Reviewerly/exHarmony huggingface dataset:
Flexible A* For Mobile Intelligent Robot Navigation

Positioning and map construction are two fundamental missions for the indoor navigation with no a preferred map of an autonomous mobile robot. A mobile robot system used for synchronous positioning and mapping (SLAM) is designed for an indoor spontaneous mobile robot. Because variant sensor modeling for laser scope finder is adopted for the extraction of some two-dimensional context characteristics and vertical margins. Applying Kalman filtering (KF) to positioning and grid map construction synchronously are showed. In addition, this research considers navigation algorithm based on A* algorithm, which is often used in game programming. Flexible A* algorithm has been implemented and tested widely in numerous simulations operates. The outcomes demonstrate that the considered method effectively distributes the robot over the environment and allows them to accomplish its mission quickly.
Travelling salesman problem: Greedy single point crossover in ordinal representation

Abstract Genetic Algorithm (GA) is a metaheuristic algorithm which has stages; coding, selection, crossover, and mutation. The crossover operator plays an important role in producing offspring to find solutions. Single Point Crossover (SPC) and Two Point Crossover (TPC) are classic crossover operators that are easy to apply to ordinal representation coding rather than path representation coding. The greedy algorithm is applied to the SPC operator to solve the Traveling Salesman Problem (TSP) to improve the performance of the genetic algorithm in the ordinal representation coding scheme. The results show that the proposed Greedy Single Point Crossover (GSPC) has higher performance in finding the global optimal solution for all population sizes and cities used, but it costs computation time.
Fast Imaging Sensor Identification

We consider identification of imaging devices by analysing images they produce. The problem is studied in the literature, yet the existing solutions are rather computationally demanding. We propose a high-speed algorithm for identification of imaging devices. The aim is to provide additional security by identification of legitimate imaging devices or an identification for forensics. The experimental evaluation confirms efficient identification of devices models and brands by the proposed algorithm, compared with the state-of-the-art method. Moreover, our algorithm is approximately two orders of magnitude faster, which is very important in resource-constrained IoT ecosystems or very large databases.
Algebraic Restriction Codes and Their Applications

Abstract Consider the following problem: You have a device that is supposed to compute a linear combination of its inputs, which are taken from some finite field. However, the device may be faulty and compute arbitrary functions of its inputs. Is it possible to encode the inputs in such a way that only linear functions can be evaluated over the encodings? I.e., learning an arbitrary function of the encodings will not reveal more information about the inputs than a linear combination. In this work, we introduce the notion of algebraic restriction codes (AR codes), which constrain adversaries who might compute any function to computing a linear function. Our main result is an information-theoretic construction AR codes that restrict any class of function with a bounded number of output bits to linear functions. Our construction relies on a seed which is not provided to the adversary. While interesting and natural on its own, we show an application of this notion in cryptography. In particular, we show that AR codes lead to the first construction of rate-1 oblivious transfer with statistical sender security from the Decisional Diffie–Hellman assumption, and the first-ever construction that makes black-box use of cryptography. Previously, such protocols were known only from the LWE assumption, using non-black-box cryptographic techniques. We expect our new notion of AR codes to find further applications, e.g., in the context of non-malleability, in the future.
Predicting Performance in Understudy Areas Using AI Novel Algorithms

Execution assessment of understudies is fundamental for check the plausibility of progress. Standard assessment improves the exhibition of the understudy as well as it helps in understanding where the understudy is deficient. It's anything but a ton of manual exertion to finish the assessment interaction as even one school may contain a great many students. Precisely predicting understudies future execution reliant upon their advancing insightful records is huge for effectively doing basic informative intercessions to ensure understudies' on-time and pleasing graduation. Disregarding the way that there is a rich composition on anticipat-ing understudy execution when handling issues or perusing for courses using data driven approaches, predicting understudy execution in completing degrees is extensively less inspected and faces new challenges: (1) Understudies contrast hugely with respect to establishments and picked courses; (2) Courses are not comparably illuminating for making exact assumptions; (3) Understudies' creating progression ought to be joined into the assumption. In this paper, we develop a novel AI procedure for expecting understudy execution in degree programs that can address these key challenges. Decisively following and expecting understudies future execution subject to their advancing insightful records to ensure understudies on-time and great graduation. Following and expecting understudy execution reliant upon degrees and here faces new challenges those are: Students shift gigantically in regards to establishments and picked courses, Courses are not comparably helpful for making careful figures, Students creating headway ought to be joined into the assumption. This paper proposed a computerized answer for the exhibition assessment of the understudies utilizing AI. A limit based division is utilized to finish the assessment strategy over MATLAB reenactment device. The exhibition of AI is assessed by exactness and mean square blunder
Data Room Management System Based on Target Detection Algorithm

