Reviewerly/exHarmony huggingface dataset:
FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES

We propose to study the problem of few-shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. Despite several interesting GNN variants being proposed recently for node and graph classification tasks, when faced with scarce labeled examples in the few-shot setting, these GNNs exhibit significant loss in classification performance. Here, we present an approach where a probability measure is assigned to each graph based on the spectrum of the graph’s normalized Laplacian. This enables us to accordingly cluster the graph base-labels associated with each graph into super-classes, where the L^p Wasserstein distance serves as our underlying distance metric. Subsequently, a super-graph constructed based on the super-classes is then fed to our proposed GNN framework which exploits the latent inter-class relationships made explicit by the super-graph to achieve better class label separation among the graphs. We conduct exhaustive empirical evaluations of our proposed method and show that it outperforms both the adaptation of state-of-the-art graph classification methods to few-shot scenario and our naive baseline GNNs. Additionally, we also extend and study the behavior of our method to semi-supervised and active learning scenarios.
Resource Provisioning for MapReduce Computation in Cloud Container Environment

MapReduce is a major computing model for big data solutions through distributed virtual computing environment. Cloud container environment is one of the platforms to compute MapReduce tasks. However, a new challenge lies on the lack of resource provisioning for containerized MapReduce computations with deadline requirements. There are two major resource provisioning strategies to solve this challenge: static and dynamic, but neither of them can satisfactorily solve it. This paper presents a resource provisioning framework, integrating semi-static and dynamic strategies, to address this challenge. The framework includes a performance model to estimate minimum resource requirements under deadline limitation, and a scheduler to adjust resource allocation. Experimental results show that the proposed semi-static framework can complete the MapReduce computation with less resource utilization and meeting the given deadline. However, proposed dynamic resource provisioning is not suitable for our scenario caused by resource overhead and late completion.
Penerapan Model Pembelajaran Model Pembelajaran Student Facilitator And Explaining Untuk Meningkatkan Hasil Belajar Siswa Pada Mata Pelajaran Matematika Di Kelas V Sekolah Dasar Negeri 164 Pekanbaru

This research aimed at increasing student learning achievement on Mathematics subject throughthe implementation of Student Facilitator and Explaining learning model at thefifth grade of State Elementary School 164 Pekanbaru. It was instigatedby the low of student learning achievement that could not pass the minimum standard of passing grade determined 75. It was a Classroom Action Research. The subjects of this research were the teachers and students. The objects were Student Facilitator and Explaining learning model and learning achievement. This research was conducted for two cycles, and every cycle comprised two meetings. Observation, learning achievement test, and documentation were the techniques of collecting the data. The technique of analyzing the data was Descriptive qualitative with percentage. Based on the research findings and data analyses,Student Facilitator and Explaining learning model could increase learning achievement. It could be seen from student learning achievement that the mean was 68.61 before the action with 47.22% classical mastery, and it was on poor category. After implementing Student Facilitator and Explaining learning model in the first cycle, student learning achievement mean increased to 73.88 with 66.66% classical mastery, and it was on enough category. In the second cycle, student learning achievement mean increased to 80.27with 86.11% classical mastery, and it was on perfect category. Thus, Student Facilitator and Explaining learning model achieved success indicators that were expected at thefith grade of state of student school 164 Pekanbaru.
Comparison of scalar point multiplication algorithms in a low resource device

Since it was invented, Elliptic Curve Cryptography (ECC) is considered an ideal choice for implementing public key cryptography in resource constrained devices, thanks to its small keys. Scalar point multiplication is the central and the complex operation in the cryptographic computations of ECC that requires a lot of optimizations on execution time and energy consumption especially in low computing power devices such as embedded systems. Thus, many scalar point multiplication algorithms are proposed, each using their own computational techniques and mathematical formulas. In this paper, we have combined the computational techniques with some optimized mathematical formulas and we have implemented them on some Elliptic Curves scalar point multiplication algorithms over finite field. The aim of this work is to identify the most efficient algorithm that combine the best computational technique and the mathematical formulas, and consequently offer less-memory requirements and faster field arithmetic operations. The results show that Montgomery ladder algorithm in co-Z addition with update formula gives better results compared to the other algorithms implemented in this paper.
Robust adaptive online sequential extreme learning machine for predicting nonstationary data streams with outliers

