Reviewerly/exHarmony huggingface dataset:
The importance of operator knowledge in evaluating virtual reality cue fidelity

Research, development, testing, and operator training of large agricultural harvesting equipment has become increasingly expensive and complex. Operational simulators can offset these costs, but it is critical to evaluate the fidelity of the simulator experience to ensure that it will lead to operator behaviors that match those in the real world. This research describes a validation process for new visual cues within simulators and focuses on the presentation and fidelity of cues in a combine harvest simulator. The fully functional immersive combine simulator in this work provides a mixed-reality interface that combines a physically accurate operator interface with displays of virtual reality (VR) combine header and grain. Grain combine operators participated to assess how well they perceived the VR cues as representing actual field and crop activity. Results showed that operators successfully identified 85% of the visual cues in the combine simulator and recognized when these cues indicated machine settings adjustment needs but that operators' ability to choose the correct action to take correlated with their knowledge of the combine. Results also showed wide variation in the number of the grain combine’s head reel adjustments made by operators, likely reflecting strong individual preferences or habits around reel adjustment. Operators' number of adjustment interactions with the reel was also significantly correlated with the number of correct actions chosen, suggesting that careful attention to the reel is related to good farming performance. This research demonstrates the importance of taking operator knowledge and preferences into account when designing new agricultural products and offers a systematic method of validating cues within an agricultural simulator.
Cross-modality motion parameterization for fine-grained video prediction

While predicting video content is challenging given the huge unconstrained searching space, this work explores cross-modality constraints to safeguard the video generation process and seeks improved content prediction. By observing the underlying correspondence between the sound and the object movement, we propose a novel cross-modality video generation network. Via adversarial training, this network directly links sound with the movement parameters of the operated object and automatically outputs corresponding object motion according to the rhythm of the given audio signal. We experiment on both rigid object and non-rigid object motion prediction tasks and show that our method significantly reduces motion uncertainty for the generated video content, with the guidance of the associated audio information.
SGSF

With the rapid development of Artificial Intelligence (AI) in the Internet of things (IoT), information fusion becomes one of the key technology. Serial fusion plays an important role in information fusion, which concatenates the features obtained from different sources to get a super-feature with higher discrimination. However, the performance of the serial fusion is usually affected by the poor quality of the indistinguishable real-world datasets, especially the problem of noise and imbalance. The existing noise data removal methods may destroy the data distribution, and the existing over-sampling methods may create noise samples. They may affect the result of feature reduction and further reduce the performance of the classification. To address these big practical challenges, a novel Small-Groups-based Serial Fusion method (SGSF) is proposed. SGSF consists of two main parts: a Small-Groups-based Noise Removal method (SGNR) and a small-groups-based Positive Region Oriented Oversampling method (PROO). Both of these two methods are based on the Neighborhood Rough Set (NRS) theory. SGNR identifies and removes low-density noise samples whilst keeps the high-density outlier samples, targeting to enlarge the limited NRS positive region and preserve the data distribution. PROO generates new samples towards the NRS positive region so that it can balance the dataset and increase the number of samples with more explicit features. Intensive experiments are carried out to verify the performance of SGSF based on 3 publicly available datasets and 6 self-collected medical datasets. The results demonstrate that SGSF increases the classification accuracy from existing 82.2% to 94.3%.
Enhancing Predictive Power of Cluster-Boosted Regression With Text-Based Indexing

Clustering prior to regression analysis improves the accuracy of prediction in clinical decision making. However, most previously described methods focused on numerical data only. This paper investigated how well textual features can improve the accuracy of regression predictions. Preliminary diagnosis, diagnosis summary, and drug names used in prescriptions as provided in the MIMIC II dataset were used to derive textual features. We proposed the bag-of-entities indexing method, which relies on named entity recognition, a machine learning technique used for locating and identifying words into predefined classes. The proposed technique captured meaningful phrases from texts in health records and represented them in numerical vector format. Dimensionality of the data space was reduced using principal component analysis. The additional well-tuned textual features were then combined with existing numerical features in using cluster-boosted regression to predict patient mortality in ICU. The experimental results showed prediction improvement obtained from textual features over the use of numerical features only. We found that using the proposed indexing method outperformed traditional word-vector representation approaches (bag-of-words and bag-of-bigrams) as well as a state-of-the-art approach (Doc2vec) in terms of resulting accuracy in predicting death status. Moreover, instead of directly interpreting, the identifiable individual features were grouped into types and summarized. The summarized de-identified data of textual features handled by the proposed framework can support predictive classification while also reducing privacy concerns. Grouping of similar patients based on their electronic health records also benefits physicians through the improved differential diagnosis and effective treatment planning.
Combining Outlier Detection and Reconstruction Error Minimization for Label Noise Reduction

