Reviewerly/exHarmony huggingface dataset:
Reinforcement-learning generation of four-qubit entangled states

We have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states with four qubits. This way, the algorithm is able to generate representative states for some of the 49 true SLOCC classes of the four-qubit entanglement states. In particular, it is possible to reach at least one true SLOCC class for each of the nine entanglement families. The quantum circuits synthesized by the algorithm may be useful for the experimental realization of these important classes of entangled states and to draw conclusions about the intrinsic properties of our universe. We introduce a graphical tool called the state-link graph (SLG) to represent the construction of the quality matrix (Q-matrix) used by the algorithm to build a given objective state belonging to the corresponding entanglement class. This allows us to discover the necessary connections between specific entanglement features and the role of certain quantum gates, which the algorithm needs to include in the quantum gate set of actions. The quantum circuits found are optimal by construction with respect to the quantum gate-set chosen. These SLGs make the algorithm simple, intuitive, and a useful resource for the automated construction of entangled states with a low number of qubits.
Machine learning for Arabic phonemes recognition using electrolarynx speech

&lt;p&gt;&lt;span lang="EN-US"&gt;Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%.&lt;/span&gt;&lt;/p&gt;
Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot

Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles. This study uses semantic segmentation-based drivable area estimation as an alternative representation to assist with local motion planning. Notably, a realistic 3D simulator based on an Unreal Engine was created to generate a synthetic dataset under different weather conditions. A transfer learning technique was used to train the encoder-decoder model to segment free space from the occupied sidewalk environment. The local planner uses a nonlinear model predictive control (NMPC) scheme that inputs the estimated drivable space, the state of the robot, and a global plan to produce safe velocity commands that minimize the tracking cost and actuator effort while avoiding collisions with dynamic and static obstacles. The proposed approach achieves zero-shot transfer from a simulation to real-world environments that have never been experienced during training. Several intensive experiments were conducted and compared with the dynamic window approach (DWA) to demonstrate the effectiveness of our system in dynamic sidewalk environments.
A two-step singular spectrum analysis method for robust low-rank approximation of seismic data

The singular spectrum analysis (SSA) method can detect the low-rank structure of data and therefore has become a powerful tool in seismic data processing and analysis. In particular, the SSA method can effectively suppress seismic random noise according to the different behaviors of coherent signal and random noise in the singular spectrum. However, there has been much research and experimentation indicating that the basic SSA method performs poorly when the noise becomes erratic. One of the reasons is that the quadratic misfit adopted in SSA is sensitive to non-Gaussian disturbances. One solution to this problem is to iteratively reweight the low-rank approximation of using the SSA method and finally achieve a particular kind of robust misfit. The low-rank approximation is robustified in this study by a more direct strategy, which consists of two main steps, prediction and elimination, and is called the two-step SSA method. The whole algorithm only runs the SSA filtering twice and hence is more computationally efficient compared to the iteratively reweighted SSA technique. The two-step SSA method adopts two criteria to calculate a weighting matrix and predict the erratic disturbance. This strategy also can be generalized into an iterative reweighting-based technique (e.g., the iteratively reweighted SSA technique) for robust denoising. The performance of the proposed two-step SSA method is tested using simulated and real seismic data. The results demonstrate its feasibility.
Research on Novel TLS Protocol Network Traffic Management and Monitoring Method

With the development of Internet technology, more and more websites use the novel TLS encryption technologies (such as TLS1.3, DOH, ESNI, etc.). The full encryption technology of network traffic brings great challenges to identify and prevent threats in encrypted traffic. In the face of new threat behavior, the traditional network management needs a long-term experimental process such as observation and analysis, feature extraction and online recognition. The whole process is time-consuming and labor-consuming. With the full encryption of the novel protocol, this confrontation will continue to escalate and unsustainable. This paper proposes a new idea of network management, which changes the traditional confrontation management into cooperation management, and can compromise privacy protection and network management. The main idea is to study the traffic management method combining "endpoint" and "pipe" and the novel protocol extension method. Securely transmit the secret key and data senseless decryption method through the hidden channel method to obtain the real URL access path of users. On this basis, this paper proposes and designs two traffic management methods: key acquisition and covert transmission method and TLS protocol upgrade implementation method. The first method requires the cooperation of the client. By studying the extension of the TLS protocol, we can decrypt the original traffic and realize network management; the second method requires the cooperation of the client and the server. By upgrading the handshake process and data of the TLS protocol transmission implementation, we can implement network management. On the network management side, it is necessary to decrypt and restore the network management packets, while the user request and response data cannot be decrypted. These two approaches are a novel solution idea under the popular application of novel protocols.
Face expression understanding by geometrical characterization of deep human face representation

