Reviewerly/exHarmony huggingface dataset:
Internet of Things Implementation for Development of Smart Agriculture Applications

Abstract The era of the industrial revolution 4.0 is an era that puts forward the concept that puts forward the Internet of Things (IoT). This concept changes from object-oriented to smart objects and makes connections not limited by time and space. The agricultural sector is one sector that adapts to the direction of automation and data processing with the development of a smart agriculture system. The purpose of this research is to change the pattern of agricultural land management from conventional concepts to become much more productive and efficient. The utilization of internet of things technology is carried out through a control and monitoring system. Manager agricultural land can regulate and predict crop yields so that agricultural productivity is more optimal. Some of the supporting devices for the agriculture system are the ESP microcontroller 8266, soil moisture sensor, temperature sensor, humidity sensor DHT-11, and jet pump mini. Sketch Arduino IDE (Integrated Development Environment) as a programming application and interface for the ESP 8266 device. The development of this smart agriculture application uses the method Design Science Research Method (DSRM). The testing phase is carried out with function and performance tests by using the ThingView mobile device.
Neural Network Back-Propagation Method as Forecasting Technique

Abstract Uncontrolled population increase has a direct impact on the development of human resource quality, and the rate of economic growth is becoming increasingly challenging. It was the purpose of the study to apply artificial intelligence tools to examine the forecasts of active family planning participants. Between 2019 and 2020, Pematangsiantar’s family planning participants will be counted as part of the dataset. The dataset was gathered through observation and interviews. The problem was solved using an artificial intelligence technique with a backpropagation algorithm. The analysis is carried out with the aid of Matlab R2011a. Six other architectural models, including 6-4-1, 6-4-2-1, 6-6-8-1, and 6-8-10-1, were evaluated for their suitability. The 6-4-2-1 architectural model on Backpropagation may be used to forecast the number of active family planning participants with a training accuracy rate of 75% and a test accuracy rate of 88%, according to the study’s findings. Results are expected to shed light on the necessity and availability of contraception for Pematangsiantar’s active family planning participants.
Image Segmentation using Color Value of the Hue in CT Scan Result

Abstract Object recognition with visual computer techniques is a process necessary to detect different objects in a three-dimensional image to get precise results. Color extraction process Red Green Blue (RGB), Red, Green, Blue (HSV) and other color spaces can produce high accuracy and minimize the number of parameters to speed the computing process. In this study the diagnosis of pneumonia was based on a CT scan of the lungs in the color image segmentation process. The removal of color features is done by using the HSV color segmentation method to classify the pneumonia in CT scans, which makes it possible to detect an image segmenting object as a blob. The color sample generates a value that is the reference point for the filter range during the segmentation process, based on the test results. In this process, it is clear how many objects are found as a color-based pneumonia diagnosis by changing the HSV and RGB thresholds.
A System for Converting and Recovering Texts Managed as Structured Information

This paper introduces a system that incorporates several strategies based on scientific models of how the brain records and recovers memories. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any language. A special case is studied and tested regarding the Spanish language. The applications of this proposal are vast because, in general, information such as text way, reports, emails, and web content, among others, is considered unstructured and, hence, the repositories based on SQL databases usually do not handle this kind of data correctly and efficiently. The conversion of unstructured textual information to structured one can be useful in contexts such as Natural Language Generation, Data Mining, and dynamic generation of theories, among others.
GraphIC: A graph-based approach for identifying complaints from code-mixed product reviews

Enterprises and online merchants can benefit from automatically detecting complaints from social media platforms and shopping websites. Prior studies on automatically identifying complaints on social media have relied chiefly on analyzing complaints in English, with little consideration for code-mixed complaints. We manually annotated the emotion and sentiment classes in the Product Review dataset, a code-mixed language complaint corpus. The extended corpus consists of 3711 instances annotated with the complaint, sentiment, and emotion labels. We present a Graph Attention Network (GAT) based multi-task framework that intends to simultaneously learn three closely related tasks, complaint detection (primary task), sentiment classification, and emotion recognition (auxiliary task). Based on a quantitative and qualitative study on the extended version of the Product Review corpus, the proposed approach attains an accuracy of 72.82% and a macro-F1 of 71% for the intended task of recognizing complaints, outperforming several single-task, multi-task baselines.1
Virtual leaders: Can customizing authoritarian and democratic business leader avatars influence altruistic behavior and leadership empowerment perceptions?

