Reviewerly/exHarmony huggingface dataset:
Impact of Educational Coaching Programs and Mentoring Services on Users’ Perception and Preferences: A Qualitative and Quantitative Approach

This study determines how educational supporting services and mentoring programs can be improved based on the users’ preferences and perception by benefiting from a data-driven design model and process innovation. To this end, the study proposed a data-driven support and decision model (DSDM) that uses a mixed methodology (qualitative and quantitative) to analyze data collected from different writing centers and coaching programs who responded to an online survey that comprised of both quantitative and qualitative items. Quantitatively, we applied a Linear and Mixed-effect logistic regression analysis to investigate the critical factors that motivate the users in seeking educational support services. And, qualitatively apply the Text mining technique to analyze the opinions given by the participants in order to establish its level of impact towards the several services offered by the coaching programs. Theoretically, the proposed research model is grounded on integration of a data-structure approach that builds on the descriptive decision theory; which studies the rationality of the decisions that users are disposed to make by means of the statistical method and textual data quantification. In turn, the method allowed us to capture the influential factors and state-of-the-art in faculties-students development and coaching programs, and to provide solutions to the ever-increasing need to improve educational supporting services and quality in a competitive and rapidly changing educational environment or market that have not been done before. Although there may be a considerable variation between the different categories of educational services offered by the coaching centers. The results of our study shows that the users hold services such as “tutoring” as a major factor that influences their visit and recommendations to the writing centers or coaching programs. Moreover, most users of the coaching programs are motivated by the need to gain “support” with their educational/academic performance and productivity.
Metaknowledge Extraction Based on Multi-Modal Documents

The triplet-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.
Lightweight Blockchain-Based System for Internet of Things Security

Nowadays, smart cities deploy several smart computing technologies such as Internet of Things (IoT), cloud computing, and interconnected network architecture for the purpose of providing innovative solutions and better collaboration between residents and local governments while maintaining an optimal utilization of the available resources. However, these technologies suffer from some security and privacy issues which are very critical for many applications. Blockchain technology is a valuable infrastructure solution that addresses these issues. The decentralized approach of the blockchain technology facilitates building a resilient ecosystem where IoT devices can function effectively and securely. Moreover, the architecture of the blockchain provides reliable communication by eliminating single point of failure and enabling users to maintain anonymity by employing privacy-preserving cryptographic algorithms. However, employing the blockchain technology in an IoT-based system is challenging due to the centralized nature of IoT-based systems and the resource constraints of its interconnected devices, which lead to several serious security and privacy issues. In this paper, we propose a blockchain infrastructure design that provides security and privacy to IoT-based systems of critical governmental and industrial applications. The proposed design includes a blockchain connected gateway, which adaptively and securely maintains user privacy preferences. Moreover, it handles the blockchain computation instead of overwhelming the IoT devices with heavy computation.
Semantic Text Analysis for Detection of Compromised Accounts on Social Networks

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for semantic analysis of text messages coming out from an account to detect compromised accounts. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream. We study the effectiveness of the proposed semantic features using a Twitter data set. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.
WITHDRAWN: Feature extraction of underwater images using principle component analysis with image registration

Digital imaging becomes the foremost important techniques in environmental monitoring and exploration. Underwater images are used to assess variety and profusion of objects are adopted by marine biologists. The quality of underwater image is necessary in the field of deep-sea study for object annotations, identifications and classifications. Automatic processing of analyzing the images working to attain the objectives in reduced price and period. There are many challenges involved such as complex background, deformation, low resolution and light propagation. With the advancement automatic detection, the deep neural network has led in real-time scenarios to detect and recognize the objects. We used the basic image processing operations such as registration and registered and fusion before performing the feature extraction. By using these techniques the proposed system takes advantages and extracted more features. The Harries Detection method is used for detecting the matched points and principal component analysis is used for feature matching. We have implemented with MATLAB and compared results with the existing systems by calculating the Entropy (E), Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR). The proposed system shows 98% of accuracy compared to the existing systems.
Improving Model Capacity of Quantized Networks with Conditional Computation

Network quantization becomes a crucial step when deploying deep models to the edge devices as it is hardware-friendly, offers memory and computational advantages, but it also suffers performance degradation as the result of limited representation capability. We address this issue by introducing conditional computing to low-bit quantized networks. Instead of using a fixed, single kernel for each layer, which usually does not generalize well across all input data, our proposed method tries to use multiple parallel kernels dynamically in conjunction with the winner-takes-all gating mechanism to select the best one to propagate information. Overall, our proposed method improves upon the prior work, without adding much computational overhead, results in better classification performance on the CIFAR-10 and CIFAR-100 datasets.
Uncertainty query sampling strategies for active learning of named entity recognition task

