Reviewerly/exHarmony huggingface dataset:
Real-time face recognition based on IoT: A comparative study between IoT platforms and cloud infrastructures

The use of the Internet of Things (IoT) is steadily increasing in a wide range of applications. Among these applications, safety and security are some of the prominent applications. Through surveillance systems, we can restrict access to our premises and thus secure our assets. Nowadays face detect ion and recognition enabled surveillance systems are available in the market, which can detect faces from video frames captured using IP cameras, and then recognize those faces by comparing them with existing databases. However, higher prices and low accuracy are impeding the large scale deployment of those systems. In this paper, we have proposed a generic architecture for face detection and recognition system from real-time video frames that have been captured through IP cameras and processed using low-cost IoT devices by utilizing Cloud computing services. We have selected two IoT platforms: Eclipse Mosquitto IoT broker and Kaa IoT middleware to implement our proposed architecture. The face detection part is deployed in the IoT devices and the computation-intensive task, i.e., face recognition is carried out in backend Cloud servers. We have executed our experiments in two different Cloud infrastructures: Core Cloud and Edge Cloud and measured the total processing time in different scenarios. The experimental results show that the performance of the Mosquitto broker in terms of total processing time is better than Kaa middleware. Total processing time can be further reduced by deploying a face recognition application from Core Cloud to Edge Cloud. Furthermore, the k-nearest neighbor algorithm shows promising results compared to other face recognition algorithms.
K-means based method for overlapping document clustering

Overlapping clustering algorithms have shown to be effective for clustering documents. However, the current overlapping document clustering algorithms produce a big number of clusters, which make them little useful for the user. Therefore, in this paper, we propose a k-means based method for overlapping document clustering, which allows to specify by the user the number of groups to be built. Our experiments with different corpora show that our proposal allows obtaining better results in terms of FBcubed than other recent works for overlapping document clustering reported in the literature.
Performance Analysis of Decentralized Ethereum Blockchain System

Crowdfunding is a form of fundraising in which we collect small amounts of money from a large number of people, which totals to a large amount. In short it is the pooling of large funds which are required for any projects or ideas. Over the years there has been a decrease in overall investment and had remained a complicated and a troubled domain due to lack of transparency and control by a central authorities being some of the primary reasons. There have been many attempts on solving this issue but none have been completely successful. Blockchain is a technology through which we can successfully achieve transparency in transactions up to some extent.
An Efficient Video Indexing and Retrieval Algorithm using Ensemble Classifier

In the recent times, video are swamping over Internet in applications such as social networks, business multimedia systems and websites. These applications adopt video as foremost significant form of information or communication. Videos can be generally accessed as a whole and it will not be indexed in visual content. For instance, videos are generally uploaded as manually, short clips, cut clips with user provided tags, keywords and annotations for retrieval. In this investigation, a classification model has been designed to categorize the video based on similarity and dissimilarity. This classification model address two limitations: it eliminates the intervention of human in video retrieval. Next is time complexity of retrieving video content is lesser in contrast to prevailing state of the art approaches. In this work, initially, a series of images from any sample dataset or from available video is considered. Subsequently, perform feature extraction using two approaches termed as Independent feature extraction and principal feature selection based dimensionality reduction. Finally, classification for video retrieval is performed with logitboost ensemble classifier. Simulation is carried out in MATLAB environment, performance metrics like accuracy, precision, recall and time complexity is attained. Here, the proposed model shows higher accuracy, precision and recall value, whereas time complexity is reduced in contrast to prevailing approaches. The proposed model shows better trade of than existing approaches.
Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling

Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.
Time-Sensitive Collaborative Interest Aware Model for Session-Based Recommendation

Session-based recommendation, which aims at predicting user's action based on anonymous sessions, is a challenging problem due to the uncertainty of user's behaviors and limited clicked information. Existing methods model users' interests to relieve the uncertainty of user's behavior prediction. However, most methods mainly focus on the current session, ignoring collaborative information (i.e., collaborative interest) in neighborhood sessions with similar interests. We argue that relying on limited implicit feedbacks within a session is insufficient to precisely infer user's interest, especially in the absence of user's profiles and historical behaviors. This paper proposes a novel model called Time-Sensitive Collaborative Interest Aware (TSCIA) to tackle this problem. It explicitly aggregates similar interests from neighborhood sessions to model the general collaborative interest, and simultaneously takes users' interest drifts into account. Finally, both current session and collaborative information are used for next-item prediction. Extensive experiments on public datasets demonstrate the effectiveness of our model.
How social instant messaging questions affect replies: a randomised controlled experiment

