Reviewerly/exHarmony huggingface dataset:
Real Time Face Mask Detection using Google Cloud ML and Flutter

Abstract Coronavirus (Covid-19) pandemic has impacted the whole world and has forced health emergencies internationally. The contact of this pandemic has been fallen over almost all the development sectors. A lot of precautionary measures have been taken to control the Covid-19 spread, where wearing a face mask is an essential precaution. Wearing a face mask correctly has been essential in controlling the Covid-19 transmission. Moreover, this research aims to detect the face mask with fine-grained wearing states: face with the correct mask and face without mask. Our work has two challenging tasks due to two main reasons firstly the presence of augmented data set available in the online market and the training of large datasets. This paper represents a mobile application for face mask detection. The fully automated Machine Learning Cloud service known as Google Cloud ML API is used for training the model in TensorFlow file format. This paper highlights the efficiency of the ML model. Additionally, this paper examines the advancement of the cloud technology used for machine learning over the traditional coding methods.
Neural Factorization Applied to Interaction Matrix for Recommendation

Matrix factorization methods have successfully been used for rating prediction. The interactions between users and items are recorded in the interaction matrix. In this paper, a neural matrix factorization method is proposed that is applied to the interaction matrix. More specifically, the normalized interaction matrix is given as input to the neural network in order to extract user and item embeddings. The estimated ratings are obtained by the inner product between the extracted user and item embeddings. The proposed method is assessed in movie rating prediction by employing three MovieLens datasets. It is demonstrated that the proposed neural factorization attains competitive performance and is less computationally demanding against the state-of-the-art methods in movie rating prediction.
Model Selection Strategies for Determining the Optimal Number of Overlapping Clusters in Additive Overlapping Partitional Clustering

Abstract In various scientific fields, researchers make use of partitioning methods (e.g., K -means) to disclose the structural mechanisms underlying object by variable data. In some instances, however, a grouping of objects into clusters that are allowed to overlap (i.e., assigning objects to multiple clusters) might lead to a better representation of the underlying clustering structure. To obtain an overlapping object clustering from object by variable data, Mirkin’s ADditive PROfile CLUStering (ADPROCLUS) model may be used. A major challenge when performing ADPROCLUS is to determine the optimal number of overlapping clusters underlying the data, which pertains to a model selection problem. Up to now, however, this problem has not been systematically investigated and almost no guidelines can be found in the literature regarding appropriate model selection strategies for ADPROCLUS. Therefore, in this paper, several existing model selection strategies for K -means (a.o., CHull, the Caliński-Harabasz, Krzanowski-Lai, Average Silhouette Width and Dunn Index and information-theoretic measures like AIC and BIC) and two cross-validation based strategies are tailored towards an ADPROCLUS context and are compared to each other in an extensive simulation study. The results demonstrate that CHull outperforms all other model selection strategies and this especially when the negative log-likelihood, which is associated with a minimal stochastic extension of ADPROCLUS, is used as (mis)fit measure. The analysis of a post hoc AIC-based model selection strategy revealed that better performance may be obtained when a different—more appropriate—definition of model complexity for ADPROCLUS is used.
Study on Automatic Evaluation Method of Spoken English Based on Multimodal Discourse Analysis Theory

Aiming at the low confidence of traditional spoken English automatic evaluation methods, this study designs an automatic evaluation method of spoken English based on multimodal discourse analysis theory. This evaluation method uses sound sensors to collect spoken English pronunciation signals, decomposes the spoken English speech signals by multilayer wavelet feature scale transform, and carries out adaptive filter detection and spectrum analysis on spoken English speech signals according to the results of feature decomposition. Based on multimodal discourse analysis theory, this evaluation method can extract the automatic evaluation features of spoken English and automatically recognize the speech quality according to the results. The experimental results show that, compared with the control group, the designed evaluation method has obvious advantages in confidence evaluation and can solve the problem of low confidence of traditional oral automatic evaluation methods.
Integrating Character-level and Word-level Representation for Affect in Arabic Tweets