Label noise is a common phenomenon when labeling a large-scale dataset for supervised learning. Outlier detection is a recently proposed method to handle this issue by treating the outliers of each class as potential data points with label noise and remove them before training. However, this approach could lead to high false positive rate and hurt the performance. In this paper, we propose a novel and effective method to deal with this issue by combining the strength of outlier detection and reconstruction error minimization (REM). The main idea is add a second verification step (i.e., REM) to the outputs of outlier detection so as to reduce the risk of discarding those points which do not fit the underlying data distribution well but with correct label. Particularly, we first find the outliers in each class by a robust deep autoencoders-based outlier detector, through which not only did we get candidate mislabeled data but also a group of well-learned deep autoencoders. Then a reconstruction error minimization based approach is applied to these outliers to further filter and relabel the mislabeled data. The experimental results on MNIST dataset show that the proposed method could significantly reduce the false positive rate of outlier detection and improve the performance of both data cleaning and classification in the presence of label noise.
AntRS: Recommending Lists Through a Multi-objective Ant Colony System

When people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms.
A spatial phase modulation laser interferometer

We propose and realize a spatial phase modulation laser interferometer (SPMLI), which mainly consists of a laser source, an optical fiber, a transparent plate and a linear charge coupled device (CCD). The reflected beams from the upper and lower surfaces of the transparent plate produce two-beam interference. The generated spatial phase modulation interference image is taken by the CCD. The spatial frequency of the image is proportional to the wave number of the laser light and the thickness of the transparent plate. Experimental results show that the wavelength and thickness can be measured in picometer-level and nanometer-level resolution, respectively. Compared with other interferometers, the proposed interferometer has the advantages of simple structure, low cost and no need for mechanical scanning components.
Design and Analysis of LMMSE Filter for MR Image Data

This paper deals with the method of removing the noise in MRI images- During data capture and transmission, the data is disturbed by a noise component that cannot be completely reproduced exclude. Noise is defined in signal theory as additive information that was added to the original purchasing equipment or during the transport. Study of noise models is a very important part of image processing. On the images are applied noise generators, and design LMMSE filter, which is used for shaded images. They were tested salt and pepper noise, Gaussian noise and Rican noise. For each noise, more than one level of this noise. Another task was objective and subjective evaluation of the success of the filtration.
Evaluation Model for Big Data Integration Tools

Given the growing demand and need by enterprises for data and information to positively support the decision-making process, there is no doubt about the importance of selecting the correct and appropriate integration tool for the different types of business. For this reason, the essential objective of this study is to create a model that will serve as a basis to evaluate the different alternatives and solutions that exist in the market able to overcome the big data integration challenges. The evaluation process of data integration product begins with the definition and prioritisation of critical requirements and criteria. In this evaluation model, the characteristics evaluated are categorised into three main groups: ease of integration and implementation, quality of service and support, and costs. After identifying the essential criteria and characteristics, it is necessary to determine the weights that these criteria should have in the evaluation. Then, it needs to verify which solutions existing in the market best fit the needs of the business and can satisfy them more effectively. And lastly, compare those solutions adopting this framework. It is essential to carry out a weighted evaluation, based on well-defined criteria like ease of use, quality of technical support, data privacy and security. This process is fundamental to verify if the solution offers what the organization needs if it meets the business requirements and their integration needs.
Attendance and Security System Based on Building Video Surveillance

