Reviewerly/exHarmony huggingface dataset:
Sensitive Feature Extraction of Telemetry Vibration Signal Based on Referenced Manifold Spatial Fusion Learning

The telemetry vibration signal contains a large number of state characteristic information in the test process of the aircraft, which has the characteristics of nonlinearity, nonstationary, and strong noise. How to effectively extract the weak sensitive features on the operating state of the system and accurately detect the abnormal pattern are directly related to the accuracy of state analysis for flights. To solve these problems, a sensitive feature extraction and anomaly detection method for the telemetry vibration signal is proposed based on referenced manifold spatial fusion learning. First, the multiscale analysis method is used to orthogonally decompose the signals into each of the scale bands and the multiscale feature is extracted to construct the high-dimensional feature set. Then, the same normal signal sample is combined with the same type of abnormal sample to establish the exclusive reference model, and the linear manifold learning is used to obtain the multiscale manifold feature difference of each reference model to enhance the sensitivity of anomalous features. The projection matrix for each reference model is used to make the original feature set robust and obtain the low-dimensional multiscale sensitive manifold feature. Finally, the input to the classifier is used to realize the state recognition of the unknown sample. The measured signal is used to verify the effectiveness of the algorithm.
Review of Deep Web Data Extraction

The Web has rich and comprehensive data resources. Deep Web is larger than the surface web, with wider data coverage, higher quality and stronger structure. This paper summarizes the data extraction technology in Deep Web, expounds the collection method of structured and unstructured Deep Web data, and looks forward to the challenges and development direction of Deep Web data collection research.
Field Extensions and Kronecker’s Construction

Summary This is the fourth part of a four-article series containing a Mizar [3], [2], [1] formalization of Kronecker’s construction about roots of polynomials in field extensions, i.e. that for every field F and every polynomial p ∈ F [ X ] \F there exists a field extension E of F such that p has a root over E . The formalization follows Kronecker’s classical proof using F [ X ] /&lt;p&gt; as the desired field extension E [6], [4], [5]. In the first part we show that an irreducible polynomial p ∈ F [ X ] \F has a root over F [ X ] /&lt;p&gt; . Note, however, that this statement cannot be true in a rigid formal sense: We do not have F ⊆ F [ X ] / &lt; p &gt; as sets, so F is not a subfield of F [ X ] /&lt;p&gt; , and hence formally p is not even a polynomial over F [ X ] / &lt; p &gt; . Consequently, we translate p along the canonical monomorphism ϕ : F → F [ X ] /&lt;p&gt; and show that the translated polynomial ϕ ( p ) has a root over F [ X ] /&lt;p&gt; . Because F is not a subfield of F [ X ] /&lt;p&gt; we construct in the second part the field ( E \ ϕF )∪ F for a given monomorphism ϕ : F → E and show that this field both is isomorphic to F and includes F as a subfield. In the literature this part of the proof usually consists of saying that “one can identify F with its image ϕF in F [ X ] /&lt;p&gt; and therefore consider F as a subfield of F [ X ] /&lt;p&gt; ”. Interestingly, to do so we need to assume that F ∩ E = ∅, in particular Kronecker’s construction can be formalized for fields F with F ∩ F [ X ] = ∅. Surprisingly, as we show in the third part, this condition is not automatically true for arbitrary fields F : With the exception of ℤ 2 we construct for every field F an isomorphic copy F′ of F with F′ ∩ F′ [ X ] ≠ ∅. We also prove that for Mizar’s representations of ℤ n , ℚ and ℝ we have ℤ n ∩ ℤ n [ X ] = ∅, ℚ ∩ ℚ[ X ] = ∅ and ℝ ∩ ℝ[ X ] = ∅, respectively. In this fourth part we finally define field extensions: E is a field extension of F iff F is a subfield of E . Note, that in this case we have F ⊆ E as sets, and thus a polynomial p over F is also a polynomial over E . We then apply the construction of the second part to F [ X ] /&lt;p&gt; with the canonical monomorphism ϕ : F → F [ X ] /&lt;p&gt; . Together with the first part this gives – for fields F with F ∩ F [ X ] = ∅ – a field extension E of F in which p ∈ F [ X ] \F has a root.
Multilayer deep features with multiple kernel learning for action recognition