Face expressions understanding is a key to have a better understanding of the human nature. In this contribution we propose an end-to-end pipeline that takes color images as inputs and produces a semantic graph that encodes numerically what are facial emotions. This approach leverages low-level geometric details as face representation which are numerical representations of facial muscle activation patterns to build this emotional understanding. It shows that our method recovers social expectations of what characterize facial emotions.
Classification of heterogeneous Malayalam documents based on structural features using deep learning models

The proposed work gives a comparative study on performance of various pretrained deep learning models for classifying Malayalam documents such as agreement documents, notebook images, and palm leaves. The documents are classified based on their visual and structural features. The dataset was manually collected from different sources. The method of research proceeds with preprocessing, feature extraction, and classification. The proposed work deals with three fine-tuned deep learning models such as visual geometry group-16 (VGG-16), convolutional neural network (CNN) and AlexNet. The models attained high accuracies of 99.7%, 96%, and 95%, respectively. Among the three models, the fine-tuned VGG-16 model was found to perform better attaining a very high accuracy on the dataset. As a future work, methods to classify the documents based on content as well as spectral features can be developed.
Influence of ambient temperature in the city of Portoviejo, Ecuador on the energy performance of photovoltaic modules

&lt;span lang="EN-US"&gt;The research analyzes the influence of the average temperature on the performance of specific photovoltaic solar modules under the environmental conditions of the city of Portoviejo, province of Manabí, Ecuador. The research is carried out using the qualitative methodology. Its main objective is to determine the influence of temperature on the energy performance of photovoltaic modules under the region's environmental conditions. Two electrical diagrams with different configurations were designed, which were analyzed and simulated using the MATLAB Simulink software. The results obtained show the direct relationship between the electrical parameters of voltage, current intensity, and power with the temperature value on the surface of the modules. It is concluded that the modules generate a higher value of voltage, electric current intensity, and electric power at a lower temperature, regardless of the level of solar irradiation they receive.&lt;/span&gt;
Attacks Against Artificial Intelligence Systems: Classification, The Threat Model and the Approach to Protection

The rapid growth of tasks solved with the help of machine learning leads to an increase in the importance of artificial intelligence systems (AISs). These systems are gradually being integrated into the business structure and widely used in various applications. The data sets and trained models used in AISs are of great interest to competitors, and bypassing and disabling AISs is of great interest to attackers. The paper presents an analysis of existing attacks against AISs, which take into account the stages of machine learning and can exploit the information infrastructure of the system. A classification of attacks based on the analysis of the AIS operation process is proposed. The classes of attacks that are the most relevant and dangerous for AISs are identified. An AIS Threat Model is introduced that considers data privacy threats, AIS operation threats, and information system threats. A methodical approach to protection attacks against AISs is proposed and experimentally evaluated.
Identification of Daily Living Recurrent Behavioral Patterns Using Genetic Algorithms for Elderly Care

A person’s routine is a sequence of activities of daily living patterns recurrently performed. Sticking daily routines is a great tool to support the care of persons with dementia, and older adults in general, who are living in their homes, and also being useful for caregivers. As state-of-the-art tools based on self-reporting are subjective and rely on a person’s memory, new tools are needed for objectively detecting such routines from the monitored data coming from wearables or smart home sensors. In this paper, we propose a solution for detecting the daily routines of a person by extracting the sequences of recurrent activities and their duration from the monitored data. A genetic algorithm is defined to extract activity patterns featuring small differences that relate to the day-to-day contextual variations that occur in a person’s daily routine. The quality of the solutions is evaluated with a probabilistic-based fitness function, while a tournament-based strategy is employed for the dynamic selection of mutation and crossover operators applied for generating the offspring. The time variability of activities of daily living is addressed using the dispersion of the values of duration of that activity around the average value. The results are showing an accuracy above 80% in detecting the routines, while the optimal values of population size and the number of generations for fitness function evolution and convergence are determined using multiple linear regression analysis.
Spatially Consistent Transformer for Colorization in Monochrome-Color Dual-Lens System