The attendance system plays a very important role in the modern enterprise’s operation, and the security of the building has always been a matter of concern to the people. Based on networked surveillance video, this paper integrates the attendance and security functions and fuses video image processing, deep learning, and face recognition to design an intelligent attendance and security system. We propose a sliding average method to identify persons’ identities. The experimental results verify the effectiveness of our method. The false reject rate (FRR) in our system reaches 0.51%, the false accept rate (FAR) reaches 2.52%, and the correct identification rate reaches 98.85%. The system is applied to some video surveillance areas, with advantages of nonintrusive, passive attendance and multiple persons’ attendance at the same time.
Artificial Fish Swarm-Inspired Whale Optimization Algorithm for Solving Multimodal Benchmark Functions

Multimodal benchmark function optimization has gained a growing interest exclusively in the evolutionary computation research field which involves achieving all or most of the multiple solutions contrasting a single best solution. A large number of real-world optimization problems can be considered as multimodal function optimization. Recently introduced Whale Optimization Algorithm (WOA) algorithm is inspired by the hunting behavior of humpback whales. The performance of WOA is very promising but the robustness and convergence need further improvement. In this paper, ‘step equation’ of Artificial Fish Swarm Algorithm (AFSA) was incorporated to enhance the robustness and convergence of the original WOA considering five multimodal test functions (F1–F5) for global numerical optimization. The proposed variant of WOA showed improved performances compared to original WOA in terms of average best fitness, robustness and convergence.
Research on the Cultivation of English Translation Ability of Undergraduates in Smart Learning Environment

Nowadays, college students have grown up in the information age. How to effectively use advanced information technology to improve college English teaching, and how to translate Internet terminals into powerful tools for English teaching have become an important topic in college English education. On the basis of discussing the superiority of smart learning environment in college English teaching, this paper uses teaching practice and elaborates the concrete class design in the context of smart class. Although there are difficulties in the building and application of smart class platform, the practice of smart class helps to change the teaching idea and reform teaching methods, improve students’ learning interest, and solve the problems existing in current teaching. Eventually, it will provide a new direction and thought for the improvement of teaching effect of college English.
Self-paced Robust Deep Face Recognition with Label Noise

Deep face recognition has achieved rapid development but still suffers from occlusions, illumination and pose variations, especially for face identification. The success of deep learning models in face recognition lies in large-scale high quality face data with accurate labels. However, in real-world applications, the collected data may be mixed with severe label noise, which significantly degrades the generalization ability of deep models. To alleviate the impact of label noise on face recognition, inspired by curriculum learning, we propose a self-paced deep learning model (SPDL) by introducing a negative $$l_1$$-norm regularizer for face recognition with label noise. During training, SPDL automatically evaluates the cleanness of samples in each batch and chooses cleaner samples for training while abandons the noisy samples. To demonstrate the effectiveness of SPDL, we use deep convolution neural network architectures for the task of robust face recognition. Experimental results show that our SPDL achieves superior performance on LFW, MegaFace and YTF when there are different levels of label noise.
The NICE Challenge Project: Providing Workforce Experience Before the Workforce

This article explains how the National Initiative for Cybersecurity Education (NICE) Challenge Project (NCP) provides students with the opportunity to apply their knowledge, skills, and abilities to a realistic hands-on scenario similar to what would be expected of them in actual cybersecurity work roles. It also describes use cases, lessons learned, and the future focus of the project.
Implementation of a Dance Teaching Live Broadcasting System Based on the Distance Education Platform

With the rapid development of the modern information technologies, to promote the education informatization and share the educational and teaching resources, the live classroom in the distance education emerges as the times require. The live classroom expands the space-time boundaries in our education and teaching, and changes the traditional teaching methods. It is a new model of the distance education. In order to recognize and understand the impact of the live teaching on the dance teaching and promote the development of the live teaching in the dance education, this paper, from the perspective of the distance education, uses the theories and methods of the distance education to analyze the characteristics of the live teaching, which is a feasible way.
The negativity of partial transpose versus the negativity of Wigner function

We investigate the multipartite entanglement in the phase space using the negativity of Wigner function (NWF) and in the Hilbert space using the negativity of partial transpose (NPT). We analyze comparatively these quantities and the different types of entanglements that are present in two major classes — GHZ and [Formula: see text] — made of coherent states. We show that the negativity of Wigner function can be used as a good measure of genuine entanglement in multipartite systems. However, the negativity of partial transpose is a good quantifier for only the bipartite entanglement in tripartite systems.
Affordance Theory Applied to Agile Development: A Case Study of LC2EVO

