Reviewerly/exHarmony huggingface dataset:
Model-Based Clustering and Classification for Data Science: With Applications in R

In statistics, data are not regarded as just numbers, but realizations of random elements. Suppose we have data y∼P . Then data analysis is essentially the process of uncovering the data generating...
The 27th International Conference on Case‐Based Reasoning

The 27th International Conference on Case‐Based Reasoning was held September 8–12, 2019, in Otzenhausen, Germany. The theme of the conference was explainable artificial intelligence. The conference featured four invited talks, an invited panel, four workshops, a doctoral consortium, four technical paper sessions, and a poster and system demo session. This report summarizes conference highlights.
Measurement-Device-Independent Entanglement Witness of Tripartite Entangled States and Its Applications

Entanglement witness is of great importance in characterizing quantum systems. The imperfections in conventional entanglement witness schemes could lead to the misidentification of a separated state as an entangled state. Measurement-device-independent entanglement witness (MDIEW) has been proposed and demonstrated to resolve the imperfect measurement devices. So far, however, the MDIEW has been restricted to a two-party qubit entangled state. Here, for the first time, we demonstrate MDIEW for multipartite entangled states. We experimentally detect the genuine entanglement and the entanglement structure of a tripartite entangled state based on an eight-photon interferometry. Furthermore, with the verified multipartite entangled state, we demonstrate quantum randomness generation and open-destination quantum key distribution in an measurement-device-independent manner. Our research presents an important step toward building a robust and secure quantum network.
Toward Better Storylines with Sentence-Level Language Models

We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.
Trace-Relating Compiler Correctness and Secure Compilation

Abstract Compiler correctness is, in its simplest form, defined as the inclusion of the set of traces of the compiled program into the set of traces of the original program, which is equivalent to the preservation of all trace properties. Here traces collect, for instance, the externally observable events of each execution. This definition requires, however, the set of traces of the source and target languages to be exactly the same, which is not the case when the languages are far apart or when observations are fine-grained. To overcome this issue, we study a generalized compiler correctness definition, which uses source and target traces drawn from potentially different sets and connected by an arbitrary relation. We set out to understand what guarantees this generalized compiler correctness definition gives us when instantiated with a non-trivial relation on traces. When this trace relation is not equality, it is no longer possible to preserve the trace properties of the source program unchanged. Instead, we provide a generic characterization of the target trace property ensured by correctly compiling a program that satisfies a given source property, and dually, of the source trace property one is required to show in order to obtain a certain target property for the compiled code. We show that this view on compiler correctness can naturally account for undefined behavior, resource exhaustion, different source and target values, side-channels, and various abstraction mismatches. Finally, we show that the same generalization also applies to many secure compilation definitions, which characterize the protection of a compiled program against linked adversarial code.
Ensemble Genetic Programming

Ensemble learning is a powerful paradigm that has been used in the top state-of-the-art machine learning methods like Random Forests and XGBoost. Inspired by the success of such methods, we have developed a new Genetic Programming method called Ensemble GP. The evolutionary cycle of Ensemble GP follows the same steps as other Genetic Programming systems, but with differences in the population structure, fitness evaluation and genetic operators. We have tested this method on eight binary classification problems, achieving results significantly better than standard GP, with much smaller models. Although other methods like M3GP and XGBoost were the best overall, Ensemble GP was able to achieve exceptionally good generalization results on a particularly hard problem where none of the other methods was able to succeed.
FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques

While there exists a plethora of datasets on the Internet related to Food, Energy, and Water (FEW), there is a real lack of reliable methods and tools that can consume these resources. This hinders the development of novel decision-making applications utilizing knowledge graphs. In this paper, we introduce a novel software tool, called FoodKG, that enriches FEW knowledge graphs using advanced machine learning techniques. Our overarching goal is to improve decision-making, knowledge discovery, and provide improved search results for data scientists in the FEW domain. Given an input knowledge graph (constructed on raw FEW datasets), FoodKG enriches it with semantically related triples, relations, and images based on the original dataset terms and classes. FoodKG employs an existing graph embedding technique trained on a controlled vocabulary called AGROVOC, which is published by the Food and Agriculture Organization of the United Nations. AGROVOC includes terms and classes in the agriculture and food domains. As a result, FoodKG can enhance the knowledge graph with semantic similarity scores and relations between different classes, classify the existing entities, and allow FEW experts and researchers to use scientific terms for describing FEW concepts. The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset. We observed that this model outperformed its competitors based on the Spearman Correlation Coefficient score.
Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world.In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data.For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter.Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment.On the other hand, they have also been used to discover patterns between sentiments.Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts.The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them.To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign.The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case.Parallelisms can be established in these patterns that match perfectly with real life events.
EngMeta: metadata for computational engineering