We study the colorization problem in monochrome-color dual-lens camera systems, i.e. colorizing the gray image from the monochrome camera using the color image from the color camera as reference. In related methods, cost volume based CNN methods achieve the state-of-the-art results, but they are costly in GPU memory due to building the 4D cost volume. Recently, some slice-wise cross-attention based methods are proposed for related problems. The slice-wise cross-attention has much less costs in GPU memory but directly using them for this colorization problem cannot generate competing results. We make use of the non-local computation property of cross-attention to propose a transformer based method. To overcome the limitations of straight-forward slice-wise cross-attention, we propose the spatially consistent cross-attention (SCCA) block to encourage pixels of slices across different epipolar lines in the gray image to find spatially consistent correspondence with pixels of the reference color image. And, to further reduce the memory cost while keeping the colorization accuracy, we design a pyramid processing strategy to cascade a series of SCCA blocks with smaller slice size and perform the colorization from coarse to fine. To extract more powerful image features, we use several regional self-attention (RSA) blocks with U-style connections. Experimental results show that we outperform the state-of-the-art methods largely on the synthesized datasets of Cityscapes, Sintel, and SceneFlow, and the real monochrome-color dual-lens dataset.
CLAST: Contrastive Learning for Arbitrary Style Transfer

Arbitrary style transfer aims at migrating the style of a reference style painting to a target content image. Existing methods find it challenging to achieve good content fidelity and style migration at the same time. Moreover, they all rely on manually defined content and style, which is of limited universality and robustness. In this paper, we propose to introduce contrastive learning into style transfer, instructing the network to automatically learn to model the structural content and artistic style based on natural contrastive relationships in style transfer. Compared with existing methods, our learned modeling of content and style is more robust and universal. In addition, we further propose instance-wise contrastive style losses and a patch-wise contrastive content loss to guide style transfer. Combining the proposed contrastive losses and two self-reconstruction strategies, we develop a new style transfer framework, which is pluggable and can be flexibly applied to various style transfer modules. Experimental results demonstrate that our method has strong flexibility and synthesizes stylized images with higher quality.
Proof-of-Stake Longest Chain Protocols

The Nakamoto longest chain protocol is remarkably simple and has been proven to provide security against any adversary with less than 50% of the total hashing power. Proof-of-stake (PoS) protocols are an energy efficient alternative; however existing protocols adopting Nakamoto's longest chain design achieve provable security only by allowing long-term predictability, subjecting the system to serious bribery attacks. In this paper, we prove that a natural longest chain PoS protocol with similar predictability as Nakamoto's PoW protocol can achieve security against any adversary with less than 1/(1+e) fraction of the total stake. Moreover we propose a new family of longest chain PoS protocols with a formal proof of their security against a 50% adversary, while only requiring short-term predictability.
The Effect of Security Information Sharing and Disruptive Technology on Patient Dissatisfaction in Saudi Health Care Services During Covid-19 Pandemic

This study is an investigation into the factors affecting patient dissatisfaction among Saudi hospitals.The selected factors considered for analysis are security of information sharing, operational practices, disruptive technologies, and the ease of use of EHR patient information management systems.From the literature review section, it was clear that hardly any other studies have embraced these concepts in one as was intended by this study.The theories that the study heavily draws from are the service dominant logic and the feature integration theory.The study surveyed 350 respondents from three large major hospitals in three different metropolitan cities in the Kingdom of Saudi Arabia.This sample came from members of the three hospitals that were willing to participate in the study.The number 350 represents those that successfully completed the online questionnaire or the limited physical questionnaires in time.The study employed the structural equation modelling technique to analyze the associations.Findings suggested that security of information sharing had a significant direct effect on patient satisfaction.Operational practice positively mediated the effect of security of information sharing on patient dissatisfaction.However, ease of use failed to significant impact this association.The study concluded that to improve patient satisfaction, Saudi hospitals must work on their systems to reinforce them against the active threats on the privacy of patients' data by leveraging disruptive technology.They should also improve their operational practices by embracing quality management techniques relevant to the healthcare sector.
Artificial Intelligence based Integrated and Distributed System for Preventing Covid-19 Spread Using Deep Learning

