Reviewerly/exHarmony huggingface dataset:
Email thread sentiment sequence identification using PLSA clustering algorithm

• SentiWordNet lexicon is used for generating sentiment features of email data. • Probabilistic Latent Semantic Analysis algorithm is used for clustering. • The sentiment sequence of thread and thread size are identified. • Evaluated the proposed model using accuracy, precision, recall and F-measure. Email messaging is the most common way of providing effective communication between internauts. Consequently, the total sent and received emails count will be increased. But, the internaut can't remember all such emails. Even though email thread identification approaches give satisfactory benefits to the internauts, but they may fail to alert them for a cause to identify the sentiments behind an email thread. To address, this issue Probabilistic Latent Semantic Analysis clustering algorithm has been used in this paper to identify the email sentiment thread sequence. The sentiment and the thread sequence within the emails have been discovered as clustering sentiment polarity and temporal categories with the help of PLSA clusters. At the initial stage, we used three feature extraction methods, latent semantic analysis (LDA), bag of words (BoW), TF-IDF and SentiWordNet (SWN) lexicon for generating sentiment features of email. Next, Probabilistic Latent Semantic Analysis algorithm is used to form email clusters based on sentiment features. Thus, it helps to identify thread sentiment and sequence of sentiment threads. Email threads give a mechanism by which any user will be able to find out the sequence in the thread on the basis of sentiment analysis of email related to a specific set of communication during a specific time period. Various parameters evaluation measures have been considered in this work to evaluate the proposed model such as accuracy, precision, recall and F-measure, and the proposed algorithm is compared with other standard algorithms. Furthermore, a statistical test has also been performed.
Incomplete Multiview Clustering With Cross-View Feature Transformation

Incomplete multiview clustering (IMC) has attracted considerable attention as it can flexibly fuse the multiview information when part of the view samples are unobserved. Considering that the main challenge of IMC is the unobserved samples causing the information loss, in this article, we propose a novel IMC model to complete the unobserved samples, named cross-view feature transformation-based incomplete multiview clustering (CFTIMC). Unlike the previous completion methods that simply complete the unobserved samples with the average value or involve a third subspace, CFTIMC directly models the alignment relation between views by the cross-view feature transformation for completion, where only the overlapping views and its overlapping parts are selected for avoiding the affection of the nonoverlapping parts. Simultaneously, this completion process is integrated into multiview graph learning, where they can boost each other. To solve the proposed model, we develop an alternate optimization scheme-based iterative algorithm, together with its computational cost analysis and convergence analysis. Finally, the clustering experiments on several benchmark datasets verify that our method can outperform the related state-of-the-art methods.
Research on Path Planning and Tracking Control of Automatic Parking System

In this paper, we focus on the parking path planning and path tracking control under parallel parking conditions with automatic parking system as the research object. In order to solve the problem of discontinuity of curvature in the path planning of traditional arc-straight combined curve, a quintic polynomial is used to smooth the path. we design a path tracking controller based on the incremental model predictive control (MPC). The preview control based on pure tracking algorithm is used as the comparison algorithm for path tracking. The feasibility of the controller is verified by building a Simulink/CarSim co-simulation platform. In addition, the practicality of the parking controller is further verified by using the ROS intelligent car in the laboratory environment.
Citywide reconstruction of cross-sectional traffic flow from moving camera videos

Analysis of traffic fl ow pa rameters is ne cessary for Intelligent Transportation Systems (ITS) and autonomous driving research. Deep learning-based vehicle detection techniques have been widely used in reconstructing traffic fl ow parameters from video images. This research proposes a novel cross-sectional traffic fl ow es timation al gorithm to re construct tr affic volume from moving camera videos. We develop a vehicle detection dataset with more than one million annotations of vehicles with orientation and train a YOLOv4 based object detection network. We leverage the accurate vehicle detection model in tracking and estimating the distance of detected vehicles using Simple Online and Realtime Tracking (SORT) and photogrammetry techniques. The estimated distances and forward bearing of the observing vehicle are then utilized to calculate the GPS position of detected vehicles and used in the algorithm to estimate cross-sectional traffic fl ow. We ut ilize th e pr oposed al gorithm to es timate the traffic flow of 580 OpenStreetMap (OSM) road links and achieve an average accuracy of 84.30% verified a gainst 1 1 t raffic police sensor data in Susono city in Japan. The proposed large-scale dataset and cross-sectional traffic flow estimation algorithm open new avenues for ITS and autonomous driving research.
Does monetary policy fuel bitcoin demand? Event-study evidence from emerging markets

