Reviewerly/exHarmony huggingface dataset:
Social Interaction Data-sets in the Age of Covid-19: a Case Study on Digital Commensality

Research focusing on social interaction often leverages data-sets, allowing annotation, analysis, and modeling of social behavior. When it comes to commensality, researchers have started working on computational models of food and eating-related activities recognition. The growing research area known as Digital Commensality, has focused on meals shared online, for instance, through videochat. However, to investigate this topic, traditional data-sets recorded in laboratory settings may not be the best option in terms of ecological validity. Covid-19 restrictions and lock-downs have increased in online gatherings, with many people becoming used to the idea of sharing meals online. Following this trend, we propose the concept of collecting data by recording online interactions and discuss the challenges related to this methodology. We illustrate our approach in creating the first Digital Commensality data-set, containing recordings of food-related social interactions collected online during the Covid-19 outbreak.
Time scrambling and decoy-state quantum key distribution for optical physical layer encryption

We propose an optical physical layer encryption method based on real-time optical signal temporal scrambling and decoy-state quantum key distribution. The real-time optical signal processing is operating with an array of tunable Fabry-Perot cavities by using the updating and synchronization keys. Based on this scheme, an experimental demonstration is given to realize secure communication for optical physical layer in a standard 10 Gbps data transmission with quantum key distribution in real time. An on-line transmitted data over 100 km of a standard single mode fiber is realized with power penalty of as low as 3.45 dB, where the average key generation rate is better than 800 bps. The experimental results indicate that, the proposed encryption scheme of dynamic scrambling with quantum keys exhibits significantly potential for improving the confidentiality of optical physical layer.
Developing student codesigned immersive virtual reality simulations for teaching of challenging concepts in molecular and cellular biology

Abstract Molecular biology theory represents a critical scaffold, which underpins multiple disciplines within life sciences education. However, it is well-documented that undergraduate students can struggle to achieve deeper understanding of key concepts and/or their application. One challenging, contributory aspect is the “invisible” nature of molecular biology processes compounded by critical 3D spatial orientations of the principal components and their interactions. Molecular theory specifically requires students to construct accurate, mental spatial models to develop their understanding. However, much of the traditional teaching and examination of such theory is limited to 2D representations. Technology-enhanced, complementary teaching and examination approaches, which engage students with spatial aspects of theoretical concepts, offer an exciting opportunity to support student learning in this area. In this study, we have explored the integration of an immersive virtual reality simulation based on a challenging molecular biology concept within an existing module taught at University College Cork. A mixed methods approach, grounded in learning theory, was undertaken to assess the student user and learning experience. The consensus response from students was one of enhanced learning, understanding, engagement, and motivation. Student partnership in the process of simulation design and integration was key to delivering the fully integrated experience.
Automatic question generation for subordinate conjunctions of Marathi

As the internet is growing fast, a lot of multilingual e-content is now easily accessible; this e-content contains information from various domains. This data is being used for a wide range of uses and applications. As systematic evaluation and appraisal methods are evolving, so does the need for a large Question Bank (QB). Manual question creation is a time-consuming and costly process that necessitates domain experts, and there is an increasing movement toward using question banks to generate the question papers for various tests and assessments. As a result, the issue of an automated question generation (AQG) has piqued the interest of the researchers; this problem can be resolved using the Natural Language Processing (NLP) techniques. The reported study here addresses the issues involved in automatic question generation in the context of subordinate conjunctions of Marathi text. We have investigated the utility of conjunctions in the context of AQG at sentence level. Our AQG engine comprises of three stages: question word selection, clause selection and automatic question generation. The central idea of our AQG methodology is to generate the questions at the parts of speech (POS) tagging level only. We have tested our AQG methodology on a test corpus of 100 Marathi complex sentences. To test our AQG methodology, we have used six subordinating conjunctions. Performance of our AQG methodology is measured with manually evaluated syntactic and semantic correctness. We have obtained the precision of 0.83 and a recall of 0.80 on the corpus chosen from standard sixth, seventh and eighth science books. The error analysis is also presented in this paper.
Research on Characterization and Shaping of Chinese Heroic Characters in Online Media Based on Deep Neural Network Evaluation

