Reviewerly/exHarmony huggingface dataset:
Unsupervised Few Shot Key Frame Extraction for Cow Teat Videos

A novel method of monitoring the health of dairy cows in large-scale dairy farms is proposed via image-based analysis of cows on rotary-based milking platforms, where deep learning is used to classify the extent of teat-end hyperkeratosis. The videos can be analyzed to segment the teats for feature analysis, which can then be used to assess the risk of infections and other diseases. This analysis can be performed more efficiently by using the key frames of each cow as they pass through the image frame. Extracting key frames from these videos would greatly simplify this analysis, but there are several challenges. First, data collection in the farm setting is harsh, resulting in unpredictable temporal key frame positions; empty, obfuscated, or shifted images of the cow’s teats; frequently empty stalls due to challenges with herding cows into the parlor; and regular interruptions and reversals in the direction of the parlor. Second, supervised learning requires expensive and time-consuming human annotation of key frames, which is impractical in large commercial dairy farms housing thousands of cows. Unsupervised learning methods rely on large frame differences and often suffer low performance. In this paper, we propose a novel unsupervised few-shot learning model which extracts key frames from large (∼21,000 frames) video streams. Using a simple L1 distance metric that combines both image and deep features between each unlabeled frame and a few (32) labeled key frames, a key frame selection mechanism, and a quality check process, key frames can be extracted with sufficient accuracy (F score 63.6%) and timeliness (&lt;10 min per 21,000 frames) for commercial dairy farm setting demands.
Smallholder Rice Farmers’ Perceptions on Usefulness of Mobile-Phone Technology in Bahi District, Tanzania

Mobile Phone Technology is increasingly becoming a powerful method of interpersonal communication. This paper documents smallholder rice farmers’ perception of the usefulness of this technology in communicating agricultural production and market information. Data were collected from 282 smallholder farmers selected randomly from rice farmers in Bahi District, Tanzania. The findings revealed that 97.2% of the sampled smallholder farmers owned the phone for more than three years. All phones were able to perform the basic function of sending and receiving short messages and making and receiving calls, with very few (18.8%) capable of accessing the internet. 48% and 78% of the respondents perceived the network coverage and costs of buying and running the technology respectively as moderate, whilst 54.3% perceived the technology as useful in communicating agricultural production and market information. However, only less than 36% of the respondents confirmed to have used the technology to communicate agricultural production and market information, with very few (3.2%) communicating with extension officers. It is recommended that intervention intended to improve farming through mobile phone technology should be configured around the basic functions of mobile phones. More so, extension officers should increase the use of mobile phone technology in advising farmers through automated short message services or a stationed officer on calls.
Optimized deep learning model for spatio-temporal detection and localization of object removal video forgery with multiple feature extraction

Abstract Video forgery (VF) is an approach for manipulating the fake videos by modifying, coordinating or generating new contents among the video sequence. The identification of this type of video forgery is complex. For extracting multiple features, developing a novel approach still remains major challenge in this area. In this work, an optimized deep learning (DL) model for the video forgery detection (VFD) and localization with multiple feature extraction is proposed. Initially, key-frame extraction takes place with the aid of Gaussian mixture model (GMM) to extract frames from the forged videos. Then, pre-processing stage is manipulated for the conversion of RGB frame into grayscale image. To study the nature of the forged videos, there is a need of extracting multi-features from the pre-processed frames. In our proposed study, SURF, PCA-HOG, MBFDF, COA and PRG features are extracted. The dataset used for proposed work is collected from REWIND of about 80 forged and authenticated videos. With the help of DL approach, video forgery can be detected and localized. Thus, this research mainly focus on the detection and localization of forged video based on ResNet152V2 model hybrid with Bi-GRU to attain the maximum accuracy and efficiency. The performance of this model is finally compared with existing approachesin terms of accuracy, precision, F-measure, sensitivity, specificity, FNR, FDR, FPR, MCC and NPV. The proposed methodology assures the performance of96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC, 96% NPV respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for GMM model attained about 104 and 27.95 respectively.
Development of Exercise Program Providing System Based on Convergence Indicator Using Virtual Reality: Children with Developmental Disabilities Study