Computational engineering generates knowledge through the analysis and interpretation of research data, which is produced by computer simulation. Supercomputers produce huge amounts of research data. To address a research question, a lot of simulations are run over a large parameter space. Therefore, handling this data and keeping an overview becomes a challenge. Data documentation is mostly handled by file and folder names in inflexible file systems, making it almost impossible for data to be findable, accessible, interopable and hence reusable. To enable and improve a structured documentation of research data from computational engineering, we developed EngMeta as a metadata model. We built this model by incorporating existing standards for general descriptive and technical information and adding metadata fields for disciplinespecific information like the components and parameters of the simulated target system and information about the research process like the used methods, software and computational environment. EngMeta functions, in practical use, as the descriptive core for an institutional repository. In order to reduce the burden of description on scientists, we have developed an approach for automatically extracting metadata information from the output and log files of computer simulations. Through a qualitative analysis, we show that EngMeta fulfills the criteria of a good metadata model. Through a quantitative survey, we can show that it meets the needs of engineering scientists.
SSD Object Detection Model Based on Multi-Frequency Feature Theory

In order to further improve the accuracy and real-time performance of the traditional Single Shot Multibox Detector (SSD) object detection model, an improved SSD multi-object detection model is proposed. Firstly, aiming at the defect of weak correlation between prediction object score and positioning accuracy in the traditional SSD model, the improved model enhanced the correlation between the two by adding Intersection Over Union(IoU) prediction loss branch. Secondly, in order to reduce the spatial redundancy of traditional SSD model, a multi-frequency feature component convolution module is designed, which greatly reduces the calculation overhead and hardware overhead of the traditional model. Finally, in order to accelerate the convergence speed of the improved model, the Adaptive and Momental Bound (AdaMod) optimizer is introduced to modify the adaptive learning rate of the improved model which is too large in the training process. Experimental results show that the improved model has stronger detection capabilities, better overall detection results, and improved detection accuracy and real-time detection.
TextGAIL: Generative Adversarial Imitation Learning for Text Generation

Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. As previous text GANs suffer from high variance of gradients, we apply contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task.
Complete calibration of a structured light stripe vision sensor through a single cylindrical target

In a complex industrial environment, it is not appropriate to use the traditional Zhang's calibration method to shoot multiple checkerboard images in different positions, and due to the contour characteristics of the cylindrical target, camera calibration and line-structured calibration can be simultaneously performed. Therefore, a complete calibration method for a line-structured light system based on a single cylindrical target is proposed. The calibration process is as follows: Placing the cylindrical target at least once and illuminate it with the light strip of the laser projector. The vision sensor captures the target image and then extracts the laser stripes and contour images of the cylindrical target. According to the contour characteristics of the cylindrical target and the definition of the collinear vector, the extracted contour image is used to solve the internal parameters of the camera. Because the line-structured light plane and cylindrical target intersection line in the axial of cylindrical is a standard circle, the equation of the light plane is established on the camera coordinate system. The effectiveness of the method is verified by simulation and physical experiments. The experimental results show that the RMS errors of the 30 distance is 0.07 mm. The proposed method does not need to rely on the constraints of multiple images and is simple to operate, meeting the requirements of industrial 3D measurement technology.
Systems-deposits-commodities-critical minerals table for the earth mapping resources initiative

To define and prioritize focus areas across the United States with resource potential for 35 critical minerals in a few years' time, the U.S Geological Survey Earth Mapping Resources Initiative (Earth MRI) required an efficient approach to streamline workflow.A mineral systems approach based on current understanding of how ore deposits that contain critical minerals form and relate to broader geologic frameworks and the tectonic history of the Earth was used to satisfy this Earth MRI need.This report describes the rationale for, and structure of, a table developed for Earth MRI that relates critical minerals and principal commodities to the deposit types and mineral systems in which they are concentrated.The hierarchical relationship between systems, deposits, commodities, and critical minerals makes it possible to define and prioritize each system-based focus area once for all of the critical minerals that it may contain.This approach is advantageous because mineral systems are much larger than individual ore deposits and they generally have geologic features that can be "imaged" by the topographic, geologic, geochemical, and geophysical mapping techniques deployed by Earth MRI.
Data imputation and compression for Parkinson's disease clinical questionnaires

Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of them having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performance of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches.
Realistic training in VR using physical manipulation

We propose an interaction method for mixed reality with a direct application on training applications. It is based on the combination of two key ideas: providing real user embodiment within virtual spaces by segmenting user real hands and allowing the manipulation of physical objects, which may preserve or change their real appearance, in the immersive scenario. We deployed a functional prototype of this concept using Unity for the base scenario, a color-based algorithm for object segmentation and Aruco library for object tracking. Then, to take advantage of gamification, we built a five-minute escape room game designed to validate all components: real embodiment, real objects, augmented real objects and control devices. We carried out a thorough evaluation of our system with 53 users and a fair comparison scheme: each user had to play the escape room twice, once with real objects and hands, and once with purely virtual objects and avatars. After each run, users had to answer a survey derived from standard questionnaires measuring presence, embodiment and quality of experience. Some quantitative data related to performance were also extracted from the game runs. Results validate our hypothesis that this approach significantly improves key factors such as presence or embodiment with respect to the counterpart virtual solution. We foresee our solution to provide a significant advance in the implementation of virtual training applications.
Detecting Fake Accounts on Twitter Social Network Using Multi-Objective Hybrid Feature Selection Approach

