Reviewerly/exHarmony huggingface dataset:
Longitudinal and Lateral Trajectory Planning for the Typical Duty Cycle of Autonomous Load Haul Dump

With the development of intelligent mining, autonomous driving will be the basic function of intelligent Load Haul Dump (LHD).Trajectory planning is the key part of autonomous driving.Due to the articulated structure, the motion state variables of the front and rear bodies are strongly nonlinear, leading to complex nonlinear collision avoidance constraints.In addition, the articulated angle and angular velocity of the LHD have physically constraints.At the same time, the terminal attitude constraint should be considered since LHD is a mining equipment.All these factors bring great difficulties to LHD trajectory planning and none of the existing methods can deal with these factors directly.In order to solve these problems, according to the most common working scenario, a novel trajectory planning method is proposed for autonomous LHD based on numerical optimization, leading to a safe and feasible time-space trajectory for efficient production.The novelty of this work is the introduction of a longitudinal and lateral trajectory planning for the typical duty cycle of LHD.More importantly, by the ingenious concept for modeling, there are two salient features of the proposed method.Firstly, no angles are used as the decision variable, and secondly, the collision constraints of the rear car are not directly considered.Through this way, the number of nonlinear constraints and the complexity of the model can keep in a reasonable level, which makes the model easy to solve.At the same time, by giving the collision-free condition and limiting the heading angular velocity, the generated trajectory is collision-free and satisfies the physical constraints of articulated angle.Case studies confirm the effectiveness of the proposed method.Adopting the proposed method to generate a spatiotemporal trajectory is beneficial to ensure safety and improve production efficiency.
Quantum steering of a two-mode Gaussian state using a quantum beat laser

We study the quantum steering of a two-mode Gaussian state evolved by a simple quantum beat laser with the cavity field being initially in arbitrary two single-mode Gaussian states and the lasing medium being driven by a strong classical field. In presence of the cavity damping, the quantum steering time of the two-mode Gaussian state increases with the relative intensity of the coupling fields, whereas the relative phase switches the steerable states into the non-steerable and vice versa. We further show that the higher the non-classicality of the initial cavity modes, the longer the time of steerability in the two-mode Gaussian state. Nonetheless, the quantum steering is irrespective of the influence from the purity of the initial cavity modes of the laser system.
THE PERFORMANCE COMPARISON OF THREE METAHEURISTIC ALGORITHMS ON THE SIZE, LAYOUT AND TOPOLOGY OPTIMIZATION OF TRUSS STRUCTURES

The structural optimization problem mostly deals with the weight minimization of the structural system. This issue can be assessed from the size, layout and topology aspects. No matter which of these aspects are targeted, to solve them an optimization technique is required. In the last decades the metaheuristic techniques, as the non-gradient optimization algorithms, are widely applied on solving these classes of problems. In the structural optimization, the most time consuming part of the process is the objective function evaluation. Based on this fact, in the current work, these techniques are divided into three main groups as single phase, double phase and multi-phase algorithms. Then based on the author knowledge, three representative methods are picked for each group and their search performance comparatively inspected on solving size, shape and topology optimization of truss structures. To meet this aim, Integrated Particle Swarm Optimization (iPSO), Teaching and Learning Based Optimization (TLBO) and Drosophila Food-Search Optimization (DSO) algorithms are selected, respectively. Different properties like accuracy, convergence rate and complexity of the algorithms are investigated. The outcomes are provided via illustrative diagrams and tables. Based on the achieved results, DSO shows the most complexity level among the other algorithm while the iPSO and TLBO can outperform it on both accuracy and convergence rate. Consequently, iPSO presents a higher accuracy level on finding optimal solutions and TLBO with the lowest standard deviation value through the process shows the highest level of stability on finding optimal solutions.
Adding Linkability to Ring Signatures with One-Time Signatures

We propose a generic construction that adds linkability to any ring signature scheme with one-time signature scheme. Our construction has both theoretical and practical interest. In theory, the construction gives a formal and cleaner description for constructing linkable ring signature from ring signature directly. In practice, the transformation incurs a tiny overhead in size and running time. By instantiating our construction using the ring signature scheme [13] and the one-time signature scheme [12], we obtain a lattice-based linkable ring signature scheme whose signature size is logarithmic in the number of ring members. This scheme is practical, especially the signature size is very short: for $$2^{30}$$ ring members and 100 bit security, our signature size is only 4 MB. In addition, when proving the likability we develop a new proof technique in the random oracle model, which might be of independent interest.
Asynchronous Deep Interaction Network for Natural Language Inference

Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence.Existing methods typically have framed the reasoning problem as a semantic matching task.The both sentences are encoded and interacted symmetrically and in parallel.However, in the process of reasoning, the role of the two sentences is obviously different, and the sentence pairs for NLI are asymmetrical corpora.In this paper, we propose an asynchronous deep interaction network (ADIN) to complete the task.ADIN is a neural network structure stacked with multiple inference sub-layers, and each sub-layer consists of two local inference modules in an asymmetrical manner.Different from previous methods, this model deconstructs the reasoning process and implements the asynchronous and multi-step reasoning.Experiment results show that ADIN achieves competitive performance and outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail.
Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews

To automatically assess the helpfulness of a customer review online, conventional approaches generally acquire various linguistic and neural embedding features solely from the textual content of the review itself as the evidence.We, however, find out that a helpful review is largely concerned with the metadata (such as the name, the brand, the category, etc.) of its target product.It leaves us with a challenge of how to choose the correct key-value product metadata to help appraise the helpfulness of free-text reviews more precisely.To address this problem, we propose a novel framework composed of two mutualbenefit modules.Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the freetext review attend to this value to obtain better neural representations for helpfulness assessment.The predictor is directly optimized by SGD with the loss of helpfulness prediction, and the selector could be updated via policy gradient rewarded with the performance of the predictor.We use two real-world datasets from Amazon.com and Yelp.com, respectively, to compare the performance of our framework with other mainstream methods under two application scenarios: helpfulness identification and regression of customer reviews.Extensive results demonstrate that our framework can achieve state-of-the-art performance with substantial improvements.
On Making Stochastic Classifiers Deterministic

Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e.g. for fairness, churn, or custom losses. Despite their utility, the inherent randomness of stochastic classifiers may cause them to be problematic to use in practice for a variety of practical reasons. In this paper, we attempt to answer the theoretical question of how well a stochastic classifier can be approximated by a deterministic one, and compare several different approaches, proving lower and upper bounds. We also experimentally investigate the pros and cons of these methods, not only in regard to how successfully each deterministic classifier approximates the original stochastic classifier, but also in terms of how well each addresses the other issues that can make stochastic classifiers undesirable.
Relationship Network Augmented Web Services Clustering

Clustering Web services can promote the quality of services discovery and management within a service repository. Traditional clustering methods primarily focus on using the semantic distance between service features, i.e., latent topics learned from WSDL documents, to measure the service content similarity between Web services. Few works exploited the structural information generated during the usage of Web services, i.e., the service compositing and tagging behaviors. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex service relationship network. The rich network relations inherently reflect either positive or negative categorical relevance between services, which can be strong supplement of service semantics in characterizing the functional affinities between services. In this paper, we propose to utilize the services relationship network for augmented services clustering algorithm design. We first learn semantic information from service descriptions based on the widely used Doc2Vec model. Then, we propose a revised K-means algorithm for service clustering that benefits simultaneously from service semantics and network relations, where the service relations are previously preserved in a set of low-dimensional vectors achieved based on a recently proposed network embedding technique. Experiments on a real-world dataset demonstrated that the proposed clustering approach yields an improvement of 6.89% than the state-of-the-art.
Characterizing Software Stability via Change Propagation Simulation

Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>S</mml:mi><mml:mi>S</mml:mi></mml:math> (software stability), to measure software stability. Our <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>S</mml:mi><mml:mi>S</mml:mi></mml:math> metric is validated theoretically using the widely accepted Weyuker’s properties and empirically using a set of open source Java software systems. The theoretical results show that our <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>S</mml:mi><mml:mi>S</mml:mi></mml:math> metric satisfies most of Weyuker’s properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems.
Identifying Predictive Causal Factors from News Streams

We propose a new framework to uncover the relationship between news events and real world phenomena.We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams.This graph is constructed by measuring how the occurrence of a word in the news influences the occurrence of another (set of) word(s) in the future.We show that PCG can be used to extract latent features from news streams, outperforming other graph-based methods in prediction error of 10 stock price time series for 12 months.We then extended PCG to be applicable for longer time windows by allowing time-varying factors, leading to stock price prediction error rates between 1.5% and 5% for about 4 years.We then manually validated PCG, finding that 67% of the causation semantic frame arguments present in the news corpus were directly connected in the PCG, the remaining being connected through a semantically relevant intermediate node.
Towards Robust Named Entity Recognition for Historic German

Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference.We apply pre-trained language models to low-resource named entity recognition for Historic German.We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with lowresource datasets.Our pre-trained characterbased language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6%.Our pre-trained language and NER models are publicly available 1 .
Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning

In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1).We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features.Our system achieves an accuracy of 69.3% macro F1-score with an improvement of 1.4% accuracy from the baseline model on the DEV dataset.Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0.12% macro F1-score behind the top ranked system.
Research on Moving Target Tracking Based on FDRIG Optical Flow

Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.
Internet of Things Security using New Chaotic System and Lightweight AES