Data streams online modeling and prediction is an important research direction in the field of data mining. In practical applications, data streams are often of nonstationary nature and containing outliers, hence an online learning algorithm with dynamic tracking capability as well as anti-outlier capability is urgently needed. With this in mind, this paper proposes a novel robust adaptive online sequential extreme learning machine (RA-OSELM) algorithm for the online modeling and prediction of nonstationary data streams with outliers. The RA-OSELM is developed from the famous online sequential extreme learning machine algorithm, but it uses a more robust M-estimation loss function to replace the conventional least square loss function so as to suppress the incorrect online update of the learning algorithm with respect to outliers, and hence enhances its robustness in the presence of outliers. Moreover, the RA-OSELM adopts a variable forgetting factor method to automatically track the dynamic changes of the nonstationary data streams and timely eliminate the negative impacts of the outdated data, so it tends to produce satisfying tracking results in nonstationary environments. The performances of RA-OSELM are evaluated and compared with other representative algorithms with synthetic and real data sets, and the experimental results indicate that the proposed algorithm has better adaptive tracking capability with stronger robustness than its counterparts for predicting nonstationary data streams with outliers.
An Investigation on Face Detection Applications

Face detection in uncontrolled ambiance continues to be a challenge to traditional face detection methods due to the large difference in facial expressions.The task of alignment is particularly difficult when the face comes from an extremely unconditional environment.To overcome these problems, the present paper is a reliable and deeply considerable application that allows users to detect a face(s) in real-time and process them.Database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them.Then methods for morphing, warping, swapping and averaging of faces are presented.As a result, faces can be very effectively detected, aligned/ oriented and used for the above methods.In addition, based on results this approach can detect faces and eyes in difficult conditions without explicitly simulating their variation.Evaluating the tests of the application, it can confidently said that this face detection method is accurate and effective and reaches the most modern level of performance.The same methodology can easily be generalized to other tasks, as well as the detection of a common object.
SWSDM: SOCIAL WEB SERVICE DISCOVERY MODEL

The number of services is growing at an explosive rate, creating great challenges in efficiently extracting the target services for users.Moreover, during recent years, social networks have emerged with the progress of the Web 2.0 and the popularity of social networks websites like Twitter and Facebook has increased rapidly.Adding social dimension in the Service-Oriented Architecture have facilitated web services discovery.Thus, web services can be treated as an active entities by being part of social networks.This paper proposes a social web services discovery model relying on the integration of social web services, and more social users in the discovery process.We aim to involve both users and web services in the discovery process, instead of focusing only on the web services.The results of using our framework show that the discovery process becomes easier if we take advantage of the relations among web services in their own social networks and linking both services and users in the same network.
Real Time object detection and multilingual speech synthesis

We introduce a technique for real time deep learning based image detection with multilingual neural text-to-speech (TTS) synthesis; to generate different voices from a single model. In this work, we show improvement to the existing single lingual approach for a single-model based neural text to speech synthesis. This model, constructed with higher performance building block of a neural network (Inception4 model), demonstrated high level significant audio signal quality improvement on the images detected in real life. We show that a single deep learning model with a single neural TTS system can generate multiple languages with unique voices and display them in real life environment. We adopted transfer learning method for the image detection and recognition purpose and retrained the top layer of the model. This work introduces user friendly image-to-speech tracking system for easy navigation and valuable information extraction especially for the visually impaired people with multiple language capabilities.
A Novel Pseudo-Random Number Generator Algorithm based on Entropy Source Epoch Timestamp

Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The lack of assurance about the random numbers generated can cause serious damage to cryptographic protocols, prompting vulnerabilities to be exploited by the attackers. In this paper, a new pseudo - random number generator algorithm that uses dynamic system clock converted to Epoch Timestamp as PRNG seed was developed. The algorithm uses a Linear Congruential Generator (LCG) algorithm that produces a sequence of pseudo - randomized numbers that performs mathematical operations to transform numbers that appears to be unrelated to the Seed. Simulation result shows that the new PRNG algorithm does not generate repeated random numbers based on the frequency of iteration, a good indicator that the key for random numbers is secured. Numerical analysis using NIST Test Suite results concerning to random sequences generated random numbers has a total average of 0.342 P-value. For a p-value ≥ 0.001, a sequence would be considered to be random with a confidence of 99.9%. This shows that robustness and unpredictability were achieved. Hence, It is highly deterministic in nature and has a good quality of Pseudo-Random Numbers. It is therefore a good source of a session key generation for encryption, reciprocal in the authentication schemes and other cryptographic algorithm parameters that improve and secure data from any type of security attack.
A Cognitive Load Perspective on the Design of Blocks Languages for Data Science