In recent years, streaming media services have been widely used in data room management systems. In order to record the user operations, host machine alarms and reminder in real time, target detection algorithms for streaming media have played an important role. Conventional target detection methods, limited by detection speed, are not suitable for such real-time application. So, in this paper, we applies SSD (Single Shot MultiBox Detector) algorithm to data room management system. Firstly, we constructs a host operating image data set, and then perform data amplification by adding noise and rotation. Secondly, the SSD model is used to train a model. Finally, after obtaining the target detection results, the NMS (Non-Maximum Suppression) algorithm is used to avoid the redundant detection frame. At the same time, some improvement measures are put forward to solve the problem of small target, leading to the difficulty of feature extraction and low detection accuracy. The experimental results demonstrate that the model can detect and track the target better in the video and meet the requirements of the real-time performance and accuracy of the system.
Use of Deterministic Transforms to Design Weight Matrices of a Neural Network

Self size-estimating feedforward network (SSFN) is a feedforward multilayer network. For the existing SSFN, a part of each weight matrix is trained using a layer-wise convex optimization approach (a supervised training), while the other part is chosen as a random matrix instance (an unsupervised training). In this article, the use of deterministic transforms instead of random matrix instances for the SSFN weight matrices is explored. The use of deterministic transforms provides a reduction in computational complexity. The use of several deterministic transforms is investigated, such as discrete cosine transform, Hadamard transform, Hartley transform, and wavelet transforms. The choice of a deterministic transform among a set of transforms is made in an unsupervised manner. To this end, two methods based on features' statistical parameters are developed. The proposed methods help to design a neural net where deterministic transforms can vary across its layers' weight matrices. The effectiveness of the proposed approach vis-a-vis the SSFN is illustrated for object classification tasks using several benchmark datasets.
Image-Based Visual Servoing Control for Automatic Carrier Landing

This paper studies the control problem of automatic carrier landing for a fixed-wing aircraft through image-based visual servoing (IBVS) technology. An image-aircraft integration model is established by incorporating the virtual image feature into the aircraft dynamics. Based on the model, we established a control framework with the attitude control as inner loop and visual servoing guidance as outer loop. For trajectory instability at low speed of aircraft, the approach power compensation system with constant angle of attack is used. In order to eliminate the influence of wind turbulence, the nonlinear disturbance observer (NDO) is designed to estimate disturbance. The Lyapunov analysis proves the stability of the system, and the simulation verifies the proposed control scheme can make the aircraft track the desired trajectory and land on the carrier with good performance.
Acoustic characteristics of resyllabification process in Korean

Purpose: This study aims to analyze acoustic characteristics of Korean words and nonwords according to resyllabification and meaningfulness.Methods: The experimental data consisted of 10 homonyms and 10 corresponding words. Computerized Speech Lab (CSL) 4150B was used in a quiet place for recording. Moreover, the randomized word list was presented to 20 subjects, and they were asked to read naturally as if they were talking comfortably to the subjects. The analysis program was Praat 6151 win 64bit (Boersma &amp; Weenink, 2021). Pitch, intensity, and duration of the words and the first and the second syllables were measured, and the resyllabification liaison rules and resyllabification influenced them. To investigate acoustic characteristics according to resyllabification, independent sample t-test and multivariate test were conducted using SPSS 26 for the statistical processing of a syllable’s pitch, intensity, and duration changes.Results: First, there was a significant difference between the groups in post-syllable pitch ratio in words and nonwords, which was 40s–50s pitch change was greater than that of 20s–30s. Second, the post-syllable pitch ratio was a significant difference between gender groups and according to the effect of the liaison rule. Third, the post-syllable duration ratio showed a significant difference between age groups. The post-syllable pitch ratio was a significant difference according to the effect of the liaison rule.Conclusions: Therefore, when resyllabifications are generated by the liaison rule, the change of the post-syllable pitch can be explained by the focus prosody, and further research will be needed to establish a solid basis for this study.
Committee-Voting mechanism based on Graph for Semisupervised Person Re-identification

Abstract Recently, person re-identification technology has made great progress, mainly thanks to a large amount of annotated data. However, the current million-dollar annotations are becoming increasingly difficult to scale. Therefore, we work on the problem of semi-supervised person re-identification using only a small amount of annotated data. Inspired by QBC, we propose the committee-voting mechanism based on graph (CVG), which consists of two modules, the committee, and the chairman. The committee proposes different opinions and constructs multiple sets of information pairs for unlabelled data, and the chairman is responsible for aggregating the opinions of all committee members and making the final decision. As a sound selection mechanism, which effectively improves the accuracy of semisupervised person re-identification. Extensive experiments on three large-scale datasets demonstrate the effectiveness of the proposed method.
Facial Expressions Recognization Based on DRLBP and CNN Classification

