achieved an operating frequency up to 78 MHz, and successfully passed all the NIST statistical tests.
NormaChain: A Blockchain-Based Normalized Autonomous Transaction Settlement System for IoT-Based E-Commerce

Internet of Things (IoT)-based E-commerce is a new business model that relies on autonomous transaction management on IoT-devices. The management system toward IoT-based E-commerce demands autonomy, lightweight, and legitimacy. As blockchain is an innovative technology that is competent in governing the decentralized network, we adopt it to design the autonomous transaction management system on IoT E-commerce. However, current blockchain solutions, most namely cryptocurrencies, have fatal drawbacks of nonsupervisability and huge computational overhead, and hence cannot be directly applied on IoT-based E-commerce. In this paper, we propose NormaChain, a blockchain-based normalized autonomous transaction settlement system for IoT-based E-commerce. By designing a special three-layer sharding blockchain network, we can significantly increase transaction efficiency and system scalability. Additionally, by designing an innovative decentralized public key searchable encryption scheme (decentralized public key encryption with keyword search (PEKS) scheme), we can uncover illegal and criminal transactions and achieve crime traceability. Our new decentralized PEKS scheme cryptographically eliminates the dependence of a trusted central authority in the original PEKS scheme and instead expands it to a fully decentralized governance, which distributes the supervision power equally among all parties. More importantly, by proving NormaChain is secure against chosen ciphertext attacks and against the stealing of the secret key, we show that NormaChain prevents a legitimate user’s privacy from being violated by banks, supervisors or malicious adversaries. Finally, we deliver the NormaChain system with design details and full implementations. Experiments show that the average transaction-per-second on IoT devices is around 113, and the supervision accuracy is 100% when proper target illegal keywords are provided.
Lightweight and Privacy-Friendly Spatial Data Aggregation for Secure Power Supply and Demand Management in Smart Grids

The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing.However, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities.Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on publickey cryptographic primitives such as homomorphic encryption, elliptic curve digital signature algorithms (ECDSA), etc. which are ill-suited for the resource constrained smart meters.On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed.However, these schemes cannot ensure some of the imperative security properties such as consumer's privacy, sender authentication, etc.In this paper, we first propose a lightweight and privacyfriendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids.Our scheme only uses lightweight cryptographic primitives such as hash functions, exclusive-OR operations, etc. Subsequently, we propose a secure billing solution for smart grids.As compared to existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids.
Random property enhancement of a 1D chaotic PRNG with finite precision implementation

The random-like behavior of chaotic systems make them perfect candidates for the core of numerous pseudo-random number generators. The coupling of two or more chaotic maps has been proven to increase the random property robustness of the generated sequences. However, these properties are also strongly influenced by the datatype implementation of these generators owing to the effect of the sensitivity to initial conditions. Hence, several factors must be considered concerning the hardware implementation of these coupled chaotic map-based generators. This paper first examines the effect of fixed-point precision implementation on the periodicity of a single skew tent map and the extension of this effect on the statistical properties of the generated sequence from coupled skew tent maps. Then, the effect on cross-coupled skew tent maps is discussed. Further, a modification to the cross-coupled scheme is presented; this achieves a reduction in the fixed-point fraction length required for generating a sequence suitable for cryptographic applications while expanding the dependency on the control parameters of the maps. The generated sequences from the proposed modification met all the applied statistical and correlation test requirements, demonstrating that they possess acceptable random properties and are suitable for cryptographic applications.
Dynamic Gesture Recognition in the Internet of Things