The difficulty of learning data science is believed to arise from its deep prerequisites in statistics, programming, and machine learning. This poster explores how blocks languages may be used to reduce the cognitive load of learning data science. Unlike blocks languages for introductory programming, blocks languages for data science naturally align with a high level of abstraction and almost exclusively sequential execution. However, these gains in simplicity are offset by the high level of parameterization at the block level. Three designs for blocks languages are presented and compared, and implications for abstraction, sequential execution, and parameterization on cognitive load are discussed.
FRCA: High-efficiency container number detection and recognition algorithm with enhanced attention

With the rapid development of information technology, text recognition in natural scenes has become a hot topic of current research. In order to accurately and quickly identify the box number in the container image in the natural scene, this paper proposes a deep learning-based image text recognition model (Faster-RCNN and CNN with Attention (FRCA)), which consists of two stages: box number area detection and box number character recognition. We use the improved Faster-RCNN network to detect the location of the box number, which increase the attention mechanism in the area generation network (RPN) to speed up the detection speed while ensuring the accuracy. And we use the improved CNN to recognize the box number characters. The experiments on the benchmark dataset and the real dataset prove that compared with the connected region detection method, the Faster-RCNN and VGG-16 combination method, the FasterRCNN and ResNet-101 combined detection method, the accuracy of FRCA model in this paper is better than the former two schemes, and the speed of detection of FRCA network is faster than that of the second and third scheme due to the increase of attention mechanism.
A Fast Epipolar Line Matching Method Based on 3D Spherical Panorama

A fast epipolar line matching method based on 3D spherical panorama is proposed in this paper in order to overcome the low automation degree of feature matching in applying the 3D panoramic model measurement. First, the search scope of panoramic image matching was restricted from 2D to 1D strip-shaped buffer zone through the epipolar constraints. The search scope was further refined using color invariant correlation coefficient. Finally, a matching calculation model was constructed based on features to complete the panoramic feature matching. The experiment verified the effectiveness and feasibility of the proposed method in reducing the mismatching of spherical panoramic features. The proposed method also effectively solved the automatic search for corresponding points and resolved geometric information. The findings help lay a solid foundation for the resampling and measurement of the panoramic measurement model and the generation of depth maps.
Foreign Direct Investment, Financial Development and Growth Convergence in ECOWAS

This study examines the tendency of low-income economies in ECOWAS to converge with their high-income neighbors. It extends the frontier of knowledge by ascertaining how quickly financial development (FD) and foreign direct investment (FDI) would stimulate growth, causing low-income ECOWAS member states to catch up. Also, the required threshold for FD and FDI required to facilitate convergence were computed. To achieve the above, fifteen ECOWAS member states were examined within the period 1990 to 2017 using panel data obtained from the World Development Indicators (WDI) 2018 database. The Fully Modified Ordinary Least Square (FMOLS) technique of analysis was utilized and the study found an absence of conditional convergence among ECOWAS member countries. More so, the FD-FDI threshold level required to aid conditional convergence is 22.8% and 3.77% respectively. Therefore to ensure convergence, the study recommends that low-income member states must thrive to attract FDI and seamless credit to the private sector.
Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec

Steganalysis is used for preventing the illegal use of steganography to ensure the security of network communication through detecting whether or not secret information is hidden in the carrier. This paper presents an approach to detect the quantization index modulation (QIM) of steganography in G.723.1 based on the analysis of the probability of occurrence of index values before and after steganography and studying the influence of adjacent index values in voice over internet protocol (VoIP). According to the change of index value distribution characteristics, this approach extracts the distribution probability matrix and the transition probability matrix as feature vectors, and uses principal component analysis (PCA) to reduce the dimensionality. Through a large amount of sample training, the support vector machine (SVM) is designed as a classifier to detect the QIM steganography. The speech samples with different embedding rates and different durations were tested to verify their impact on the accuracy of the steganalysis. The experimental results show that the proposed approach improves the accuracy and reliability of the steganalysis.
Russian academy probe triggers more than 800 retractions

