Reviewerly/exHarmony huggingface dataset:
CBR-based dynamic decision-making method for emergencies

Abstract The randomness of emergencies is very important to emergency management, and case-based reasoning has attracted the attention of emergency decision-makers. In order to improve the one-sidedness of the existing emergency decision-making method based on static case-based reasoning, this paper fully considers the development and evolution stage of the event, and proposes a CBR-based dynamic emergency decision-making method. First, the emergency event is decomposed into multiple time stages; then the comprehensive attributes of each stage are determined according to the event description and treatment measures to calculate the attribute similarity; then the case similarity is calculated through the attribute similarity, and the decision-making plan of current event stage is formed by combining the treatment measures of the case with the highest similarity; at the same time, the current decision-making measures are involved in the case reasoning of the next stage. Finally, a high-rise building fire case in the city shows the feasibility of this method.
Profiling of Malicious Users Targeting Ethereum’s RPC Port Using Simple Honeypots

Blockchain is a service operated by a peer-to-peer type distributed network, and protocol control such as JSON-RPC is implemented as the interface for flexibility and operability. However, attacks that use protocol control against vulnerable and unmanaged interfaces have been reported. One of the methods to track cyber attacks on such a malicious user's network service is a honeypot that imitates the service and acquires attacker's behavior information. In this research, focusing on the Ethereum network, the behavior of malicious users is clarified using malicious communication history sent to simple honeypots installed in nine countries, Ethereum network information and darknet arrival packets. By analyzing these, the behavior of attackers and the tendency of requests were elucidated, and primary safety measures were established.
Kannada Grammar Checker Using LSTM Neural Network

Language is the most fundamental and historically normal means of communication today. Grammar plays a critical role in the excellence of a language. As individuals have already been educated throughout our existence with an accumulation of knowledge that is accrued, mastered over time with guidelines and a restriction of significance that allows us to comprehend and interact one another. But also to translate such awareness into a computer, to be capable of interpreting and classifying contextual evidence into a proper syntactical form, thereby validating that the information was in the correct form, is incredibly necessary at the current time since it is a sophisticated activity. The paper addresses the issue and asserts the advancement of such grammar verifying mechanism for the Dravidian language Kannada. Among the first account would be that the intricacy of the language poses a problem and preferring to have a rule based stance is an easier route and makes it possible to identify detected flaws competently. It takes a linguistic specialist to compile hundreds of parallel standards that are difficult to preserve. Here, a model is advocated that employs a deep learning method to train the LSTM (Long Short Term Memory) neural network trained over a massive data set to fulfill the necessary categorisation, using a context-based retention of the data attained through Word2Vec along with the TensorFlow and Keras packages. The proposed system is able to perform Grammatical Error Detection (GED) effectively.
LSDC - A comprehensive dataset for Low Saxon Dialect Classification

We present a new comprehensive dataset for the unstandardised West-Germanic language Low Saxon covering the last two centuries, the majority of modern dialects and various genres, which will be made openly available in connection with the final version of this paper. Since so far no such comprehensive dataset of contemporary Low Saxon exists, this provides a great contribution to NLP research on this language. We also test the use of this dataset for dialect classification by training a few baseline models comparing statistical and neural approaches. The performance of these models shows that in spite of an imbalance in the amount of data per dialect, enough features can be learned for a relatively high classification accuracy.
Discovering Fully Oriented Causal Networks

We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end we base our approach on the algorithmic Markov condition. Unlike the statistical Markov condition, it uniquely identifies the true causal network as the one that provides the simplest---as measured in Kolmogorov complexity---factorization of the joint distribution. Although Kolmogorov complexity is not computable, we can approximate it from above via the Minimum Description Length principle, which allows us to define a consistent and computable score based on non-parametric multivariate regression. To efficiently discover causal networks in practice, we introduce the Globe algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. Through an extensive set of experiments we show Globe performs very well in practice, beating the state of the art by a margin.
A Traffic Analysis on Serverless Computing Based on the Example of a File Upload Stream on AWS Lambda