In accurate action recognition, discriminative human-region representation as auxiliary information is critical for fusing multiple visual clues in a video and further improving the recognition performance. To this end, in this paper we propose integrating a novel representation named multilayer deep features (MDF) of both the human region and whole image area into an extended region-aware multiple kernel learning (ER-MKL) framework. To be specific, we first exploit the human cues with the help of the off-the-shelf semantic segmentation models. Then more powerful representations MDF are constructed by concatenating activations at the last convolutional layer and fully connected layer. Finally, the proposed framework termed ER-MKL is presented to learn a robust classifier for fusing human-region MDF and whole-region MDF. In addition to combining multiple kernels derived from features of heterogeneous image regions, ER-MKL also considers the sets of pre-learned classifiers and incorporates prior knowledge of different regions. Extensive evaluations on the JHMDB and UCF Sports datasets validate the effectiveness and the superiority of our proposed approach.
PENGEMBANGAN BAHAN AJAR MATA PELAJARAN BAHASA INDONESIA BERBASIS CERITA RAKYAT KABUPATEN BANJARNEGARA

This research is based on the fact that teaching materials are not yet available as a best friend for teachers in delivering Indonesian language learning at the SMA / MA class X semester 1 of KD 3.7 and 4.7. The purpose of this study was to produce teaching materials for Indonesian language for class X semesters 1 KD 3.7 and 4.7 based on Banjarnegara Regency folklore. This research is a development research with a qualitative descriptive analysis technique. According to experts teaching material has a percentage of validity level 88.5% with a valid category, according to media experts teaching material has a percentage of validity level 80% with a valid category, according to learning experts namely Indonesian language subject teachers have a percentage of validity level 83.7 % with valid categories. Based on the results of research according to class X students of SMA Muhammadiyah Banjarnegara as users, this teaching material greatly facilitates students in understanding folklore. This can be seen from the result of filling out the questionnaire about response student of class X to the teaching materials of the developmet result which have a percentage of validity 91.5%. Suggestions include for product utilization, product dissemination, and further development needs.
Image Registration Method for Satellite Image Sensing using Feature based Techniques

Identifying targets (sensed image) on large satellite images (reference image) is a difficult task and consumes time.In this paper an image registration method for satellite image sensing using feature based techniques is proposed for detecting the targets.Image registration is the process of sheathing images of the same scene with respect to a particular reference image.Classical registration methods are used for registering the images but they have certain limitations in their accuracy.Method proposed and implemented here has good response target matches and depicts 76 percent accuracy with the best combination of feature extractor and detector.
A Modified Algorithm for the Logistic Sequence Based on PCA

In order to deal with the dynamical degradation of logistic chaotic sequence caused by the structures and finite precision computing, we propose a modified algorithm based on phase space reconstruction and PCA. In the process of phase space reconstruction, it uses Cao theory after determining the delay time by the mutual information function and takes the first principal component as the result. Results of experiments demonstrate that: the algorithm erases the relevance among data points and enhances the randomness and complexity of the sequence.
DIGITAL MODERNIZATION OF THE REGION'S EDUCATIONAL MARKET AND ITS INFLUENCE ON QUALITY OF EDUCATION

The purpose of the paper is to study the experience of increase of quality of education as a result of digital modernization of the educational market in modern Russia's regions based on technologies of Industry 4.0 with the help of reorganization of regional universities and to develop recommendations for optimizing the practice of managing this process in modern Russia in the interests of the fullest implementation of the potential of mentioned problem.The research is performed by the example of the regions of the Volga Federal District of the Russian Federation.The authors perform analysis of the achieved results of increase of quality of education as a result of digital modernization of the educational market in regions of the Volga Federal District of the Russian Federation based on technologies of Industry 4.0 with the help of reorganization of regional universities and perform modeling of scenarios of increase of quality of education in this regions based on technologies of Industry 4.0 through reorganization of universities and develop recommendations for practical implementation of the most optimal scenario.This envisages the increase of quality of education in mentioned regions based on technologies of Industry 4.0 through reorganization of universities.The methods of horizontal, scenario, and regression analysis are used.It is substantiated that as of now (2019) Russia has not achieved significant results in the sphere of increase of quality of education during digital modernization of universities based on technologies of Industry 4.0 in the process reorganization (formation of regional flagship universities).The authors also try to solve the different aspects of this problem.
Android App for Improvising Sign Language Communication in English and Hausa