Shaping the image of heroic characters is an important task of melodramatic films and TV dramas, whether they are revolutionary heroes, combat heroes, leader heroes, or civilian heroes, which have always been the key objects of melodramatic films and TV dramas. However, the traditional melodramatic films and TV dramas are facing many bottlenecks in the acceptance of heroic characters in the new era. In this paper, in order to obtain a richer character text, we divide the denotation elimination into two parts; one is the denotation elimination without considering the zero pronoun phenomenon, which is mainly done by the existing end-to-end neural codenotation algorithm, and the other is the attention-based algorithm model for the zero pronoun phenomenon. Analyzing this result from the perspective of the algorithmic model, it can be speculated that the lexical vector has a superior performance in the overall predicted character score, indicating that in the Big Five personality analysis for characters, the character’s personality is more reflected in lexicality, and the scores on a person’s Big Five personality dimensions can be indirectly reflected by the frequency of using different lexicalities. The similarities and differences among heroes are explored to create a popular hero image.
Transfer and share: semi-supervised learning from long-tailed data

Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost to solve complex optimization problems, or class-balanced undersampling which can result in information loss. In this paper, we present the TRAS (TRAnsfer and Share) to effectively utilize long-tailed semi-supervised data. TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes. It then transfers the distribution to a target model such that the minority class will receive significant attention. Interestingly, TRAS shows that more balanced pseudo-label distribution can substantially benefit minority-class training, instead of seeking to generate accurate pseudo-labels as in previous works. To simplify the approach, TRAS merges the training of the traditional SSL model and the target model into a single procedure by sharing the feature extractor, where both classifiers help improve the representation learning. According to extensive experiments, TRAS delivers much higher accuracy than state-of-the-art methods in the entire set of classes as well as minority classes. Code for TRAS is available at https://github.com/Stomach-ache/TRAS .
Agile and Touchless Automation in the software industry

Software and systems are becoming the business drivers and core competencies for organisations than enabling function. Building effective and quality software systems is a crucial success factor for any organization. Technology and infrastructure capabilities have gone through a significant leap in the last few decades at one end, and the complexity of software and its expectations have become too high at another end of the spectrum. With these developments, it is essential to revisit the traditional way of developing, building, and operating software that leverages the advancements in technology and infrastructure developments, automation, agility, and industrialization practices. A survey was done to study existing industry practices, Organization's readiness in DevOps adoption, their pain points and how they aim to address the same. This study aims to provide deep insights into how the software industry is evolving so academicians understand the state of the industry and further the research for betterment.
The exact complexity of pseudorandom functions and the black-box natural proof barrier for bootstrapping results in computational complexity

Investigating the computational resources we need for cryptography is an essential task of both theoretical and practical interests. This paper provides answers to this problem on pseudorandom functions (PRFs). We resolve the exact complexity of PRFs by proving tight upper and lower bounds for various circuit models.
Clinical decision support models for oropharyngeal cancer treatment: design and evaluation of a multi-stage knowledge abstraction and formalization process

Treatment decisions in oncology are demanding and affect survival, general health, and quality of life. Expert systems can handle the complexity of the oncological field. We propose the application of a hybrid modeling approach for decision support models consisting of expert-based implementation of a decision model structure and machine-learning (ML) based parameter generation. We demonstrate our approach for the treatment of oropharyngeal cancer.We created a clinical decision model based on Bayesian Networks and iteratively optimized its characteristics using structured knowledge engineering approaches. We combined manual adaptation of individual concepts with automatic learning of parameters and causalities. Using data from 94 patient records, we targeted the needed objectivity and clinical significance.In three iteration steps, we assessed the model with cross-validations. The initial aggregated accuracy of 0.529 could be increased to 0.883 in the final version. The predictive rates of the target nodes range from 0.557 to 0.960.Combining different methodological approaches requires balancing the complexity of the clinical subject matter with the amount of information available in the dataset for ML application. Our method showed promising results because flaws of one approach can be overcome by the other approach. However, technical integrability as well as clinical acceptance must always be ensured.
Optimal Control for Unmanned Systems With One-Way Broadcast Communication

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.
Twitter Sentiment Classification with Deep Learning LSTM for Airline Tweets