Affect tasks, which range from sentiment polarity classification to finer grained sentiment strength and emotional intensity detection, have become of increasing interest due to the vast amount of user-generated content and advanced learning models. Word representation models have been leveraged effectively within a variety of natural language processing tasks. However, these models are not always effective in the context of social media. When dealing with social media posts in Arabic, the use of Arabic dialects needs to be considered. Although using informal text to train word-level models can lead to the identification of words that convey the same meaning, these models are unable to capture the full extent of the words that are used in the real world due to out-of-vocabulary (OOV) words. The inability to identify such words is one of the main limitations of word-level models. One approach of overcoming OOV is through the use of character-level embeddings as they can effectively learn the vectors of word parts or character n-grams. This study uses a combination of character-level and word-level models to identify the most effective methods by which affective Arabic words in tweets can be represented semantically and morphologically. We evaluate our generated models and the proposed method by integrating them in a supervised learning framework that was used for a range of affect tasks and other related tasks. Our findings reveal that the developed models surpassed the performance of state-of-the-art Arabic pre-trained word embeddings over eight datasets. In addition, our models enhance previous state-of-the-art outcomes on tasks involving Arabic emotion intensity, outperforming the top-systems that used advanced ensemble learning models and several additional features.
A Seismic Sensor based Human Activity Recognition Framework using Deep Learning

Activity recognition has gained attention due to the rapid development of microelectromechanical sensors. Numerous human-centric applications in healthcare, security, and smart environments can benefit from an efficient human activity recognition system. In this paper, we demonstrate the use of a seismic sensor for human activity recognition. Traditionally, researchers have relied on handcrafted features to identify the target activity, but these features may be inefficient in complex and noisy environments. The proposed framework employs an autoencoder to map the activity into a compact representative descriptor. Further, an Artificial Neural Network (ANN) classifier is trained on the extracted descriptors. We compare the proposed framework with multiple machine learning classifiers and a state-of-the-art framework on different evaluation metrics. On 5-fold cross-validation, the proposed approach outperforms the state-of-the-art in terms of precision and recall by an average of 10.68 and 23.36%, respectively. We also collected a dataset to assess the efficacy of the proposed seismic sensor-based activity recognition. The dataset is collected in a variety of challenging environments, such as variable grass length, soil moisture content, and the passing of unwanted vehicles nearby.
Extreme learning with Artifical Electric Field Algorithm (EL-AEFA) for estimation of Short-term Vehicular Traffic System

The faster learning and better approximation ability of extreme learning machine (ELM) made it a popular learning method for neural network training. However, two issues such as probable inclusion of non-optimal parameters because of random selection and size of hidden layer may unfavorably impact the network performance. To address these issues, this article combines the artificial electric field algorithm (AEFA) and ELM to construct a new learning framework called as extreme learning with AEFA (EL-AEFA). Then, EL-AEFA is used to find the optimal model parameters (i.e., weight, bias and number of hidden neurons) of a neural network with single hidden layer. The proposed method is used to estimate the short-term traffic volume through exploitation of data available publicly. The model exhibits minimal structural and computational complexity, follows adaptive training and generates lowest mean absolute percentage of error (MAPE) values compared to few other forecasts developed in a similar way. Therefore, it may be recommended as a promising tool for traffic volume prediction.
Active safe motion planning for intelligent vehicles in dynamic environments

Motion planning is an essential component in intelligent vehicle study. Rapidly-exploring Random Tree(RRT) and its variants are popular algorithms that have been successfully applied in solving motion planning problems. However, obtaining an optimal trajectory while concerning driving safety in dynamic environments is a difficult problem. In this study, we present an active safe RRT(AS-RRT) motion planning algorithm that enable the intelligent vehicle to avoid collision risks and find an efficient path in the dynamic environment. The algorithm firstly reconstructs a potential field-based configuration space for static obstacles and moving vehicles, which defines the risk regions. Then, it develops an RRT tree through samples in the space with considerations of nonholonomic constraints of the vehicles. A comprehensive cost function is used for the priority sequence mechanism to get an initial trajectory. After that, the trajectory is asymptotically optimized gradually by decreasing the cost iteratively. Simulation results demonstrated that the proposed algorithm improved the vehicles’ motion planning safety performance in dynamic environments.
Simultaneous Measurements Reading from More Than One MiBand 3 Wristbands

This paper presents the research done on simultaneous data acquisition from multipleRodak, Zuzanna Tokarz, Krzysztof Mielnik, Pawel Fojcik, Marcin smart wristbands. The wristbands, besides monitoring fitness features, can be used to monitor health parameters like heart rate, blood pressure, and speed and dynamics of movement. The movement analysis may be used to monitor progress in rehabilitation. In order to analyze human movement more accurate, it is useful to gather data from several accelerometers simultaneously. The studies were based on raw data gathered from wristbands to collect and analyze the time between the readings depending on the number of connected devices. Additionally, the analysis was made of the difference in readings of the accelerometers from several devices to estimate the mean error of measurements.
On Lossy Compression of Directed Graphs