Gesture recognition based on computer vision has gradually become a hot research direction in the field of human-computer interaction.The field of human-computer interaction is an important direction in the Internet of Things (IoTs) technology.Human-computer interaction through gestures is the direction of continuous research on IoTs technology.In recent years, the Kinect sensor-based gesture recognition method has been widely used in gesture recognition, because it can separate gestures from complex backgrounds and is less affected by illumination and can accurately track and locate gesture motions.At present, the Kinect sensor needs to be further improved on the recognition of complex gesture movements, especially the problem that the recognition rate of dynamic gestures is not high, which hinders the development of humancomputer interaction under the IoTs technology.In this paper, based on the above problems, the Kinectbased gesture recognition is analyzed in detail, and a dynamic gesture recognition method based on HMM and D-S evidence theory is proposed.Based on the original HMM, the tangent angle and gesture change at different moments of the palm trajectory are used as the characteristics of the complex motion gesture, and the dimension of the trajectory tangent is reduced by the number of quantization codes.Then, the parameter model training of HMM is completed.Finally, combined with D-S evidence theory, combinatorial logic is judged, dynamic gesture recognition is carried out, and a better recognition effect is obtained, which lays a good foundation for human-computer interaction under the IoTs technology.
Artificial bee colony optimization for the quadratic assignment problem

We propose hybrid Artificial Bee Colony (ABC) optimization algorithms for the well-known Quadratic Assignment Problem (QAP). Large problem instances of the QAP are still very challenging. Scientists have not discovered any method to obtain the exact solutions for these difficult problems yet. The ABC has been reported to be an efficient meta-heuristic for the solution of many intractable problems. It has promising results making it a good candidate to obtain (near)-optimal solutions for well-known NP-Hard problems. The proposed ABC algorithm (ABC-QAP) and its parallel version (PABC-QAP) are the first applications of the ABC meta-heuristic together with Tabu search to the optimization of the QAP. The behavior of employed, onlooker and scout bees are modeled by using the distributed memory parallel computation paradigm for large problem instances of the QAP. Scout bees search for food sources, employed bees go to food source and return to hive and share their information on the dance area, onlooker bees watch the dance of employed bees and choose food sources depending on the dance. Robust Tabu search method is used to simulate exploration and exploitation processes of the bees. 125 of 134 benchmark problem instances are solved optimally from the QAPLIB library and 0.27% deviation is reported for 9 large problem instances that could not be solved optimally. The performance of the ABC optimization algorithms is competitive with state-of-the-art meta-heuristic algorithms in literature.
Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization

Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves' disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet architecture of convolutional neural network is employed, and the training method is improved. The architecture is modified by adding the trainable weight parameters to each skip connection in DenseNet. And the training method is improved by optimizing the learning rate with flower pollination algorithm for network training. Experimental results demonstrate that the proposed method of convolutional neural network is efficient for the diagnosis of thyroid diseases with SPECT images, and it has superior performance than other CNN methods.
Deep learning for multiple object tracking: a survey

Deep learning has been proved effective in multiple object tracking, which confronts the difficulties of frequent occlusions, confusing appearance, in‐and‐out objects, and lack of enough labelled data. Recently, deep learning based multi‐object tracking methods make a rapid progress from representation learning to network modelling due to the development of deep learning theory and benchmark setup. In this study, the authors summarise and analyse deep learning based multi‐object tracking methods which are top‐ranked in the public benchmark test. First, they investigate functionality of deep networks in these methods, and classify the methods into three categories as description enhancement using deep features, deep network embedding, and end‐to‐end deep network construction. Second, they review deep network structures in these methods, and detail the usage and training of these networks for multi‐object tracking problem. Through experimental comparison of tracking results in the benchmarks in total and by group, they finally show the effectiveness of deep networks for tracking employed in different manners, and compare the advantages of these networks and their robustness under different tracking conditions. Moreover, they analyse the limitations of current methods, and draw some useful conclusions to facilitate the exploration of new directions for multi‐object tracking.
Semantic Descriptions of High-Resolution Remote Sensing Images

Image captioning has attracted more and more attention in remote sensing filed since it provides more specific information than traditional tasks, such as classification. Though image captioning has gained some developments in recent years, it is difficult to describe the image in one simple sentence. To relieve the limitation, a novel captioning task is proposed and a novel framework is proposed to solve the novel task. The proposed framework uses semantic embedding to measure the image representation and the sentence representation. The captioning performance is improved by a proposed sentence representation (collective representation). Experimental results and human evaluations on three captioning data sets in remote sensing field demonstrate that the proposed framework can lead to advancement in image captioning results.
Semantic Text Matching for Long-Form Documents