Online Q&A has become a very common way for people to find information, and sending instant messages via social networking sites offers new opportunities for companies and individuals to reach out for information at a more private level. Despite the popularity of social instant messaging, little is known about how social instant messaging requests may affect the responses received. This study offers a new contribution by reporting a randomised-controlled study on social instant messaging, in which we phrased a basic request ‘Should I watch the Jurassic World' into 16 variants along four axes: punctuation, number of sentences, scope, and emoji availability, and sent the requests to 160 strangers. The response rate, speed and perceived answer usefulness were measured, indicating that 95% questions received replies, 55% of responses were received within one minute, and 22% were perceived as useful. We also statistically investigated what factors related to the four dimensions might influence the answer usefulness. The results showed that if a request was phrased as an explicit question with ‘?’, addressed to a specific respondent ‘you’, and accompanied with a friendly emoji , it was most likely to receive useful responses. The theoretical and practical implications are discussed.
Distributed Financial Exchanges: Security Challenges and Design Principles

Implementing secure, distributed, and economically viable financial exchanges radically challenges traditional constructs such as zero knowledge and secure multiparty computation. To boost discussions of such practical challenges, we enucleate the design principles to build a secure, distributed futures exchange.
Developing Shooter Game Interaction using Eye Movement Glasses

A quadriplegic is a paralysis that affects limitations in some physical movements and psychological disorders. They have limited media to interact with computers so a suitable solution is needed in the form of a media that can recognize other body parts movements which in this research uses eye movement. one of the solutions to this problem is to propose alternative technologies to interact and play games. We propose a simple technique by using a camera mounted on the glasses that will take the eye area. This technique will help reduce unnecessary parts of eye detection so that performance increases. The eyes will be processed using basic image processing and then determined the center position of the pupil using the Mean method. This system consists of pupil movements for pointer motion control and blinking of eyes for shooting. The performance test of this method toward the system, which has used 10 people with 7 experiments, shows an accuracy of 84.86 percent, the speed of movement with a duration of 2.22 seconds and the speed of response blinking with a duration of 0.026 seconds. In addition, we can distinguish between intentional blink and unintentional blink in which intentional blink has a duration of 0.30 seconds and unintentional 0.12 seconds. It can be concluded that by using this method and this technique is able to achieve good accuracy and also able to use intentional blink as shoot trigger.
Catalytic approaches to the tree evaluation problem

The study of branching programs for the Tree Evaluation Problem (TreeEval), introduced by S. Cook et al. (TOCT 2012), remains one of the most promising approaches to separating L from P. Given a label in [k] at each leaf of a complete binary tree and an explicit function in [k]2 → [k] for recursively computing the value of each internal node from its children, the problem is to compute the value at the root node. (While the original problem allows an arbitrary-degree tree, we focus on binary trees.) The problem is parameterized by the alphabet size k and the height h of the tree. A branching program implementing the straightforward recursive algorithm uses Θ((k + 1) h ) states, organized into 2 h −1 layers of width up to k h . Until now no better deterministic algorithm was known.
Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network

Function approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding responses. In the recent past, the improved neural networks including intuitive, interpretable correlated-contours fuzzy rules for classification tasks were proposed. However, the acquired data set can contain large volume of data and noise that degrades the classification ability of the model and increases the computational time. Thus, it is important to consider this problem which was not focused on recent existing works. Furthermore, there are also some neuron regularization issues in the second layer. To solve this issue in this proposed system Bat optimization based feature selection is proposed for optimal selection of features from the available dataset. Then classification is done by using enhanced neural network including intuitive and interpretable correlated-contours fuzzy rules (EC-FR). According to fuzzy rules extraction, an appropriate framework is built-in which similarity-based directional component of data partitioning and also a model to form cloud data is presented. Neurons weight and bias values are computed by adapting wavelet functions. Finally, parameters of the fuzzy neural networks are fine-tuned using the hybrid ant colony particle swarm optimization (HASO). Performance is evaluated primarily in accordance with the subsequent metrics like precision, recall, accuracy, and error rate.
Communication Pattern Based Data Authentication (CPDA) Designed for Big Data Processing in a Multiple Public Cloud Environment