The Internet of Things (IoT)services and application were increasing during the last years in several life fields causes need to provide a secure identifier for protecting sensing data passing between IoT sensors/devices and embedded-subsystem connected by networks. This paper was proposed an algorithm for helping in IoT communication security can used in different IOT entities used in unofficial industrial machine to machine (M2M) communications, smart energy-grids, home or buildings and other computing devices. &#x0D; This paper proposed a secure system using new proposed 4D chaotic system combined with the modified lightweight Advanced Encryption Standard (AES). The proposed 4-dimension (4D) chaos system Lyapunov was tested and pass for many initial periods and get a super chaos system (4 positive Lyapunov). Generated chaos keys (used JORN) were used in the lightweight AES and the Secure Hash Algorithm version 3 (SHA3-256). The Lightweight AES was design in case to reduce CPU computation cycles and complexity of AES. Results show that computation time for proposed system decreased (has 145% speedup more). The output of modified lightweight AES encryption System has the good statistical tests near to original AES that can avoid many attacks.
A Density-based Under-sampling Algorithm for Imbalance Classification

Abstract Imbalance classification is an interesting issue in machine learning and data mining. In recent years, many related algorithms have been proposed to solve such an issue. Among them, under-sampling is an effective and timesaving data pre-processing method, which balances the dataset by removing some examples from the majority class. However, these proposed under-sampling methods often lose some useful information or ignore noise in the datasets, which will result in the performance degradation. This paper proposes a density-based under-sampling algorithm (DBU) to solve these two problems. In feature space, similar examples are close to each other and noisy example is far from other examples belonging to the same class. Thus the similar examples have a high density while the noisy example has a low density. DBU uses the local density peaks to represent the whole majority class, so that it can retain the useful information and eliminate the noisy examples automatically. To evaluate our algorithm, experiments are conducted on 15 two-class imbalanced datasets. Experimental results show that DBU achieves the better results than other under-sampling methods.
Cozy mystery construction kit

This paper presents a case study in the experience-first prototyping of a generative game. Our goal in this process was to create a PCG-based mystery story construction game ncentered on a social simulation of characters and their motivations, and driven by a set of core themes and experiences we wanted players to encounter. In pursuit of this goal, we created a series of prototypes to test how a variety of generative and AI-based techniques---including character generation, character action suggestion based on game state, story sifting, and social simulation---may be used in support of collaborative storytelling. In this paper we catalogue these prototypes and what we have learned by creating them, detailing design elements we found to be successful in supporting player creativity and that may be useful to the developers of similar games and experiences going forward.
Sistem Informasi Penjualan Furniture Berbasis Web Pada CV. Satria Hendra Jaya Pekanbaru

CV Satria Hendra jaya is a trading company engaged in the sale of furniture fixtures household appliances such as tables, chairs, cabinets, beds etc., where the promotion and sale is still conventional. This means that customers have to go to the CV Satria Hendra jaya to be able to purchase the product. Development of the e-commerce sales in the CV Satria Hendra jaya is an effort to increase sales and promotion of products that can benefit the company. The purpose of building an ecommerce sales of furniture, customers can order the products without having to come into the CV Satria Hendra jaya, the company was able to overcome the problem of product processing, booking processing thus providing convenience to consumers to get information about products CV Satria Hendra jaya. The system is built using PHP and MySQL database program and use the notepad ++ editor. The results of this research is to implement an e-commerce CV Satria Hendra jaya can be used as a sales promotion tool that can be accessed anywhere and anytime, the buying process can be directly without having to come to the store
Efficient and Flexible Multi-Factor Authentication Protocol Based on Fuzzy Extractor of Administrator’s Fingerprint and Smart Mobile Device

In an era of tremendous development in information technology and the Internet of Things (IoT), security plays a key role in safety devices connected with the Internet. Authentication is vital in the security field, and to achieve a strong authentication scheme, there are several systems using a Multi-Factor Authentication (MFA) scheme based on a smart card, token, and biometric. However, these schemes have suffered from the extra cost; lost, stolen or broken factor, and malicious attacks. In this paper, we design an MFA protocol to be the authenticated administrator of IoT’s devices. The main components of our protocol are a smart mobile device and the fuzzy extractor of the administrator’s fingerprint. The information of the authenticated user is stored in an anomalous manner in mobile devices and servers to resist well-known attacks, and, as a result, the attacker fails to authenticate the system when they obtain a mobile device or password. Our work overcomes the above-mentioned issues and does not require extra cost for a fingerprint device. By using the AVISPA tool to analysis protocol security, the results are good and safe against known attacks.
A three-stage real-time detector for traffic signs in large panoramas

Traffic sign detection is one of the key components in autonomous driving. Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis. Detecting traffic signs, moving vehicles, and lanes is important for localization and decision making. Traffic signs, especially those that are far from the camera, are small, and so are challenging to traditional object detection methods. In this work, in order to reduce computational cost and improve detection performance, we split the large input images into small blocks and then recognize traffic signs in the blocks using another detection module. Therefore, this paper proposes a three-stage traffic sign detector, which connects a BlockNet with an RPN–RCNN detection network. BlockNet, which is composed of a set of CNN layers, is capable of performing block-level foreground detection, making inferences in less than 1 ms. Then, the RPN–RCNN two-stage detector is used to identify traffic sign objects in each block; it is trained on a derived dataset named TT100KPatch. Experiments show that our framework can achieve both state-of-the-art accuracy and recall; its fastest detection speed is 102 fps.