This study investigated how avatar customization activated leadership styles which may influence subsequent altruistic behavior. Participants (N = 222) engaged in a resource allocation task after customizing avatars that combined their physical self with the traits of democratic or authoritarian business leaders relative to a control condition. Authoritarian condition participants showed decreased altruism than those in the democratic condition. Additionally, participants attributed increased leadership empowerment to their customized avatars but not to themselves. The findings supported the assumption that business leader primes can influence individuals’ behavior and provided initial evidence for how avatar customization can exert priming effects on altruism and social perceptions. Future research avenues for this line of research are further discussed.
Blockchain-based Security Mechanisms for Internet of Medical Things (IOMT)

Traditional standards and security protocols are recognized as unable to solve the security, privacy, and availability of services of the Internet of Medical Things (IoMT) ecosystem, especially during the Coronavirus (COVID-19) pandemic. Blockchain technology has then emerged as a distributed ledger technology that can manage many intelligent transactions and ensure greater security in data management. The Blockchain-based security mechanisms with specific adaptation and additional layers of authentication and verification can offer a complete resources' management system. It has demonstrated it’s superlatively as the core component of the Bitcoin cryptocurrency. In this paper, we propose a ThreeTier Blockchain Architecture in a hierarchical clustering network, with a lightweight authentication system-based API Gateway model that provides network and communication security. Reasonable implementation is proposed and the obtained results demonstrate that our approach shows satisfactory performances in terms of transfer time, energy consumption, and CPU impacts. The traffic analysis also shows that the proposed model can meet the requested security, integrity, and confidentiality of user data.
Latency-Aware Accelerator of SIMECK Lightweight Block Cipher

This article presents a latency-optimized implementation of the SIMECK lightweight block cipher on a field-programmable-gate-array (FPGA) platform with a block and key lengths of 32 and 64 bits. The critical features of our architecture include parallelism, pipelining, and a dedicated controller. Parallelism splits the digits of the key and data blocks into smaller segments. Then, we use each segmented key and data block in parallel for encryption and decryption computations. Splitting key and data blocks helps reduce the required clock cycles. A two-stage pipelining is used to shorten the critical path and to improve the clock frequency. A dedicated controller is implemented to provide control functionalities. For the performance evaluation of our design, we report implementation results for two different cases on Xilinx 7-series FPGA devices. For our case one, the proposed architecture can operate on 382, 379, and 388 MHz frequencies for Kintex-7, Virtex-7, and Artix-7 devices. On the same Kintex-7, Virtex-7, and Artix-7 devices, the utilized Slices are 49, 51, and 50. For one encryption and decryption computation, our design takes 16 clock cycles. The minimum power consumption is 172 mW on the Kintex-7 device. For the second case, we targeted the same circuit frequency of 50 MHz for synthesis on Kintex-7, Virtex-7, and Artix-7 devices. With minimum hardware resource utilization (51 Slices), the least consumed power of 13.203 mW is obtained for the Kintex-7 device. For proof-of-concept, the proposed SIMECK design is validated on the NEXYS 4 FPGA with the Artix-7 device. Consequently, the implementation results reveal that the proposed architecture is suitable for many resource-constrained cryptographic applications.
Zero-Shot Video Grounding for Automatic Video Understanding in Sustainable Smart Cities

Automatic video understanding is a crucial piece of technology which promotes urban sustainability. Video grounding is a fundamental component of video understanding that has been evolving quickly in recent years, but its use is restricted due to the high labeling costs and typical performance limitations imposed by the pre-defined training dataset. In this paper, a novel atom-based zero-shot video grounding (AZVG) method is proposed to retrieve the segments in the video that correspond to a given input sentence. Although it is training-free, the performance of AZVG is competitive to the weakly supervised methods and better than unsupervised SOTA methods on the Charades-STA dataset. The method can support flexible queries as well as different video content. It can play an important role in a wider range of urban living applications.
Static Video Compression’s Influence on Neural Network Performance