Despite mounting central bank concerns over cryptocurrency adoption, there is little evidence that monetary policy compels national currency holders to consider decentralized virtual alternatives. Exploiting cross-country variation in prescheduled announcement timing, I isolate how monetary policy announcements affect local Bitcoin demand using an event-study design on a panel of 26 emerging economies. I find that monetary policy announcements increase both measures of local Bitcoin attention and local Bitcoin trade volume, but only on days when the public is abnormally attentive to inflation. These announcement-induced Bitcoin trading spikes are only significant in countries that are not normally accustomed to inflation risk.
Resource analysis of quantum computing with noisy qubits for Shor’s factoring algorithms

Abstract We decompose two implementations of Shor’s algorithm for prime factorization into universal gate units at the logical level and predict the number of physical qubits and execution time when surface codes are used. Logical qubit encoding using a rotated surface code and logical qubits with all-to-all connectivity are assumed. We express the number of physical qubits and execution time in terms of the bit length of the number to be factorized and error rate of the physical quantum gate. We confirm the relationship between the number of qubits and the execution time by analyzing two algorithms using various bit lengths and physical gate error rates .
Identifying Compiler and Optimization Level in Binary Code From Multiple Architectures

While compiling a native application, different compiler flags or optimization levels can be configured. This choice depends on the different requirements. For example, if the application binary is intended for final release, the flags and optimization settings should be set for execution speed and efficiency. Alternatively, if the application is to be used for debugging purposes, debug flags should be configured accordingly, usually involving minor or no code optimization. However, this information cannot be easily extracted from a compiled binary. Nonetheless, ensuring the same compiler and compilation flags is particularly important when comparing different binary files, to avoid inaccurate or unreliable analyses. Unfortunately, to understand which flags and optimizations have been used, a deep knowledge of the target architecture and the compiler used is required. In this study, we present two deep learning models used to detect both compiler and optimization level in a compiled binary. The optimization levels we study are O0, O1, O2, O3, and Os in the x86_64, AArch64, RISC-V, SPARC, PowerPC, MIPS, and ARM architectures. In addition, for the x86_64 and AArch64 architectures, we also determine whether the compiler is GCC or Clang. We created a dataset of more than 76000 binaries and used it for training. Our experiments showed over 99.99% accuracy in detecting the compiler flags and between 92% to 98%, depending on the architecture, in detecting the optimization level. Furthermore, we analyzed the change in accuracy when the amount of data was extremely limited. Our study shows that it is possible to accurately detect both compiler flag settings and optimization levels with function-level granularity.
Evolutionary Architectural Search for Generative Adversarial Networks

The Generative Adversarial Network (GAN) has shown powerfulness in various real-world artificial intelligence applications. However, its network architecture is generally designed through a manual trial-and-error process, which is relatively tedious, slow, and sub-optimal. This paper hence develops an evolutionary architectural search (EAS) technique to automate the entire design process of the GAN. In particular, different objective functions are used in the generator of a GAN as variation operators. This helps train the generator with various candidate architectures and their associated weights to play adversarial training against the discriminator of the GAN. Following evaluations by the discriminator, superior candidate generators survive to the next generation and evolve for potentially better architectures and their weights simultaneously, leading to more stabilized GANs with improved performance. The GAN designed through EAS is termed an EAS-GAN in this paper and is tested against existing evolutionary and other state-of-the-art GANs. The test results show that the EAS-GAN offers better generative performance overall, with the Fréchet inception distance scoring 22.1, 38.8, and 8.3 on the CIFAR-10, STL-10, and LSUN bedroom data sets, respectively.
Complete Hyperentangled Bell States Analysis For Polarization‐Spatial‐Time‐Bin Degrees of Freedom with Unity Fidelity