Deaf and dumb persons are facing great challenges communicating with people in society despite the fact that they are part of the society and are enrolled in the normal school system where teaching and learning take place using spoken language.This challenge calls for the need to address the problem of communication through the use of mobile technology.Developing a mobile app to enhance learning and communication is of great importance and thus the focus of this paper.Obviously most of the applications available were developed using foreign languages which is not easy to use.In this paper, we introduce the Simple Sign Language Translator Application (SSLATA) that will help people to learn and master sign language.The introduced application will translate words from sign language to English or Hausa languages, text to speech to sign, speech to text to sign and vice-visa.The SSLATA through its user-friendly interface will enable users to interact with one another via either spoken language or sign language.Constructive research approach and an agile model were adopted for the study.Android Studio, SQLite and Java were the environment and language used in developing the app.The application runs on android operating system devices.
Geochronology, paleomagnetic signature and tectonic models of cratonic basins of India in the backdrop of Supercontinent amalgamation and fragmentation

strengthens the view that basin formation processes operated on a global scale, and stratigraphic basin analysis on a regional scale is a significant tool in evaluating the basins' history.The available stratigraphic, geochronologic or palaeomagnetic data from India is still inadequate, and further information is required to constrain its definite position in the context of global tectonics.
Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity

Unstructured neural network pruning is an effective technique that can significantly reduce theoretical model size, computation demand and energy consumption of large neural networks without compromising accuracy. However, a number of fundamental questions about pruning are not answered yet. For example, do the pruned neural networks contain the same representations as the original network? Is pruning a compression or evolution process? Does pruning only work on trained neural networks? What is the role and value of the uncovered sparsity structure? In this paper, we strive to answer these questions by analyzing three unstructured pruning methods (magnitude based pruning, post-pruning re-initialization, and random sparse initialization). We conduct extensive experiments using the Singular Vector Canonical Correlation Analysis (SVCCA) tool to study and contrast layer representations of pruned and original ResNet, VGG, and ConvNet models. We have several interesting observations: 1) Pruned neural network models evolve to substantially different representations while still maintaining similar accuracy. 2) Initialized sparse models can achieve reasonably good accuracy compared to well-engineered pruning methods. 3) Sparsity structures discovered by pruning models are not inherently important or useful.
Digital Literacy of STEM Senior High School Students: Basis for Enhancement Program

Digital literacy promotes students’ competitiveness and better opportunity in today’s digital world and in the fourth industrial revolution (FIRe). This descriptive-survey research determined the digital literacy of science, technology, engineering &amp; mathematics (STEM) senior high school students. A total of 130 respondents from two state-owned public high schools in Zambales, Philippines answered the digital literacy survey questionnaire. Results revealed that a typical STEM respondent came from school B, aged between 15-17, female and currently Grade 11. The STEM students are digitally literate to some extent in terms of access and evaluation of information; utilization and management of information; media analysis; creation of media products; effective application of technology; and interaction through technology. There is a significant difference in the extent of digital literacy of students when grouped according to sex and grade level. Moderate significant relationship exists across all domains digital literacy. The study recommends the implementation of the proposed digital literacy working group to enhance students’ digital proficiency and to equip them with the challenges of the FIRe. Teachers may likewise utilize digital devices and information effectively and responsibly towards developing digitally literate citizens.
Analysis of IT Graduates Employment Alignment Using C4.5 and Naïve Bayes Algorithm

