Reviewerly/exHarmony huggingface dataset:
A storage architecture for high-throughput crop breeding data based on improved blockchain technology

Abstract This paper presents an architecture to store high-throughput crop breeding data efficiently and safely, by using improved blockchain technology in a breeding information management system, Golden Seed Breeding Cloud Platform (GSBCP). The storage architecture was devised to ensure (a) efficient and safe storage of breeding data, (b) easy and quick acquisition of breeding data from the dimensions of crop planting location and breeding process, (c) easy extension of the architecture to improve system performance as data volume grows, and (d) low cost of system hardware. This storage architecture uses a light blockchain to save key breeding data. Different types of blockchains are used to store different types of breeding data. Proxy encryption technology is used to guarantee data security. To strike a balance between data security and system performance, important data are assigned a higher level of security. This storage architecture can significantly improve the efficiency of GSBCP when the amount of data is large, especially when breeders query breeding data. In addition, increasing the number of blockchain nodes can greatly improve the concurrency of the system.
On Maximizing Manipulability Index while Solving a Kinematics Task

In this paper, we investigate the problem of maximizing the manipulability index while solving a general Inverse Kinematics (IK) problem of a redundant industrial manipulator. Manipulability index has been extensively studied in the robotics literature and several formulae have been developed, nevertheless, they mainly only exploit the robot redundancy. The general IK is formulated as a Quadratic Programming (QP) that can seamlessly incorporate inequality constraints, such as collision avoidance, and we propose two new formulae to integrate the manipulability index maximization into the QP-based IK solver. We then thoroughly analyze the performance of the proposed formulae in simulation and validate them on a real Baxter research robot. The experimental results revealed the outperformance of the proposed formulae in comparison with the classical formula in the literature. Hence, providing a way to improve the manipulability index of a recorded trajectory, e.g. by learning by demonstration, or an offline generated one by a motion planning algorithm.
Autarky

As the first widely-deployed secure enclave hardware, Intel SGX shows promise as a practical basis for confidential cloud computing. However, side channels remain SGX's greatest security weakness. Inparticular, the "controlled-channel attack" on enclave page faults exploits a longstanding architectural side channel and still lacks effective mitigation.
An Improved A* Algorithm for UAV Path Planning Problems

Recently unmanned aerial vehicle (UAV) has been widely applied in military and civil fields due to its strong autonomic and adaptability. Compared with the manned vehicles, UAV has significant advantages in carrying out the dangerous work by keeping human life away from risks. Although UAV provides notable benefits to practical applications, it gives rise to a complex path planning problem. The optimal flying path of a UAV should be obtained such that the flight length and time cost can be reduced as much as possible. In this paper, we study the path planning problem and propose an improved A* algorithm to solve the problem. First, with the models of UAVs and regions, an exact formulation based on mixed integer linear programming (MILP) is introduced to completely search the solution space. Then, by improving the evaluation function and the node selection strategy, an improved A* algorithm is presented to produce an optimal flight path for UAVs. Experimental results show that the approach proposed is more effective to solve the path planning problem than the traditional algorithms.
WAGE: An Authenticated Encryption with a Twist

This paper presents WAGE, a new lightweight sponge-based authenticated cipher whose underlying permutation is based on a 37-stage Galois NLFSR over F27. At its core, the round function of the permutation consists of the well-analyzed Welch-Gong permutation (WGP), primitive feedback polynomial, a newly designed 7-bit SB sbox and partial word-wise XORs. The construction of the permutation is carried out such that the design of individual components is highly coupled with cryptanalysis and hardware efficiency. As such, we analyze the security of WAGE against differential, linear, algebraic and meet/miss-in-the-middle attacks. For 128-bit authenticated encryption security, WAGE achieves a throughput of 535 Mbps with hardware area of 2540 GE in ASIC ST Micro 90 nm standard cell library. Additionally, WAGE is designed with a twist where its underlying permutation can be efficiently turned into a pseudorandom bit generator based on the WG transformation (WG-PRBG) whose output bits have theoretically proved randomness properties.
Pragmatic-Pedagogic Value Alignment