Purpose: The present invention is to provide a personalized exercise program corresponding to developmental disabilities and exercise performance based on the results presented in the convergence indicator for children with developmental disabilities.BR Method: Based on the cognitive function test, sociality test, and physical fitness test results of children with developmental disabilities, we present an exercise program that responds to the developmental disabilities and exercise performance of children with developmental disabilities.BR Results: A test terminal that provides an exercise program with test response information for children with developmental disabilities by performing cognitive function tests, social tests, and physical tests for children with developmental disabilities, and cognitive function evaluation results and sociality evaluation results based on information collected from the test terminals and physical fitness evaluation results were calculated. An exercise program was created to create a customized exercise for children with developmental disabilities by determining the exercise type and exercise order based on the calculated evaluation results. The exercise program generating device selects an exercise related to a function with a low evaluation result based on the cognitive function result and the sociality result among the set exercise items, and the number of exercise items by exercise intensity and the order of exercise intensity based on the physical fitness result was set. The number of exercise types for each exercise intensity set by the physical fitness evaluation result was determined, and the exercise items were arranged in the order of the exercise intensity set by the physical fitness evaluation result to create an exercise program tailored to the child with developmental disabilities.BR Conclusion: The present invention will be utilized as a system that can create an exercise program suitable for individual characteristics by presenting convergence indicators through comprehensive results on cognitive function, sociality, and exercise performance of children with developmental disabilities. And through future clinical trials, children with developmental disabilities effect verification on cognitive function, sociality, and exercise performance should be conducted.
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions

Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual distractions---which are common in real scenes---from high-dimensional observations can be hurtful to the learned representations in visual RL, thus degrading the performance of generalization. To tackle this problem, we propose a novel approach, namely Characteristic Reward Sequence Prediction (CRESP), to extract the task-relevant information by learning reward sequence distributions (RSDs), as the reward signals are task-relevant in RL and invariant to visual distractions. Specifically, to effectively capture the task-relevant information via RSDs, CRESP introduces an auxiliary task---that is, predicting the characteristic functions of RSDs---to learn task-relevant representations, because we can well approximate the high-dimensional distributions by leveraging the corresponding characteristic functions. Experiments demonstrate that CRESP significantly improves the performance of generalization on unseen environments, outperforming several state-of-the-arts on DeepMind Control tasks with different visual distractions.
Design of Online Reading System Based on Human-Computer Interaction Information Fusion Technology

Information technology has the functions of visual presentation, scene blending, human-computer interaction, and information fusion. It integrates various forms of information resources such as video, audio, pictures and text, so as to integrate them into the organic whole, and better serve Chinese teachers and students provide services to bring students fresh learning experience. Aiming at the limitations of the alternative and fast-food reading modes of traditional primary school Chinese reading teaching, the online reading system is constructed based on modern information technology to provide students with expanded reading and answer-based reading. In actual teaching, it is also necessary to use information technology to create situations to stimulate students' interest in reading; use information technology to import extracurricular resources to enrich teaching materials; extend the classroom based on information technology and broaden students' horizons.
Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion

&lt;span&gt;Augmented reality (AR) applications have become increasingly ubiquitous as it integrates virtual information such as images, 3D objects, video, and more to the real world, which further enhances the real environment. Many researchers have investigated the augmentation of the 3D object on the digital screen. However, certain loopholes exist in the existing system while estimating the object’s pose, making it inaccurate for mobile augmented reality (MAR) applications. Objects augmented in the current system have much jitter due to frame illumination changes, affecting the accuracy of vision-based pose estimation. This paper proposes to estimate the pose of an object by blending both vision-based techniques and micro electrical mechanical system (MEMS) sensor (gyroscope) to minimize the jitter problem in MAR. The algorithm used for feature detection and description is oriented FAST rotated BRIEF (ORB), whereas to evaluate the homography for pose estimation, &lt;a name="_Hlk100294718"&gt;&lt;/a&gt;random sample consensus (RANSAC) is used. Furthermore, gyroscope sensor data is incorporated with the vision-based pose estimation. We evaluated the performance of augmenting the 3D object using the techniques, vision-based, and incorporating the sensor data using the video data. After extensive experiments, the validity of the proposed method was superior to the existing vision-based pose estimation algorithms.&lt;/span&gt;
Progressive Class Semantic Matching for Semi-supervised Text Classification

