Reviewerly/exHarmony huggingface dataset:
Deterministic policies based on maximum regrets in MDPs with imprecise rewards

Markov Decision Process Models (MDPs) are a powerful tool for planning tasks and sequential decision-making issues. In this work we deal with MDPs with imprecise rewards, often used when dealing with situations where the data is uncertain. In this context, we provide algorithms for finding the policy that minimizes the maximum regret. To the best of our knowledge, all the regret-based methods proposed in the literature focus on providing an optimal stochastic policy. We introduce for the first time a method to calculate an optimal deterministic policy using optimization approaches. Deterministic policies are easily interpretable for users because for a given state they provide a unique choice. To better motivate the use of an exact procedure for finding a deterministic policy, we show some (theoretical and experimental) cases where the intuitive idea of using a deterministic policy obtained after “determinizing” the optimal stochastic policy leads to a policy far from the exact deterministic policy.
Mapping-Based Using Geographic Information Systems for Smart Transportation

In everyday life, transportation plays an essential role in human activities. Smart transportation is expected to help the community with traffic information and carry out public transportation tracking. The purpose of this paper is to design a mapping system that can track the position of public vehicles using a Global Positioning System device based on a geographic information system. The methodology used to achieve the objectives of this paper is the Unified Software Development Process with stages starting from the analysis model, design model, deployment model, implementation model, and testing model, which in this paper is limited to the design model stage. This paper aims to map public transport routes based on geographic information systems, which can help the public or newcomers find information about operating public transport routes.
Research on the application of blockchain in supply chain information sharing

There are some problems in traditional supply chain, such as information asymmetry and weak traceability. To solve these problems, this paper proposes a supply chain information sharing system based on block chain technology, combining with the characteristics of block chain technology. Firstly, the framework of information sharing system based on information chain and supply chain participants was constructed. Secondly, the interaction rules of information sharing process were designed to meet the actual demand of supply chain information interaction. At the same time, based on the role-based access control model, the system authority management mechanism was established to standardize the interaction between supply chain enterprises. Then, the information intelligent contract was deployed based on the above rules, and the supply chain information sharing system was established. Finally, the scheme was compared and analyzed through experiments. The results show that the scheme has certain advantages in the overall system function, fault tolerance and throughput, and can realize the efficient and secure sharing of supply chain information.
Online Repositories, Search Costs and Cumulative Innovation

Efficient access to existing knowledge is essential to technical advance, yet little is known about how access-enhancing institutions shape intertemporal knowledge spillovers. In this paper, I investigate the cumulative technological impact of the CNIDR AIDS Database, the first, disease-targeted, online repository of electronic patent documents, launched in 1994. Tracing references from subsequent patents, I find that the marginal impact of the repository was largest (+30%) among patents for which the established disease-link was previously non-obvious to detect through standard bibliographic search, in line with predictions of stronger reduction of search costs. Further findings suggest that increased visibility and attention to more "hidden" prior art particularly benefited private sector HIV researchers, and was reflected in enhanced diffusion of technological knowledge across scientific community and geographic boundaries.
Dense Video Captioning for Incomplete Videos

Incomplete video or partially-missing video situations are rarely considered in video captioning research. Previous approaches are mainly trained and evaluated on complete video clip datasets where all the events involved are thoroughly observed. In this work, we formulate the issue of video content description for partially-missing videos. To tackle this challenge, we propose a Visual-Semantic Embedding with Context (VSEC) module to capture the missing visual content by jointly embedding the constructed contextual visual representation and corresponding textual annotation. We further employ a transformer-based captioning network to generate complete and coherent descriptions for the incomplete video. To validate the effectiveness of our method, we construct a new dataset based on ActivityNet Caption to imitate incomplete video situations in reality, named as ActivityNet Caption-P. We train and test our method both on ActivityNet Caption-P and achieve outstanding performances in most metrics.
Type-2 Fuzzy Set Aggregation and Health Status Mining from Condition-Monitoring Applications

