Reviewerly/exHarmony huggingface dataset:
Diverse Rule Sets

While machine-learning models are flourishing and transforming many aspects of everyday life, the inability of humans to understand complex models poses difficulties for these models to be fully trusted and embraced. Thus, interpretability of models has been recognized as an equally important quality as their predictive power. In particular, rule-based systems are experiencing a renaissance owing to their intuitive if-then representation.
On MDS negacyclic LCD codes

Linear codes with complementary duals (LCD) have a great deal of significance amongst linear codes.Maximum distance separable (MDS) codes are also an important class of linear codes since they achieve the greatest error correcting and detecting capabilities for fixed length and dimension.The construction of linear codes that are both LCD and MDS is a hard task in coding theory.In this paper, we study the constructions of LCD codes that are MDS from negacyclic codes over finite fields of odd prime power q elements.We construct four families of MDS negacyclic LCD codes of length n| q-1 2 , n| q+1 2 and a family of negacyclic LCD codes of length n = q -1.Furthermore, we obtain five families of q 2 -ary Hermitian MDS negacyclic LCD codes of length n| q -1 and four families of Hermitian negacyclic LCD codes of length n = q 2 + 1.For both Euclidean and Hermitian cases the dimensions of these codes are determined and for some classes the minimum distances are settled.For the other cases, by studying q and q 2 -cyclotomic classes we give lower bounds on the minimum distance.
Debugging Non-ground ASP Programs: Technique and Graphical Tools

Answer Set Programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach allowing for interactive localization of bugs in non-ground programs. The new approach points the user directly to a set of non-ground rules involved in the bug, which might be refined (up to the point in which the bug is easily identified) by asking the programmer a sequence of questions on an expected answer set. The approach has been implemented on top of the ASP solver WASP. The resulting debugger has been complemented by a user-friendly graphical interface, and integrated in ASPIDE, a rich IDE for answer set programs. In addition, an empirical analysis shows that the new debugger is not affected by the grounding blowup limiting the application of previous approaches based on meta-programming. Under consideration in Theory and Practice of Logic Programming (TPLP).
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union—mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1—the fine GT images for training and prediction, and scenario 2—the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.
Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices

Deep neural networks show high accuracy in the problem of semantic and instance segmentation of biomedical data.However, this approach is computationally expensive.The computational cost may be reduced with network simplification after training or choosing the proper architecture, which provides segmentation with less accuracy but does it much faster.In the present study, we analyzed the accuracy and performance of UNet and ENet architectures for the problem of semantic image segmentation.In addition, we investigated the ENet architecture by replacing of some convolution layers with box-convolution layers.The analysis performed on the original dataset consisted of histology slices with mast cells.These cells provide a region for segmentation with different types of borders, which vary from clearly visible to ragged.ENet was less accurate than UNet by only about 1-2%, but ENet performance was 8-15 times faster than UNet one.
Gaussian random number generator: Implemented in FPGA for quantum key distribution

Quantum Key Distribution is the process of using quantum communication to establish a shared key between two parties. It has been demonstrated the unconditional security and effective communication of quantum communication system can be guaranteed by an excellent Gaussian random number generator with high speed and an extended random period. In this paper, we propose to construct the Gaussian random number generator using Field-Programmable Gate Array (FPGA) which is able to process large data in high speed. We also compare three algorithms ofGRNgeneration: Box-Muller algorithm, polarization decision algorithm, and central limit algorithm. We demonstrate that the polarization decision algorithm implemented inFPGArequires less computing resources and also produces a high-quality Gaussian random number, through the null hypothesis test.
Medical Image Segmentation Using a Robust Edge-stop Function with 2×2 Window Patch

Edge-based active contour models using robust edge-stop functions (ESFs) have shown their effectiveness in segmenting medical images. The ESFs utilize classification scores from machine learning algorithms which are sensitives to the feature vector. The number of features influences the speed of the algorithms. Previous studies utilize image patch 3×3 which consists of 9 components in the feature vector and leads to a long computational time. This paper investigates the use of a simple feature vector for the ESFs. An image patch of 2×2 is selected and applied to a classification algorithm, namely the k-nearest neighbors (k-NN). Experimental results indicate that the feature leads to similar in accuracy but faster in computational speed.
REFINITY to Model and Prove Program Transformation Rules

is a workbench for modeling statement-level transformation rules on programs with the aim to formally verify their correctness. It is based on Abstract Execution, a verification framework for abstract programs with a high degree of proof automation, and interfaces with the program prover. We describe the user interface and functionality of , and illustrate its capabilities along the application to proving conditional correctness of a code refactoring rule.
Adversarial Learning for Zero-Shot Domain Adaptation