With the development of cloud computing, there is a growing trend of multi-cloud Collaborative Big Data Computation (CBDC).In this environment, threats from authorized insiders are of particular concerns.Based on an extreme case of distributed computation where multiple collaborators jointly perform CBDC on shared datasets using an example distributed computing framework, MapReduce (MR), deployed in a Multiple Public Cloud (MPC) environment, this paper investigates how to protect the authenticity of data used during the computation in an efficient and scalable manner by proposing and evaluating a novel data authentication solution.The solution, called a Communication Pattern based Data Authentication (CPDA) framework, ensures data authenticity and non-repudiation of origin at the finest granularity without compromising efficiency and scalability.This is achieved by using an idea of communication pattern based authentication data aggregation.The framework has been comprehensively evaluated both theoretically and experimentally.The evaluation results show that the CPDA framework offers the strongest level of data authenticity protection (equivalent to that provided by digitally signing each data object individually) but introduces much lower overhead cost than the digital signature based solution.The results demonstrate that the idea of communication pattern based authentication data aggregation brings much benefit in terms of supporting efficient and scalable data authentication in a large-scale distributed system.
Leveraging Social Media for Medical Text Simplification

Patients are increasingly using the web for understanding medical information, making health decisions, and validating physicians' advice. However, most of this content is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. Most text simplification methods employ neural seq-to-seq models for this task. However, training such models requires a corpus of aligned complex and simple sentences. Creating such a dataset manually is effort intensive, while creating it automatically is prone to alignment errors. To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text. Experiments on four datasets show that our method significantly outperforms the best known medical text simplification models across multiple automated and human evaluation metrics. Our model achieves an improvement of up to 16.52% over the existing best performing model on SARI which is the primary metric to evaluate text simplification models.
Improving Detection Accuracy for Malicious JavaScript Using GAN

Dynamic web pages are widely used in web applications to provide better user experience. Meanwhile, web applications have become a primary target in cybercriminals by injecting malware, especially JavaScript, to perform malicious activities through impersonation. Thus, in order to protect users from attacks, it is necessary to detect those malicious codes before they are executed. Since the types of malicious codes increase quickly, it is difficult for the traditional static and dynamic approaches to detect new style of malicious code. In recent years, machine learning has been used in malicious code identification approaches. However, a large number of labeled samples are required to achieve good performance, which is difficult to acquire. This paper proposes an efficient method for improving the classifiers’ recognition rate in detecting malicious JavaScript based on Generative Adversarial Networks (GAN). The output from the GAN is used to train classifiers. Experimental results show that our method can achieve better accuracy with a limited set of labeled sample.
ADORE

Online reviews play a critical role in persuading or dissuading users when making purchase decisions. And yet very few users take the time to write helpful reviews. Encouragingly, recent advances in deep neural networks offer good potential to produce review-like natural language content. However, there is a lack of large, high-quality labeled data at both the aspect and sentiment level for training. Hence, toward enabling a writing assistant framework to help users post online reviews, this paper proposes a scalable labeling method for bootstrapping aspect and sentiment labels.Concretely, the proposed approach ? Aspect Dependent Online RE-views (ADORE) - leverages the underlying distribution of reviews and a small seed set of labeled data through carefully designed review segmentation and label assignment. We then show how these labels can inform a generative model to produce aspect and sentiment-aware reviews. We study the effectiveness of ADORE under various scenarios such as how end-users perceive the quality of the labels and aspect-aware generated reviews. Our experiments indicate that the proposed effective labeling process along with a regularized joint generative model lead to high quality reviews with 90% accuracy.
Using weighted directed graphs for identification of flow of emotions in poems