Semi-supervised learning is a promising way to reduce the annotation cost for textclassification.Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance.In this work, we further investigate the marriage between semisupervised learning and a pre-trained language model.Unlike existing approaches that utilize PLMs only for model parameter initialization, we explore the inherent topic matching capability inside PLMs for building a more powerful semi-supervised learning approach.Specifically, we propose a joint semi-supervised learning process that can progressively build a standard K-way classifier and a matching network for the input text and the Class Semantic Representation (CSR).The CSR will be initialized from the given labeled sentences and progressively updated through the training process.By means of extensive experiments, we show that our method can not only bring remarkable improvement to baselines, but also overall be more stable, and achieves state-ofthe-art performance in semi-supervised text classification.
Pengembangan Video Pembelajaran dengan Bantuan Software Wondershare Filmora pada Materi Reaksi Reduksi Oksidasi

This research aims to determine the form of the development of learning videos on the subject matter of oxidation reduction reactions (redox) with the help of wondershare filmora software so that they can become effective media in the learning process and to determine their feasibility. The procedure taken to produce the product is divided into 4 stages, namely: (1) determining the material to be developed, (2) carrying out design and development, (3) producing wondershare filmora software or packaging learning videos, and (4) conducting a formative evaluation and revise development product. The results of the assessment from material experts and media experts on the feasibility of this learning video have a very good category with an overall ideal percentage of 84,69% each in terms of the material presented and 90.5% in terms of the media displayed. The results of the peer reviewer test assessment of this learning video is 80,9% with a very good category. As for the results of the assessment of student responses to this learning, it is included in the very good category with an ideal percentage of 97.25% for small group trials and 98.75% for large group trials, respectively. Keywords: Development, Learning Video, Wondershare Filmora, Oxidation Reduction Reaction
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization contains relatively little information about the source task. Instead, we show that we can learn highly informative posteriors from the source task, through supervised or self-supervised approaches, which then serve as the basis for priors that modify the whole loss surface on the downstream task. This simple modular approach enables significant performance gains and more data-efficient learning on a variety of downstream classification and segmentation tasks, serving as a drop-in replacement for standard pre-training strategies. These highly informative priors also can be saved for future use, similar to pre-trained weights, and stand in contrast to the zero-mean isotropic uninformative priors that are typically used in Bayesian deep learning.
Acute lymphoblastic leukemia detection approach from peripheral blood smear using color threshold and morphological techniques

&lt;span&gt;Acute lymphoblastic leukemia (ALL) has recently been one of the most significant concerns in cancers, especially child and old age. Therefore, crying needs to diagnose leukemia as early as possible, increasing the treatment options and patient survivability. Some basic handicraft leukemia detection processes have been introduced in this arena though these are not so accurate and efficient. The proposed approach has been introduced an automated ALL recognition system from the peripheral blood smear. Initially, the color threshold has been applied to segment lymphocytes blood cells from the blood smear. Some post-processing techniques like morphological operation and watershed have been executed to segment the particular lymphocytes cell. Finally, we used a support vector machine (SVM) classifier to classify the cancerous image frames using a statistical feature vector obtained from the segmented image. The proposed framework has achieved the highest accuracy of 99.21%, the sensitivity of 98.45%, specificity of 99%, the precision of 99%, and F1 score of 99.1%, which has beat existing and common states of art methods. We are confident that the proposed approach will positively impact the ALL detection arena.&lt;/span&gt;
Platform for Multi-User Channel-Based Encryption of Speech Communication with AES on 2.45 GHz