As intelligent systems gain autonomy and capability, it becomes vital to ensure that their objectives match those of their human users; this is known as the value-alignment problem. In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users’ objectives as they go. We argue that a meaningful solution to value alignment must combine multi-agent decision theory with rich mathematical models of human cognition, enabling robots to tap into people’s natural collaborative capabilities. We present a solution to the cooperative inverse reinforcement learning (CIRL) dynamic game based on well-established cognitive models of decision making and theory of mind. The solution captures a key reciprocity relation: the human will not plan her actions in isolation, but rather reason pedagogically about how the robot might learn from them; the robot, in turn, can anticipate this and interpret the human’s actions pragmatically. To our knowledge, this work constitutes the first formal analysis of value alignment grounded in empirically validated cognitive models.
Inter-Planetary File System Enabled Blockchain Solution For Securing Healthcare Records

The sharing of health-related data has become challenging in terms of data security which may lead to compromise patient’s privacy. Generally, once the report generated by the health provider is final, it will be uploaded to the hospital’s private repository. When it comes to the hospital as an organization, many participants [Doctor, Patient, Researcher, Insurance company] requires the report of patients for one or other reasons. Providing a single platform for all participants to share confidential data securely is a difficult and challenging task. Care should be taken such that the personal data of the patients should not be misused or tampered. Existing methods have been proved insufficient to effectively manage and secure health records. Blockchain technology, a recent research trend, has shown promising results for such secure data sharing. Since the contents of blockchain are tamper-proof, all participants can access the data but cannot change the data. By employing smart contracts and access control programs one can monitor data activity in the blockchain network. In this article, a blockchain architecture has been designed and discussed for secure and easy sharing of patient’s Personal Health Report(PHR) among the different players of health organization. Further, Inter-Planetary File System (IPFS) has also used in the proposed blockchain architecture for faster retrieval of PHR’s. We demonstrate the strengths of our proposed model, its user-centric focus and also the experimental results.
Trajectory Planning Using Artificial Potential Fields with Metaheuristics

The use of industrial robots has grown over the years, making production systems increasingly efficient. Within this context, some limitations appear that can delay the productive process causing damages to the production. These limitations are robot stops, for example. Stops can be caused by various factors, such as accidents, collisions of manipulator robots with operators or other equipment. The main contribution of this research is to improve the Artificial Potential Field (APF) using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) by optimizing the APF parameters in collision avoidance. We present as results: the trajectories generated by a planar manipulator robot; the position errors between the final position and the last position of the generated trajectories; and the computational cost of the PSO, GA and DE algorithms to find the parameters of the APF algorithm.
EHANet: An Effective Hierarchical Aggregation Network for Face Parsing

In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications.
Enabling trustworthy personal data protection in eHealth and well-being services through privacy-by-design

Users are each day more aware of their privacy and data protection. Although this problem is transversal to every digital service, it is especially relevant when critical and personal information is managed, as in eHealth and well-being services. During the last years, many different innovative services in this area have been proposed. However, data management challenges are still in need of a solution. In general, data are directly sent to services but no trustworthy instruments to recover these data or remove them from services are available. In this scheme, services become the users’ data owners although users keep the rights to access, modify, and be forgotten. Nevertheless, the adequate implementation of these rights is not guaranteed, as services use the received data with commercial purposes. In order to address and solve this situation, we propose a new trustworthy personal data protection mechanism for well-being services, based on privacy-by-design technologies. This new mechanism is based on Blockchain networks and indirection functions and tokens. Blockchain networks execute transparent smart contracts, where users’ rights are codified, and store the users’ personal data which are never sent or given to external services. Besides, permissions and privacy restrictions designed by users to be applied to their data and services consuming them are also implemented in these smart contracts. Finally, an experimental validation is also described to evaluate the Quality of Experience (in terms of user satisfaction) and Quality of Service (in terms of processing delay) compared to traditional service provision solutions.
Learning Sparse Low-Precision Neural Networks With Learnable Regularization