Facial expression appreciation typically can be a distinct one of significant investigation in the field of AI or example appreciation. The facial expression contains rich imperceptible information, which can appreciate human emotions or intentions, which extensively investigate value. Facial expression analysis is a compelling and challenging trouble that affects essential purposes in various fields such as human–computer interface and data-driven animation. Developing useful facial expressions from original face images is a necessary step in realizing face recognition. The actual evaluation is based on local statistical traits; Dominant Rotated Local Binary Patterns (DRLBP) facial expressions. Several machine learning methods have been thoroughly observed in various databases. Researchers typically use the DRLBP function, which is efficient and capable of face recognition. Cohn Canada is the current work database or programming language used in MATLAB. First, divide the face area into small areas, drag the histogram, Dominant Rotated Local Binary Patterns out of the area, and connect it to a single function vector. This feature vector outlines a well-organized face illustration and helps determine the similarity between images. It is still a severe face recognition problem to create powerful and unique features, while increasing interpersonal differences. In this article, the researchers explained how to use Dominant Rotated Local Binary Patterns (DLBP) for feature extraction and Convolutional Neural Networks (CNN) for image classification to enhance the face recognition capabilities system. The post-workout correspondence helps CNN converge faster and achieve better accuracy. Compared to other traditional methods, it has also been significantly improved to evaluate this new method's completion.
Chapter 15: Goldstrike Gold System, North Carlin Trend, Nevada, USA

Abstract The Eocene Goldstrike system on the Carlin Trend in Nevada is the largest known Carlin-type gold system, with an endowment of 58 million ounces (Moz) distributed among several coalesced deposits in a structural window of gently dipping carbonate rocks below the regional Roberts Mountains thrust. The 3.5- × 2.5-km Goldstrike system is bounded to the east by the Post normal fault system and to the south by the Jurassic Goldstrike diorite stock and is partly hosted in the favorable slope-facies apron of the Bootstrap reef margin that passes through the system. The carbonate and clastic sedimentary sequence is openly folded, cut by sets of reverse and normal faults, and intruded by the Jurassic Goldstrike stock and swarms of Jurassic and Eocene dikes, establishing the structural architecture that controlled fluid flow and distribution of Eocene mineralization. A proximal zone of permeability-enhancing decarbonatization with anomalous gold (&amp;gt;0.1 ppm) extends a few hundreds of meters beyond the ore footprint and lies within a carbonate δ18O depletion anomaly extending ~1.4 km farther outboard. The full extent of the larger hydrothermal system hosting Goldstrike and adjacent deposits on the northern Carlin Trend is outlined by a 20- × 40-km thermal anomaly defined by apatite fission-track analyses. The bulk of the mineralization is hosted in decarbonatized sedimentary units with elevated iron contents and abundant diagenetic pyrite relative to background. Gold is associated with elevated concentrations of As, Tl, Hg, and Sb, and occurs in micron-sized arsenian pyrite grains or in arsenian pyrite overgrowths on older, principally diagenetic pyrite, with sulfidation of available iron as the main gold precipitation mechanism. The intersection of a swarm of Jurassic lamprophyre dikes with the edge of the limestone reef provided a favorable deeply penetrating structural conduit within which a Jurassic stock acted as a structural buttress, whereas the reef’s slope-facies apron of carbonate units, with high available iron content, provided a fertile setting for Carlin-type mineralization. The onset of Eocene extension coupled with a southwestward-sweeping Cenozoic magmatic front acted as the trigger for main-stage gold mineralization at 40 to 39 Ma. All these factors contributed to the exceptional size and grade of Goldstrike.
A Logic for Desire Based on Causal Inference

Reasoning about desire plays a significant role in philosophy, logic, and artificial intelligence etc. In this paper, we propose an interpretation of “desiring \(\phi \)” based on whether the outcome brought about by “making \(\phi \) true” is preferred by the agent. This interpretation of desire (including conditional desire) is different from the traditional approaches which directly interpret desire as preference. In order to formalize this idea, we construct a desire-causality model by combing the betterness model in preference logic and the causal model in the logic for causal reasoning. We also develop a logic for desire based on this semantics, and an axiomatization for our formal system is given.
Attention Guidance for Autonomous Vehicles Based on Type-2 Fuzzy Sets