The concept of action recognition in smart security heavily relies on deep learning and artificial intelligence to make predictions about actions of humans. To draw appropriate conclusions from these hypotheses, a large amount of information is required. The data in question are often a video feed, and there is a direct relationship between increased data volume and more-precise decision-making. We seek to determine how far a static video can be compressed before the neural network’s capacity to predict the action in the video is lost. To find this, videos are compressed by lowering the bitrate using FFMPEG. In parallel, a convolutional neural network model is trained to recognise action in the videos and is tested on the compressed videos until the neural network fails to predict the action observed in the videos. The results reveal that bitrate compression has no linear relationship with neural network performance.
Practicum During the Pandemic: Zooming In

Universities in British Columbia pivoted to remote online learning in March 2020 and remained online in the upcoming school year. Teacher Education Programs also pivoted to remote online learning, with exception to practicum. Teacher candidates returned to K-12 schools for practicum as of the Fall 2020 term while coursework at universities remained online. Teacher candidates were placed into schools in their home communities, if possible, for practicum. At one university, some teacher candidates were supervised and formally observed and supported by the practicum mentor remotely due to limited staffing, location of placement, and COVID safety protocols. Formal observations and pre- and post-conferences with the teacher candidate were conducted using video conferencing on Zoom. Normally, remote observations are not accepted by the regulating board but during the COVID-19 pandemic some exceptions were made. There were some advantages and disadvantages to remote observations.
Application of Improved K-Means Algorithm in Collaborative Recommendation System

With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.
Likelihood ratios for categorical count data with applications in digital forensics

Abstract We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we focus on the situation in which the evidence consists of two sets of categorical count data: a set of event counts from an unknown source tied to a crime and a set of event counts generated by a known source. Using a same-source versus different-source hypothesis framework, we develop an approach to calculating a likelihood ratio. Under our proposed model, the likelihood ratio can be calculated in closed form, and we use this to theoretically analyse how the likelihood ratio is affected by how much data is observed, the number of event types being considered, and the prior used in the Bayesian model. Our work is motivated in particular by user-generated event data in digital forensics, a context in which relatively few statistical methodologies have yet been developed to support quantitative analysis of event data after it is extracted from a device. We evaluate our proposed method through experiments using three real-world event datasets, representing a variety of event types that may arise in digital forensics. The results of the theoretical analyses and experiments with real-world datasets demonstrate that while this model is a useful starting point for the statistical forensic analysis of user-generated event data, more work is needed before it can be applied for practical use.
Taking stock of teaching practice at the onset of COVID-19

COVID-19 closed schools across the USA and forced teachers and students to navigate virtual learning with little guidance. This mixed-methods study examines K–12 teachers’ beliefs about their teaching practices that were abruptly transitioned to online during the first four months of COVID-19. Surveys and interviews were analysed using sociocultural theory and the four-dimensional model of digital literacy. Teachers felt unprepared for online teaching and expressed an inability to meet their students’ needs. Concerns focused on digital access inequalities, a desire for support to build digital pedagogies, particularly to enhance student engagement. The findings point to a responsibility to support digital pedagogies across pre-service and in-service contexts so that, at a minimum, teachers can operate efficiently. For greater educational equity and outcomes, teachers need to be able to build competencies to facilitate communication, critique and creative expression across contexts, digital spaces, media and for multiple purposes.
Analysis of the efficiency of a double-axis control system of a PV power plant in a solar tracking system

Abstract A prototype control system for a PV power plant was constructed with the movement following the position of the Sun, where the position control takes place in two planes. The principle of operation is based on comparing the voltage values of small PV modules constituting the measurement system. With the use of this stand, the dynamics of changes in light intensity as a function of electricity production using a PV cell was analyzed. The value of energy from two PV cells was compared with the determined efficiency index as the ratio of energy obtained from a follow-motion controlled PV cell to the energy obtained from a stationary cell. The amount of energy generated from the follow-up PV system in the analyzed period exceeds the stationary system in the range from 5% to 74%, depending on the climatic conditions on the observed day.
Using Mobile Dual Eye-Tracking to Capture Cycles of Collaboration and Cooperation in Co-located Dyads