Poem is a spontaneous flow of emotions. There are several emotion detection systems to identify emotions from speech, gestures, and text (blogs, newspapers, stories and medical reports). Since such systems do not exist for poetry, we take the first step in building a system to recognize emotions in poetry by constructing a benchmark corpus, the PERC (Poem Emotion Recognition Corpus), of poems written by Indian poets in English. In this research a novel graphical method, Poem Emotion Trajectory System (PETS), is proposed to depict the flow of emotion in a poem. PETS is based on the construction of a weighted directed graph as a means to represent the emotion flow among the verses of a given poem. The weights represent the transition probability among the emotion states considered. The significant advantage is that a dominant path for each emotion category is identified. Emotion flow along verses is analyzed using a graph-based approach. This method, applied to each emotion category, generalizes the emotion flow in each emotion class. This PETS can be applied in poetry therapy and to enhance creative thinking and writing.
Bernoulli Multi-Target Track-Before-Detect for Maritime Radar

Detection and tracking of small maritime targets using high resolution radar is a challenging problem due to the temporally and spatially varying nature of the sea surface and the spiky returns which can resemble targets. The traditional approach to radar data processing is to first detect targets using their amplitude information (by comparing the radar returns to a suitably chosen threshold) and then feed the extracted (point) measurements into a target tracking system. An alternative approach which can be exploited to improve tracking of weak targets is to process the raw radar data directly though use of a track before detect (TBD) algorithm. This paper presents the development of a multi-target TBD algorithm for maritime radar with targets that are both widely separated and also closely spaced. The algorithm builds upon the Bayes-optimal single-target TBD filter, referred to as the Bernoulli TBD.
User Requirement Validation: Challenge Exploration in Pre-project Execution

Abstract The need for validation technique for user requirement or in other term Tender Specification at the Request for Tender (RFT) is vital. Without much resource for vendor to dispose of, the outcome of this research will proof useful. The purpose of this article is to determine the gap of initial and finish software product, method of eliminate or mitigate those gaps, list of OO artefacts and their relevancy towards the research. Finding of this article will be the basis of interconnecting user requirement specification and meta-requirement via UML. Based on the result, RE plays an important role in the survivability of a project, there are a few researches being done for solutions in the field of RFT specification validation, UML is the most relevant artefacts and usability of UML diagram ranked based on consistency is relevant toward connecting UML and meta-requirement. It is suggested that further research is to be carried out that will define the connection between user requirement specification in the form of UML to meta-requirement in order to achieve the overall objective of the research.
Geometry-Supervised Pose Network for Accurate Retail Shelf Pose Estimation

In the smart retail industry, the quality of image collection is known to heavily affect the final analysis results (like accuracy) of applications, such as commodity detection, identification, and stitching. In practice, images captured manually by a monocular camera like mobile phone contain many low-quality images caused by an irregular shoot step. After image collection, filtering out low-quality images is a key step to mitigate the aforementioned impacts. One of the most effective solutions is to filter images with huge off-angles in 3-D through the shelf pose estimation algorithm. However, most of the existing camera pose estimation algorithms are designed for natural scenes and are difficult to realize in the structured real target scenes (like shelf scenes). Meanwhile, due to the lack of shelf pose dataset in academia and industry, there is still no approach designed for the shelf pose estimation in the smart retail scenario. In this article, we try to regress the complete shelf pose within a single end-to-end network and propose a novel geometry-supervised pose network (GSPN), which supervises the shelf pose estimation by learning the intrinsically geometric properties of shelves. Furthermore, we introduce the first retail shelf pose dataset (RSPD), including 28 876 images selected from three different shelf categories and being annotated carefully, as well as a complete 3-D shelf posture. The whole networks can be trained end to end with the shelf images and well-annotated ground truth. Experiments result of five strategies show that GSPN achieves the state-of-the-art performance on RSPD.
‘URBAN &amp; SUBURBAN NATURE INTERACTIONS’, IMPACTS AND SERENDIPITOUS NARRATIVES OF THE MY NATUREWATCH PROJECT

Abstract The My Naturewatch project uses self-build wildlife cameras supporting new public engagements with nature &amp; technology. The project used Open Design methods providing self-defined, bespoke making outputs, modelling ‘environmental citizenship’, transitioning beyond sustainability. By co-designing makers were encouraged to engage with ecosystems refocusing human habitats &amp; perspectives driven by curiosity. The article shares a qualitative evaluation of the project's impact on participants, describing attitudes, understanding and behaviour towards technology &amp; nature changed by participation.