There is a significant increase in the number of graduates produced by higher education institutions every year and the number of graduates is growing immensely compared to job opportunities in the market.Data mining techniques serve as the foundation of various researches in generating knowledge from the dataset obtained from schools, universities, and companies.In this paper, the application of classification mining with the use of the C4.5 and Naïve Bayes algorithms, analyzed the dataset gathered from the Office of the Registrar of Davao del Norte State College.This study aims to determine the correlation between students' grades in their major subjects and their employment right after completing a degree in Information Technology.The experimental results showed that the Naive Bayes algorithm garnered 67.22% accuracy using 10-fold cross-validation scheme and 55.91% accuracy with the 70% training and 30% testing percentage split.Further, the C4.5 algorithm accumulated 95.52% and 92.13% accuracies using the same cross-validation method and data composition percentage split, respectively.It goes to show that the best algorithm in analyzing the employment alignment of the IT students is the C4.5.This research would help the students and the university to improve various aspects of educating the students and see to it that they would produce quality graduates in the following years.The use other algorithms to generate more significant models, as well as adding more attributes to identify other factors affecting graduate's employment is recommended.
Long-haul and High-speed Key Distribution Based on One-way Non-dual Arbitrary Basis Transformation in Optical Fiber Link

We propose a long-haul and high-speed key distribution based on one-way non-dual arbitrary basis transformation in optical fiber link. The key distribution rate of 277 Kbit/s with free key error rate is demonstrated over 300km.
Traceable and Weighted Attribute-Based Encryption Scheme in the Cloud Environment

In order to solve the problem that the importance of the user's attribute is seldom considered in the most of the existing attribute-based encryption schemes with traitor tracing, we designed a traceable and weighted attribute-based encryption scheme.In our constructed scheme, the private key of the user consisting of user's identity information is applied to trace traitors.In addition, the idea of weighted attribute is introduced, and the attributes set is transformed into the segmentation set of weighted attributes through the attributes set segmentation algorithm.Via employing a linear secret sharing scheme, the constructed scheme offers fine-grained and nimble access control mechanism.Under the assumption of q-BDHE in the standard model, we prove that the designed scheme is able to reach security against chosen-plaintext attack.By comparing with other relevant schemes, it has significant improvement in the costs of communication and computation, and it is more suitable for the application of the mobile terminal in cloud computing.
A Control Unit for Emotional Conversation Generation

Emotional conversation generation model predicts the response according to the current words and the emotional words. However, the researchers only dedicated to adding more emotional words in the conversation generation model to retain the taste of chat users without considering whether the emotion of a response is suitable for human conversations or not. In this paper, we aim to address the issue of emotion drift which indicates the emotion of a response is not the same category as its post in human conversations. We propose a control unit framework, which consists of emotional channels and word-level attention mechanism, to incorporate natural and smooth emotional words into conversation generation. Emotional channel consists six channels, namely like, sadness, disgust, anger, happiness and other ones, which provides strategy choice control unit to generate emotional words. To improve the importance of emotional content, we use the word-level attention mechanism in emotional channel for acquiring a better emotional decoding response. Experimental results suggest that the proposed model is effective not only in generate content but also in emotion.
Humans Predict Action using Grammar-like Structures

Abstract Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants.
Extreme Residual Connected Convolution-Based Collaborative Filtering for Document Context-Aware Rating Prediction

Deep learning methods can improve the performance of recommender systems over traditional methods, especially when text information is available. To extract information hidden in the text description of an item and fuse it with rating information, it has been proposed that the network be stacked with more layers. However, as the network thus deepens, the problem of the attenuation of the preamble signal can cause the convolutional neural network to break down. The gradient gradually disappears during the back-propagation process, resulting in an inability to adjust the weights. In this paper, a method called eXtreme Residual connected Convolution Collaborative Filtering (xRConvCF) is proposed to predict the rating for each item based on the textual information. It creates data branch lines to form a residual module called the eXtreme residual (xRes) connection to mitigate the problem of the vanishing gradient and enhance feature reuse. The results of experiments on empirically obtained datasets show that the proposed deep learning model significantly outperforms state-of-the-art methods of recommendation.
Multi-Person Pose Estimation using an Orientation and Occlusion Aware Deep Learning Network

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.