Semantic text matching is one of the most important research problems in many domains, including, but not limited to, information retrieval, question answering, and recommendation. Among the different types of semantic text matching, long-document-to-long-document text matching has many applications, but has rarely been studied. Most existing approaches for semantic text matching have limited success in this setting, due to their inability to capture and distill the main ideas and topics from long-form text.
An Optimal Tile-Based Approach for Viewport-Adaptive 360-Degree Video Streaming

In this paper, we propose a new adaptation approach for viewport-adaptive streaming of 360-degree videos over the Internet. The proposed approach is able to systematically decide versions of tiles according to user head movements and network conditions by taking into account not only viewport estimation errors, but also users head movements in each segment duration. The experimental results show that the proposed approach can effectively adapt 360-degree videos to both varying network conditions and user's head movements. Compared to existing approaches, the proposed approach can improve the average viewport quality by up to 3.8 dB and reduce the standard deviation of the viewport quality by up to 1.1 dB. Also, the impacts of the segment duration and the buffer size are investigated. It is found that long segment durations and large buffer sizes have significant impacts on the performances of tile selection methods.
Fine-grained data access control with attribute-hiding policy for cloud-based IoT

Ciphertext-policy attribute-based encryption (CP-ABE) is a promising approach to achieve fine-grained access control over the outsourced data in Internet of Things (IoT). However, in the existing CP-ABE schemes, the access policy is either appended to the ciphertext explicitly or only partially hidden against public visibility, which results in privacy leakage of the underlying ciphertext and potential recipients. In this paper, we propose a fine-grained data access control scheme supporting expressive access policy with fully attribute hidden for cloud-based IoT. Specifically, the attribute information is fully hidden in access policy by using randomizable technique, and a fuzzy attribute positioning mechanism based on garbled Bloom filter is developed to help the authorized recipients locate their attributes efficiently and decrypt the ciphertext successfully. Security analysis and performance evaluation demonstrate that the proposed scheme achieves effective policy privacy preservation with low storage and computation overhead. As a result, no valuable attribute information in the access policy will be disclosed to the unauthorized recipients.
Leveraging rust types for modular specification and verification

Rust's type system ensures memory safety: well-typed Rust programs are guaranteed to not exhibit problems such as dangling pointers, data races, and unexpected side effects through aliased references. Ensuring correctness properties beyond memory safety, for instance, the guaranteed absence of assertion failures or more-general functional correctness, requires static program verification. For traditional system programming languages, formal verification is notoriously difficult and requires complex specifications and logics to reason about pointers, aliasing, and side effects on mutable state. This complexity is a major obstacle to the more-widespread verification of system software. In this paper, we present a novel verification technique that leverages Rust's type system to greatly simplify the specification and verification of system software written in Rust. We analyse information from the Rust compiler and synthesise a corresponding core proof for the program in a flavour of separation logic tailored to automation. To verify correctness properties beyond memory safety, users can annotate Rust programs with specifications at the abstraction level of Rust expressions; our technique weaves them into the core proof to verify modularly whether these specifications hold. Crucially, our proofs are constructed and checked automatically without exposing the underlying formal logic, allowing users to work exclusively at the level of abstraction of the programming language. As such, our work enables a new kind of verification tool, with the potential to impact a wide audience and allow the Rust community to benefit from state-of-the-art verification techniques. We have implemented our techniques for a subset of Rust; our evaluation on several thousand functions from widely-used Rust crates demonstrates its effectiveness.
BlockSim

Both in the design and deployment of blockchains many configuration choices need to be made. Investigating different implementation and design choices is not feasible or practical on real systems. Therefore, we propose BlockSim as a framework to build discrete-event dynamic system models for blockchain systems. BlockSim is organized in three layers: incentive layer, connector layer and system layer and is implemented in Python. This paper introduces BlockSim, with a particular emphasis on the modeling and simulation of block creation through the Proof of Work consensus algorithm.
Sparse pinball twin support vector machines