Zero-shot domain adaptation (ZSDA) is a category of domain adaptation problems where neither data sample nor label is available for parameter learning in the target domain. With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain shift from an irrelevant task (IrT) to the task of interest (ToI). Specifically, we first identify an IrT, where dual-domain samples are available, and capture the domain shift with a coupled generative adversarial networks (CoGAN) in this task. Then, we train a CoGAN for the ToI and restrict it to carry the same domain shift as the CoGAN for IrT does. In addition, we introduce a pair of co-training classifiers to regularize the training procedure of CoGAN in the ToI. The proposed method not only derives machine learning models for the non-available target-domain data, but also synthesizes the data themselves. We evaluate the proposed method on benchmark datasets and achieve the state-of-the-art performances.
A cloud priority-based dynamic online double auction mechanism (PB-DODAM)

Abstract Double auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.
The improved mayfly optimization algorithm with Chebyshev map

Abstract The mayfly optimization (MO) algorithm was just proposed recently, simulation experiments proved that it was capable to optimize both the benchmark functions and the real problems we met. In this paper, the MO algorithm would be improved with Chebyshev map, simulation experiments were carried out and results showed that the improved algorithm would indeed increase the capability.
Guiding Principle for Minor-Embedding in Simulated-Annealing-Based Ising Machines

We propose a novel type of minor-embedding (ME) in simulated-annealing-based Ising machines. The Ising machines can solve combinatorial optimization problems. Many combinatorial optimization problems are mapped to find the ground (lowest-energy) state of the logical Ising model. When connectivity is restricted on Ising machines, ME is required for mapping from the logical Ising model to a physical Ising model, which corresponds to a specific Ising machine. Herein we discuss the guiding principle of ME design to achieve a high performance in Ising machines. We derive the proposed ME based on a theoretical argument of statistical mechanics. The performance of the proposed ME is compared with two existing types of MEs for different benchmarking problems. Simulated annealing shows that the proposed ME outperforms existing MEs for all benchmarking problems, especially when the distribution of the degree in a logical Ising model has a large standard deviation. This study validates the guiding principle of using statistical mechanics for ME to realize fast and high-precision solvers for combinatorial optimization problems.
Sentence Embeddings and High-Speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents

Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming and, hence, expensive. In this paper, we introduce a proof-of-concept system for annotating sentences “laterally.” The approach is based on the observation that sentences that are similar in meaning often have the same label in terms of a particular type system. We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents. Here, we present the interface of the system and empirically evaluate the approach. The experiments show that lateral annotation has the potential to make the annotation process quicker and more consistent.
Subaperture stitching wavelength scanning interferometry for 3D surface measurement of complex-shaped optics

Abstract Subaperture stitching wavelength scanning interferometry with a high-accuracy multi-axis position stage is proposed for measuring complex surface shapes such as aspherical and freeform optics. Typical optical approaches suffer due to poor fringe visibility when it comes to complex-shaped optics. Our proposed technique allows a proper solution to this challenging issue by keeping the optical probe perpendicular to the surface slope, which provides good fringe visibility during the wavelength scan regardless of how steep the surface is. Also, the full-aperture surface map of a test sample can be obtained by stitching multiple subaperture measurement results with high precision. We tested and verified our method by measuring several representative samples and comparing the measurement results with a well-established stylus method.
Attribute-based encryption and sticky policies for data access control in a smart home scenario: a comparison on networked smart object middleware

Abstract Regulating the access to the Internet of Things (IoT) network’s resources is a complex-prone task, which requires to pay a great attention on how policies are defined, shared, and enforced. The present paper considers the specific context of a smart home, which represents one of the main IoT application domains, and it focuses on two solutions proposed in the literature to cope with the aforementioned issues. On the one side, approaches based on attribute-based encryption (ABE) allow one to encrypt data for multiple recipients, in such a way that only those recipients whose attributes satisfy a given access policy can decrypt afterward. ABE guarantees a high level of customization due to the variety of attributes which can be defined, and it is also flexible enough to be adapted to different kinds of scenarios. On the other side, approaches based on sticky policies allow to attach an access policy directly to the data itself, and to employ a trusted authority to evaluate and enforce the policy itself. Sticky policies also guarantee a highly distributed and customizable enforcement of access control rules. In this paper, we compare the advantages and the drawbacks in terms of performance and robustness of such two techniques by means of their integration within the prototype of an IoT middleware, named networked smart object. Hence, the effectiveness of the presented solutions is validated by means of a real test-bed in the smart home scenario, in terms of storage occupancy, CPU load, and data retrieval delay. The final goal is to reveal the best approach to be used depending on the application’s requirements.
Generalized quantum assisted simulator

