Reviewerly/exHarmony huggingface dataset:
A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone

Activities on Unmanned Aerial Vehicle (UAV) have increased over the last years and there are many fields in which UAVs can be used. One of the basic applications is reconnaissance of a given area using multiple UAVs. To perform reconnaissance mission, there are two methods: (i) path planning to navigate the pre-determined route; and (ii) random mobility method to explore without prior knowledge. In this paper, we indicate the imbalance problem of existing random mobility models for reconnaissance and propose a new model considering reconnaissance balance based on the number of visits. We divide the scanning area into N zones and then select a zone stochastically in which the search is insufficient. We evaluated the performance of the model by focusing on the coverage rate and average inter-visiting time. The proposed model shows that the 90%-coverage reaching time is improved by about 25% and the average inter-visiting time is improved by up to 15% compared to the previous approach.
An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography

Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.
Knowledge Sharing, Organizational Culture, Intellectual Capital, and Organizational Performance

This study aims to examine the influence of knowledge sharing and organizational culture toward organizational performance with intellectual capital as an intervening variable. The questionnaire was sent to 71 general managers of manufacturing companies, and 60 questionnaires were returned. The data of this research were analyzed using structural equation modelling (SEM) method, with Partial Least Square (PLS) approach. The results of this study indicated that knowledge sharing had a significant positive effect on human capital, yet no significant effect on structural capital and relational capital. Knowledge sharing also had no significant effect on organizational performance. Meanwhile, the organizational culture had a significant positive effect on human capital, structural capital, and relational capital and also significant effect on organizational performance. The influence of knowledge sharing on organizational performance was only partially mediated by intellectual capital or only mediated by human capital, while the influence of organizational culture on organizational performance was not mediated by intellectual capital which consists of human capital, structural capital, and relational capital.
A Hybrid Heuristic with Hopkins Statistic for the Automatic Clustering Problem

Cluster Analysis is a multivariate method to handle real problems associated with several fields. This area combines several methods of unsupervised classification, which can be applied in order to identify groups in a data set. The Clustering Problems are classified as NP-Hard and, in order to obtain such classification, the number of groups k may be fixed or, in the Automatic approach, the ideal k must be identified upon evaluation of some validation index. In this paper the Silhouette Index was considered and a new proposed Hybrid Heuristic Algorithm (HHA) operates to identify the ideal number of groups. The HHA consider two heuristic algorithms based on metaheuristics: an algorithm based on Iterated Local Search (ILS) that considers a density-based approach and a literature Evolutionary Algorithm (EA). Besides, the HHA have a heuristic algorithm that verify clustering tendency, considering the Hopkins Statistic. Basically, according with the clustering tendency level, the HHA use a specific heuristic (ILS or EA). The computational experiments used three literature data sets with eighty-two instances, and all of them were considered and reported by different researchers. The effectiveness and the efficiency of the proposed heuristic are reflected in substantially lower computational time and in the solutions quality, that are competitive when compared with the best results reported in the literature.
Meningkatkan Kognitif Siswa SMAN I Jambi Melalui Modul Berbasis E-Book Kvisoft Flipbook Maker

This research was aimed to: produce module based on e-book using Kvisoft Flipbook Maker application that proper to increase learning interest and cognitive learning outcome, know the increasing interest of senior high school students which use learning media module based on e-book using Kvisoft Flipbook Maker application, and know the increasing of cognitive learning outcome of senior high school students which use learning media module based on e-book using Kvisoft Flipbook Maker application. The result show that: the module based on e-book using Kvisoft Flipbook Maker application is proper to increase learning interest and cognitive learning outcome of senior high school students seen from very good and good results category of practitioner appraisal and student’s response, the increasing of student’s learning interest which use module based on e-book using Kvisoft Flipbook Maker application by low category standard gain, and the increasing of student’s cognitive learning outcome which use module based on e-book using Kvisoft Flipbook Maker application by medium category standard gain
Post-synaptic Potential Regularization Has Potential