The study focuses on health monitoring applications performing Fault Identification (FI) and Maintenance Indication (MI). In industry, third-party algorithms based on artificial intelligence could be supplied in closed-source. For this reason, they may lack of customization, or retraining, that leads to a possible poor performance. The work proposes a system called Fuzzy Set Aggregator (FSA) receiving in input unstable classifier predictions over time and provides a stable MI signal with a reasonable degree of anticipation. Uncertainty quantification and management are embedded in the aggregation process by using the type-2 fuzzy set theory. Four system variants are tested through simulation with data extracted from a real-case study. Two FSA variants were able to anticipate the expected reference values, providing valuable signals to plan maintenance at a reduced cost, and showing a performance improvement when the uncertainty due to expert opinion is greater.
TRENDS TO SIMULACRIZATION OF THE EDUCATIONAL ENVIRONMENT

The article analyzes the problems associated with the upbringing of a personality in the modern educational environment. Paradigmatically, the study is based on the principle of dividing educational technologies into effective and ineffective. With regard to the latter, the term "simulacrum" is used, which is found in the Latin translations of Plato as an analogue of the word "ειδωλον". Digital technologies used in the educational environment can then and only then contribute to personal development when they connect students with the history of their own country, sometimes contradictory, but at the same time attracted to the eternal truth.
NEO: NEuro-Inspired Optimization—A Fractional Time Series Approach

Solving optimization problems is a recurrent theme across different fields, including large-scale machine learning systems and deep learning. Often in practical applications, we encounter objective functions where the Hessian is ill-conditioned, which precludes us from using optimization algorithms utilizing second-order information. In this paper, we propose to use fractional time series analysis methods that have successfully been used to model neurophysiological processes in order to circumvent this issue. In particular, the long memory property of fractional time series exhibiting non-exponential power-law decay of trajectories seems to model behavior associated with the local curvature of the objective function at a given point. Specifically, we propose a NEuro-inspired Optimization ( NEO ) method that leverages this behavior, which contrasts with the short memory characteristics of currently used methods (e.g., gradient descent and heavy-ball). We provide evidence of the efficacy of the proposed method on a wide variety of settings implicitly found in practice.
Optoelectronic Readout of STT-RAM Based on Plasmon Drag Effect

An opto-electronic readout of the memory state in spin transfer torque random access memory (STT-RAM) cells is proposed. A single optical beam illuminating an array of STT-RAM cells creates electrical potential along each STT-RAM cell due to optical rectification that can be further enhanced through plasmon drag effect (PLDE). Then, the photo-induced voltage is assumed to be measured by conventional electronics. Here, a theoretical modeling is performed, where the results provide an estimate of the photo-induced voltage in each memory cell and its variation with respect to the changes in state of the memory cell (parallel vs anti-parallel). Our study shows that the plasmonic enhancement facilitates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sim }$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</i> times enhancement in the voltage change due to state change of the each memory cell, compared to the case of out-of-resonance excitation. This enhancement potentially improves the memory readout rate if a proper supporting electronic circuit is available.
Adaptive Sorting Method of Big Data with Scattered Defects Based on Cloud Computing

In order to better sort the massive scattered defect data and ensure the effect of data processing, an adaptive sorting method based on cloud computing is proposed. In order to improve the convergence of data, multi-objective processing is carried out in the cloud computing environment. Combined with the fuzzy adaptive sorting algorithm, the difference value of multi-objective variation is calculated to achieve the balanced adjustment of variation data. In order to speed up the data convergence, balance the local optimal sorting value, obtain the initial value of scattered defect data, and complete the adaptive sorting of scattered defect big data. Finally, experiments show that the adaptive sorting method based on cloud computing can better repair and accurately sort the scattered data, and improve the data convergence effect.
LABELNET: Recovering Noisy Labels

Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. Recent studies improve the robustness of deep models against noisy labels without the knowledge of true labels. In this paper, we advocate to derive a stronger classifier which proactively makes use of the noisy labels in addition to the original images - turning noisy labels into learning features. To such an end, we propose a novel framework, LABELNET, composed of Amateur and Expert, which iteratively learn from each other. Amateur is a regular image classifier trained by the feedback of Expert, which imitates how human experts would correct the predicted labels from Amateur using the noise pattern learnt from the knowledge of both the noisy and ground truth labels. The trained Amateur and Expert proactively leverage the images and their noisy labels to infer image classes. Our empirical evaluations on noisy versions of MNIST, CIFAR-10, CIFAR-100 and real-world data of Clothing1M show that the proposed model can achieve robust classification against a wide range of noise ratios and with as little as 20-50% training data, compared to state-of-the-art deep models that solely focus on distilling the impact of noisy labels.
Midrange Exploration Exploitation Searching Particle Swarm Optimization in Dynamic Environment

Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate.
An Answer Driven Model For Paragraph-level Question Generation

Answer-aware question generation aims to generate answerable questions from a given context and answers. Most of the current models are based on the attention-based sequence-to-sequence ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> eq2seq) structure. However, these models do not make full use of the answer information, resulting in generating questions unrelated to the answer. We propose an answer driven model, which dynamically incorporates the interactive information between answer and previously generated words in the decoder to help the model decide which aspect of the question to focus on. Further, we use the answer distribution difference as a reward and use reinforcement learning to fine-tune the model. Experimental results show that our model performs better than the baseline models.
Early development of horizon detection system based on OpenCV and Python

To stabilize and control a flying vehicle, attitude sensors are needed. Relative attitude sensors such as gyroscope tend to accumulate errors if not corrected periodically using absolute attitude sensors. Horizon sensor is one of the absolute sensors that is commonly used in satellites and recently in autonomous UAVs. This paper describes our early development of a horizon detection system, assuming the horizon as a straight line. The input image is pre-processed by converting to grayscale and applying a Gaussian filter. Then the Canny method performs edge detection and the detected edges are given to the line fitting algorithm to find the best fit line equation. We use the least square method to do line fitting. From the line equation obtained, roll and pitch angles are calculated. We implement the system using OpenCV and Python. We have conducted lab-scale experiments using images and video files. We make parameter adjustments to obtain good results. For the images we used, the recommended parameter values are Canny threshold (200, 250) and kernel size 3x3, and Gaussian filter size 3x3 to 5x5 in the Pre-processing stage. For other images, the values may need to be adjusted accordingly.
Blind over-the-air computation for federated learning

The performance of over-the-air computation-based fast model aggregation approach presented in the previous chapters heavily depends on instantaneous channel state information (CSI). Acquiring CSI for federated learning systems with massive mobile devices leads to large amount of signaling overhead. To address this issue, in this chapter, we provide a blind over-the-air computation approach for low-overhead fast model aggregation by exploiting the blind demixing technique for transceivers design without CSI information. We formulate a bilinear unconstrained optimization problem and present a Wirtinger flow solution, which enjoys global convergence guarantee. Simulation results demonstrate that the aggregation results can be efficiently computed over-the-air without knowing instantaneous CSI.
An improved algorithm for disparity estimation of SGM stereo matching based on edge detection

Abstract In the fields of depth measurement and computer vision, generating sufficiently accurate disparity maps through various stereo matching algorithms is an important foundation. The SGM semi-global matching algorithm is one of the best stereo matching algorithms, But its filling accuracy is poor in the invalid parallax area. Therefore, on the original basis, an improved algorithm of SGM stereo matching disparity filling based on edge detection is proposed. The experiment uses a standard data set to verify the algorithm. The results show that compared with the original SGM stereo matching algorithm, the paper algorithm can achieve better disparity estimation results in the invalid disparity area.
An Example of Synthesis on the Neural Networks for the Linear Objects with Multiple Inputs