The frequency of fake accounts or social bots is considered as one of serious challenges of online social networks, which are controlled by automatic operators and often used for malicious purposes.The researchers have performed many efforts to identify these entities in online social networks, which Machine Learning classifier technique using a distinctive feature set is the most common one and feature selection is the principal process of such feature-based classifiers.The present study was carried out the fake accounts detection by using a multi-objective hybrid feature selection approach that helps the feature set selection with optimal classification performance.First, the candidate feature set was identified based on the highest relation to the target class and the least redundancy among the features by the Minimum Redundancy -Maximum Relevance algorithm (mRMR).Then, the stable feature set with the minimum number of features, which can achieve optimal performance, is selected as the final feature set for the detection operations.The proposed approach is tested on two datasets from Twitter's social network and the results were compared to the results of efficient existing methods.According to the results, the performance of the proposed classifier approach is higher compared to existing methods.
Memory-aware gated factorization machine for top-N recommendation

Factorization machine (FM) has recently become one of the most popular methods in collaborative filtering due to its flexibility of incorporating auxiliary information, e.g., user demographics and item genres. However, standard FM method and its deep variants (e.g., NFM and DeepFM) suffer from two key issues: (1) failing to effectively leverage user historical records, i.e., all historical records are treated equally without considering the relevance to the targeted user–item pair and (2) failing to adaptively weigh the importance of auxiliary information, i.e., auxiliary information may have negative effects on the accuracy in certain cases but existing methods cannot effectively detect and eliminate the negative effects. To this end, this paper proposes a memory-aware gated factorization machine (MAGFM), which improves the FM method by introducing two new components: (1) an external user memory matrix is introduced to each user, which can enrich the representation capacity by leveraging user historical items and the auxiliary information associated with the historical items and (2) gated filtering units are applied on top of the embedding of user/item auxiliary information, which can adaptively filter out the features with negative effects to achieve higher accuracy. Experimental studies on real-world datasets demonstrate that MAGFM can substantially outperform FM, NFM and DeepFM methods by 0.31% – 12.77% relatively in top-N recommendation.
The blended learning with Whatsapp media on Mathematics creative thinking skills and math anxiety

The blended learning was a learning model that combines offline and online learning. There are two types of blended learning models used in this research, namely the flipped classroom model and the station rotation model. In these models, the teacher would use WhatsApp as a media for online learning. The purpose of this research was to determine the effect of blended learning models on mathematical creative thinking skills and math anxiety of public junior high school students in Sukoharjo Regency, Central Java Province. The research method used was quasi-experimental by sampling using stratified cluster random sampling techniques. There were three schools selected as research samples, namely Mojolaban 1 Public Junior High School, Mojolaban 2 Junior High School, and Grogol 3 Public Junior High School. The data collection used a written test and questionnaires methods which were carried out after the treatment was given. The data analysis technique used a one-way multivariate analysis of variance. This research shows that the blended learning models were better than the direct learning model on mathematical creative thinking skills, but judging from the magnitude of math anxiety, the direct learning model was better than the station rotation learning model.
Assessment of the GOES-16 Clear Sky Mask Product over the Contiguous USA Using CALIPSO Retrievals

Cloud initialization is a challenge in numerical weather prediction. Probably the most relevant observations for this task come from geostationary satellites. These satellites provide the cloud mask with high spatio-temporal resolution and low latencies. The low latency is an attractive option for nowcasting systems such as the solar irradiance nowcasting model MAD-WRF. In this study we examine the potential of using the cloud mask from the GOES-16 satellite over the contiguous U.S. for this particular application. With this aim, the GOES-16 cloud mask product is compared against CALIPSO retrievals during a two year period. Both the GOES-16 data and the CALIPSO retrievals are interpolated to a grid that covers the contiguous U.S. at 9 km of horizontal grid spacing that is being used in MAD-WRF nowcasts. Results indicate a probability of detection, or accuracy, of all sky conditions of 86.0%. However, the accuracy is higher for cloud detections, 90.9% than for clear sky detections 74.8%. The lower performance of clear sky retrievals is a result of missdetections during daytime. This is especially clear for summer, and for regions to the north of parallel 36 during winter. However, regions to the south of parallel 36 show acceptable performance during both daytime and nighttime. It is over these regions wherein the cloud mask product should show its largest potential to enhance the cloud initialization in the MAD-WRF model.