Reviewerly/exHarmony huggingface dataset:
Reflectance spectroscopy in combination with cluster analysis as tools for identifying the provenance of Neolithic flint artefacts

The provenance of flint artefacts found in archaeological excavations is very important for archaeologists to better understand artefact displacement, exchange networks, mobility patterns, prehistoric migrations and various cultural processes such as commerce, procurement strategies, territorial boundaries, etc. Reflectance Spectroscopy is an analytical technique that measures the radiation reflected by any type of surface, as it is sensitive to electronic and vibrational processes, both at the atomic and molecular level, which results in spectral signatures and absorption bands of the material. In this study the flint material of 16 quarries with evident signs of having been used to manufacture artefacts during the Neolithic period have been characterized by means of their spectral signatures and used to identify the origin of 5 flint blades found in a dolmen close to one of the quarries. The application of cluster analysis to compare the spectral signatures of both the quarries and the archaeological blades has ruled out one of them as not being sourced from the studied quarries, as well as assigning a very probable origin to the other four. Interestingly, none of the blades studied have been sourced to the nearest quarry, as one might presume. The results of this study confirm that reflectance spectroscopy combined with the multivariate statistical analysis could provide a powerful tool for determining the origin of flint artefacts which have been deposited in museums and other institutions.
Annealing-behaved 100-Digit challenge problem optimization

In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named Annealing Krill Quantum Particle Swarm Optimization Algorithm (AKQPSO), is proposed, and it is based on annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which is proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on the benchmark problems.
Variational analytic programming for synthesis of optimal control

In this paper we use analytic programming method for optimal control synthesis. We developed variation version of the analytic programming to improve the search process eﬃciency. The method perform search over the set of the small variations of the given basic solution. Search eﬃciency depends on the basic solution. We give an example of optimal control synthesis for the three-dimension system with the state constraints over the set of the initial states using this method.
Classification of Covid-19 Tweets Using Deep Learning Techniques

In this digital era, there is an exponential growth of text-based content in the electronic world. Data as texts exist in the form of documents, social media posts on Facebook, Twitter, etc., logs, sensor data, and emails. Twitter is a social platform where users express their views on various aspects in a day to day life. Twitter produces over 500 million tweets daily that is 6000 tweets per second. Twitter data is, by definition, very noisy and unstructured in nature. Text classifications based on the machine learning techniques have problems like poor generalization ability and sparsity dimension explosion. Classifiers based on deep learning techniques are implemented to improve accuracy to overcome shortcomings of machine learning techniques and to avoid feature extraction processes and have high prediction accuracy and strong learning ability. In this work, the classification of tweets is performed on Covid-19 dataset by implementing deep learning techniques namely Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Recurrent Convolution Neural Network (RCNN), Recurrent Neural Network with Long Short Term Memory (RNN+LSTM), and Bidirectional Long Short Term Memory with Attention (BI-LSTM + Attention). The algorithms are implemented using two-word embedding techniques namely Global Vectors for Word Representation (GloVe) and Word2Vec. RNN with Bidirectional LSTM model has performed better than all the classifiers considered. It has classified the text with an accuracy of 93% and above when used with GloVe and Word2Vec.
A new two-step approach for solving a control system synthesis problem by symbolic regression methods

The problem of synthesizing a control system using symbolic regression methods is considered. A new approach suggested in this work considers the control system synthesis problem as the set of optimal control problems and suggests a two-step algorithm to solve it. At the first step evolutionary algorithms are used to solve the optimal control problem numerically for each initial state from a given domain. As a result of the first step a set of optimal trajectories is obtained. At the second step symbolic regression methods are considered to solve the problem of approximating the found in the first step set of trajectories optimally. The result of the second step is a solution of the control system synthesis problem as a multidimensional control function of object’s state vector. Compared to the known approach to solve the control system synthesis problem using symbolic regression methods, the suggested approach provides betters results since the search of control function is carried out based on the set of optimal trajectories. Computational experiment presents the solution of the applied problem of synthesizing the optimal control system for the spacecraft landing on the Moon. It is experimentally demonstrated that the synthesized control system provides a close to optimal landing trajectory for any initial state from a given domain.
The objective contents of an artistic text: from phenomenology towards mereology