Visual perception is an important part in the system of autonomous vehicles. The choice of image focus will directly affect decision-making and driving safety, so how to analyze the attention problem and select the appropriate attention area is of great significance. In this paper, an attention guidance for autonomous vehicles is proposed, which can help the system to understand attention areas. According to empirical analysis from different drivers, attention allocations of human drivers are summarized, and membership functions are established. Based on the theory of type-2 fuzzy sets and fuzzy comprehensive evaluation, the rationality of three drivers' visual focus are evaluated in the example.
Reproducible Improvement of Images Quality Through Nature Inspired Optimisation

Transparent and reproducible image processing is fundamental in science and engineering, whether to prove the efficiency of an algorithm or to prepare a new corpus of images. In this paper, we propose a Nature Inspired Optimisation algorithm based on Image Quality Assessment methods to obtain a reproducible sequence of transformations that improves the quality of a given image. Preliminary tests were realized on state-of-the-art benchmarks.
LSTM - Based Model of Conversation Sentiment Tendency Analysis

Natural language processing (NLP) has made great progress in recent years. However, there is a lack of relevant research on natural language processing of conversational situations, and many natural language processing methods for conversational situations have the problem of unbalanced information processing. In this paper, a weighted fusion Long Short-Term Memory (LSTM) neural network is proposed. The information of the two sides of the dialogue is processed step by step and weighted together to comprehensively consider the roles of the two sides in the context. Finally, a sentiment analysis model for the conversation environment is implemented, and the advantages of LSTM model are verified through comparative experiments.
Optimal Recharging of Teams of Mobile Robots

In this paper, we propose an approach to extend the operational time of teams of battery-based robots by introducing a set of charging stations. We assume that the robots are heterogeneous (having different energy limits and being able to service different types of customers) and have access to a priori known map of the environment. The map is modeled as a directed, connected, and finite graph whose nodes are charging stations or customers, and arcs denote the possibility of traveling. To this end, we first formulate a task assignment and path planning problem that aims at optimizing energy consumption as well as the time needed to complete the tasks, including the time spent for recharging. Next, we propose four offline optimization techniques and one online algorithm, where the robots can dynamically adjust their paths in response to the presence of uncertainties imposed by the physical environment. Our proposed algorithms are validated through both simulation and a real-world case study on a team of unmanned aerial vehicles (UAVs) performing a joint search mission.
The Use of the Progressive in Light of the AH in Monolingual EFL-Instructed Spanish Learners at University Level: A Longitudinal Learner Corpus-Based SLA Study

Despite recent interest in the analysis of the progressive in light of the Aspect Hypothesis (AH), little information is available on the use of the progressive by EFL Spanish learners. To gain a better understanding of the use of the progressive in EFL-instructed Spanish learner writing at advanced levels, this longitudinal learner corpus-based SLA study examines the frequency of use of the progressive, as well as two of the associations of the AH: (i) the progressive with dynamic verbs; (ii) and, no overextension of the progressive to stative verbs. The effects derived from factors or variables such as the tense employed, target- and non-target-like uses, students’ academic year and expected higher proficiency level, task type and individual preferences are also discussed as a way to fine-tuning the strong version of the AH to the use of the progressive by this learner group
Design of Data Center Network Architecture for Cloud Computing in Forestry Information

Cloud Computing Oriented Data Center network architecture in forestry information is one ofthe key technologies of intelligent manufacturing data center. Data center network in forestryinformation is a new research hotspot driven by the actual needs of the industry under thecondition of cloud computing. It has a very broad research prospect and is of great significanceto the development of information technology. This paper summarizes the main research resultsof data center network, and summarizes the methods of constructing large-scale network. At thesame time, this paper analyzes the server centric data center network and switch centric datacenter network, as well as the construction method of container based data center network,which is very popular at present. In order to solve the problem of whether the cloud computingcarrying entity data center network structure is compatible with the cloud computing system,according to the characteristics of cloud computing data center and cloud computing data centernetwork, this paper discusses the requirements of cloud computing technology for data centernetwork and the corresponding key technologies step by step.
A Clicks-and-Mortar Smart System

1E-commerce nowadays has made its official way into the clicks-and-mortar era. The advantage of a clicks-and-mortar model is that the "mortar" counterpart can enjoy logistics support, which complements any operational inadequacy on the side of the virtual website - the "clicks" counterpart. In a clicks-and-mortar smart contract system, the clicks-and-mortar application enables any transaction to automatically execute each operational step. When a consumer selects a product and completes the payment for it, the smart contract can proceed to execute any terms established in its contents, ensuring the proper practice of every item in the contract.