Fraud commission's work causes "tension and conflict."
Blockchain and cryptocurrencies: essential tools in a two-tier financial system

The announcement by Facebook1 on 18 June 2019 of its plans to launch a new cryptocurrency shook the markets and caused renewed global attention to cryptocurrencies. Facebook ‘Libra’, which will be an asset-backed cryptocurrency using the blockchain technology, is to be launched in 2020, and Facebook hopes that it will be widely accepted and be used by people to ‘… .send, receive, spend, and secure their money’.2 Libra will be supported by a basket of real currencies and assets, which will offer it intrinsic value, and, Facebook hopes, also price stability.3 The announcement gave a significant boost to the valuations of the other cryptos, which were already recovering from the 2017/18 crash. By June 26 Bitcoin, the market leader, surged above $13,000 registering an overall return of more than 250 per cent for the year.4 Other cryptocurrencies followed with market analysts seeing evidence of a...
Cinévoqué: Design of a Passively Responsive Framework for Seamless Evolution of Experiences in Immersive Live-Action Movies

In this paper, we present a passively responsive framework for immersive movies, called Cinévoqué. The framework seamlessly alters the narrative and visual elements within an immersive live-action movie, based on real-time passive data, such as gaze direction and system time, obtained during the experience. The paper primarily focuses on the design and storytelling aspects of Cinévoqué, such as possible narrative structures and the design challenges involved in creating responsive experiences. We further examine the potential of this framework through two prototypes of varying complexity and responsive features, and the insights from them are used to suggest approaches that can lead to effective seamless narrative experiences.
Question Generation for Reading Comprehension of Language Learning Test : -A Method using Seq2Seq Approach with Transformer Model-

In this paper, we propose a method using seq2seq approach with the Transformer model to generate questions for reading comprehension of language learning test. First, the method uses the attention mechanism to extract sentences from given articles and combines the extracted sentences with the corresponding question to make pairs of sentences and a question. Second, the method trains the sentences-question pairs to make a seq2seq model. The model can generate questions once sentences are input. Evaluation results showed that more than 50% of the generated questions were appropriate as it is reasonable to be used in reading comprehension tests. Through the analysis of the generated questions, we found the differences between the types of generated questions: one is the question directly related to the given article (DR question) and the other is a common question (CM question). We discussed the reasons why the model tends to generate more CM questions than DR questions. Through the discussion, we found a future direction to improve the method to generate more DR questions.
Searching Correlated Patterns From Graph Streams

Mining the correlation has attracted widespread attention in the research community because of its advantages in understanding the dependencies between objects. In this paper, a correlated graph pattern searching scheme has been proposed, that is, provided with a query g as a structured pattern (i.e., a graph), our algorithm is capable of retrieving the top-k graphs that most likely correlated with g. Traditional methods treat graph streams as static records, which is computational infeasible or ineffective because of the complexity of searching correlated patterns in a dynamic graph stream. In this paper, by relying on sliding windows to separate graph streams in chunks, we propose a Hoe-PGPL algorithm to handle the top-k correlated patterns searching from a dynamic perspective. Our algorithm applies Hoeffding bound and twolevel (Sliding window level and local batch level) candidate inspection to discover potential graph candidates and determine the similarity of these candidates without double-checking the previous stream. Theoretical analysis shows that our method can guarantee the quality of the returned answers, and our experiments also present that Hoe-PGPL has an excellent performance with aspects of precision, recall, runtime, and resource consumption.
Real-time P-wave picking for earthquake early warning system using discrete wavelet transform

P-wave picking is one of the important steps for earthquake parameter determination. Although manual inspection of P-wave arrival timing is the most accurate method for detection but, using automated algorithm is a must in order to facilitate this continuous task. In this research article an automated real-time algorithm is introduced to determine P-wave arrival timing of multiple of stations in a sub-network of the Egyptian National Seismic Network (ENSN). This algorithm generates a daily report of all events recorded by this sub-network. The main advantage of this proposed algorithm is its small processing time to give accurate P-wave arrival timings. This merit had been invested to make a proposal for Earthquake Early Warning (EEW) system using this algorithm. Additionally, this algorithm can detect very small events starting from micro-earthquakes due to the use of multiresolution analysis (MRA) of discrete wavelet transform (DWT). Results show a high rate of successful detections of 94.6%with low false alarm rate.