The method of types presented by Csiszár and Körner is a central tool used to develop and analyze the basic properties and constraints on sequences of data over finite alphabets. A central problem considered using these tools is that of data compression, and specifically lossy data compression. In this work we consider this very problem, however, instead of sequences of data we consider directed graphs. We show that given a more natural distortion measure, fitting the data structure of a directed graph, the method of types cannot be applied. The suggested distortion measure aims to preserves the local structure of a directed graph. We build on the recent work of Barvinok and extend the method of types to the two dimensional setting of directed graphs. We see that the extension is quite natural in many ways. Given this extension we provide a lower and upper bound on the rate-distortion problem of lossy compression given the suggested distortion measure.
An Automatic Approach to Extracting Requirement Dependencies based on Semantic Web

For a complex system with a large number of requirement texts, manual analysis of the dependencies between the requirement texts is very time-consuming and requires heavy workload. This paper proposes an automatic approach to extract requirement dependencies based on semantic web. Firstly, requirements text is formalized as a set of keywords by segmenting the requirement text and making part-of-speech tagging. Secondly, the requirement dependencies are automatically obtained by constructing the semantic web ontology and defining inference rules. Finally, the results of requirement dependence extraction and expert extraction are compared and analyzed. The results show that the proposed method has high feasibility and accuracy which can save a lot of human resources and cost.
Information Visualization: Features and Challenges in the Production of Data Stories

Information visualization is a communicative discipline that has experienced a continuous growth and development through the centuries. From its origins and first vestiges in the ancient civilizations of Egypt and Mesopotamia to the most up-to-date forms marked by the development of information technology, visual communication has been an effective tool. Furthermore, over the last few years its presence as a journalistic genre has been strengthened, especially with the emergence of new needs arising from the increasing availability of data. This chapter explores some of the challenges that information visualization has to face in our days, like the need to adapt the content to the particularities of different platforms and screens. The authors also address the particularities of interaction in these projects, one of their most salient features due to his usefulness in organizing and supporting storytelling.
Black-Box Adversarial Attacks against Audio Forensics Models

Speech synthesis technology has made great progress in recent years and is widely used in the Internet of things, but it also brings the risk of being abused by criminals. Therefore, a series of researches on audio forensics models have arisen to reduce or eliminate these negative effects. In this paper, we propose a black-box adversarial attack method that only relies on output scores of audio forensics models. To improve the transferability of adversarial attacks, we utilize the ensemble-model method. A defense method is also designed against our proposed attack method under the view of the huge threat of adversarial examples to audio forensics models. Our experimental results on 4 forensics models trained on the LA part of the ASVspoof 2019 dataset show that our attacks can get a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>99</mn> <mo>%</mo> </math> attack success rate on score-only black-box models, which is competitive to the best of white-box attacks, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>60</mn> <mo>%</mo> </math> attack success rate on decision-only black-box models. Finally, our defense method reduces the attack success rate to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>16</mn> <mo>%</mo> </math> and guarantees <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>98</mn> <mo>%</mo> </math> detection accuracy of forensics models.
A Hybrid Multi-answer Summarization Model for the Biomedical Question-Answering System

In natural language processing problems, text summarization is a difficult problem and always attracts attention from the research community, especially working on biomedical text data which lacks supporting tools and techniques. In this scientific research report, we propose a multi-document summarization model for the responses in the biomedical question and answer system. Our model includes components which is a combination of many advanced techniques as well as some improved methods proposed by authors. We present research methods applied to two main approaches: an extractive summarization architecture based on multi scores and state-of-the-art techniques, presenting our novel prosper-thy-neighbor strategies to improve performance; EAHS model (Extractive-Abstractive hybrid model) based on a denoising auto-encoder for pre-training sequence-to-sequence models (BART). In which we propose a question-driven filtering phase to optimize the selection of the most useful information. Our propose model has achieved positive results with the best ROUGE-1/ROUGE-L scores being the runner-up by ROUGE-2 <tex>$F1$</tex> score by extractive summarization results (over 24 participated teams in MEDIQA2021).
Research on Design Document Tampering Detection and Location Based on Blockchain Technology