The shift towards microservisation which can be observed in recent developments of the cloud landscape for applications has led towards the emergence of the Function as a Service (FaaS) concept, also called Serverless. This term describes the event-driven, reactive programming paradigm of functional components in container instances, which are scaled, deployed, executed and billed by the cloud provider on demand. However, increasing reports of issues of Serverless services have shown significant obscurity regarding its reliability. In particular, developers and especially system administrators struggle with latency compliance. In this paper, following a systematic literature review, the performance indicators influencing traffic and the effective delivery of the provider’s underlying infrastructure are determined by carrying out empirical measurements based on the example of a File Upload Stream on Amazon’s Web Service Cloud. This popular example was used as an experimental baseline in this study, based on different incoming request rates. Different parameters were used to monitor and evaluate changes through the function’s logs. It has been found that the so-called Cold-Start, meaning the time to provide a new instance, can increase the Round-Trip-Time by 15%, on average. Cold-Start happens after an instance has not been called for around 15 min, or after around 2 h have passed, which marks the end of the instance’s lifetime. The research shows how the numbers have changed in comparison to earlier related work, as Serverless is a fast-growing field of development. Furthermore, emphasis is given towards future research to improve the technology, algorithms, and support for developers.
Single-Modal Video Analysis of Personality Traits using Low-Level Visual Features

The ability to analyze the first impression of a person automatically enables novel applications in human-computer interaction and other areas. A person's first impression can decide about a positive or negative outcome in different daily-life situations. The human brain is able to get a picture of the counterpart's personality at short notice. The main aim of this paper is to show how a system based on a standard Convolutional Neural Network (CNN) as well as a 3D-CNN architecture, can be built to solve a multi-label regression task using only visual low-level features. This paper investigates how various pre-processing methods, such as face-extraction and data-augmentation, influence the predicted personality confidences. Furthermore, it explores different training strategies and optimization techniques e.g. regularization in order to improve the model performance. The results of this paper demonstrate an image-based as well as a time-sequence-based system to predict the Big-Five personality dimensions of humans in short video sequences by solving a multi-label regression task. The approaches reach an overall accuracy of 0.891 by using visual features only.
Detection through deep neural networks

The Reservoir Computing, a neural computing framework suited for temporal information processing, utilizes a dynamic reservoir layer for high-dimensional encoding, enhancing the separability of the network. In this paper, we exploit a Deep Learning (DL)-based detection strategy for Multiple-input, Multiple-output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) symbol detection. To be specific, we introduce a Deep Echo State Network (DESN), a unique hierarchical processing structure with multiple time intervals, to enhance the memory capacity and accelerate the detection efficiency. The resulting hardware prototype with the hybrid memristor-CMOS co-design provides the in-memory computing and parallel processing capabilities, significantly reducing the hardware and power overhead. With the standard 180nm CMOS process and memristive synapses, the introduced DESN consumes merely 105mW of power consumption, exhibiting 16.7% power reduction compared to shallow ESN designs even with more dynamic layers and associated neurons. Furthermore, numerical evaluations demonstrate advantages of the DESN over state-of-the-art detection techniques in the literate for MIMO-OFDM systems even with a very limited training set, yielding a 47.8% improvement against conventional symbol detection techniques.
Multi-sensor optical profilometer for measurement of large freeforms at nm-level uncertainty

Abstract Optical measurement of large freeform samples is often limited by the resolution, size and slope limits of measurement devices. This trade-off can be solved using stitching—which, however, creates several difficulties often linked to accuracy of movement of the sample or objective. We present a stitching multisensor freeform topography instrument based on scanning white light interferometer, confocal sensor and accurate movements of the sample tracked using laser interferometers. The interferometers track the sample in 2D at an accuracy of a few nm over a 10 cm × 10 cm area. The instrument is thoroughly characterized and uncertainty is estimated to ensure traceable results. Based on the characterization results the instrument allows topography measurement of freeform sample with 54 nm standard uncertainty for datasets of a few hundred sub-images.
A quantum beat of quantum discord induced by the number of qubits in non-Markovian environments

