Reviewerly/exHarmony huggingface dataset:
Adaptive Profiling Model for Multiple Residents Activity Recognition Analysis Using Spatio-temporal Information in Smart Home

In this research, we look at the notion of spatio-temporal and create context information from the smart home environment for multiple resident activity recognition analysis. We created context features from the smart home environment in order to correlate with the ambient sensor pattern from activities of daily life (ADL) of the multiple residents that reside in the same environment. An Adaptive Profiling model was generated based on the possible interaction activity using Expectation-Maximization (EM) clustering. The clusters parameter is adaptively generated from the active label sets from the real-world datasets at the processing stage. The proposed Adaptive Profiling model was classified using multi-label classification using the label combination approach and compared with different classifiers such as Random Forest, Support Vector Machine, and k-NN method. The model with Adaptive Profiling achieved higher accuracy when it was compared with only sensor triggering patterns. The results obtained also were compared with the previous works to justify its performance.
Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models

Arguably the most popular application task in artificial intelligence is image classification using transfer learning. Transfer learning enables models pre-trained on general classes of images, available in large numbers, to be refined for a specific application. This enables domain experts with their own—generally, substantially smaller—collections of images to build deep learning models. The good performance of such models poses the question of whether it is possible to further reduce the effort required to label training data by adopting a human-in-the-loop interface that presents the expert with the current predictions of the model on a new batch of data and only requires correction of these predictions—rather than de novo labelling by the expert—before retraining the model on the extended data. This paper looks at how to order the data in this iterative training scheme to achieve the highest model performance while minimising the effort needed to correct misclassified examples. Experiments are conducted involving five methods of ordering, using four image classification datasets, and three popular pre-trained models. Two of the methods we consider order the examples a priori whereas the other three employ an active learning approach where the ordering is updated iteratively after each new batch of data and retraining of the model. The main finding is that it is important to consider accuracy of the model in relation to the number of corrections that are required: using accuracy in relation to the number of labelled training examples—as is common practice in the literature—can be misleading. More specifically, active methods require more cumulative corrections than a priori methods for a given level of accuracy. Within their groups, active and a priori methods perform similarly. Preliminary evidence is provided that suggests that for “simple” problems, i.e., those involving fewer examples and classes, no method improves upon random selection of examples. For more complex problems, an a priori strategy based on a greedy sample selection method known as “kernel herding” performs best.
HIGHER EDUCATION STUDENTS AND ONLINE INCIVILITY: A GENDERED ISSUE?

During the pandemic of Covid-19, people turned more to their screens. The tendency towards platformization and the use of mobile media was intensified during the Covid-19 pandemic (Nielsen, 2021). The adoption of technologically mediated spaces either for leisure or for work purposes occurred at an unprecedented level, creating and expanding online social interactions, including harmful dynamics. Social media were one of the most important - sometimes, the most important - means to fight social isolation and to relate to others. Social networking platforms became a privileged locus for public discussion and the expression of personal beliefs. However, they have also become a prolific field for incivility (Esau, 2021; Papacharissi, 2004; Oz, Zhen & Chen, 2018). Online incivility comprehends various expressions of behavior that does not respect others. It ranges from rude and impolite comments to threats and other kinds of violence and oppression, among other forms. Previous research has shown that online hate speech is a growing phenomenon that reproduces offline practices of structural oppression regarding gender and sexuality (Matamoros-Fernández & Farkas, 2021). This paper aims to contribute to the study of online incivility precisely as a gendered experience: how do higher educations female students experience online incivility? For this study, we conducted two focus groups with 10 female students from a higher education institution in Portugal (both Portuguese and Brazilian). The research was conducted to grasp the content of the discussions, but also to get deeper insights into the participants’ perceptions of the exposition of women to online incivility in social media. The results suggest that being a foreign woman raises the possibilities of suffering from online incivility, which occurs mainly through xenophobic comments and sexual harassment. The students from the study suggest that Portuguese higher education institutions should provide more protection to their female students when both offender and victim are students, despite being online. The results also point to the importance of promoting the discussion of these issues among students. This paper brings a deeper knowledge of how online incivility is experienced by female students with possible implications in the development of internal policies within higher education institutions.
Online Mail Junk Penetration by Using Genetic Algorithm Probabilistic Weights and Word Compute