This paper outlines a hardware platform for half-duplex, real-time encrypted digital speech communication on 2.45 GHz. The platform is based on a STM32F415 MCU with a low power ADF7242 transceiver for wireless communication. The Advanced Encryption Standard (AES) Electronic Code Book encryption scheme is performed on the transmitted packets with symmetrically generated channel-based keys. Encryption keys of 128-bit are obtained, based on the unique reciprocal channel characteristics of the employed link. Key reconciliation is applied by means of a Hamming code and a cyclic redundancy check (CRC) to correct key mismatch. A multi-user system is introduced by splitting voice key distribution and speech communication into different abstraction layers. Measurements reveal a maximum communication range of 400 m without significant packet loss. A high key entropy of 0.94 bit is established with a packet rate of 167 packets/s and a key bit generation delay of four received signal strength (RSS) measurements.
Selective Segmentation Model for Vector-Valued Images

One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.
Opinion Triplet Extraction for Aspect-Based Sentiment Analysis Using Co-Extraction Approach

In aspect-based sentiment analysis, tasks are diverse and consist of aspect term extraction, aspect categorization, opinion term extraction, sentiment polarity classification, and relation extractions of aspect and opinion terms. These tasks are generally carried out sequentially using more than one model. However, this approach is inefficient and likely to reduce the model’s performance due to cumulative errors in previous processes. The co-extraction approach with Dual crOss-sharEd RNN (DOER) and span-based multitask acquired better performance than the pipelined approaches in English review data. Therefore, this research focuses on adapting the co-extraction approach where the extraction of aspect terms, opinion terms, and sentiment polarity are conducted simultaneously from review texts. The co-extraction approach was adapted by modifying the original frameworks to perform unhandled subtask to get the opinion triplet. Furthermore, the output layer on these frameworks was modified and trained using a collection of Indonesian-language hotel reviews. The adaptation was conducted by testing the output layer topology for aspect and opinion term extraction as well as variations in the type of recurrent neural network cells and model hyperparameters used, and then analysing the results to obtain a conclusion. The two proposed frameworks were able to carry out opinion triplet extraction and achieve decent performance. The DOER framework achieves better performance than the baselines on aspect and opinion term extraction tasks.
Deterministic Entanglement Swapping with Hybrid Discrete- and Continuous-Variable Systems

The study of entanglement between discrete and continuous variables is an important theoretical and experimental topic in quantum information processing, for which entanglement swapping is one of the interesting elements. Entanglement swapping allows two particles without interacting with each other in any way, to form an entangled state by the action of another pair of entangled particles. In this paper, we propose an experimentally feasible scheme to realize deterministic entanglement swapping in the hybrid system with discrete and continuous variables. The process is achieved by preparing two pairs of entangled states, each is formed by a qubit and two quasi-orthogonal coherent state elements of a cavity, performing a Bell-state analysis through nonlocal operations on the continuous variable states of the two cavities, and projecting the two qubits into a maximally entangled state. The present scheme may be applied to other physical systems sustaining such hybrid discrete and continuous forms, providing a typical paradigm for entanglement manipulation through deterministic swapping operations.
THE EFFECT OF JOB CONTEXT AND COVID-19 ON PERCEIVED AUDIT QUALITY BEHAVIOUR:

The unprecedented impact of Covid-19 pandemic has caused untoward consequences to almost all professions and auditors are no exception. This study investigated perceived audit quality reduction behaviour through job context, COVID-10 effect and mediated by job satisfaction. An online survey involving 103 junior auditors were conducted during the Covid-19 lockdown and that the collected data were analysed using partial least square (PLS). The results revealed that human capital training and perceived compensation can reduce perceived audit quality reduction behaviour during the pandemic while other factors such as time pressure and role ambiguity do not contribute to perceived audit quality reduction behaviour. Job satisfaction also has a significant relationship with perceived audit quality reduction behaviour but it does not mediate any relationship between job context factors and perceived audit quality reduction behaviour. The findings provide new insights into the effect of COVID-19 on audit quality reduction behaviour and its influence on the job context factors. Inevitably, in the midst of pandemic, human capital training appears to be insurmountable and such skills facilitation and knowledge if not being prioritised would likely to impede the auditing process. As a consequence, audit personnel in particular; the audit managers, directors and partners should take heed of the essential human development in the relevant auditing profession over time. Our research findings revealed that COVID-19 pandemic had a profound effect on audit quality behaviour and its influence on the job context factors. It is therefore imperative for audit profession to prepare for such unprecedented and the perceived long-lasting endemic that had thus far impacted to a significant extent on people all around the world.