Abstract Hyperentangled states can outperform their classical counterparts on solving certain tasks. Here, a simplified scheme for completely distinguishing two‐photon hyperentangled Bell states in polarization, spatial, and time‐bin degrees of freedom (DOFs) is presented. Unity fidelity can be achieved in principle without strong couple limitation between photon and quantum dot (QD), and the incomplete and imperfect QD‐cavity interactions are prevented by single‐photon detectors. In addition, auxiliary photons or DOFs are not required in the scheme. The necessary linear optical elements are fewer than the parity‐check‐based one.
Discrete space-based route planning for rotary-wing UAV formation in urban environments

The route planning problem for rotary-wing UAV formation is studied in discrete urban environments. First, a discrete-space consensus algorithm (DSCA) integrating asynchronous planning and grouping mechanism is developed to make the rotary-wing UAVs converge to the desired formation. Then the DSCA is combined with the improved rapidly-exploring random tree (IRRT) algorithm to enable the rotary-wing UAV formation to avoid the obstacles. Finally, the time information of waypoints is complemented by resolving the conflicts among multiple rotary-wing UAVs, thus reducing the rotary-wing UAVs' time gap of reaching the destinations. The proposed method extends the original consensus theory in the discrete space and for the obstacle avoidance issue. Also, it is valid in the route planning problem for rotary-wing UAV formation considering the real urban environments.
A modified biogeography‐based optimization algorithm based on cloud theory for optimizing a fuzzy <scp>PID</scp> controller

Abstract In recent years, the heuristic algorithms used to improve the PID controller have attracted increasing attention. Additionally, there have been many significative explorations into the field of the fuzzy PID controller. Therefore, this article presents a modified biogeography‐based optimization (BBO) algorithm to optimize the fuzzy PID controller. We utilize cloud theory to adapt migration operator and mutation operator of the BBO algorithm to enhance its exploration ability and exploitation ability for better convergence speed and accuracy. The final algorithm is called cloud theory‐biogeography‐based optimization algorithm (CTBBO). The great performance of the CTBBO algorithm is illustrated in the benchmark functions testing compared with other heuristic algorithms. Lastly, the CTBBO algorithm was used in an engineering case—optimizing the fuzzy PID controller in a 180°C‐die heater to demonstrate its practical application value.
Verifying Solidity Smart Contracts via Communication Abstraction in SmartACE

Solidity smart contract allow developers to formalize financial agreements between users. Due to their monetary nature, smart contracts have been the target of many high-profile attacks. Brute-force verification of smart contracts that maintain data for up to $$2^{160}$$ users is intractable. In this paper, we present SmartACE, an automated framework for smart contract verification. To ameliorate the state explosion induced by large numbers of users, SmartACE implements local bundle abstractions that reduce verification from arbitrarily many users to a few representative users. To uncover deep bugs spanning multiple transactions, SmartACE employs a variety of techniques such as model checking, fuzzing, and symbolic execution. To illustrate the effectiveness of SmartACE, we verify several contracts from the popular OpenZeppelin library: an access-control policy and an escrow service. For each contract, we provide specifications in the Scribble language and apply fault injection to validate each specification. We report on our experience integrating Scribble with SmartACE, and describe the performance of SmartACE on each specification.
An Introduction to the Federated Learning Standard

With the growing concern on data privacy and security, it is undesirable to collect data from all users to perform machine learning tasks. Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns.
Adaptively Exploits Local Structure With Generalised Multi-Trees Motion Planning

Sampling-based motion planners perform exceptionallywell in robotic applications that operate in high-dimensional spaces. However, most previous works often constrain the planning workspace rooted at some fixed locations, do not adaptively reason on strategies for narrow passages, and ignore valuable local structure information. In this letter, we propose Rapidly-exploring Random Forest ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rrf</small> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> )—a generalised multi-trees motion planner that combines the rapid exploring property of tree-based methods and adaptively learns to deploys a Bayesian local sampling strategy in regions that are deemed to be bottlenecks. Local sampling exploits the local-connectivity of spaces via Markov Chain random sampling, which is updated sequentially with a Bayesian proposal distribution to learn the local structure from past observations. The trees selection problem is formulated as a multi-armed bandit problem, which efficiently allocates resources to the most promising tree accelerating planning runtime. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rrf</small> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> learns the region that is difficult to perform tree extensions and adaptively deploys local sampling in those regions to maximise the benefit of exploiting local structure. We provide rigorous proofs of completeness and almost-surely asymptotic optimal convergence, and experimentally demonstrate that the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rrf</small> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ’s adaptive multi-trees approach allows it to perform well in a wide range of problems.
Coherent and incoherent laser pump on a five-level atom in a strongly coupled cavity-QED system