Active learning approach is well known method for labeling huge un-annotated dataset requiring minimal effort and is conducted in a cost efficient way. This approach selects and adds most informative instances to the training set iteratively such that the performance of learner improves with each iteration. Named entity recognition (NER) is a key task for information extraction in which entities present in sequences are labeled with correct class. The traditional query sampling strategies for the active learning only considers the final probability value of the model to select the most informative instances. In this paper, we have proposed a new active learning algorithm based on the hybrid query sampling strategy which also considers the sentence similarity along with the final probability value of the model and compared them with four other well known pool based uncertainty query sampling strategies based active learning approaches for named entity recognition (NER) i.e. least confident sampling, margin of confidence sampling, ratio of confidence sampling and entropy query sampling strategies. The experiments have been performed over three different biomedical NER datasets of different domains and a Spanish language NER dataset. We found that all the above approaches are able to reach to the performance of supervised learning based approach with much less annotated data requirement for training in comparison to that of supervised approach. The proposed active learning algorithm performs well and further reduces the annotation cost in comparison to the other sampling strategies based active algorithm in most of the cases.
Deep Facial Emotion Recognition System Under Facial Mask Occlusion

Over the past few decades, automated machine based Facial Emotion Recognition (FER) has made a significant progress, guided by its relevance for applications in various areas of neuroscience, health, defense, entertainment and Human Robot Interaction (HRI). Most of the work on FER is focused on controlled conditions for non-occluded faces. In the present Covid-19 scenario, when more individuals cover their face partially to prevent spread of corona virus, developing a system/technique that can recognize facial emotion with facial mask constraint is desirable. In this research work, a FER system built using Convolution Neural Networks (CNN) technique is proposed. In the proposed work, eyes and forehead segment is used to train model. This system is implemented using Raspberry Pi 3 B + and Robotic Process automation (RPA) for HRI. The proposed system achieves 79.87% average accuracy with 5-fold cross validation and also tested in real time scenario.
Performance Evaluation of Various Feature Selection Techniques for Offline Handwritten Gurumukhi Place Name Recognition

The relevance of feature selection technique is found in decreasing the dimensionality of the feature set by rejecting the irrelevant and redundant features. The feature selection technique considers only relevant and significant features which are required to classify the word image into one of the multiple classes. In this article, four feature selection techniques such as principal component analysis (PCA), correlation feature set (CFS), consistency-based analysis (CBA) and chi squared attribute (CSA) are used to optimize the boundary extent features extracted from the word images. The proposed approach is used to recognize offline handwritten Gurumukhi place names based on holistic approach which recognize the whole word as an independent entity without going for its explicit segmentation. The experiments are evaluated on benchmark dataset of 40,000 handwritten words of Gurumukhi script using two classification techniques, namely decision tree and random forest classifiers. To evaluate the performance of different feature selection techniques, various evaluation parameters are used. Based on experiments, CSA feature selection technique achieved the best results using random forest classifier and surpassed the other three feature selection techniques in terms of various considered evaluation parameters. CSA feature selection technique along with random forest classifier achieved best recognition rate of 87.42%.
Performance of particle swarm optimization bin packing algorithm for dynamic virtual machine placement for the consolidation of cloud server

Abstract Infrastructure as a service offered by the cloud computing is one of the most important service. It allows physical machines to get virtualized by creating many instances of virtual machines. Mapping virtual machines on physical machine has become the major challenge in cloud data centres. The dynamic virtual machine placement methods are used to solve this issue with objectives like maximizing the resource utilization, minimizing the energy consumption and maximizing the scalability of data centres. In this paper a virtual machine placement-based bin packaging algorithm is proposed and analysed with four different fitness strategies to obtain the optimal solution. The unimodal (Sphere, Step) and multimodal (Graywang and Rastridge) benchmark functions are used with proposed algorithm for the analysis and obtain the quantitative measurements. The results show that optimizing the mass of particles using the best fitting strategy reduces the energy consumption, resource utilization and improved the scalability of data centres.
Convolution-Based Attention Model With Positional Encoding For Streaming Speech Recognition On Embedded Devices

On-device automatic speech recognition (ASR) is much more preferred over server-based implementations owing to its low latency and privacy protection. Many server-based ASRs employ recurrent neural networks (RNNs) to exploit their ability to recognize long sequences with a limited number of states; however, they are inefficient for single-stream implementations in embedded devices. In this study, a highly efficient convolutional model-based ASR with monotonic chunkwise attention is developed. Although temporal convolution-based models allow more efficient implementations, they demand a long filter-length to avoid looping or skipping problems. To remedy this problem, we add positional encoding, while shortening the filter length, to a convolution-based ASR encoder. It is demonstrated that the accuracy of the short filter-length convolutional model is significantly improved. In addition, the effect of positional encoding is analyzed by visualizing the attention energy and encoder outputs. The proposed model achieves the word error rate of 11.20% on TED-LIUMv2 for an end-to-end speech recognition task.
Socio-hydrogeology and Geoethics—State of the Art and Future Challenges