Abstract We investigate the protection of the quantum discord of two qubits, individually originated by two generalized amplitude-damping models. Here, each generalized amplitude-damping model consists of N non-interacting qubits interacting with a common zero-temperature reservoir. First, for initial states with extended Werner-like states, we find that the robust quantum discord of two qubits against decoherence can be obtained by controlling the number of qubits in the reservoir. More interestingly, we find that a quantum beat for quantum discord can occur when both the number of qubits in each reservoir is large and when the number of qubits have a tiny difference, even in the resonant case. Our results shed new light on the protection of quantum discord by means of a quantum-reservoir engineering approach in quantum-information processing.
Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the tradeoff between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this tradeoff can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself, minimizing the download cost. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with multiple file download.
Faster Unsupervised Object Detection For Symbolic Representation

Symbolic artificial intelligence has seen a wave of intense research in the late 20th century. More recently, the field of deep learning and deep reinforcement learning has been making large stride ...
The development of an immersive three-dimensional virtual reality system for identifying hand–eye coordination in badminton

Hand-eye coordination (HEC) is an important ability in a variety of sports, as it involves the ability to quickly and accurately control motion using visual information. This ability is extensively involved in badminton smash reception. In existing studies on HEC evaluation, this ability was evaluated according to information collected after the motion had been performed. Accordingly, it is impossible to determine the specific phase of perception or motion that causes problems in HEC. The purpose of this study was to develop a system for identifying the abilities of perception involved in HEC as it relates to badminton smash it is considered as badminton's most effective shot. Two systems were developed to identify HEC perceptual abilities, one focused on perception in a static state (i) and the other focused on perception-to-motion comprehensiveness (ii). Next, two types of tests were conducted, one involving (i) and the other using (ii). The tests were randomly administered for a total of 18 participants in three groups: six people who had previously played badminton, six people who had played ball sports other than badminton, and six people who were inexperienced at ball sports. Participants were healthy adults. The tests aimed to identify factors of perceptual ability by comparing them across systems. The results showed no significant difference between experienced badminton players and others according to the conducted tests, where test (i) was used for measuring the distance between the shuttlecock and gaze. A significant difference between the group of experienced badminton players and the other two groups was, however, observed in the test using (ii). The results suggested the ability to follow the shuttlecock with the eyes to be a perceptual ability related to HEC in badminton smash receptions. The results of this study can help evaluate perceptual ability during smash receptions.
Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network

Objective: In this study, it is aimed to classify prostate cancer, compare the predictions of these two models and determine the factors associated with the disease by applying Multilayer Perceptron Neural Network (MLPNN) and Radial-Based Function Neural Network (RBFNN) methods on the open access Prostate cancer dataset.Methods: In this study, the dataset named "Prostate Cancer Data Set" was used by obtaining from https://www.kaggle.com/sajidsaifi/prostate-cancer address. To classify prostate cancer, MLPNN and RBFNN methods, which are artificial neural network models, is used. The classification performance of the models was evaluated with the sensitivity, specificity, accuracy, negative predictive value and positive predictive value, which are among the classification performance metrics. Prostate cancer related factors were estimated by using MLPNN and RBFNN models.Results: With the applied MLPNN model, performance metric values were obtained as AUC 0.937, Sensitivity 100%, accuracy 92.5%, Selectivity 84.6%, Positive predictive value 87.5% and Negative predictive value 100%. With the RBFNN model, the performance metric values were obtained as AUC 0.921, Sensitivity 83.3%, accuracy 86.6%, Selectivity 91.6%, Positive predictive value 93.7% and Negative predictive value 78.5%. When the effects of variables in the dataset in this study on prostate cancer are examined; The three most important variables for the MLPNN model were obtained as perimeter, area and compactness, respectively. For the RBFNN model, the three most important variables were obtained as perimeter, area and compactness, respectively.Conclusion: It was seen that MLPNN and RBFNN models used in this study gave successful predictions in the classification of prostate cancer. In addition, estimating the significance values of factors associated with the disease with these classification models made it different from similar studies with the same dataset.
Is neural always better? SMT versus NMT for Dutch text normalization

Social media texts have become one of the most used forms of written language and a valuable source of information for companies. However, despite the wealth of information hidden in social media language for profiling, information extraction and sentiment analysis purposes, this user-generated content is also characterized by non-standard language. This makes it difficult to process using standard NLP techniques as these are typically trained on standard text. Text normalization is often used as a preprocessing step to overcome this problem. In this work, we take a machine translation perspective on text normalization and investigate both statistical and neural methods. We perform text normalization in a low-resource scenario and report both SMT and NMT experiments for three different Dutch user-generated text types: tweets, message board posts and text messages. Furthermore, we look into overcoming the over-normalization problem in NMT using different techniques, viz. teacher forcing, copy mechanism and byte pair encoding. Our results reveal that even though the SMT approach obtains the best results, the NMT system using CopyNet shows promising results even in this low-resource setting, solving more normalization issues than SMT although not solving all the over-normalization problems.
A Multitask Active Learning Framework for Natural Language Understanding