We consider learning deep neural networks (DNNs) that consist of low-precision weights and activations for efficient inference of fixed-point operations. In training low-precision networks, gradient descent in the backward pass is performed with high-precision weights while quantized low-precision weights and activations are used in the forward pass to calculate the loss function for training. Thus, the gradient descent becomes suboptimal, and accuracy loss follows. In order to reduce the mismatch in the forward and backward passes, we utilize mean squared quantization error (MSQE) regularization. In particular, we propose using a learnable regularization coefficient with the MSQE regularizer to reinforce the convergence of high-precision weights to their quantized values. We also investigate how partial L2 regularization can be employed for weight pruning in a similar manner. Finally, combining weight pruning, quantization, and entropy coding, we establish a low-precision DNN compression pipeline. In our experiments, the proposed method yields low-precision MobileNet and ShuffleNet models on ImageNet classification with the state-of-the-art compression ratios of 7.13 and 6.79, respectively. Moreover, we examine our method for image super resolution networks to produce 8-bit low-precision models at negligible performance loss.
A Systematic Literature Review on the Application of Multicriteria Decision Making Methods for Information Security Risk Assessment

In today's fast, agile, complex and interconnected business world, one of the main goals and concerns is to find an efficient and effective way of managing information security risks.So, one of the means is usage of multicriteria decision-making techniques for such purposes.The vast majority of research begins with some form of literature review.Thus, the review of the literature must be done thoroughly and impartially in order to obtain certain scientific value.This paper provides a systematic literature review (SLR) of relevant and recent literature from both research domains, namely information security risk management and multicriteria decision-making, identifying the standards, methods, techniques and tools that are considered to be the most relevant in the research areas observed.The main purpose of the paper is to discover complementary ISRA and MCDM methods that could be used as a basis to create a new hybrid model for more efficient evaluation of critical IT solutions.The related context, main goals, review methods, relevant results of each research phase along with the findings, papers' analysis, recommendations and conclusions are all given in this review article in order to fully comply with the SLR requirements.
Rapid phase calibration of a spatial light modulator using novel phase masks and optimization of its efficiency using an iterative algorithm

We develop an improved phase calibration method of a reflective SLM using interferometry by employing novel phase masks. In the process, we definitively determine the actual maximum phase throw of our SLM which provides a recipe for users to verify supplier specifications. We generate optimised phase masks by using Iterative Fourier Transform Algorithm (IFTA) and compare their performance with global linear corrections in the look-up table (LUT) and find that the former perform with around 20% better efficiency. Besides obtaining an array of 1D/2D spots having high uniformity (90%) using IFTA, our result exemplifies the use of iterative algorithms for improving efficiency of phase limited SLMs. Finally, our improved phase calibration method enables threefold faster phase measurements, and to the best of our knowledge, is the first endeavour directed towards enabling rapid phase characterisation of an SLM using interferometric measurements. We believe that it can have very useful applications in settings which may require fast phase calibrations as well as for real-time, multi-wavelength spectroscopic applications.
Hash functions from superspecial genus-2 curves using Richelot isogenies

Abstract In 2018 Takashima proposed a version of Charles, Goren and Lauter’s hash function using Richelot isogenies, starting from a genus-2 curve that allows for all subsequent arithmetic to be performed over a quadratic finite field 𝔽 p 2 . In 2019 Flynn and Ti pointed out that Takashima’s hash function is insecure due to the existence of small isogeny cycles. We revisit the construction and show that it can be repaired by imposing a simple restriction, which moreover clarifies the security analysis. The runtime of the resulting hash function is dominated by the extraction of 3 square roots for every block of 3 bits of the message, as compared to one square root per bit in the elliptic curve case; however in our setting the extractions can be parallelized and are done in a finite field whose bit size is reduced by a factor 3. Along the way we argue that the full supersingular isogeny graph is the wrong context in which to study higher-dimensional analogues of Charles, Goren and Lauter’s hash function, and advocate the use of the superspecial subgraph, which is the natural framework in which to view Takashima’s 𝔽 p 2 -friendly starting curve.
On K-means clustering-based approach for DDBSs design

Abstract In Distributed Database Systems (DDBS), communication costs and response time have long been open-ended challenges. Nevertheless, when DDBS is carefully designed, the desired reduction in communication costs will be achieved. Data fragmentation (data clustering) and data allocation are on popularity as the prime strategies in constant use to design DDBS. Based on these strategies, on the other hand, several design techniques have been presented in the literature to improve DDBS performance using either empirical results or data statistics, making most of them imperfect or invalid particularly, at least, at the initial stage of DDBSs design. In this paper, thus, a heuristic k-means approach for vertical fragmentation and allocation is introduced. This approach is primarily focused on DDBS design at the initial stage. Many techniques are being joined in a step to make a promising work. A brief yet effective experimental study, on both artificially-created and real datasets, has been conducted to demonstrate the optimality of the proposed approach, comparing with its counterparts, as the obtained results has been shown encouraging.
A statistical pattern based feature extraction method on system call traces for anomaly detection