Textual semantics is studied on the basis of subjective — objective reciprocal feedback, where the objective field is reconstructed from the eidetic images with the inversion of their inner relations. The priority belongs to the textual inner world, and the objective field is determined with an epoch's concrete historical conditions. Mereology is developed within the framework of phenomenology as the logical theory dealing with the relation of "part –whole" that in difference to hermeneutics is based on the irreducibility of the whole to the parts and the reciprocity of this relation. The parts are divided into the autonomous and dependent, inner and outer ones. A text is regarded as a hologram where each part represents its functions within the whole. It is the textual frameworks that play the decisive role in textual integration together with reciprocal references of the distant parts and complementary relations. The paragon of framework is to be found in the cues of a role of a dramatic persona. It is due to distanced relations that the dimensionality of a text grows, it surpasses linear and planar limits, its depth and volume are revealed. Such means of frameworks as anaphora procure the textual division onto closed cycles.
Promoting Security Mindset through Hands-on Exercises for Computer Science Undergraduate Students

Software developers play a crucial role in the security of a system. The lack of security knowledge and skills by professionals who are involved in the design and development of software could lead to serious security consequences. The security mindset concept, which involves thinking about how to break the systems as attackers think, allows developers to gain a deeper understanding of how a vulnerability can be successfully exploited. Computer Science students learning to design and develop software or web applications should also be aware of things that make the system fails. In this paper, we discussed hands-on web security exercises that promote security mindset for computer science undergraduate students that other computer science instructors may find useful.
Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp

Performance of neural models for named entity recognition degrades over time, becoming stale.This degradation is due to temporal drift, the change in our target variables' statistical properties over time.This issue is especially problematic for social media data, where topics change rapidly.In order to mitigate the problem, data annotation and retraining of models is common.Despite its usefulness, this process is expensive and timeconsuming, which motivates new research on efficient model updating.In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training.We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset.Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution. 1
A Novel Partitioning Algorithm to Process Large-Scale Data

In mathematics and graph theory, the graph partitioning problem defines the reduction of graphs into smaller graphs by partitioning its set of nodes into mutually exclusive groups. Fundamentally, finding a partition that simplifies a graph is very hard to figure out and hence, the problem falls under the NP-hard category. Numerous algorithms and mechanisms exist for the evaluation of graph partitioning. In this paper, we have discussed a novel partitioning process named pairwise partitioning, where the prime focus is to derive all possible pairs of vertices to simplify any graph. The process of partitioning has been developed using an equality-inequality mechanism. The entire graph will be decomposed into meaningful pairs represented through certain sets where each set must contain distinct pairs of vertices. Nowadays, data management is an essential task to be performed. To deal with a large amount of data, it should be very difficult and next to impossible. In this situation, our proposed algorithm (pairwise partitioning algorithm) can provide a good solution. As we can simplify a graph in the form of pairs, so, a better understanding should be developed to analyze any graph. Throughout this paper, we have discussed the working principle of the pairwise partitioning algorithm and its significant impact on big data analysis.
Attitude Control in Unmanned Aerial Vehicles Using Reinforcement Learning—A Survey

Unmanned Aerial Vehicles (UAVs) have great potential in various fields. With ongoing research, the day is not far when they would be directly impacting our lives. The promise of deep learning techniques like reinforcement learning has created a window of opportunity for their use in a plethora of tasks, like the attitude control problem of UAVs. Attitude of a UAV is the angle at which it is flying relative to the ground. Attitude control is the management of the orientation of a UAV with respect to the inertial frame. In this paper, we have surveyed reinforcement learning algorithms to learn attitude control of UAVs and be able to take decisions in unforeseen circumstances. Reinforcement learning is the branch of deep learning where there is no human intervention in training the model. Instead, the system learns over time by trial and error. Since navigation in the air presents scenarios that may be new and unexpected for a UAV, reinforcement learning presents a viable option for their use in attitude control in them.
A Good Start is Half the Battle Won: Unsupervised Pre-training for Low Resource Children’s Speech Recognition for an Interactive Reading Companion