Natural language understanding (NLU) aims at identifying user intent and extracting semantic slots.This requires sufficient annotating data to get considerable performance in real-world situations.Active learning (AL) has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU.However, no research has been done on investigating how the relation information between intents and slots can improve the efficiency of AL algorithms.In this paper, we propose a multitask AL framework for NLU.Our framework enables poolbased AL algorithms to make use of the relation information between sub-tasks provided by a joint model, and we propose an efficient computation for the entropy of a joint model.Experimental results show our framework can achieve competitive performance with less training data than baseline methods on all datasets.We also demonstrate that when using the entropy as the query strategy, the model with complete relation information can perform better than those with partial information.Additionally, we demonstrate that the efficiency of these active learning algorithms in our framework is still effective when incorporate with the Bidirectional Encoder Representations from Transformers (BERT).
Hardware Implementation of Depthwise Separable Convolution Neural Network

In this paper, an efficient architecture of depthwise separable convolutional neural network is presented. By simplifying the network architecture and adopting 16-bit fixed point form, the computation cost is substantially reduced with slightly decrease in image classification precision based on CIFAR10 dataset. In order to improve energy efficiency and reduce memory access, the custom processing element (PE) is proposed, which supports zero skipping, local accumulation and memory, as well as function multiplexing (for different convolution operations). Besides, the specific hardware architecture based on our custom PE is proposed and the hardware architecture can also support two dataflow modes in order to customize traditional convolution and depthwise separable convolution (DWC) dataflow. The hardware architecture is implemented on a Xilinx Zynq 7Z020 field-programmable gate array (FPGA) platform and the experimental results are implemented. By exploiting parallelism and data reuse, post synthesis simulation with a clock frequency of 50 MHz shows that the network achieves a peak performance of 9.6 GOPS and an energy efficiency of over 90.56 GOPS/W for single-frame runtime inference, achieving a 59× higher energy efficiency compared with the CPU Intel i5-8400. The results show that the proposed accelerator can classify each picture from Cifar10 in 10 ms, which is about 100 frames per second.The FPGA design achieves 41x speedup if compared to CPU, achieving real-time image classification at 100 fps.
Decision Support System For Student Scholarship Recipients Using Simple Additive Weighting Method with Sensitivity Analysis

The scholarship recipients should ideally be given to the appropriate students. Many methods have been widely used to assist the school management in deciding the scholarship recipients. However, such methods do not give additional information and other methods of comparison. The purpose of this research is to provide a systematic and objective scholarship selection recommendation system and using sensitivity analysis to compare the two decision support methods used, i.e. the Simple Additive Weighting and the Weighted Product methods. The Simple Additive Weighting method provides the highest assessment results, namely alternatives with a preference value of 13.27. The Weighted Product method provides the highest assessment results, namely alternatives with a preference value of 0.046. The results of the sensitivity analysis show that the total change value of the Simple Additive Weighting method was 6%, while in the Weighted Product method the total change value was 0.2%. Therefore, the sensitivity analysis showed that the Simple Additive Weighting method better than Weighted Product in determining the scholarship recipient recommendation because it has a greater total change value.
Chinese text classification method using FastText and term frequency-inverse document frequency optimization

Abstract With the development of information technology, obtaining information quickly and accurately has become an indispensable part of people’s lives. Text classification can filter and organize massive text data to find valuable information, and its practical application is of great significance. This paper proposes Chinese text classification method using FastText and term frequency-inverse document frequency optimization(I-FastText). This method introduces term frequency-inverse document frequency(TF-IDF) optimization into the input layer of FastText model, removes words with high frequency and low discrimination ability, and achieves high-quality word vectors to train the text classification model. The experimental results on the THUCNews dataset shown that the Chinese text classification accuracy of I-FastText is significantly better than TF-IDF and FastText methods.