As a result of increase in internet usage, there is a massive amount of information available to web users, as well as a massive amount of new information being created daily. To facilitate internet pick-up, trading ideas, and disseminating assessments, the internet has evolved into a stage of large volumes of data. Facebook, and Twitter generate a lot of data every day. As a result, text handling is crucial in making decisions. Sentiment analysis has surfaced as a method for analyzing Twitter data. In this paper, we collected a Kaggle dataset with airline tweets. It contains three variants of tweets: neutral, positive, negative. First, we used NLP methods to clean the text data. Later, we applied RNN, LSTM, stacked LSTM, bidirectional LSTM, and GRU techniques for classifying tweets in three different ways: positive vs negative sentiment analysis, neutral vs positive sentiment analysis, and neutral vs negative sentiment analysis. We achieved an accuracy of 93% for the classification of positive and negative airline reviews. We achieved accuracy values of 84.5%,83.8% for neutral vs positive and neutral vs negative tweets. The results show that, the proposed RNN/LSTM/GRU model performed well for sentiment classification. Keywords-Sentiment analysis, Tweets, RNN, LSTM, GRU.
Neural Network Algorithm with Dropout Using Elite Selection

A neural network algorithm is a meta-heuristic algorithm inspired by an artificial neural network, which has a strong global search ability and can be used to solve global optimization problems. However, a neural network algorithm sometimes shows the disadvantage of slow convergence speed when solving some complex problems. In order to improve the convergence speed, this paper proposes the neural network algorithm with dropout using elite selection. In the neural network algorithm with dropout using elite selection, the neural network algorithm is viewed from the perspective of an evolutionary algorithm. In the crossover phase, the dropout strategy in the neural network is introduced: a certain proportion of the individuals who do not perform well are dropped and they do not participate in the crossover process to ensure the outstanding performance of the population. Additionally, in the selection stage, a certain proportion of the individuals of the previous generation with the best performance are retained and directly enter the next generation. In order to verify the effectiveness of the improved strategy, the neural network algorithm with dropout using elite selection is used on 18 well-known benchmark functions. The experimental results show that the introduced dropout strategy improves the optimization performance of the neural network algorithm. Moreover, the neural network algorithm with dropout using elite selection is compared with other meta-heuristic algorithms to illustrate it is a powerful algorithm in solving optimization problems.
AI Model to Generate SQL Queries from Natural Language Instructions through Voice

Abstract Data plays the most important role in the development of industries, small businesses. Even world leaders need the data to make analyses and make better policies for people. In almost every field where the work process is digitized need to store data and then retrieve it. According to statistics most of the data is stored in the relational database and for manipulations of the data, Structured Query Language(SQL) is commonly used. So for handling databases a person need to have specialized knowledge regarding the queries and had to remember the syntax of many complex queries. So to enhance data manipulation using SQL and to efficiently get the required query, the paper proposes a method for the generation of SQL query from natural language input, spoken(audio input) by the user. The model is constructed on NLP (Natural Language Processing) and Neural Networks (Deep Learning) technologies. Long Short Term Memory(LSTM) Model is used for predicting queries and is trained on the dataset with natural language as input and returns outline skeletal structure of the query as output. Then the output will be processed and the final query will be displayed to the user. The project also aims to benefit the people who are suffering from Repetitive Stress Injury (RSI), causing pain in the finger joints, which has been attributed to work requiring a long period of typing and also to those who are not familiar with SQL queries. As this system will readily provide the required query.
Design, Simulation, and Implementation of connected IoT for Disabled People

Taking care of the elderly is difficult, especially when they have chronic diseases. Its difficulty is to focus and address the challenges experienced by differently-abled persons, especially if our patient is visually, audibly, or vocally disorderly. The paper's goal is to design and implement a wearable device solution that is simple, quick, accurate, and cost-efficient; through the benefit of the Internet of Things (IoT) concept with a mobile application to facilitate the patient's caregivers. In this paper, there are two scenarios, wherein in both scenarios, the patients are diagnosed with Alzheimer's disease, and they will be wearing a health band to detect and record their health vitals and allocate their position. The first one is that the Alzheimer's patient is audibly and vocally impaired. There is an implemented program within the wearable band and the mobile application to detect the voice and convert speech-to-text and convert text-to-speech, in which the speaker outputs the speech. In the second scenario, the patient has lost the sense of sight. There are two solutions, face recognition using a camera and the third eye using an ultrasonic sensor. The output of this will easier the communication for the deaf, dumb, and blind people so that they can easily communicate with each and everyone around them and easier their life.
Theater of Public Punishment in Pakistan: A Discourse Analysis of Demand for Public Hanging

