Reviewerly/exHarmony huggingface dataset:
Intelligent agent based low level control of complex robotic systems

The use of intelligent agents, trained with reinforcement learning methods for control of complex mechanical systems, like humanoid robots has the potential to revolutionize the way we think about control problems. This way of learning is very similar to how we humans learn most of the things in our early age, thus proving really promising if we want to make robots able to learn tasks that require some form of intelligence. Throughout the research presented in this paper, a deep neural network based intelligent agent, with Actor-Critic architecture was trained with the Deep Deterministic Policy Gradient algorithm for the purpose of controlling a custom designed humanoid robot. For the training of the agent a simulation model of the physical robot is developed and integrated into a customizable simulated environment. The idea of low, actuator level control of complex systems by neural networks formulates the problem into a more abstract form while keeping the full control of the system without having to deal with the actual level of complexity. This can be further enhanced by expanding the abstraction from the software level to include some part of the hardware as well.
Bridging the scales: model-driven integrative interpretation of archaeological and geophysical data.

Geophysical prospecting in wetland environments is continuously under debate because of the critical unstable environment, but at the same time the richness in cultural heritage. This thesis is part of the interdisciplinary project SFB 1266 of the CRC ‘Scales of transformation’ at Kiel University, which focuses on reconstructing the prehistoric landscape transformation during human occupation. It aims to examine the potential for conventional geophysical survey methods (resistivity, ground penetrating radar and seismics) as site prospection and landscape investigation tools in peatland environments. Two case studies are presented in which multi-geophysical investigations have been performed and validated by archaeological excavations and stratigraphic information.Kettle holes are common ice decay features in formerly glacial landscapes like those in Southern Scandinavia and Northern Germany. In 2017 the Horsens Museum (Denmark) carried out a rescue excavation at Tyrsted which revealed Late Palaeolithic flint of the Bromme type (12.000-11.000 BCE) and worked reindeer antlers. Nowadays, the organic artefact inventory from the Bromme culture is largely unknown due to the scarcity of organic remains and the general lack of proper stratigraphic observations. The available dates concentrate in the (late) Allerod and early Younger Dryas chronozones, but unfortunately most of these dating are tentative only. Therefore, this connection in a Late Glacial horizon at Tyrsted is unique and it has the potential to provide new information in the current archaeological debate. The aim is to investigate a small kettle hole (site 8) using ground penetrating radar (GPR), electromagnetic induction (EMI) and electrical resistivity tomography (ERT) to estimate the extension of the feature. Shear wave reflection and refraction seismics (SH Seismics) were able to detect the whole shape and the bottom sediment of the former lake. Furthermore a seismic event is visible which can be associated to the transition between the Allerod and Younger Dryas sediment making the detection of the Bromme horizon possible. After the non invasive investigation, a location for an open excavation has been chosen in a way to groundtruth the geophysical results allowing the direct comparison with the stratigraphy. These results allow the archaeologists to identify key excavation areas focused on the investigation of the Allerod and Younger Dryas layers in a way to improve the dating information about the Bromme horizon collected so far. At the Mesolithic hunter-gatherers site of Duvensee (10000-6500 BCE) a multi methodological investigation has been carried out too, aiming to reconstruct the ancient landscape during human occupation. GPR, ERT and SH-Seismic have been performed together with corings, DP-EC logs and soil analyses as well for ground-truthing. It turned out that each method is able to distinguish between sediments that differ in grain size, in particular between peat, lake sediment (gyttjas and clay) and basal glacial sand deposits. GPR…
Parallel particle swarm optimization algorithm based on spatial equal-scale segmentation and hybrid strategy

According to the characteristics of classical PSO algorithm(s), this paper uses the spatial equal-scale segmentation method (PESS) to effectively reduce the data dimension because of the low efficiency of PSO algorithm caused by big data and large population size. Diversity selection and global optimization, according to the law of evolution selection, the local optimization is focused on the two extremes of minimum value and maximal value, thus reducing the number of calculations of the fitness function of the algorithm. On this basis, we introduce a time-hop and elite Gaussian mixture strategy that can overcome the particle plunging into local optimum, while improving the efficiency of the algorithm, the generalization ability of the algorithm is taken into account. In order to prove the performance of FSPSO, we chose PSO and the self-organizing migration algorithm (SOMA) evolved from PSO that were tested in 30, 60 and 100 comparison tests on 5 different test function set with different degrees of complexity. The experimental results show that we proposed FSPSO algorithm has good effectiveness, robustness, complexity and universality in solving global optimization problems.
Un modelo preliminar para la recomendación de ítems basada en resúmenes textuales [Not available in English]

No English translation of this document was provided by the author or conference organizers.
Blockchain: Structure, Uses, and Applications in IoT