The Theory of Affordances considers the interaction between the people and their environment based on their mental models of the environment. This study analyses a software development process on the theory of affordances. The LC2EVO software has been developed in Italy. There were two onsite team groups located five kilometers apart in separate buildings who were using the agile framework in their development. The product was delivered successfully under this framework. Team members in the case study were allowed inside of their environment to arrange their workspaces. By utilizing the works of Gibson and Norman on affordances, we found that arrangement of the workspace broadly met with both ideas of affordances. The examined case study demonstrates that a collaborative process on the design of a work space inside of an agile methodology increases opportunities for collaboration and communication among team members. The design of the work space affect the application of the agile methodology. Worker’s affordances with the environment, both through their perception and interaction is fundamental to consider for the optimal and productive environment setup.
Cascaded Iterative Training Model and Parallel Multi-Classifiers for Visual Object Tracking

Because of the superiority in distinguishing the target from the background by learning mechanism, discriminative classifier attracts a great deal of attention in target detection and tracking systems. Generally, a discriminative classifier is learned from the initial image patch. However, the other algorithms, using only one image frame inhibit the performance of the classifier. A novel training strategy of cascade iteration, which is based on the multiple image frames, is used to initialize the discriminative classifier for the purpose of weakening the initialization limitation and improving the algorithms' adaptability and robustness. Furthermore, to cope with the changes in natural images, online learning is usually adopted to enhance the performance of classifiers, but it easily leads to tracking failures, especially in the case of full occlusion. In order to attenuate the influence of full occlusion, a multi-classifiers parallel tracking algorithm is proposed by designing the multi-classifiers and inducing extra constraints. The experiments are performed on the visual tracking benchmark, and the proposed algorithm is more robust while the target is fully occluded. The simulation results show that the proposed algorithm exhibits better performance compared to the state-of-the-art trackers, especially with KCF, TLD, and STRUCK.
The efficiency indicator for information visualization system of the Tokamak experimental device operating in repetitively pulsed mode

The multi-objective method evaluating the performance of the information visualization system of tokamak-type experimental device is presented. The method takes into account special features of the tokamak operation. The indicator includes not only evaluation of the space-time distribution of information in the shared display space of the information visualization system, but also relevance of information. The task of multiple criteria decision making is performed using Pareto optimization. This method is used to design the information visualization system of control console for the KTM tokamak.
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining

With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task. Although there are many available metrics that rank companies, there is an inherent need for a generalized metric that takes into account the different aspects that constitute employee opinions of the companies. In this work, we aim to overcome the aforementioned problem by generating aspect-sentiment based embedding for the companies by looking into reliable employee reviews of them. We created a comprehensive dataset of company reviews from the famous website Glassdoor.com and employed a novel ensemble approach to perform aspect-level sentiment analysis. Although a relevant amount of work has been done on reviews centered on subjects like movies, music, etc., this work is the first of its kind. We also provide several insights from the collated embeddings, thus helping users gain a better understanding of their options as well as select companies using customized preferences.
One Step at a Time

Big-step and small-step are two popular flavors of operational semantics. Big-step is often seen as a more natural transcription of informal descriptions, as well as being more convenient for some applications such as interpreter generation or optimization verification. Small-step allows reasoning about non-terminating computations, concurrency and interactions. It is also generally preferred for reasoning about type systems. Instead of having to manually specify equivalent semantics in both styles for different applications, it would be useful to choose one and derive the other in a systematic or, preferably, automatic way. Transformations of small-step semantics into big-step have been investigated in various forms by Danvy and others. However, it appears that a corresponding transformation from big-step to small-step semantics has not had the same attention. We present a fully automated transformation that maps big-step evaluators written in direct style to their small-step counterparts. Many of the steps in the transformation, which include CPS-conversion, defunctionalisation, and various continuation manipulations, mirror those used by Danvy and his co-authors. For many standard languages, including those with either call-by-value or call-by-need and those with state, the transformation produces small-step semantics that are close in style to handwritten ones. We evaluate the applicability and correctness of the approach on 20 languages with a range of features.