In host-based anomaly detection, feature extraction on the system call traces is important to build an effective anomaly detection model. Different kinds of feature extraction methods are recently proposed and most of them aim at preserving the positional information of the system calls within a trace. These extracted features are generally named from system calls, therefore, cannot be used directly in the case of cross platform applications. In addition, some of these feature extraction methods are very costly to implement. This paper presents a new feature extraction method. It aims at extracting features that are irrelevant to the names of system calls. The samples represented by the extracted features can be directly used in the case of cross platform applications. In addition, this method is lightweight in that the feature values are not expensive to compute. The proposed method firstly transforms the system calls in a trace into frequency sequences of n-grams and then explores a fixed number of statistical features on the frequency sequences. The extracted features are irrelevant to the names/indexes of system calls on a platform. The calculation of feature values works on the frequency sequences rather than on system call sequences. These feature vectors built on the training set with only normal data are then used to train a one class classification model for anomaly detection. We compared our method with four previously proposed feature extraction methods on system call traces. When used on the same platform, even though our method does not always obtain the highest AUC, overall, it performs better than all the compared methods. When testing on cross platform, it performs the best among all compared methods. The features extracted by our method are platform-independent and are suitable for anomaly detection across platforms.
Adaptive Quadrature Schemes for Bayesian Inference via Active Learning

Numerical integration and emulation are fundamental topics across scientific fields. We propose novel adaptive quadrature schemes based on an active learning procedure. We consider an interpolative approach for building a surrogate posterior density, combining it with Monte Carlo sampling methods and other quadrature rules. The nodes of the quadrature are sequentially chosen by maximizing a suitable acquisition function, which takes into account the current approximation of the posterior and the positions of the nodes. This maximization does not require additional evaluations of the true posterior. We introduce two specific schemes based on Gaussian and Nearest Neighbors (NN) bases. For the Gaussian case, we also provide a novel procedure for fitting the bandwidth parameter, in order to build a suitable emulator of a density function. With both techniques, we always obtain a positive estimation of the marginal likelihood (a.k.a., Bayesian evidence). An equivalent importance sampling interpretation is also described, which allows the design of extended schemes. Several theoretical results are provided and discussed. Numerical results show the advantage of the proposed approach, including a challenging inference problem in an astronomic dynamical model, with the goal of revealing the number of planets orbiting a star.
Multi-Strategy Ensemble Whale Optimization Algorithm and Its Application to Analog Circuits Intelligent Fault Diagnosis

The whale optimization algorithm (WOA) is a new swarm intelligence (SI) optimization algorithm, which has the superiorities of fewer parameters and stronger searching ability. However, previous studies have indicated that there are shortages in maintaining diversity and avoiding local optimal solutions. This paper proposes a multi-strategy ensemble whale optimization algorithm (MSWOA) to alleviate these deficiencies. First, the chaotic initialization strategy is performed to enhance the quality of the initial population. Then, an improved random searching mechanism is designed to reduce blindness in the exploration phase and speed up the convergence. In addition, the original spiral updating position is modified by the Levy flight strategy, which leads to a better tradeoff between local and global search. Finally, an enhanced position revising mechanism is utilized to improve the exploration further. To testify the superiorities of the proposed MSWOA algorithm, a series of comparative experiments are carried out. On the one hand, the numerical optimization experimental results, which are conducted under nineteen widely used benchmark functions, indicate that the performance of MSWOA stands out compared with the standard WOA and six other well-designed SI algorithms. On the other hand, MSWOA is utilized to tune the parameters of the support vector machine (SVM), which is applied to the fault diagnosis of analog circuits. Experimental results confirm that the proposed method has higher diagnosis accuracy than other competitors. Therefore, the MSWOA is successfully applied as a novel and efficient optimization algorithm.