Reviewerly/exHarmony huggingface dataset:
Engaging Parents and Teens in an Asynchronous, Remote, Community-Based Method for Understanding the Future of Voice Technology

Voice agents are becoming deeply integrated into the daily lives of millions of people, yet adults and children perceive and use them differently. Therefore, it is important to understand how parents and children envision the future of these agents. We enrolled 22 parent-teen dyads in an asynchronous remote community-based (ARC) method study. Each dyad consisting of a parent and a teen, participated in nine weekly activities over a period of eight weeks, followed by an exit interview. The participants discussed their desires and fears, such as those regarding agency, emotion recognition, voice characteristics and forms, the timing of their interactions, and voice agents’ acting on behalf of others. The dyads appreciated the flexibility in participation, their ability to send responses privately to the researchers, their access to a channel for socializing with other participants, and the opportunity to build on ideas through sequential interactions during ARC activities. In the end, we discuss how our findings might inform the design of future voice agents and present recommendations for adapting ARC method while designing with parent-teen dyads.
Locked in and locked out: Covid-19 and teaching “remotely”

Covid-19 has rendered education "remote", opening a chasm in space and time between teachers and students, between how teaching and learning was practiced before and how it is practiced now and for the foreseeable, uncertain future. As many educators find themselves both locked in and locked out, this article seeks to sort through the implications of this remoteness. The article builds on the work of William F. Pinar and George Grant, to argue that technology is an ontology shaping how we encounter who we are and the world in which we live. Caught within the tightening circle of a Covid-19 environment predicated on keeping our distance from one another, while we are connecting technologically, at risk is the complicated conversation, as well as attunement, that lie at the heart of teaching, even as teachers know that it is only through improvisational variations on these that one can hope to chart an ethical course forward.
Pros and cons of Blockchain-based approaches in Education

Blockchain has improved rapidly due to its unique characteristics such as security, decentralization, reliability and data integrity. Blockchain technology is used in several areas, for instance, health, financial, and Education. This article is a systematic review and state of art. It concentrates on Blockchain technology usage in Education; it focuses on three main areas: 1. Educational applications based on Blockchain technology 2. Advantages of use Blockchain in Education among real case study 3. Drawbacks of implementing application based on Blockchain technology. Detailed analysis and discussion based on the results and findings are taken into consideration. Due to the Covid-19 pandemic in 2020, eLearning becomes vital for any educational institute in the world. Covid-19 consequences in the eLearning are discussed and analyzed in our study. In conclusion, we briefly describe our future work to present the new Blockchain model in Education.
A No Reference Deep Learning Based Model for Quality Assessment of UGC Videos

Recent years have witnessed a rapid growth of user generated videos thanks to the availability of affordable video recording devices and the extreme popularity of social media platforms. Accordingly, there is a challenge for designing highly efficient video quality assessment models to monitor, control, and optimize this content. In this contribution, a novel no-reference video quality metric for user generated video is presented. It exploits the spatial and temporal information contained in the center patch of video frames and a Support Vector Regressor system for computing the objective score. Experimental results show the effectiveness of the proposed approach. To promote reproducible research and public evaluation, an implementation of RM3VQA has been made available online: https://github.com/pramitmazumdar/RM3VQA.
e-Nelayan the Fishery Marketplace App

Smartphones have become an essential device that not only acts as a communication media, but it is also able to assist its user to do multiple tasks. A fisherman is an example of a community member that uses a smartphone. If a smartphone is fully utilised, it can be a huge help for the fishermen to sell their catch and fishery products. However, there is no proper medium, such as an mobile application, for this group of people to sell their catch. Hence, the e-Nelayan Marketplace App is introduced. This app enables the fishermen mainly in Kota Samarahan to sell their catch and other fishery products more effectively. The functionalities of this app include the ability to advertise the catch and let customers and fishmongers know the type of fish being sold. In addition, an interactive graphical user interface was designed to display the output of each functional module. In order to evaluate the user acceptance towards the app, several tests were conducted, such as the performance, portability, compatibility, and usability testing. With the development of this project, it is hoped that the application will benefit not only fishermen but also customers and fishmongers.
Just‐in‐time defect prediction for software hunks

Abstract Just‐in‐time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just‐in‐time prediction for software files (JIT‐F). JIT‐F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just‐in‐time defect prediction for software hunks (JIT‐H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT‐H. Experimental results show that JIT‐H is effective at predicting defects in software hunk changes. Compared with JIT‐F, JIT‐H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.
Feature Group Importance for Automated Essay Scoring

One of the challenges in essay scoring is that it is highly subjective to the human graders. There have been numerous research projects conducted on improving computerised Automated Essay Scoring (AES). AES systems generally rely on hand-crafted linguistic features to construct a classification model for essay scoring. The majority of the AES systems’ classification algorithm inputs are based on three main feature groups; lexical, grammatical, and semantic feature groups. This paper presents an empirical study to explore the influence of each feature group on the performance of AES classification models based on a general approach of the AES system. The results uncovered that the grammatical and semantic feature groups are lacking due to their poor performance and typical over-fitting of the classification models when using the features in the feature group.
The use of GIS technologies for the classification of underwater topography and estimation of their ore production (on the example of the Magellan seamounts, the Pacific Ocean)