Improving generalization is one of the main challenges for training deep neural networks on classification tasks. In particular, a number of techniques have been proposed, aiming to boost the performance on unseen data: from standard data augmentation techniques to the $$\ell _2$$ regularization, dropout, batch normalization, entropy-driven SGD and many more. In this work we propose an elegant, simple and principled approach: post-synaptic potential regularization (PSP). We tested this regularization on a number of different state-of-the-art scenarios. Empirical results show that PSP achieves a classification error comparable to more sophisticated learning strategies in the MNIST scenario, while improves the generalization compared to $$\ell _2$$ regularization in deep architectures trained on CIFAR-10.
Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.
Cognate Projection for Low-Resource Inflection Generation

We propose cognate projection as a method of crosslingual transfer for inflection generation in the context of the SIGMORPHON 2019 Shared Task.The results on four language pairs show the method is effective when no low-resource training data is available.
Accelerometer-Based Mobile Device Identification System for the Realistic Environment

With the rapid development of the Internet and mobile technology, the frequency of online fraud incidents continues to increase, and the fraudulent means are increasingly diverse. Traditional anti-fraud methods can no longer effectively deal with the serious security situation at present because of their own various shortcomings. In this paper, a device identification system based on acceleration sensor is constructed which takes into consideration the imperfections of the sensor in the manufacturing process and assembley process. The system can identify the user's device by taking advantages of the data collected in a flat static state, and thus confirm the user's identity. The acceleration data of the device is first collected, and then the data in flat static state is extracted through the state discrimination algorithm. After the feature extraction process, a 20-dimensional feature vector is obtained. Finally, a device identification model combining one-class classifier and multi-class classifier is trained. The model can identify unknown devices and classify known devices. The method proposed in this paper solves the problem that the device state cannot be determined in the real world. Further on we propose a multi-layer device identification model that can identify unknown device without training threshold. Compared with the previous studies in the literature, the biggest innovation of our method is to consider the user authentication of device fingerprint in the real world.
Probing Word and Sentence Embeddings for Long-distance Dependencies Effects in French and English

The recent wide-spread and strong interest in RNNs has spurred detailed investigations of the distributed representations they generate and specifically if they exhibit properties similar to those characterising human languages.Results are at present inconclusive.In this paper, we extend previous work on long-distance dependencies in three ways.We manipulate word embeddings to translate them in a space that is attuned to the linguistic properties under study.We extend the work to sentence embeddings and to new languages.We confirm previous negative results: word embeddings and sentence embeddings do not unequivocally encode fine-grained linguistic properties of longdistance dependencies.
Learning a Layout Transfer Network for Context Aware Object Detection

We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains.
Derivational Morphological Relations in Word Embeddings

Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes.In this paper, we explore the potential of word embeddings to identify properties of word derivations in the morphologically rich Czech language.We extract derivational relations between pairs of words from DeriNet, a Czech lexical network, which organizes almost one million Czech lemmata into derivational trees.For each such pair, we compute the difference of the embeddings of the two words, and perform unsupervised clustering of the resulting vectors.Our results show that these clusters largely match manually annotated semantic categories of the derivational relations (e.g. the relation 'bake-baker' belongs to category 'actor', and a correct clustering puts it into the same cluster as 'governgovernor').
Transparent Pronunciation Scoring Using Articulatorily Weighted Phoneme Edit Distance

For researching effects of gamification in foreign language learning for children in the "Say It Again, Kid!" project we developed a feedback paradigm that can drive gameplay in pronunciation learning games.We describe our scoring system based on the difference between a reference phone sequence and the output of a multilingual CTC phoneme recogniser.We present a white-box scoring model of mapped weighted Levenshtein edit distance between reference and error with error weights for articulatory differences computed from a training set of scored utterances.The system can produce a humanreadable list of each detected mispronunciation's contribution to the utterance score.We compare our scoring method to established black box methods.
On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution

This paper presents the 3rd-place-winning solution to the GAP coreference resolution shared task.The approach adopted consists of two key components: fine-tuning the BERT language representation model (Devlin et al., 2018) and the usage of external datasets during the training process.The model uses hidden states from the intermediate BERT layers instead of the last layer.The resulting system almost eliminates the difference in log loss per gender during the cross-validation, while providing high performance.
A Key Verification Protocol for Quantum Key Distribution

Sharing a secret key between two physically separated nodes, Alice and Bob, is possible through the use of quantum key distribution (QKD) techniques.In the presence of an eavesdropper, Alice's key may not be identical with Bob's key, due to the characteristics of a quantum channel.To obtain identical keys at Alice and Bob, we propose a block-based key verification protocol that relies on Newton's polynomial interpolation.As the nodes solely share random numbers and indices of the removed blocks, no information is revealed about the secret message, at a cost of higher computational complexity.The error propagation through the key verification process is prevented by the characteristics of the proposed approach.
Role of the "Green" Philosophy and Bionics on the Development of EcoDesign Clothing

The article is devoted to the fundamental role and evolution of costume in human ethnography , its material and spiritual culture, public relations and family life, the disclosure of its nature-like characteristics and the impact on the development of modern trends in the world of the clothing industry.Folk costume as one of the most essential types of decorative and applied art is of the greatest interest and fully embodies the diverse connections of a person with the outside world.Its design is one of the most dynamic types of the whole variety , all changes in the life of society are reflected in it through the use of current signs and symbols.The authors carried out the systematic work on the study of "Green" philosophy, the principle of respectful management of the environment.Careful attitude to nature and the search for means of preserving the environment through eco-design clothing, unique ways to improve the eco-system and acute pollution control due to the use of natural and eco-materials in fabrics and clothing are taken into consideration.The laws of shaping in the design of the national costume in the context of "Green" philosophy and bionics together with the historical method allow to consider the evolution of the formation and accumulation of knowledge about bionic principles and formulate its fundamental importance for the development of artistic culture.
A Hierarchical Task Assignment for Manual Image Labeling

Manual image labeling (selecting an appropriate “category” for an image) is very tedious and time consuming especially when selecting labels from a large number of categories. In this study, we propose a hierarchical assignment of labeling tasks where the labelers recursively classify images in a category group into sub category groups, working on a single level at a time. This significantly makes each labeler's task easier, reducing the number of choices from 1,000 to 27 on average. In the user study, we compared our hierarchical assignment to a normal (non-hierarchical) assignment for a labeling task. The results show that the hierarchical assignment requires less total time to complete the labeling task. In addition, the learning effect in the labeling process is more profound in the hierarchical assignment.
CLMed: A Cross-lingual Knowledge Graph Framework for Cardiovascular Diseases

Currently, knowledge graphs can be used to better query complex related information, understand user intentions from the semantic level, and improve search quality. With the increase in cross-lingual medical data recently, the construction of cross-lingual knowledge graph in the medical domain is urged. Meanwhile, there also exist many challenges due to the specific features in the medical area: (1) the scarcity of Chinese knowledge and the inner relations within the entities in existing open source cross-lingual knowledge bases; (2) the scarcity of prior knowledge defined by medical experts; (3) the availability of Electronic Medical Records (EMRs) and (4) the inconsistent semantic rules in different languages. To overcome these limitations, we propose CLMed platform which can be used in constructing the disease-specific, cross-lingual medical knowledge graphs from multiple sources. This platform provides several toolsets to process the cross-lingual data. Meanwhile, based on this platform, we generate a large scale, cross-lingual medical knowledge graph named CLKG.
Deep Learning Hyper-parameter Tuning for Sentiment Analysis in Twitter based on Evolutionary Algorithms