The goal of this paper is to bring new insights to the study of social learning processes by designing measures of collaboration using high-frequency sensor data. More specifically, we are interested in understanding the interplay between moments of collaboration and cooperation, which is an understudied area of research. We collected a multimodal dataset during a collaborative learning activity typical of makerspaces: learning how to program a robot. Pairs of participants were introduced to computational thinking concepts using a block-based environment. Mobile eye-trackers, physiological wristbands, and motion sensors captured their behavior and social interactions. In this paper, we analyze the eye-tracking data to capture participants' tendency to synchronize their visual attention. This paper provides three contributions: (1) we use an emerging methodology (mobile dual eye-tracking) to capture joint visual attention in a co-located setting and replicate findings that show how levels of joint visual attention are positively correlated with collaboration quality; (2) we qualitatively analyzed the co-occurrence of verbal activity and joint visual attention in low and high performing groups to better understand moments of collaboration and cooperation; (3) inspired by the qualitative observations and theories of collaborative learning, we designed a new quantitative measure that captures cycles of collaborative and cooperative work. Compared to simple measures of joint visual attention, we found it to increase correlation coefficients with learning and collaboration scores. We discuss those results and describe how advances in analyzing sensor data can contribute to theories of collaboration. We conclude with implications for capturing students' interactions in co-located spaces using Multimodal Learning Analytics (MMLA).
Voting-Based Contour-Aware Framework for Medical Image Segmentation

Accurate and automatic segmentation of medical images is in increasing demand for assisting disease diagnosis and surgical planning. Although Convolutional Neural Networks (CNNs) have shown great promise in medical image segmentation, they prefer to learn texture features over shape information. Moreover, recent studies have shown the promise that learning the data in a meaningful order can make the network perform better. Inspired by these points, we aimed to propose a two-stage medical image segmentation framework based on contour-aware CNN and voting strategy, which could consider the contour information and a meaningful learning order. In the first stage, we introduced a plug-and-play contour enhancement module that could be integrated into the encoder–decoder architecture to assist the model in learning boundary representations. In the second stage, we employed a voting strategy to update the model using easy samples in order to further increase the performance of our model. We conducted studies of the two publicly available CHAOS (MR) and hippocampus MRI datasets. The experimental results show that, when compared to the recent and popular existing models, the proposed framework can boost overall segmentation accuracy and achieve compelling performance, with dice coefficients of 91.2 ± 2.6% for the CHAOS dataset and 88.2 ± 0.4% for the hippocampus dataset.
DRMT: A Decentralized IoT Device Recognition and Management Technology in Smart Cities

The Internet of Things (IoT) faces many security and privacy risks due to the large number of devices accessing the network while providing convenience to the current society. At present, the management of IoT devices is centralized. All terminal devices are managed in a centralized manner, and the data accessing IoT devices can only be connected to the network with the permission of the centralized nodes. In this scenario, the increase in the amount of equipment can cause central node overload or single node failure. Moreover, flexible expansion requirements are difficult to achieve in such a centralized architecture. This paper proposes a safe and efficient IoT device recognition and management scheme based on blockchain and edge computing, DRMT. This scheme realizes feature extraction, clustering, and learning of IoT devices’ traffic based on the characteristics of distributed deployment of edge servers. In this process, the recognition model can be continuously updated. The scheme also has an advantage that it is possible to enhance the management efficiency and scalability of the system by establishing a blockchain network between edge servers to share identification information and automate deployment security. This paper also verifies the scheme’s effectiveness by building an experimental network and a blockchain system. The results showed that DRMT would help realize the effective management of large-scale IoT devices in smart cities.
A Framework for Modifying Orchestral Qualities in Computer-Aided Orchestration

Abstract This article introduces the Orchidea Orchestral Qualities framework (OOQ), an extension of the Orchidea environment for computer-aided orchestration. Traditional target-based orchestration generally reconstructs a target sound “as faithfully as possible” with a collection of samples. But more often than not, composers do not have specific targets in mind while performing orchestration tasks. A large class of orchestration practices deal with the transformation of musical material to enhance or reduce certain of its qualities (such as making a score more “brilliant,” “blurry,” “dense,” and so on). The OOQ framework implements this idea by making use of an analogy with digital signal processing. Scores and sounds are no longer used as targets, but rather as “sources” to be processed, not unlike what happens within a channel strip of a modern digital audio workstation. This article presents the rationale behind the OOQ framework, describes the behavior of its modules, and traces a path for future research on the subject.