The use of GIS
Upper Bounds on Communication in Terms of Approximate Rank

We show that any Boolean function with approximate rank r can be computed by bounded error quantum protocols without prior entanglement of complexity $$O( \sqrt{r} \log r)$$ . In addition, we show that any Boolean function with approximate rank r and discrepancy $$\delta $$ can be computed by deterministic protocols of complexity O(r), and private coin bounded error randomized protocols of complexity $$O((\frac{1}{\delta })^2 + \log r)$$ . Our deterministic upper bound in terms of approximate rank is tight up to constant factors, and the dependence on discrepancy in our randomized upper bound is tight up to taking square-roots. Our results can be used to obtain lower bounds on approximate rank. We also obtain a strengthening of Newman’s theorem with respect to approximate rank.
AI And Precision Medicine For Oncology

AI can help to essentially accelerate the process of analysis of vast amounts of data, anchoring patterns in the data, resulting in faster and recommended decision-making. Sophisticated prescient models are produced using algorithms that extract the patterns from data and predict results. AI is categorized into three stages namely, artificial narrow intelligence (ANI), artificial general intelligence(AGI), and artificial super intelligence(ASI). Precision medicine is a rising methodology for disease prevention and treatment based on the specific hereditary, environment conditions, and lifestyle choices of an individual patient. AI algorithms have been found to be compelling in streamlining malignant growth screening and discovery. AI frameworks are able to recognize singular drug-response variability, making recommendations based on patterns learned from vast amounts of public and exclusive information, and can help expand the frontier of precision medicine, specially of cancer genomics.
Why Did the Robot Cross the Road?

This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human-robot interaction (HRI) scenario. In doing so, this work situates “explainable AI” (XAI) in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences.
Features of Speech Commands Recognition Using an Artificial Neural Network

The article is devoted to the features of speech commands recognition using an artificial neural network. The existing methods of speech recognition are considered. The algorithm of operation of the speech recognition system is presented. The stages of speech signal processing are described, indicating widely used methods. It is shown that the use of neural network analysis increases the efficiency of speech signal processing. As a type of neural network architecture, we used a recurrent bidirectional deep learning neural network Bidirectional Long Short-Term Memory and a method for obtaining mel-frequency cepstral coefficients. The choice of this neural network is due to the fact that the network uses both a priori and a posteriori information. To train the neural network in the study, a ready-made open database of speech commands Google Speech Commands Dataset was used. An experimental study of speech command recognition using BiLSTM artificial neural network was carried out using the Matlab software package and the AudioLabeler additional application.
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser

Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation method is proposed consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal. A novel layer, to be used in deep learning architectures, is proposed in this work, bringing back the concept of chained equations for multiple imputation []. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives. The code is made available on Github.
AEE-Net: An Efficient End-to-End Dehazing Network in UAV Imaging System

Because it can provide real-time images for the first time, UAV plays a massive role in disaster relief, environmental observation, and information collection. However, the quality of images collected by UAV is always affected by fog. Therefore, the research on how to remove the fog in the image becomes more and more critical. In recent years, the role of convolutional neural networks (CNN), which can automatically extract features and efficiently process high-dimensional data, has received more and more attention in many disciplines. To improve the imaging quality of UAV in a foggy environment, this paper proposes an image dehazing model built with a convolutional neural network (CNN), called an effective end-to-end dehazing Network (AEE-Net). Our proposed method has a faster running speed than traditional models due to the simple structure of the model and the design based on the modified atmospheric scattering model. Our method combines the characteristics of dehazing processes and the advantages of deep learning. Experimental results on the training set and raw images show that the proposed method has better performance than traditional methods. This method can improve the quality of UAV-captured images under foggy conditions and can meet the input requirements of UAV vision tasks.
Near future prediction of rainfall distribution pattern by Adaptive Neuro Fuzzy Interference System in Central Java, Indonesia

Rainfall distribution pattern is very important for sustainable production in the agricultural sector in Tropical region. The near future rainfall amount under changing climate was predicted from 2009 to 2028 by Adaptive Neuro Fuzzy Inference System (ANFIS), which was trained with the rainfall observation data from 1979 to 2013, spatiotemporally in the Central Java Province, Indonesia. Our analysis showed that the predicted rainfall data using ANFIS can represent actual rainfall conditions. Rainfall predicted from 2009 – 2028 in Central Java will experience a decrease in high rainfall in an area of 1615125,2 hectare, which can cause drought. The area that predicted to experience drought in the future are Kebumen, Jepara, Pati, Rembang, Kudus, Grobogan, Blora, Sragen, and parts of the regions of Purworejo, Cilacap, Banyumas, Brebes, Wonogiri, Karanganyar, and Demak.
A Mobilenet Accelerator with High Processing-Element-Efficiency On FPGA