Children's speech recognition is a challenging task because of the inherent speech production characteristics of children's articulatory structure as well as their linguistic usage. In the context of developing automated reading companions, the problem is compounded by lack of training data. Most of the available data is recorded under clean and controlled conditions leading to a performance degradation in presence of uncontrolled and realistic acoustic environments. In this study, we address these challenges by leveraging a publicly available large unlabeled read speech corpus to learn generalized audio representations. These learned representations are then employed to augment the features used for training the acoustic model of limited in-domain children's speech. The representations are learned via a deep convolutional architecture optimized on a noise contrastive binary classification task to distinguish a true future audio sample from negatives. We obtain upto 24.87% relative improvement in the Word Error Rate (WER) of our speech recognition system using these generalized audio embeddings and show the effectiveness of using a pre-trained model when training data is limited.
Research on the application strategy of new media technology in Modern University Law Teaching

The arrival of the new media era has a certain impact on the teaching environment of universities in China. The rapid development of new media has also profoundly affected the thinking mode, behavior style and psychological consciousness of college students. This paper puts forward some innovative teaching modes under the background of new media information technology, including the online simulation court, the construction of interactive dynamic teaching website and so on. It realizes the deep integration between law teaching and modern new media technology, and gradually forms an open and diversified teaching mode. The research content of this paper has far-reaching significance for promoting the teaching of new media technology and enhancing the pertinence and effectiveness of College Students’ legal education.
Sarcasm Detection on Twitter Data Using R and Python

A challenging task in Opinion Mining is to detect sarcasm. Sarcasm is a way of telling things in a positive way where the intended meaning is negative. Sarcastic texts lead to wrong classification of data due to the usage of positive words to convey negative emotions. It is important to detect sarcasm on social media data because there is no face-to-face conversation happening to understand the context. When this data is taken for sentiment analysis and decision-making process it leads to wrong inferences. The proposed work classifies the dataset into sarcastic and non-sarcastic using machine learning algorithms and the same has been implemented using R and Python for finding the difference in accuracy. The dataset has been collected from Twitter. Naïve Bayes algorithm is used for the classification. The result obtained from sarcasm detection on social media data will be helpful in areas such as sentiment analysis, natural language processing, opinion mining, business-related decision making, and so on. The result of the proposed method classified the given data into sarcastic and non-sarcastic with an accuracy of 0.91 and 0.88 using R and Python respectively.
Proprioceptive Stimulation Added to a Walking Self-Avatar Enhances the Illusory Perception of Walking in Static Participants

When immersed in virtual reality, users who view their body as a co-located virtual avatar that reflects their movements, generally develop a sense of embodiment whereby they perceive the virtual body to be their own. One aspect of the sense of embodiment is the feeling of agency over the avatar, i.e., the feeling that one is producing the movements of the avatar. In contexts such as physical rehabilitation, telepresence and gaming, it may be useful to induce a strong sense of agency in users who cannot produce movements or for whom it is not practical to do so. Being able to feel agency over a walking avatar without having to produce walking movements could be especially valuable. Muscle vibrations have been shown to produce the proprioceptive perception of movements, without any movement on the part of the user. The objectives of the current study were to: 1-determine if the addition of lower-limb muscle-vibrations with gait-like patterns to a walking avatar can increase the illusory perception of walking in healthy individuals who are standing still; 2-compare the effects of the complexity of the vibration patterns and of their synchronicity on the sense of agency and on the illusory perception of walking. Thirty participants viewed a walking avatar from a first-person perspective, either without muscle vibrations or with one of four different patterns of vibrations. These five conditions were presented pairwise in a two-alternative forced choice paradigm and individually presented, after which participants answered an embodiment questionnaire. The displacement of center of pressure of the participants was measured throughout the experiment. The results show that all patterns of proprioceptive stimulation increased the sense of agency to a similar degree. However, the condition in which the proprioceptive feedback was realistic and temporally aligned with the avatar’s leg movements led to significantly larger anteroposterior sway of the center of pressure. The frequency of this sway matched the cadence of the avatar’s gait. Thus, congruent and realistic proprioceptive stimulation increases the feeling of agency, the illusory perception of walking and the motor responses of the participants when viewing a walking avatar from a first-person perspective.
Teacher-Supervised Generative Adversarial Networks