This paper investigates the discourse of public hanging of rapists on the social media space, that is, Twitter in Pakistan. It also examines how this discourse is interdiscursively related to the power relations particularly implicated in the discourses on religion and gender and the possible effects of this discursive struggle on society. By employing Critical Discourse Analysis (CDA) for analysis, this research uses Laclau and Mouffe’s Discourse Theory as theoretical framework. We conclude that the discourse approving death by hanging for the rapists is a device to propagate the power and the fear of the State. It has a “repetition induced effect” and implies sovereign’s exception; and women are silenced in their demands for justice against the patriarchal social structures which are responsible for crime against women’s body. It also reifies the patriarchal structures and controls societies through vengeance rather than reformative justice.
Hierarchical Attention Neural Network for Event Types to Improve Event Detection

Event detection is an important task in the field of natural language processing, which aims to detect trigger words in a sentence and classify them into specific event types. Event detection tasks suffer from data sparsity and event instances imbalance problems in small-scale datasets. For this reason, the correlation information of event types can be used to alleviate the above problems. In this paper, we design a Hierarchical Attention Neural Network for Event Types (HANN-ET). Specifically, we select Long Short-Term Memory (LSTM) as the semantic encoder and utilize dynamic multi-pooling and the Graph Attention Network (GAT) to enrich the sentence feature. Meanwhile, we build several upper-level event type modules and employ a weighted attention aggregation mechanism to integrate these modules to obtain the correlation event type information. Each upper-level module is completed by a Neural Module Network (NMNs), event types within the same upper-level module can share information, and an attention aggregation mechanism can provide effective bias scores for the trigger word classifier. We conduct extensive experiments on the ACE2005 and the MAVEN datasets, and the results show that our approach outperforms previous state-of-the-art methods and achieves the competitive F1 scores of 78.9% on the ACE2005 dataset and 68.8% on the MAVEN dataset.
Exploiting domain transferability for collaborative inter-level domain adaptive object detection

Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial levels (e.g., image-level, instance-level, RPN-level) in a two-stage detector via adversarial training. However, individual levels in the object detection pipeline are closely related to each other and this inter-level relation is unconsidered yet. To this end, we introduce a novel framework for DAOD with three proposed components: Multi-scale-aware Uncertainty Attention (MUA), Transferable Region Proposal Network (TRPN), and Dynamic Instance Sampling (DIS). With these modules, we seek to reduce the negative transfer effect during training while maximizing transferability as well as discriminability in both domains. Finally, our framework implicitly learns domain invariant regions for object detection via exploiting the transferable information and enhances the complementarity between different detection levels by collaboratively utilizing their domain information. Through ablation studies and experiments, we show that the proposed modules contribute to the performance improvement in a synergic way, demonstrating the effectiveness of our method. Moreover, our model achieves a new state-of-the-art performance on various benchmarks.
A Multimedia Learning for Chinese Character Image Recognition via Human-Computer Interaction Network

As science and technology continue to develop, Chinese character image recognition technology is being used in a wide range of fields. This computer-based technology is a practical way of automatically recognizing images of text. Typically used in Chinese character education, it provides a new form of human–computer interaction for students. In addition, multimedia technology can provide a rich learning environment for students, which can present information about Chinese characters in the form of pictures, sounds, and videos, thus compensating for the disadvantages of learning Chinese characters by rote in the traditional educational process. The combination of Chinese character image recognition technology and multimedia technology can not only enrich the process of learning Chinese characters, but also promote students’ motivation to learn, thus providing a new and more modern approach to Chinese character education. Based on the study of Chinese character image recognition technology, this research combines it with multimedia information, to achieve the image recognition of Chinese character and multimedia information representation. The combined technology can provide significant references for course design and Chinese learners.