The original twin support vector machine (TWSVM) formulation works by solving two smaller quadratic programming problems (QPPs) as compared to the traditional hinge-loss SVM (C-SVM) which solves a single large QPP — this makes the TWSVM training and testing process faster than the C-SVM. However, these TWSVM problems are based on the hinge-loss function and, hence, are sensitive to feature noise and unstable for re-sampling. The pinball-loss function, on the other hand, maximizes quantile distances which grants noise insensitivity but this comes at the cost of losing sparsity by penalizing correctly classified samples as well. To overcome the limitations of TWSVM, we propose a novel sparse pinball twin support vector machines (SPTWSVM) based on the ϵ-insensitive zone pinball loss function to rid the original TWSVM of its noise insensitivity and ensure that the resulting TWSVM problems retain sparsity which makes computations relating to predictions just as fast as the original TWSVM. We further investigate the properties of our SPTWSVM including sparsity, noise insensitivity, and time complexity. Exhaustive testing on several benchmark datasets demonstrates that our SPTWSVM is noise insensitive, retains sparsity and, in most cases, outperforms the results obtained by the original TWSVM.
Solar radiation estimation at high latitudes: Assessment of the CMSAF databases, ASR and ERA5

There is a growing demand for the estimation of solar energy potential at high latitude locations. This study compares four datasets; Cloud, Albedo, Radiation dataset Edition 2 (CLARA), Surface Solar Radiation dataset – Heliosat Edition 2 (SARAH), ECMWF Reanalysis 5 (ERA5) and Arctic System Reanalysis v2 (ASR) on high latitude locations. Global horizontal irradiance (GHI) from these datasets is compared with in-situ ground-measurements over multiple locations in Norway. The first two datasets are mainly based on satellite estimation of solar radiation, while the latter two are based on a combination of a weather-prediction model, satellite data, and other observations. The datasets are evaluated against quality-controlled in-situ measurements of solar radiation from pyranometers. Overall, CLARA, SARAH, and ERA5 show moderate errors, while those of ASR are considerably larger. Monthly averages of global horizontal irradiance have mean absolute deviation (MAD) of 5.6 Wm−2, 5.0 Wm−2, 6.8 Wm−2, and 16.1 Wm−2 for CLARA, SARAH, ERA5, and ASR, respectively. Seasonal error analysis of these datasets reveals that CLARA and SARAH have low errors in all seasons. The datasets are classified into clear-sky, intermediate-cloudiness, and overcast categories, by using two thresholds of cloudiness based on the ratio of radiation at ground to its corresponding clear-sky value (clear-sky index). The categories obtained from satellite and reanalysis data are then compared against estimates based on corresponding in-situ observations; this analysis shows that both CLARA and SARAH perform better than ERA5 and ASR for these categories. SARAH and CLARA perform similarly in all types of conditions, but a gradual increase in errors for an increase of cloudiness is observed for ERA5 and ASR. Yearly energy analysis shows that CLARA performs better than other datasets for locations above latitude 65°N, and SARAH performs better in locations below 65°N. A further analysis is performed to assess the cloud sensing abilities of ERA5. On a shorter time scale, there are errors due to inaccurate representation of clouds, however on longer time scales i.e. months and years, these errors are considerably reduced. ERA5 is observed to overestimate TCWC (the total cloud water content defined as the mass of water and ice in a cloud) in clear-sky and intermediate-cloudy categories, while in overcast category it is underestimated. Generally, an overestimation of solar radiation is observed in reanalysis and an underestimation is observed in satellite methods.
Establishing the representational faithfulness of financial accounting information using multiparty security, network analysis and a blockchain

This paper aims to develop a design for an accounting information system that will enhance the representational faithfulness of financial reporting information. One of the functions of financial reporting is to aggregate and report the entity's private data. This paper shows that recognizing that some of the firm's private data is already shared with others allows the methods of multiparty security to be applied to the reporting and audit processes. We contend that using both public key cryptography and network analysis allows the identity of an entity to be modelled as a place on a network. We also develop accounting recordkeeping techniques to balance public access with privacy using a blockchain. Taken together, these three design ideas can enhance the representational faithfulness of financial reporting systems because they use shared data from independent entities, a transparent system, and open-access immutable storage. Faithful representation is enhanced because information from this system can be used by auditors to support their audit opinion or by stakeholders who need credible information about the entity.