This paper decomposes the multi-element DXF format design file, extracts the most important layer, block, entity element and the number of entities according to the elements, and constitutes the characteristics of the design file; Then, the extracted features are taken as the underlying leaves, and the Merkel tree based on MD5 algorithm is used to get the tamper proof code. By comparing the tamper resistant code, we can detect whether tampering occurs and locate the tampering location, and store the tamper resistant code and tampering location information on the chain, using the characteristics of the blockchain to ensure that they are not tampered and traceable.
Performance Improvement of FORCE Learning for Chaotic Echo State Networks

Echo state network (ESN) is a kind of recurrent neural networks (RNNs) which emphasizes randomly generating large-scale and sparsely connected RNNs coined reservoirs and only training readout weights. First-order reduced and controlled error (FORCE) learning is an effective online training approach for chaotic RNNs. This paper proposes a composite FORCE learning approach enhanced by memory regressor extension to train chaotic ESNs efficiently. In the proposed approach, a generalized prediction error is obtained by using regressor extension and linear filtering operators with memory to retain past excitation information, and the generalized prediction error is applied as additional feedback to update readout weights such that partial parameter convergence can be achieved rapidly even under weak partial excitation. Simulation results based on a dynamics modeling problem indicate that the proposed approach largely improves parameter converging speed and parameter trajectory smoothness compared with the original FORCE learning.
Design of Bluetooth-Controlled Floor Cleaning Robot

Floor cleaning is one of the most useful and hygienic practices done at all houses as well as workplaces to keep them neat and clean by removing the stains, dirt, litter, and obstructions on the floor. The process of floor cleaning not only cleans the floors but also makes them look beautiful. Although it is an essential practice, it is hectic and time consuming too. To overcome this problem, the floor cleaning robot was introduced into the market. The first-floor cleaning robot was introduced in the market by a company named iRobot in 2002 followed by other competitive players. These robots, equipped with various types of tools and features, are technologically advanced that makes them more attractive and effective in their operations. In this paper, the making and the working process of a Bluetooth-controlled floor cleaning robot are discussed. The key purpose of this work is cost cutting and introducing the floor cleaning robot at affordable rates in the market. The design consists of different components, namely Arduino, L293D motor driver board, DC motors, Bluetooth module, and water pump. The design also consists of other structural components to form the robot that can clean the floor on the commands given from a smartphone application. This robot serves the cleaning purpose effectively and efficiently, hence reducing the labor force as well as fulfilling the motto of cost cutting.
Integration of ICT into Education: Lessons Learnt at the State University of Zanzibar and the Midlands State University in Zimbabwe

The provision of education using ICT has been adopted by many institutions in Africa. The use of ICT is critical in knowledge-based societies such as those in Zanzibar and Zimbabwe. This study looks at how the Midlands State University (MSU) and State University of Zanzibar (SUZA) have adopted the use of ICT in many ways. ICTs do not work for everyone in the same way. It has become inevitable, in the current digital era for educators to integrate ICT in their teaching and gradually replace traditional teaching methods with modern ones which are ICT led. The main objective of this study is to find out challenges and opportunities of using ICT in education.
Modelling Context with Graph Convolutional Networks for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis is a fine-grained natural language processing task that aims to predict a specific target's sentiment polarity in its context. Existing researches mainly focus on the exploration of the interaction between the sentiment polarity of aspects and contexts. Models based on the self-attention mechanism can fully explore the syntactic structure of sentences. In contrast, models based on a convolutional neural network have the ability to make aspects and the semantics of contextual words alignment. These methods all have some limitations; that is, they lack the ability to make full use of syntactic information and long-range word dependencies to carry out relevant syntactic constraints while associating the target’s sentiment with the local context. And they are not able to handle affective ambivalence in text. In this paper, we propose a stacked ensemble method for predicting the sentiment polarity by combining a local context embedding and a global graph convolutional network. It uses a Graph Convolutional Network (GCN) to supplement local information to improve the accuracy of the aspect sentiment classifier with revealing multi-level sentiments. Experimental results on three commonly used datasets show that our approach outperforms the state-of-the-art models in the Semeval-2014 dataset.
Realization of the quantum Toffoli gate based on a six-level atomic system

In this paper, we propose a scheme for implementing a Toffoli gate in a system with an atom that has six levels in a lambda configuration, interacting with a high-Q cavity containing four modes. Here, we reduce a six-level system into an effective three-level behavior by applying the adiabatic elimination method. Next, we calculate the probabilities of the states of the interest as well as the fidelity. We also study the effects of photonic and atomic decay rates on the evolution of the system which is reasonably less sensitive to decoherence.