Junk email sifting is a high volume space for exploration, and they are developing with time. The vast majority of the spam sends are limited time in nature. Consequently, spam sends are not unsafe for the PCs, but rather these sends are irritating for client. Spam sends can be separated utilizing spam sifting techniques like Bayes and Gullible Bayes arrangements. Arrangement is one based on substance of the by email and based on specifically on names or likelihood can determined of discovering an instruction from junk and non-spam classifier words. As the count is not many names or weight of information which can be found in both junk and non-spam sends, hence limit based component is attractive for right characterization. For right arrangement utilizing Bayes and Innocent Bayes dataset ought to be colossal in a perfect world number of sends ought to be boundless. However, in genuine applications a plan is wanted which is versatile in nature and can furnish great outcomes with a couple of sends. The comparative way, in this paper a hereditary calculation based spam identification strategy is definite which is basic and give great outcomes restricted dataset.
Local Trust in Internet of Things Based on Contract Theory

Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any two participating nodes to share sensitive information, nominally a challenging proposition for a large, heterogeneous network where node participation is constantly in flux. This work explores a contract-theoretic framework that exploits the principles of network economics to crowd-source trust between two arbitrary nodes based on the efforts of their neighbors. Each node in the network possesses a trust score, which is updated based on useful effort contributed to the authentication step. The scheme functions autonomously on locally adjacent nodes and is proven to converge onto an optimal solution based on the available nodes and their trust scores. Core building blocks include the use of Stochastic Learning Automata to select the participating nodes based on network and social metrics, and the formulation of a Bayesian trust belief distribution from the past behavior of the selected nodes. An effort-reward model incentivizes selected nodes to accurately report their trust scores and contribute their effort to the authentication process. Detailed numerical results obtained via simulation highlight the proposed framework's efficacy and performance. The performance achieved near-optimal results despite incomplete information regarding the IoT nodes' trust scores and the presence of malicious or misbehaving nodes. Comparison metrics demonstrate that the proposed approach maximized the overall social welfare and achieved better performance compared to the state of the art in the domain.
Few-shot relational triple extraction with nearest neighbor matching

Current relational triple extraction approaches rely on a large amount of labeled data which is hard for many real-world applications. How to extract relational triples has become a concern in few-shot settings. At present, the researches in few-shot settings are rare on the joint extraction tasks. The main problem is that the accuracy of subject and object recognition is relatively low. To improve the accuracy of subject and object recognition, we propose an end-to-end few-shot relational triple extraction model with nearest neighbor matching. Specifically, we recognize subject and object in the sentence according to the semantic similarity of words, and propose the new method to discriminate the subject and object. Also, we add the loss of the subject and object type as a penalty item for model training. Experiments results demonstrate that our method greatly improves the performance of subject and object recognition on the public English dataset FewRel, and achieves the state-of-the-art on the few-shot relational triple extraction task.
Underwater Image Enhancement using a Light Convolutional Neural Network and 2D Histogram Equalization

Underwater images usually have low contrast, blurring, and extreme color distortion because the light is refracted, scattered, and absorbed as it passes through the water. These features can lead to challenges in image-based processing and analysis. In this paper, a two-step method based on a deep convolution network is proposed for solving these problems and enhancing underwater images. First, through a light global-local structure, the initial image enhancement is performed and the color distortion and degradation of the images are partially covered. Then, two-dimensional histogram equalization is used as an appropriate complement to the network. Two-dimensional histogram equalizing is able to produce clear results and prevents excessive contrast. The results show that the proposed method performs better than other methods in this field in terms of qualitative and quantitative criteria.
Edge boundary variable neighborhood strategy adaptive search for a vegetable crop land allocation problem