The state of the art in Sentiment Analysis is defined by deep learning methods, and currently the research efforts are focused on improving the encoding of underlying contextual information in a sequence of text.However, those neural networks with a higher representation capacity are increasingly more complex, which means that they have more hyper-parameters that have to be defined by hand.We argue that the setting of hyper-parameters may be defined as an optimisation task, we thus claim that evolutionary algorithms may be used to the optimisation of the hyper-parameters of a deep learning method.We propose the use of the evolutionary algorithm SHADE for the optimisation of the configuration of a deep learning model for the task of sentiment analysis in Twitter.We evaluate our proposal in a corpus of Spanish tweets, and the results show that the hyper-parameters found by the evolutionary algorithm enhance the performance of the deep learning method.
Remote Sensing Image Registration Based on Dynamic Threshold Calculation Strategy and Multiple-Feature Distance Fusion

Remote sensing image registration is widely used in civilian and military applications such as target recognition, environmental transformation monitoring, and military damage assessment. The severe outliers in the extraction of feature points caused by nonrigid transformation and viewpoint changes in the process of capturing remote sensing images increase the difficulty of registration. Therefore, we present a remote sensing image registration method based on dynamic threshold calculation strategy (DTCS) and multiple-feature distance fusion. The main idea of our approach is to maximize inliers while ensuring the optimal correspondence. First, DTCS gradually screens reliable inliers to reduce the negative effect of outliers over the iterations. The multiple-feature distance fusion Gaussian mixture model is then introduced to compensate for the defect of a single feature, and DTCS acting as the prior probability combines with the deterministic annealing to achieve the optimal mapping from local to global scale. Moreover, structure constraint based on local applying force is added into the global constraint to control the alignment of feature points more accurately in the overlapping area, so as to guide the subsequent image transformation. Extensive experiments show that our method performs better in most cases compared with nine state-of-the-art methods.
Optimal VM Placement Model for Load Balancing in Cloud Data Centers

In cloud data centers, there are numerous hosts as well as application requests that have been developed. These requests need to allocate resources dynamically which raises the traffic scalability issue. The authors formulate the idea of selecting the most optimal host allocation scheme which can improve the power consumption as well as resource utilization ratio. We propose an Optimal VM Placement for Load Balancing (OPLB) using Maximum Likelihood estimation for parallel and distributed applications. The problem is formulated in a speculative framework based on CPU, Memory and Energy estimations to capture the improvements in throughput and failure rate. The performance evaluation demonstrates that the proposed method achieves significant traffic scalability improvement upto 49.54%, 32.63% and 19.23% over random, sequential and LB-BC virtual machine placement heuristics respectively.
The Learning Paradox: The Digital Generation Seeks A Personal, Human Voice

This study examines undergraduate students’ perceptions of academic teaching and factors that affect these perceptions, whether social background, secondary education, academic background, or learning habits. The study is based on the findings of a previous study conducted by the authors, which indicated two patterns of learning habits among undergraduate students: those oriented toward the use of digital and technological devices, and those oriented toward the use of traditional technology-resistant means. These learning habits were incorporated as explanatory variables in the current study, which included 772 undergraduate students in 14 disciplines from 37 academic institutions. A complex picture emerges from the research findings. On one hand, students’ attitudes toward teaching indicate a preference for teaching that stresses personal rather technological aspects. While students’ learning patterns also reflect traditional learning patterns, students do not resist technology: they are exposed to and actively use technologies, both within and outside of the learning context. Moreover, it emerges that traditional learning habits have a formative effect on perceptions of technology-oriented teaching. That is to say, despite a conventional mode of learning and a preference for personal frontal teaching, students appear to subscribe to the notion that academic institutions should be at the cutting edge of teaching technologies. Thus, a gap is identified between students’ personal preferences and their perceptions of ideal academic teaching. This gap may stem from issues related to learning and/or from the social and academic climate. Investigation of the gap and of related issues is a recommended subject for future research.