We provide a noisy intermediate-scale quantum framework for simulating the dynamics of open quantum systems, generalized time evolution, non-linear differential equations and Gibbs state preparation. Our algorithm does not require any classical-quantum feedback loop, bypass the barren plateau problem and does not necessitate any complicated measurements such as the Hadamard test. We introduce the notion of the hybrid density matrix, which allows us to disentangle the different steps of our algorithm and delegate classically demanding tasks to the quantum computer. Our algorithm proceeds in three disjoint steps. First, we select the ansatz, followed by measuring overlap matrices on a quantum computer. The final step involves classical post-processing data from the second step. Our algorithm has potential applications in solving the Navier-Stokes equation, plasma hydrodynamics, quantum Boltzmann training, quantum signal processing and linear systems. Our entire framework is compatible with current experiments and can be implemented immediately.
Deeplabv3+ semantic segmentation model based on feature cross attention mechanism

Abstract Aiming at the problem that the deeplabv3+ model is not accurate in segmentation of the image target edge, the image feature fitting is slow, and the attention information cannot be effectively used. It is proposed to add a feature cross attention module (FCA) to the model. The cross-attention network is composed of two branches and a feature cross attention module. Among them, the shallow branch is used to extract low-level spatial information, and the deep branch is used to extract high-level context features to make important feature extraction more refined. This paper designs and realizes the connection between Feature Cross Attention module and Deeplabv3+ coding module, input the output features of the Deeplabv3+ encoding module into the feature cross attention module for convolution operation to realize the recalibration of the original features. The decoding module of Deeplabv3+ obtains spatial features and channel features from two branches respectively, and then merges the obtained features to obtain more important features. The improved model was validated by the Pascal Voc2012 data set, and the results showed that the ratio of average intersection and the average pixel accuracy were increased by 1.96% and 2.84%, respectively. The model added with FCA can effectively improve the shortcomings of the original model, can segment the target more finely, and better solve the problem of rough segmentation boundary.
Differentially Private Learning Needs Better Features (or Much More Data)

We demonstrate that differentially private machine learning has not yet reached its "AlexNet moment" on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we show that private learning requires either much more private data, or access to features learned on public data from a similar domain. Our work introduces simple yet strong baselines for differentially private learning that can inform the evaluation of future progress in this area.
Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.
Obstacle Avoidance of UAV Based on Neural Networks and Interfered Fluid Dynamical System

Obstacle avoidance is the prerequisite guarantee for the unmanned aerial vehicle (UAV) to fly safely in the three-dimensional dynamic complex environment. In this paper, a three-dimensional real-time obstacle avoidance method is proposed by combining neural network and the Interfered Fluid Dynamical System (IFDS) for the first time. First, in order to solve the problem of insufficient samples, sample data are generated based on the sparrow search algorithm (SSA) and receding horizon control (RHC). Second, training neural network offline, the relative position between UAV, destination and obstacle from sample data as input of neural network, and the IFDS parameters are used as the feature extraction of the output terminal of the neural network. Third, the trained neural network is used to adjust the coefficients of the IFDS according to environment in real time. Finally, the simulations demonstrate effectiveness of the proposed method.
Raspberry Pi Assisted Safety System for Elderly People: An Application of Smart Home

Nowadays, the care of elderly peoples is a significant problem and needs more time and attention from the caregivers. In the elderly population, accidental fall is a common cause of injury due to the weakness or any other disease such as high temperature and blood pressure. Moreover, in developing countries, the use of natural gas for older adults is widespread for room heating. The use of a gas heater in a room can be hazardous, especially at night and it has reportedly said to turn OFF the gas heater while going to sleep. However, it is ignored quite often that results in many deaths not only to the elderly but to all age groups. To cope with these issues and to provide a safer environment, especially to the older people where the caretaker can be informed well on-time, a raspberry Pi assisted cost-effective safety system is proposed. The 3-axis accelerometer of gas and flame sensors are used to detect an event when accidental fall occurs precisely, or any gas leakage or flame is detected. The caretaker will be informed using a mobile app or text alert to take action immediately. Results demonstrate that the modules for fall and gas/open flame detection accurately detected true events by 92% and 95% respectively. The primary purpose of the proposed system is to provide a reliable, cost-effective, and efficient method for the safety of the elderly population.