This paper focuses on optimizing cropland allocation for planting vegetables for farmer cooperatives. In this research, planning about land allocation for crops including what to grow and what is in demand during specific periods is performed while considering important factors such as production yield per area, crop planting and harvesting time, price fluctuations of vegetables and plant incompatibility, so that farmers can plan their crops accordingly, and achieve the optimal prices and annual income. To solve the problem, the Edge Boundary Variable Neighborhood Strategy Adaptive Search (EB-VaNSAS), which is the first extension of Variable Neighborhood Strategy Adaptive Search (VaNSAS), was applied to significantly improve the solution quality of the traditional Variable Neighborhood Strategy. Two local searches, called Track Transition Method (TTM) and Multiplier Factor (MF) were developed and used for the first time in this paper. The numerical results show that the EB-VaNSAS outperforms all other proposed methods since EB-VaNSAS could find new optimal solutions more often during the simulation, while the local search-based heuristics tend to be trapped in some local optima, and the DE lacks search intensification.
Weight regulation in British and Irish Bronze Age gold objects: a reanalysis and reinterpretation

Sophisticated metrological systems were common in the European Bronze Age and mass-regulation has been argued for various classes of object, including gold artefacts. A recent study published in Antiquity used Cosine Quantogram Analysis to demonstrate mass-regulation in a small sample of gold objects from Britain, Ireland and France. Since then, substantial quantities of new data from British Bronze Age gold objects have been collated. Here, the author presents the results of Cosine Quantogram Analysis on nearly 1000 such objects—the largest sample analysed to date. The results demonstrate that, even though some regularities can be discerned, mass-regulation is no longer a tenable interpretation of gold objects from Bronze Age Britain.
Sign Language to Text for Deaf and Dumb

Hand-Sign Language Gestures are nonverbal messages that help in communication and can be understood with vision. Since the only disability Deaf &#x0026; Dumb people have is communication related to speech therefore they cannot use spoken languages hence the only way for them to communicate with the people having same disability is through this hand-sign language. This causes a language barrier as normal people can not understand their hand sign language and vice a versa also since most of the people are not familiar with the same and interpreters are not very user-friendly they end up in a deadlock. Therefore, this paper proposes a CNN-based method for deciphering sign language and then converting it to text. In the proposed scheme of this paper the main focus is on fingerspelling and an additional feature of emotion recognition to support the interpretation with the 3rd component of sign language i.e non-manual features, a real-time solution for easy interpretation of sign language for normal human beings as well as Deaf &#x0026; Dumb people using convolutional neural networks breaking the language barrier. The experimental results show that this paper achieved an accuracy score of 99.8&#x0025; on testing data which is better than the majority of the recent research papers on American gesture-based communication
Artificial Conversational Entity with Regional Language

During the lockdown period, people suffered a lot with much misinformation and conflicts regarding the hospitals and the dangerous COVID infection. A large number of people have common and simple doubts regarding the infection, quarantine, bed availability, oxygen cylinder availability. So, a patient with minor issues does not need to reach the hospital in person to consult with doctors for simple and similar queries in that lockdown situation. To neglect these difficulties, the chatbots were proposed with an AIML platform to provide details about infection rate, information regarding treatments, decision making regarding hospital admissions. Artificial Conversational Entity otherwise called chatbots acts as the conversational agent or talkbot or chatterbot which has the capability of performing an intellectual conversation with a human. There are many chatbots built using Watson, Google DialogFlow, Keras Seq2Seq, Gradient Descent algorithm, Beam search decoding methods, etc., Even though many chatbots respond in regional languages, not all the chatbots are appreciated for their efficacy and attractive customization. So, a chatbot with both the national language which is English, and the regional language which is the Tamil language, using the RASA framework has been developed. RASA NLU is well known for its efficacy and good customization methodologies. RASA NLU has a higher level of Application Programming Interface (API). So, the proposed method can be connected with a web page, and carousels are added to project the model very much attractive and deliver effective visualization. The carousels provide a wide range of information in regional language and it can be customized according to the user&#x0027;s requirements.
Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob's decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve's classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.
Batch Normalization and Dropout Regularization in Training Deep Neural Networks with Label Noise