With the continuous increase in the applications of blockchain, the research and academic circles are constantly exploring and providing guidelines for the use of blockchain in new directions. The use of this technology has given new dimension in the Internet of Things (IoT) systems. In this chapter, the authors are providing an outline and structure of blockchain and its various applications in IoT. Blockchain technology helps in building trustless and efficient secure environment in IoT. Therefore, the authors think it is necessary to aim the basic shape of this technology and explain its use and applications in IoT in a concise and comprehensive way. This chapter starts with introduction of blockchain and IoT supported by its working in various applications of IoT. The authors believe that this chapter will provide a basic idea to users in understanding blockchain technology in IoT applications.
Performance Analysis of MCOD Algorithm With Varying Parameters

Outlier detection has become one of the prominent and most needed technologies these days. Outliers can be anything in our daily life like credit card fraud, intrusion in a network, aberrant condition detection in condition monitoring data. There are numerous methodologies to detect outliers. In the past few years many tools have come up in the outlier detection in data streams. In this chapter, the authors discuss the tool MOA (massive online analysis) to detect anomalies and the best performing algorithm amongst the prescribed algorithms of MOA. The authors elaborately discuss that MCOD (micro-cluster-based algorithm) is one of the best in the prescribed algorithms of the MOA (massive online analysis) tool which outperforms all other algorithms. In this paper, the authors will deeply discuss the performance of MCOD algorithm. The authors will also discuss which factor of MCOD separates its performance from others and also what the different parameters that influence the performance of MCOD are.
WaveAR: A Real-Time Sensor-Based Augmented Reality Implementation for Operating Deflection Shapes

We present WaveAR, the first sensor-based augmented reality system for structural dynamic measurements of operating deflection shapes. WaveAR offers a significant simplification of the configuration effort of vibration sensor measurements by an automatically sensor tracking coupled by an efficient 3D scan generation of the structure. AR markers are used for sensor tracking which are placed on the sensor. This allows the determination of the sensor pose relative to the scanned structure. For this purpose the vibration sensors must be covered with AR markers. This allows a universal easy application also for existing sensor equipment. For 3D scanning and sensor tracking, a low-cost stereo depth camera is used. Because the AR application should be independent from data acquisition hardware, we have implemented a universal HTTP Rest-API interface for data acquisition connection. Through this, it is also supported to use wireless sensors. Based on this configuration, a real-time visualization in the form of an augmented reality application of the operating deflection shapes can be performed under real operating conditions. This enables the user to view the measurement from different perspectives by moving the camera around the object to be examined.
Data Mining Techniques for Breast Cancer Detection: A Survey

Breast Cancer impacts almost 2.1 million women and it accounts for the death of 15% of women worldwide. Though there is no permanent solution to this disease, diagnosing it at an early stage can change the outcome in a very significant way. It can greatly reduce the risk of spreading the tumour to other parts of the Breast. In the past, Machine learning tools has proved to be an eminent way for diagnosing Breast Cancer. Supervised techniques have been used for classification. This survey paper analyses various papers based on the concept of Decision Tree algorithms to diagnose breast cancer. The studies show that the results can also be improvised further with the Random Forest algorithm which is based on a supervised learning model. Random Forest works on an ensemble learning algorithm called Bootstrap Aggregation or Bagging for Classification and Regression.
Night Vision Based Optimum Robot Path Planning in Rescue Operations

This paper presents a novel approach for optimum shortest path planning at night for a robot based rescue operations. Once the obstacle is identified, the next step is to find the position of the obstacle (even moving), identify the optimum robot path for navigation. Next,applying the Blob analysis,to find the position of the obstacle. Later, by using Line algorithm, lines are drawn from source to destination position of the robot.The robotâ€™s optimum shortest path is computed by adopting obstacle slope line detecting technique. Hence,the robot takes an optimum path for its navigation.
Augmenting Discriminative Correlation Filters with Stereo Blob Tracking for Long-Term Tracking of Underwater Animals

This paper presents a vision-based model-free longterm tracking algorithm to be used on-board autonomous underwater vehicles (AUVs) for long duration marine animal observation missions. During underwater tracking missions, drifting and losing track of targets after they leave the field of view are two major problems with state-of-the-art tracking algorithms. To achieve the long-term tracking goal, the proposed method gained drift resistance and target re-capturing ability by combining the merits of two mature short-term trackers: stereo blob tracking and discriminative correlation filter (DCF). In our approach, stereo blob tracking acts as complementary supervision to correct drift and to guide DCF to learn target appearances online before any tracking interruptions. The target information learned is then used to help re-capture the target after a tracking failure. In our experiments on field data, compared to running DCF alone, running the proposed augmented tracker increased average bounding box accuracy by 45% and eliminated drift-caused tracking failures. Our tracking algorithm also achieved 86% target re-capturing success.
Solving the two-way Number-Partitioning Problem using a Spatial-Photonic Ising Machine