MobileNet is a kind of Convolutional neural networks (CNNs), but the existing MobileNet accelerators on Field Programmable Gate Array (FPGA) show low processing element (PE) efficiency. One of the reasons is that the structure of MobileNet makes the accelerators fall into memory bandwidth bottleneck. For this reason, this work proposed a network mapping strategy to improve the computational efficiency of FPGA implementation, based on knapsack algorithm and simulated annealing algorithm. Also, experiment results show that the implementation with this strategy can achieve a processing PE efficiency up to 72.7% with lower resource utilization, compared with other implementations.
The artificial intelligence revolution: Achievements and prospects

The commercially successful application of neural network algorithms in artificial intelligence (AI) systems and devices after 2010 has significantly accelerated the process of achieving new successes in solving “intellectual problems. Further development of work on AI will affect not only the technological order, but also social relations in human society, and it is necessary to think about the possible consequences of such an influence right now.
Predicting Relative Thresholds for Object Oriented Metrics

Object-oriented software metrics provide a numerical characterization of software quality. They have also been used in the assessment and identification of technical debt. However, metrics generally need to be used with thresholds as reference points that help to interpret their values properly and objectively. The problem is that, while there are many proposed metrics, there are relatively few studies on thresholds and threshold calculation methods; hence, the effective application of metrics in practice has been limited. Moreover, although it has been acknowledged that thresholds should not be absolute, but rather relative to certain contextual factors, the context is still not considered in most threshold studies. In this paper, the relationship between system size (as a contextual factor) and metric thresholds is investigated. The objective is to build predictive models that estimate thresholds based solely on system size, and to assess the feasibility of this approach as a threshold estimation method. An empirical study is conducted for this purpose using 36 defect- prediction datasets and six metrics. The results show that the proposed threshold estimation method is feasible, and it can achieve an accuracy remarkably comparable to more complex threshold models.
On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backward-in other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.
EFFECT OF WAVELET DE-NOISING ON THE CLASSIFICATION OF PIG BEHAVIOUR

To efficiently eliminate the noise generated by the triaxial accelerometer when collecting pigs' behavioural data, this paper adopted SNR and MSE as the indexes to evaluate the de-noising effect of pigs' acceleration signal under various combinations of wavelet basis, decomposition layer, threshold rule and threshold function. Based on the optimal wavelet parameter combinations, the de-noised data were divided into a training dataset and test dataset to conduct a 3-fold cross validation. The results showed that Db4 wavelet can achieve a satisfactory de-noising effect when used as a wavelet basis for 8 layers wavelet decomposition based on Rigrsure threshold rules and the new improved threshold function. As a result, compared with traditional wavelet hard threshold de-noising, soft threshold de-noising and EMD de-noising method, the improved threshold function improved the stability of signal filtering, which was shown to be more practical, effective and feasible. As such, wavelet de-noising was found to significantly improve the classification accuracy of all four behaviour classes (lying, standing, walking and exploring) considered for this study, and the overall major mean accuracy was improved from 0.680 to 0.826.
Public opinion hot topic discovery based on improved bacterial foraging algorithm

In order to find the hot topic from public opinion and solve the problem that the topic mining accuracy is not high enough, the topic is disturbed by unrelated links and the topic cannot be focused on a point, the swarm intelligence algorithm is applied to topic mining, and an improved bacterial foraging algorithm is proposed. The algorithm improves the chemotaxis, copy and migration operations in the original algorithm, which makes up for the shortcomings of the original algorithm, and greatly improves the accuracy and convergence. The correlation value of web page is taken as the measurement of web page heat, and the heat evaluation model is established. The improved bacterial foraging algorithm is used to cluster the web pages. The results of experiments and other algorithms show that the algorithm has better clustering effect.
Explainability using Decision Trees and Monte Carlo Simulations

The prominence of AI algorithms has reached new heights in todays world. Algorithms are implemented in each and every aspect of our life with the primary intention of improving it. However, the working of most of these AIs is not entirely understood, this causes the problem. Since the models are not understood, the researchers do not know how to manually improve on it, creating an artificial ceiling. Understanding these algorithms is the key to realize how these machines think and not only learn from them but also teach it to understand better. The primary idea put forward in this paper is to explain a black-box model using a mimicking simulation, rather than the usual calculative approaches. The primary idea put forward is to understand how an AI works using a decision tree that will be designed to mimic the AI. It proposes the use of a Decision Tree based approach along with randomization using Monte Carlo simulations for a more precise simulation of the black-box.
Evolving from Data to Knowledge Mining to Uncover Hidden Relationships

Nowadays, law enforcement agencies – LEAs – are forced to deal with extreme volumes of data, being in need to analyse from heterogeneous data sources, uncover hidden relationships, trends and patterns of incidents and ultimately reach solid evidence to be used in court. In this chapter, a system is presented that can assist LEA officers in fighting crime, that, following the collection of the primary data, it applies semantic reasoning tools that allow the system to relate pieces of data, based on their inner relationships, and extract new information based on the asserted facts and rules defined by the LEAs. Then, the results derived by the reasoners and the initial data are fused using appropriate tools including a trajectory, a person and an event fusion tool, to be finally visualized by the proposed bipartite graphs. The proposed system is expected to decrease the time required to solve a crime by LEA’s officers.