This contribution aims at exploring the nexus between the recently developed discipline of socio-hydrogeology and geoethics. Socio-hydrogeology targets the inclusion of social sciences into hydrogeological assessment, with the overall goal of assessing the reciprocity between people and groundwater. For doing so, it promotes the integration of hydrogeological assessments with tools and activities typical of the social sciences (e.g. social network analysis, social impact assessment, public engagement). A closer look at the social implications of any hydrogeological assessment would not only permit to better frame the research and propose more adequate science-based managed practice, but also to ensure that (still existing) gap between science and society is effectively bridged. Indeed, socio-hydrogeology fosters the development of mutual trust between scientists and concerned stakeholders: a fundamental requisite for the sustainability of any action targeted to the long-term protection of water resources. Socio-hydrogeology hence encourages hydrogeology goes beyond classical sectorial approaches, by looking at ethical, social, and cultural implications of hydrogeological assessments. As a result, they will be capable of promoting new management strategies that will not only based on sound scientific knowledge, but also on the adequate assessment of the social dimension of groundwater.
Optimization of threshold functions over streams

A common stream processing application is alerting, where the data stream management system (DSMS) continuously evaluates a threshold function over incoming streams. If the threshold is crossed, the DSMS raises an alarm. The threshold function is often calculated over two or more streams, such as combining temperature and humidity readings to determine if moisture will form on a machine and therefore cause it to malfunction. This requires taking a temporal join across the input streams. We show that for the broad class of functions called quasiconvex functions, the DSMS needs to retain very few tuples per-data-stream for any given time interval and still never miss an alarm. This surprising result yields a large memory savings during normal operation. That savings is also important if one stream fails, since the DSMS would otherwise have to cache all tuples in other streams until the failed stream recovers. We prove our algorithm is optimal and provide experimental evidence that validates its substantial memory savings.
A Survey on K-means Algorithm Centroid Initialization

The K-mean Clustering technique used for identifying object and group similar objects based on features. Clustering algorithm capable of handling a massive chunk of data. Initial points as it affects local optimization affect the K mean clustering result. The optimization process is NP-hard a survey on local optimization is helpful to find a better solution. We will compare different initialization techniques like Random Sampling MEAN, Random Sampling, Moving K-Means (MKM), Distance optimization Methods, Density Estimation Methods.
Vision and Language Navigation using Multi-head Attention Mechanism

In the wake of the developments of deep learning, more and more research focus on the intersection fields of natural language processing and machine vision, vision and language navigation (VLN) is one of them. The VLN task needs an embodied agent to carry out the natural language instruction and navigate inside a real 3D environment with the help of visual information, planning a trajectory from start point to goal location. In this paper, inspired by the previous works, we introduce a multi-head attention module with a parallel attention computing method which apply multi-head attention mechanism on visual and textual input to enhance the performance of the model. Specifically, first we design a multihead attention module with trainable parameters, which can extract associated attention from the textual and visual information, extracted attention variables are used to promote the agent aware of which part of the sentence or image is more important. Second, in order to help the agent perceives more useful information, we perform a parallel computing method on extracted attention and input, i.e. visual and textual input feature, then use a layer normalization to combine them. The results of experiments indicate our proposed module enables the model to obtain better performance and surpasses the baseline model. The success rate of our model is 51% and oracle success rate is 62% with low navigation error.
Unsupervised Moving Object Detection through Background Models for PTZ Camera

Moving object detection in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN-based methods usually require the user's supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.
LSTPR: Graph-based Matrix Factorization with Long Short-term Preference Ranking

Considering the temporal order of user-item interactions for recommendation forms a novel class of recommendation algorithms in recent years, among which sequential recommendation models are the most popular approaches. Although, theoretically, such fine-grained modeling should be beneficial to the recommendation performance, these sequential models in practice greatly suffer from the issue of data sparsity as there are a huge number of combinations for item sequences. To address the issue, we propose LSTPR, a graph-based matrix factorization model that incorporates both high-order graph information and long short-term user preferences into the modeling process. LSTPR explicitly distinguishes long-term and short-term user preferences and enriches the sparse interactions via random surfing on the user-item graph. Experiments on three recommendation datasets with temporal user-item information demonstrate that the proposed LSTPR model achieves significantly better performance than the seven baseline methods.
<i>‘I’m the boss of the Stiemerbeek valley!’</i>Reconsidering children’s empowerment in participatory design from the perspective of infrastructuring

Although researchers and designers have paid attention to children’s empowerment in Participatory Design (PD) processes, there is a lacuna in research into manifestations of empowerment in and as a result of PD processes concerned with the building of dynamic infrastructures. In a response, we conducted a case study in which 10–12-year-olds participated in designing prototypes related to a local nature area and reconsidered existing views on empowerment. The findings resulted in (1) a broad operationalisation of the critical form of empowerment; (2) a mapping of the possibilities for empowerment in informal time frames next to traditional, project-related time frames; and (3) an extended analytical framework for the span of activities considered to be empowering in PD processes. The contribution of this study is a comprehensive understanding and lingua franca to consider children’s empowerment in relation to infrastructuring. This infrastructural view on empowerment allows to include notions of active citizenship and empowerment-in-use in the discourse and analysis on children’s empowerment, placing design participation in a holistic context, beyond the formal participation in the design process.