We propose a cavity quantum electrodynamic system consisting of a five-level atom coupled to a single mode of the cavity electromagnetic field. The study is focused on the regime of strong coupling between the cavity and atom. Pump laser fields and cavity fields connect the split energy levels of the atom. Instead of the well-known two-level Dicke model obtained by adiabatic elimination of the high-energy levels, we consider the pump lasers' detunings to the atomic transitions to be very small such that we can examine the influence of the higher-energy states. We have studied the effect of an external coherent drive and incoherent pumping on these higher-energy levels and observed the enhancement of intracavity photon numbers due to quantum coherence effects. The amplification of intracavity photons is achieved even without a population inversion. However, the effect of the coherent and incoherent drive is negligible for very large detunings when the higher-energy states are adiabatically eliminated. At zero and small detunings, the system reaches the steady state at an earlier instant of time for higher incoherent pumping. We find an almost agreeable steady-state behavior of the system's exact full quantum dynamics model and its semiclassical approximation. Our model tries to accurately simulate the open system by considering the cavity decay, spontaneous decay, and dephasing of the system.
A New Framework for Politics, Law, and Government in the Digital Era

In the 21st century, the digital world has taken over law and politics. Political war is on online platforms, and various decisions are made based on the digital data. Another problem is to provide security of online data. Most of the world is unsatisfied with the government and policymakers. A lack of satisfaction among the people leads towards civil war or it can cause the fall of an entire selected government, or it can collapse the law systems of the world. To cope with this problem, the authors propose the judge-based political system (JBPS). The new political system can deal with the advancement of technology and cybersecurity. Judge-based politics will help to control this type of threat and provide satisfaction in the upcoming era of democracy.
Deep Reinforcement Learning With Adversarial Training for Automated Excavation Using Depth Images

Excavation, which is one of the most frequently performed tasks during construction often poses danger to human operators. To reduce potential risks and address the problem of workforce shortage, automation of excavation is essential. Although previous studies have yielded promising results based on the use of reinforcement learning (RL) for automated excavation, the properties of excavation task in the context of RL have not been sufficiently investigated. In this study, we investigate Qt-Opt, which is a variant of Q-learning algorithms for continuous action space, for learning the excavation task using depth images. Inspired by virtual adversarial training in supervised learning, we propose a regularization method that uses virtual adversarial samples to reduce overestimation of Q-values in a Q-learning algorithm. Our results reveal that Qt-Opt is more sample-efficient than state-of-the-art actor-critic methods in our problem setting, and we verify that the proposed method further improves the sample efficiency of Qt-Opt. Our results demonstrate that multiple optimal actions often exist within the process of excavation and the choice of policy representation is crucial for satisfactory performance.
Governance quality and economic growth in Sub-Saharan Africa: the dynamic panel model

Purpose The main objective of this article is to analyze the role of governance quality in influencing the economic growth of 22 selected Sub-Saharan African Countries. Design/methodology/approach The study applied the panel dynamic Generalized Method of Moments (GMM) to analyze the data obtained from the World Bank database over the period from 2002 to 2020. Findings The overall finding indicated that the composite governance index has a positive significant effect on the economic growth of the countries; where a unit improvement in the aggregate governance index leads to a 3.05% increase in GDP. The disaggregated result has shown that corruption control and government effectiveness have a negative significant effect on growth performance, whereas, the rule of law and regulatory quality showed a positive significant effect. Political stability and voice and accountability have an insignificant effect on economic growth. Research limitations/implications Due to data limitations, this study could not address the whole members of Sub Sahara African Countries and could not see the causal relationship. Practical implications The study suggested a strong commitment to the implementation of policy and reform measures on all governance factors. This may add to the need to devise participatory corruption control mechanisms; to closely look at the proper implementation of policies and reforms that constitute the government effectiveness factors, and properly implement the rule of law at all levels of the government with a strong commitment to realizing it so that citizens at all levels can have full confidence in and abide by the rules of society. Originality/value Even though there are some studies conducted using conventional methods of panel data analysis such as random effect or fixed effects, this empirical study used more advanced panel dynamic generalized moment of methods to examine the role of improvement in governance quality on economic growth.