Although generative adversarial networks (GANs) show impressive effects on image generation, existing GANs suffer an unstable training process, and thus result in poor image quality sometimes. To solve this problem, we first introduce a supervision mechanism into GANs and propose a teacher-supervised GAN (GAN-T) model. Specifically, we design a teacher supervision mechanism to inspect whether the features of generated images are as real as those of real images. If not, we add action into the generator. The action takes the encoding of the real image as prior knowledge to guide the generation of samples. We then apply our proposed method to existing GANs to show its compatibility with them. Finally, we conduct extensive experiments on the tasks of noise-to-image generation and image translation, and experimental results show that our proposed method can significantly stabilize the training process of the generator and improve the quality of generated images.
Interface-Free Connection of Mobile Robot Cells to Machine Tools Using a Camera System

Part complexity increase the ratio of manual tasks on machine tools, i.e. feeding, to maintain high productivity. Mobile robot cells automate these tasks but require cost-intensive retrofittings for additional interfaces. This paper focuses on a new mobile robot cell without any mechanical or electrical connection to the machine tool. Therefore, the solution uses a camera system for wireless machine tool localization and signal transmission.
The Classification of Fetus Gender Based on Fuzzy C-Mean Using a Hybrid Filter

Abstract This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT &amp; LBP) and (median &amp; Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median &amp; Laplace has recorded a better accuracy. Our experimental evaluation on real data from the Kadhimiya teaching hospital shows that the proposed CUHF is a better method when compared to the accuracy of the other integrated filters.
Packing of spanning mixed arborescences

Abstract In this paper, we characterize a mixed graph which contains edge and arc‐disjoint spanning mixed arborescences , such that for each , the cardinality of lies in some prescribed interval. This generalizes both Nash‐Williams and Tutte's theorem on spanning tree packing for undirected graphs and the previous characterization on digraphs which was given by Cai and Frank.
Reducing the detection of genuine entanglement of n qubits to two qubits

We propose a criterion for the detection of genuine entanglement of pure multiqubit states. To this aim, we define an operator called the losing one qubit operator, which is different from the reduced density operator. The states obtained from a multiqubit state by applying the losing one qubit operator are referred to as its projected states. We show that all of the projected states of a pure product n-qubit state are pure product states provided that it cannot be written as a product of a single qubit state and a genuinely entangled ( $$n-1$$ )-qubit state. We also show that a pure n-qubit state is genuinely entangled provided that the state has at least two genuinely entangled ( $$n-1$$ )-qubit projected states. By repeating the losing process, we reduce the detection of entanglement of pure n-qubit states to the one of pure two-qubit states. Also we write a LISP program for the reduction process.
Variable structure interacting multiple model for highly aperiodic sensors with poor measurement precision

The issue of aircraft tracking using passive sensors with poor measurement precision and a variable update rate is investigated. First, the need for a time-dependent Markov chain transition matrix for the Interacting Multiple Model (IMM) is thoroughly evaluated using simulation and real data from an experimental multilateration system. Three different methods for computing the time-dependent matrix are compared, showing their superiority for highly aperiodic sensors. Second, the Variable Structure Interacting Multiple Model (VS-IMM) for passive sensors with poor geometry is proposed, in which the mode set update is based on local dilution of precision or distance root mean square error. The performance of the estimator is compared with the IMM using real data. The VS-IMM results in the best performance for areas of both high and low measurement precision.
The Development of the Traditional Metrology in China

In this chapter, a history of the origin and the development of metrology in ancient China has been given. An analysis of the conversion from the traditional metrology to the modern metrology in China has also been conducted.
Advertisement Click Fraud Detection

Mobile ads are tormented with deceitful clicks that may be an important test for the advertising network. Albeit documented promotion systems utilize various methods to acknowledge click fraud, they don’t shield the client from conceivable intrigue among distributors and advertisement systems. What’s additional, promotion systems can’t screen the client’s action for click fraud location once they're entertained to the advertising web content following clicking the ad, i.e., in a web site there will be focuses or some specific spots after we click on the actual spot it'll be entertained to the advertising web site. We propose another publically support-based mostly framework referred to as Click Fraud Crowdsourcing (CFC) that works in conjunction with the two promoters and advertisement organizes therefore as to defend the two gatherings from any conceivable click dishonorable acts. The framework profits by each a worldwide view, wherever it accumulates various promotion demands examining to various advertising organize distributer publicist blends, and a nearby read, wherever it will follow the clients’ commitment in every advertising website.