The method of synthesis of the neural controllers using, that is based on the reinforcement learning approaches, is proposed in the paper. The proposed method should be applied to multichannel objects with several control inputs and a single output. Such objects are complex objects from the controller synthesis point of view and the application of the known classical methods particularly of the polynomial synthesis method might not give results. On the other hand, the synthesis of controllers with neural networks allows to regulate such types of objects. An example of the application of the proposed method to the object with two inputs and one output is shown in the paper.
Workshop on Recommenders in Tourism (RecTour)

The Workshop on Recommenders in Tourism (RecTour) 2021, which is held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.
Targeted BERT Pre-training and Fine-Tuning Approach for Entity Relation Extraction

Entity relation extraction (ERE) is an important task in the field of information extraction. With the wide application of pre-training language model (PLM) in natural language processing (NLP), using PLM has become a brand new research direction of ERE. In this paper, BERT is used to extracting entity-relations, and a separated pipeline architecture is proposed. ERE was decomposed into entity-relation classification sub-task and entity-pair annotation sub-task. Both sub-tasks conduct the pre-training and fine-tuning independently. Combining dynamic and static masking, new Verb-MLM and Entity-MLM BERT pre-training tasks were put forward to enhance the correlation between BERT pre-training and Targeted NLP downstream task-ERE. Inter-layer sharing attention mechanism was added to the model, sharing the attention parameters according to the similarity of the attention matrix. Contrast experiment on the SemEavl 2010 Task8 dataset demonstrates that the new MLM task and inter-layer sharing attention mechanism improve the performance of BERT on the entity relation extraction effectively.
Visual analytics for nonlinear programming in robot motion planning

Abstract Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming. Graphic abstract
Design of Fault-Tolerant Quantum Ripple Carry Adder

The performance of existing technology for implementing digital circuits based on irreversible logic is closely reaching its limit. Conventional logic circuits result in heat dissipation whenever there is an information loss. Hence reversible logic is attaining major consideration in the perspective of evolving technologies such as quantum computing as there isno information loss and there is a unique mapping between the input vectors and output vectors. A reversible gate is supposed to be parity preserving if there existsimilarity between input parityand the outputparity.To construct reliable fault-tolerant reversible systems elements should be of fault-tolerant reversiblestructures.Adder circuits are studied extensively since their performance can directly affect the entire digital system. This paper presents a Toffoli gate (TG) based fulladderwhich serves as a fundamental component in constructing a ripple carry adder. Further a parity preserving Toffoligate based full adder is presented which was utilized to develop a fault-tolerant Ripple Carry Adder without compromising on its performance.The proposed designs have been simulated using IBM Quantum Simulator and have been verified for their correctness. The proposed adders will serve as vital design components in developing fault-tolerant reversible arithmetic and logic unit.
Flexible and Explainable Solutions for Multi-Agent Path Finding Problems

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths.The real-world applications of MAPF require flexibility (e.g., solving variations of MAPF) as well as explainability.In this study, both of these challenges are addressed and some flexible and explainable solutions for MAPF and its variants are introduced.
Multinomial-Based Decision Synthesis of ML Classification Outputs

With the explosion of interest in machine learning (ML)-based classification algorithms applied to streaming data sources, there is a decided benefit to the rapid decisions that a neural network (NN) can provide. However, there is also a desire to integrate successive real-time decisions on streaming data, which likely have temporal correlations, into an overall higher confidence result. This paper offers a predictive approach to aggregating the results of discrete classification outputs based upon that duration of temporal correlation, with specific examples for both an image processing (facial mask recognition) application and two radio frequency (RF) ML applications (specific emitter identification and automatic modulation classification). The decision aggregation technique employs a multinomial distribution representation of conditional decision probabilities, drawn from the confusion matrices of the classification problems, to show that an ML classifier possessing even a marginal ability to improve upon random guessing has the potential to drastically improve overall decision accuracy when operating on large continuous streams of data, boosting confidence in the resulting decision.