Reviewerly/exHarmony huggingface dataset:
Development of multiple mobile networks call detailed records and its forensic analysis

Call Detailed Records (CDR) are generated and stored in Mobile Networks(MNs) and contain subscriber's information about active or passive usage of the network for various communication activities. The spatio-temporal nature of CDR makes them a valuable dataset used for forensic activities. Advances in technology have led to the seamless communication across Multiple Mobile Network (MMN), which poses a threat to the availability and integrity of CDR data. Present CDR implementation is capable of logging peer-to-peer communications over single connection only, thus necessitating improvements on how the CDR data is stored for forensic analysis. In this paper, the problem is solved by identifying and conceptually modelling six new artifacts generated by such communication activities. The newly identified artifacts are introduced into the existing CDR for an incident capturing of the required data for forensic analysis during investigations involved in the MMN communication. Results show an improved absolute speed of 0.0058 s for the MMN-CDR to associate a suspect with an incident, which is 0.0038 s faster than the speed of 0.0097s for the existing CDR to associate a suspect with an accomplice. Thus, a novel method for forensically tracking calls over the MMN has been developed. The MMN-CDR, when forensically analyzed, reveals an increase in time efficiency over the existing CDR due to its high absolute speed. Also, higher accuracy and completeness percentage are both obtained.
Predicting slow and fast neuronal dynamics with machine learning

In this work, we employ reservoir computing, a recently developed machine learning technique, to predict the time evolution of neuronal activity produced by the Hindmarsh-Rose neuronal model. Our results show accurate short- and long-term predictions for periodic (tonic and bursting) neuronal behaviors, but only short-term accurate predictions for chaotic neuronal states. However, after the accuracy of the short-term predictability deteriorates in the chaotic regime, the predicted output continues to display similarities with the actual neuronal behavior. This is reinforced by a striking resemblance between the bifurcation diagrams of the actual and of the predicted outputs. Error analyses of the reservoir’s performance are consistent with standard results previously obtained.
Optimizing Deep Learning RNN Topologies on Intel Architecture

Recurrent neural network (RNN) models have been found to be well suited for processing temporal data. In this work, we present an optimized implementation of vanilla RNN cell and its two popular variants: LSTM and GRU for Intel Xeon architecture. Typical implementations of these RNN cells employ one or two large matrix multiplication (GEMM) calls and then apply the element-wise operations (sigmoid/tanh) onto the GEMM results. While this approach is easy to implement by exploiting vendor-optimized GEMM library calls, the data reuse relies on how GEMMs are parallelized and is sub-optimal for GEMM sizes stemming from small minibatch. Also, the element-wise operations are exposed as a bandwidth-bound kernel after the GEMM which is typically a compute-bound kernel. To address this discrepancy, we implemented a parallel blocked matrix GEMM in order to (a) achieve load balance, (b) maximize weight matrix reuse, (c) fuse the element-wise operations after partial GEMM blocks are computed and while they are hot in cache. Additionally, we bring the time step loop in our cell to further increase the weight reuse and amortize the overhead to transform the weights into blocked layout. The results show that our implementation is generally faster than Intel MKL-DNN library implementations, e.g. for RNN, forward pass is up to ~3× faster whereas the backward/weight update pass is up to ~5× faster. Furthermore, we investigate high-performance implementations of sigmoid and tanh activation functions that achieve various levels of accuracy. These implementations rely on minimax polynomial approximations, rational polynomials, Taylor expansions and exponential approximation techniques. Our vectorized implementations can be flexibly integrated into deep learning computations with different accuracy requirements without compromising performance; in fact, these are able to outperform vectorized and reduced accuracy vendor-optimized (Intel SVML) libraries by 1.6–2.6× while speep up over GNU libm is close to two orders of magnitude. All our experiments are conducted on Intel’s latest CascadeLake architecture.
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification

Triggered by Internet development, a large amount of information is published in online sources. However, it is a well-known fact that publications are inundated with inaccurate data. That is why fact-checking has become a significant topic in the last 5 years. It is widely accepted that factual data verification is a challenge even for the experts. This paper presents a domain-independent fact checking system. It can solve the fact verification problem entirely or at the individual stages. The proposed model combines various advanced methods of text data analysis, such as BERT and Infersent. The theoretical and empirical study of the system features is carried out. Based on FEVER and Fact Checking Challenge test-collections, experimental results demonstrate that our model can achieve the score on a par with state-of-the-art models designed by the specificity of particular datasets.
CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present constrained text generation task, CommonGen associated with benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given set of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to generate coherent sentence describing an everyday scenario using these concepts (e.g., a man throws frisbee and his dog catches it). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through combination of crowdsourced and existing caption corpora, consists of 79k commonsense descriptions over 35k unique concept-sets. Experiments show that there is large gap between state-of-the-art text generation models (e.g., T5) and human performance. Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
Encoding Classical Information Into Quantum Resources

We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance.
Splitting Steepest Descent for Growing Neural Architectures

We develop a progressive training approach for neural networks which adaptively grows the network structure by splitting existing neurons to multiple off-springs. By leveraging a functional steepest descent idea, we derive a simple criterion for deciding the best subset of neurons to split and a \emph{splitting gradient} for optimally updating the off-springs. Theoretically, our splitting strategy is a second order functional steepest descent for escaping saddle points in an $\Linfty$-Wasserstein metric space, on which the standard parametric gradient descent is a first-order steepest descent. Our method provides a new computationally efficient approach for optimizing neural network structures, especially for learning lightweight neural architectures in resource-constrained settings.
The Trend of Mathematics Teaching Method Has Change From Fragments To Systematics

According to the school observation in China, There are still fragmentation and superficiality problems in front-line teachers’ mathematics teaching method. The reason is that the general system theory has not been rooted in the mathematics teaching method. This study aims to develop Dick-Carey model of systematic design and used in the learning activity. Based on system analysis model, Dick-Carey model of systematic design, and the “Six Questions” cognitive concepts, this paper tries to construct a systematic plan of teaching mathematics. In order to restore the structure, improve the breadth, depth and openness of teaching plan and to promote the systematic model into better teaching method. At the same time, taking the design of "intersecting lines" as an example, this paper explores the effective strategies of systematic design in mathematics teaching.
Entrainment2Vec: Embedding Entrainment for Multi-Party Dialogues

Entrainment is the propensity of speakers to begin behaving like one another in conversation. While most entrainment studies have focused on dyadic interactions, researchers have also started to investigate multi-party conversations. In these studies, multi-party entrainment has typically been estimated by averaging the pairs' entrainment values or by averaging individuals' entrainment to the group. While such multi-party measures utilize the strength of dyadic entrainment, they have not yet exploited different aspects of the dynamics of entrainment relations in multi-party groups. In this paper, utilizing an existing pairwise asymmetric entrainment measure, we propose a novel graph-based vector representation of multi-party entrainment that incorporates both strength and dynamics of pairwise entrainment relations. The proposed kernel approach and weakly-supervised representation learning method show promising results at the downstream task of predicting team outcomes. Also, examining the embedding, we found interesting information about the dynamics of the entrainment relations. For example, teams with more influential members have more process conflict.
The Effect of Feature Selection on Classification Algorithms in Credit Approval

Data mining applications usually use datasets with large dimensions. Unfortunately, the large dimension in a dataset affects the processing time and outcome of the classification. One solution to this problem is to select relevant features for the reduce dimensions. The selection of the right features can increase the accuracy of the classification process. This study proposes a proper feature selection model for increasing the accuracy for specific classifier models by comparing several existing feature selection models and some of the classifier. The feature selection, we use information gain, gain ratio, and correlation-based feature selection (CBFS) while the classifier we employee K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Artificial Neural Network and Decision Tree. Evaluation is done using several public databases from UCI Machine Learning Repository. Evaluation results indicate that all feature selection methods are increasing the accuracy, but Decision Tree only increases in CBFS. Based on the result, feature selection does not always improve classifier accuracy but depends on the characteristics and algorithms used.
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2+s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019).
Embarrassingly Parallel Inference for Gaussian Processes

Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice of model structure can ameliorate this problem. A mixture-of-experts model that uses a mixture of $K$ Gaussian processes offers modeling flexibility and opportunities for scalable inference. Our embarrassingly parallel algorithm combines low-dimensional matrix inversions with importance sampling to yield a flexible, scalable mixture-of-experts model that offers comparable performance to Gaussian process regression at a much lower computational cost.
Experimental Assessment of the Performance of Data Augmentation with Generative Adversarial Networks in the Image Classification Problem

Deep Learning algorithms have almost become a key standard for majority of vision and machine learning problems. Despite its common usage and high performance for many applications, they have certain disadvantages. One major problem with deep learning methods is the size of the dataset to be used for training. The methods require a large dataset for an adequate training. However, a large dataset may not be available for all problems. In such a case, researchers apply data augmentation methods to obtain a larger dataset from a given dataset. For the image classification problem, conventional method for data augmentation is the application of transformation based methods; such as flipping, rotation, blurring etc. Recently, generative models which apply deep learning methods are also commonly used for data augmentation. On the other hand, in case of a too large dataset the classifiers may overfit and end up with a lack of generalization. In this study, we explore the usage of generative adversarial networks for data augmentation in the image classification problem. We evaluate the classification performance with three types of augmentation methods. Dataset is first augmented by two conventional methods; Gaussian blurring and dropout of regions, then by generative adversarial networks. Meanwhile, we observe the behavior of the classifier for various sized datasets with and without data augmentation. We observe that especially in datasets of certain sizes generative adversarial networks can be effectively used for data augmentation.
Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing

In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).
Scene Text Detection with Polygon Offsetting and Border Augmentation

Scene text localization is a very crucial step in the issue of scene text recognition. The major challenges—such as how there are various sizes, shapes, unpredictable orientations, a wide range of colors and styles, occlusion, and local and global illumination variations—make the problem different from generic object detection. Unlike existing scene text localization methods, here we present a segmentation-based text detector which can detect an arbitrary shaped scene text by using polygon offsetting, combined with the border augmentation. This technique better distinguishes contiguous and arbitrary shaped text instances from nearby non-text regions. The quantitative experimental results on public benchmarks, ICDAR2015, ICDAR2017-MLT, ICDAR2019-MLT, and Total-Text datasets demonstrate the performance and robustness of our proposed method, compared to previous approaches which have been proposed.
FORECASTING CRYPTOCURRENCY PRICE MOVEMENT USING MOVING AVERAGE METHOD : A CASE STUDY OF BITCOIN CASH

The aim of this study is to develop forecasting cryptocurrency price movement using moving average.The cryptocurreny that selected in this study is Bitcoin Cash.The observation periods involved in this study are starting from 1 st October 2019 until 20 th December 2019.The price of Bitcoin Cash are collected from https://www.coindesk.com.The moving average forecasting method implemented using 2-days, 3days, 4-days and 7-days calculation.The value of mean absolute error percentage for 2-days moving average forecasting method is 3.1 %.The significant of this study is it can help investors to determine the price movement of cryptocurrency in selecting best option for investment portfolio.
Triggers analysis of an agile transformation: the case of a central bank

Agile transformation is one of the central topics for many IT departments in large organizations. Convinced by the benefits, large companies have entered a generalization phase of these approaches at the level of all information system projects. But little research informs practitioners about how a company goes from an experimentation phase to the generalization. Knowing that agile methods generate several changes in roles, processes and culture, our paper aims to answer the following question: what are the triggers of a decision that will enable an organization moving from experimentation to a wide generalization of an agile method? Based on a qualitative research design via a case study seeking to explain this complex phenomenon. We investigated the adoption process of agile methods during the last 13 years in the IT department of the French central bank. Contributions and practical implications highlight successful ingredients allowing to organize the implementation of agile methods in a large public organization.
Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.