The COVID-19 pandemic is causing a worldwide emergency in healthcare. This virus mainly spreads through droplets which emerge from a person infected with coronavirus and poses a risk to others. The risk of transmission is highest in public places. Many measures have been suggested, such as maintaining a social distance, and wearing a face mask to avoid the spread of this virus. There are three modules in this work, in the first module a mask detection system which detects whether a person wears a mask or not using deep learning techniques such as MobileNet V2 architecture along with Facenet and Masknet. Accuracy of 98.6% is achieved in this module with one or two people in the frame. Barricade has been set which does not allow people who does not wear mask and allows people who wears a mask. LED light indicators and LCD displays are used as alerts, and they are programmed to provide information that is both worn and not worn, depending on the output. In the second module, a system has been designed which detects the temperature of the person and detects whose temperature is above normal body temperature and alerts accordingly. In the third module a social distancing system has been designed which detects people who does not follow social distancing protocol and alerts them using deep learning techniques. The YOLOv3 algorithm is used which creates a square box around people that displays green or red color box according to the measurement output. The transfer learning methodology is also implemented to increase the accuracy of this module. The accuracy of 98.2% is achieved for social distance detection module using YOLOv3 detection model with transfer learning. All three modules are integrated so it automatically monitors human body temperature, detects mask and social distancing at the barricade system.
Neurosymbolic repair for low-code formula languages

Most users of low-code platforms, such as Excel and PowerApps, write programs in domain-specific formula languages to carry out nontrivial tasks. Often users can write most of the program they want, but introduce small mistakes that yield broken formulas. These mistakes, which can be both syntactic and semantic, are hard for low-code users to identify and fix, even though they can be resolved with just a few edits. We formalize the problem of producing such edits as the last-mile repair problem. To address this problem, we developed LaMirage, a LAst-MIle RepAir-engine GEnerator that combines symbolic and neural techniques to perform last-mile repair in low-code formula languages. LaMirage takes a grammar and a set of domain-specific constraints/rules, which jointly approximate the target language, and uses these to generate a repair engine that can fix formulas in that language. To tackle the challenges of localizing errors and ranking candidate repairs, LaMirage leverages neural techniques, whereas it relies on symbolic methods to generate candidate edits. This combination allows LaMirage to find repairs that satisfy the provided grammar and constraints, and then pick the most natural repair. We compare LaMirage to state-of-the-art neural and symbolic approaches on 400 real Excel and Power Fx formulas, where LaMirage outperforms all baselines. We release these benchmarks to encourage subsequent work in low-code domains.
Semi-symbolic inference for efficient streaming probabilistic programming

A streaming probabilistic program receives a stream of observations and produces a stream of distributions that are conditioned on these observations. Efficient inference is often possible in a streaming context using Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall back on sampling approximations when necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal of streaming probabilistic programming is to automatically generate such efficient inference implementations given input probabilistic programs. In this work, we propose semi-symbolic inference, a technique for executing probabilistic programs using a runtime inference system that automatically implements Rao-Blackwellized particle filtering. To perform exact and approximate inference together, the semi-symbolic inference system manipulates symbolic distributions to perform exact inference when possible and falls back on approximate sampling when necessary. This approach enables the system to implement the same RBPF a developer would write by hand. To ensure this, we identify closed families of distributions – such as linear-Gaussian and finite discrete models – on which the inference system guarantees exact inference. We have implemented the runtime inference system in the ProbZelus streaming probabilistic programming language. Despite an average 1.6× slowdown compared to the state of the art on existing benchmarks, our evaluation shows that speedups of 3×-87× are obtainable on a new set of challenging benchmarks we have designed to exploit closed families.