We demonstrate an adiabatic evolution scheme to evolve from the ground state of a known Hamiltonian to a Mattis-spin glass Hamiltonian, which corresponds to solving a two-way Number-Partitioning Problem (NPP), using a Spatial-Photonic Ising Machine (SPIM) with a magnified readout plane.
Improvement on Attribute Weighting in Attribute Coordinate Comprehensive Evaluation Method

Attribute coordinate comprehensive evaluation method is essentially a qualitative evaluation method. Its feature lies in that on the same total score plane, on the basis of considering the comprehensive scores of all attributes, it focuses on considering the weight of certain attributes. The weight of an attribute is obtained by the evaluator's ratings, i.e. several samples are randomly selected from the hyperplane with the same total score for the evaluator to rate. However, the samples can be randomly selected and may not reflect all the characteristics of the entire attribute system, thus the ratings cannot reflect the evaluator’s preference well or the comprehensive factors of the attributes, further resulting in unreasonable evaluation results. This paper proposes a new attribute weighting method to fundamentally address this problem. Simulation experiments also verify the effectiveness of this method.
Synthesis of Adequate Mathematical Descriptions of Dynamic Systems for the Purposes of Forecasting, Control and Diagnostics

Abstract The article proposes to use adequate mathematical descriptions of dynamical systems which in some cases allow making a reasonable forecast of the behaviour of real processes, control of motion and diagnostics in deterministically statement. Two criteria for the adequacy are used (quantitative and qualitative types). The possibilities of adequate mathematical descriptions are investigated if both criteria are met. Several algorithms of synthesis of such descriptions are proposed.
Rethinking the Information Inside Documents for Sentiment Classification

Document-level sentiment classification aims to predict the sentiment rating of a particular document. Most of the methods take word embeddings as the inputs of a neural network. However, most existing methods fail to account for the fact that a specific word usually contains a certain amount of redundant information, and generally different words contain different amounts of redundancy. Such ambiguous word representation will cause models to misunderstand a text, and thus to incorrectly predict the sentiment, meanwhile simply treating words with different amounts of redundancy equally is not appropriate. Moreover, these methods take the user ratings as the training target, which leads to the fact that the information selected by the model is usually limited to the rating itself and there is no obvious sentiment tendency. Accordingly, we propose a Rethinking mechanism (R-TM) to rethink the information inside documents. More specifically, R-TM filters out redundancy contained in different words from different levels, and selects information from positive and negative two different perspectives. Our experimental results demonstrate that the proposed mechanisms can achieve consistent improvements compared to state-of-the-art methods.
Multiple Fisheye Camera Calibration and Stereo Measurement Methods for Uniform Distance Errors throughout Imaging Ranges

This paper proposes calibration and stereo measurement methods that enable accurate distance and uniform distribution of the distance error throughout imaging ranges. In stereo measurement using two fisheye cameras, the distance error varies greatly depending on the measurement direction. To reduce the distance error, the proposed method introduces an effectual baseline weight into the stereo measurement using three or more fisheye cameras and their calibration. Accurate distance is obtained because this effectual baseline weight is the optimum weight in the maximum likelihood estimation. Experimental results show that the proposed methods can obtain an accurate distance with a 94% reduction in error and make the distribution of the distance error uniform.
De Finetti Theorems for Quantum Conditional Probability Distributions with Symmetry

Abstract The aim of device-independent quantum key distribution (DIQKD) is to study protocols that allow the generation of a secret shared key between two parties under minimal assumptions on the devices that produce the key. These devices are merely modeled as black boxes and mathematically described as conditional probability distributions. A major obstacle in the analysis of DIQKD protocols is the huge space of possible black box behaviors. De Finetti theorems can help to overcome this problem by reducing the analysis to black boxes that have an iid structure. Here we show two new de Finetti theorems that relate conditional probability distributions in the quantum set to de Finetti distributions (convex combinations of iid distributions) that are themselves in the quantum set. We also show how one of these de Finetti theorems can be used to enforce some restrictions onto the attacker of a DIQKD protocol. Finally we observe that some desirable strengthenings of this restriction, for instance to collective attacks only, are not straightforwardly possible.
Action Relational Graph for Weakly-Supervised Temporal Action Localization