Availability of large annotated datasets in computer vision, speech understanding, or natural language processing is one of the main reasons for deep neural networks popularity. Unfortunately, such data can suffer from label noise, introduced by incorrectly labelled patterns. Since neural networks, as data-driven approaches, are strongly dependent on the quality of training data, the results of building deep neural structures with noisy examples can be unreliable. In this paper, we present preliminary experimental results on how two regularization techniques, namely dropout and batch normalization, influence vulnerability to incorrect labels. On popular MNIST and CIFAR-10 datasets we demonstrate that combination of these two approaches can be considered as a tool to improve network robustness to mislabelled training examples.
Comprehensive Evaluation and Analysis of Ecological Language Development in Consideration of Q-Learning Algorithm

The article uses the Q-learning algorithm to investigate the development of ecological language of college students in some cities, and analyzes the results of the investigation. Including the analysis of the language ability of the university, the analysis of the impact of the language environment of the students on the language ability, the analysis of the difference in language use and the analysis of the difference in language behavior. On this basis, summarizing the usage habits and behaviors of some students and giving solutions. In terms of social factors, analyzing the status quo of students and the Mandarin mode of college students. Analyzing the causes of college students’ “bilingualism” problems from the perspectives of sociolinguistics and psycholinguistics, improving language proficiency, and providing targeted solutions from the three perspectives of school, family, and individuals. The results show that only 9.9% of the respondents think their Mandarin is “very good,” only 19% of the respondents who can speak a little Mandarin think that their Mandarin is “very good.” Mandarin is very fluent, and the corresponding respondents rated their Mandarin as “very good” accounting for 32.1%. It can be seen that the Mandarin level of the surrounding contact objects has a great influence on the Mandarin level of the surveyed persons, and there is a positive correlation.
Pytearcat: PYthon TEnsor AlgebRa calCulATor A python package for general relativity and tensor calculus

This paper introduces the first release of Pytearcat, a Python package developed to compute tensor algebra operations in the context of theoretical physics, for instance, in general relativity. Given that working with tensors can become a complex task, people often rely on computational tools to perform tensor calculations. We aim to build a tensor calculator based on Python, which benefits from being free and easy to use. Pytearcat syntax resembles the usual physics notation for tensor calculus, such as the Einstein notation for index contraction. This version allows the user to perform many tensor operations, including derivatives and series expansions, along with routines to obtain the typical General Relativity tensors. A particular concern was put in the execution times, leading to incorporate an alternative core for the symbolic calculations, enabling to reach much faster execution times. The syntax and the versatility of Pytearcat are the most important features of this package, where the latter can be used to extend Pytearcat to other areas of theoretical physics.
Representation learning for temporal networks using temporal random walk and deep autoencoder

Network representation learning is a promising direction towards applying machine learning over graph-structured data. Most of the recent researches focus on embedding static networks to a low dimensional vector space and performing traditional network mining tasks using this latent space. But, to study many complex interactions between real-world entities, we need to model the data into a time-varying network (temporal network), where the edge connectivity patterns may vary with time. We focus on the problem of temporal network representation learning, where the network is represented with edges time-stamped with the time of interaction. We design a random surfing model for the temporal network using a non-homogeneous Markov chain to generate a node similarity matrix. Further, we perform non-linear dimensionality reduction on the node similarity matrix using a denoising deep autoencoder to generate node representations. We also evaluate the quality of the embeddings generated using a temporal link prediction benchmark.