The task of weakly-supervised temporal action localization (WTAL) is to recognize plentiful unstructured actions in untrimmed videos with only video-level class labels. As various actions may occur in an untrimmed video, it is desirable to capture the correlation among different actions to effectively identify the target actions. In this paper, we propose a novel Action Relational Graph Network (ARG-Net) to model the correlation between action labels. Specifically, we build a co-occurrence graph using Graph Convolutional Network (GCN), where the graph nodes and edges are represented by word embedding of action labels and relations between two labels, respectively. Then we apply the GCNs to project the action label embeddings into a set of correlated action classifiers which are multiplied with the learned video representations for video-level classification. To facilitate discriminative video representation learning, we employ the attention mechanism to model the probability of a frame containing action instances. A new Action Normalization Loss (ANL) is proposed to further alleviate the confusion from irrelevant background frames (i.e., frames containing no actions). Experimental results on THUMOS14 and ActivityNet1.2 datasets demonstrate that our ARG-Net outperforms the state-of-the-art methods.
Application of Search Engine Optimization in the College’s Official Website from the Perspective of Website Diagnosis

Abstract The official website is a window for the school to publicize and a “business card” that reflects the strength of running a school. Use relevant tools to diagnose the official website of Hainan College of Software Technology, and propose internal and external optimization suggestions to improve the website’s inclusion, ranking and traffic in search engines. This is of great significance for school brand building and website enrollment consultation.
Answer Selection in Community Question Answering Using LSTM

Websites like Quora, Yahoo! Answers, and Reddit are examples of community question answering (CQA) systems that enable users to ask questions as well as to answer questions. Answer selection is the most challenging task in CQA systems to get the good and relevant answer for the user questions. The shortcomings in the current approaches are lexical gap between text pairs, dependency on external sources, and manual features which lead to lack of generalization ability. These shortcomings are resolved by already proposed work, but they lack generalization, and their performance is not satisfying. Whereas to focus on rich quality answers, attention mechanism can be integrates with neural network. This chapter proposes two models BLSTM and BLSTM with attention mechanism. Attention mechanism aligns question to the answer with the answer's more informative part. So, when it is applied in the model, BLSTM with attention mechanism model surpasses the top approaches.
교사 내부마케팅 요인이 조직신뢰 및 고객지향성에 미치는 영향

Purpose: The main purpose of this research is to analyze that how internal marketing factors implemented on teachers can have an effect on organizational trust and customer orientation. The various supporting systems to teachers must take precedence in order to obtain educational competitiveness in educational institutions.Research design, data, and methodology: This research has been initiated and completed on 245 questionnaires of the elementary and junior high school teachers for about a month in October, 2019, by utilizing for frequency analysis, technical analysis, exploratory factor analysis, cronbach’s alpha, covariance analysis to verify its hypothesis by SPSS 23.0 & AMOS tool.Results: First of all, 5 major factors(management support, education & training, authority delegation, reward system, and internal communication) were selected among various internal marketing factors of teachers. Secondly, imposing internal marketing factors have been proven to display positive effects in terms of above 5 major factors for improving on organizational trust. Thirdly, above 5 major factors have been lead to positive influences on the development of the internal customer orientation, too. Lastly, organizational trust also are appeared to yield positive effects on customer orientation.Conclusions: This research has verified that implementation of internal marketing factors could potentially lead to enhance teacher’s organizational trust, and also lead to positive effects on customer orientation to students. In comparison with existing research contents, internal marketing factors might have different effects on organizational trust among occupational differences. More objective measurement tools must need to be developed and lead to further research which are prepared for the effects on teacher’s internal marketing factors.
Time-Lag Aware Multi-Modal Variational Autoencoder Using Baseball Videos And Tweets For Prediction Of Important Scenes

A novel method based on time-lag aware multi-modal variational autoencoder for prediction of important scenes (TI-MVAE-PIS) using baseball videos and tweets posted on Twitter is presented in this paper. This paper has the following two technical contributions. First, to effectively use heterogeneous data for the prediction of important scenes, we transform textual, visual and audio features obtained from tweets and videos to the latent features. Then TI-MVAE-PIS can flexibly express the relationships between them in the constructed latent space. Second, since there are time-lags between tweets and the corresponding multiple previous events, Tl-MVAE-PIS considers such time-lags in their relationship estimation for successfully deriving their latent features. Therefore, these two contributions enable accurate important scene prediction. Results of experiments using actual baseball videos and their corresponding tweets show the effectiveness of TI-MVAE-PIS.
Morpho-syntactic changes in the Trentino dialect of Piracicaba

The paper focuses on two morpho-syntactic phenomena of the dialect spoken by heritage speakers of Trentino in Piracicaba, a city in the state of San Paolo, Brazil. The two investigated phenomena are: a) the selection of auxiliaries with some classes of verbs (pronominal, weak unaccusative and meteorological verbs); b) the use of the partitive clitic ne with quantified pronominal objects. To collect the relevant data, a questionnaire was administered to six speakers in Trentino and six speakers in Brazil. The results show a clear distinction between the sentences produced by Brazilian speakers older than 70 years who still use the original dialectal structures, and the sentences produced by Brazilian speakers younger than 70 years, in which both attrition and contact with the Portuguese language lead to simplified structures.