Reviewerly/exHarmony huggingface dataset:
A Study of Grammar-Concept Understanding Problem for Python Programming Learning

Python Programming has become commonly used among IT and non-IT engineers for a variety of applications such as data analysis, prototyping, web application systems, and embedded systems. It offers rich libraries and short coding capabilities. To further promote this useful language, a high-quality self-study tool has been strongly demanded, since it is still rare to open Python programming courses at universities. In this paper, we study the Grammar-concept Understanding Problem (GUP) for self-study of Python programming by novice students. A GUP instance consists a source code and a set of questions describing the definitions of grammar or library keywords appearing in the code. The correctness of the answer from a student is marked through string matching with the correct keyword. In this study, we selected 114 keywords and common functions, and made the corresponding questions for them. Then, we generated 24 GUP instances with 142 questions and assigned them to 9 students in Okayama University. The results confirmed the effectiveness of the proposal.
Product image retrieval using category-aware siamese convolutional neural network feature

Product image retrieval in the customer-to-shop setting uses similarity learning instead of a predefined distance to address the cross-domain matching problem. Similarity learning can be done using a Siamese convolutional network (SCN) model with pairwise or triplet image sampling. The model training uses product item labels as the target without considering the product category. However, images in the e-shop are inherently have hierarchically structured from the category to the individual image. Therefore, category information should be involved to improve the discriminating factor of the image feature. To accommodate this, we propose a SCN model that involves category and item labels in training to produce the category-aware feature. Our model is based on SCN with modification in training procedure that simultaneously learns the category and item label. Our category-aware Siamese CNN is implemented using MobileNet as the backbone and single-layer network for the mid-feature learner. The results show that our method can improve the accuracy of product image retrieval using SCN based features.
Investigating classification performance of hybrid deep learning and machine learning architectures on activity recognition

Abstract Human activity recognition (HAR) has become a popular field to recognize people's activities from signals obtained using various types of body placed sensors. The increase in the elderly population will increase cognitive and physical decline due to aging and may cause to serious injuries and deaths if immediate assistance is not provided. For this reason, temporal dynamics and important features should automatically be extracted in order to support the daily life of the elderly and to recognize their physical activities correctly and real time. In the study, the classification performances of deep models (CNNLSTM: CNN long short‐term memory network, ConvLSTM: convolutional LSTM, LSTM: long short‐term memory network) and four other machine learning algorithms (SMVs: support vector machines, k‐NN: k ‐nearest neighbor classifier, DT: decision tree classifier, ERF: ensemble random forest) which are known to be successful in HAR were investigated. Features used features used in deep models are automatically generated however in machine learning models were generated by hand. The deep models are developed using with a huge set of activities containing 2520 tests. In the tests, each activity of the volunteers was recorded with three axis accelerometer, gyroscope and magnetometer sensors placed in the waist region of body. As a result, ConvLSTM reached the highest accuracy with 99.86% in deep models, while SVMs achieved the highest accuracy in fall detection among machine learning algorithms with 98.47%. When the classification of 36 activities was examined, the highest accuracy was obtained with CNNLSTM and SVMs with 86.94% and 74.58%, respectively. Deep models proposed in this study are considered to be more applicable in real‐world HAR scenarios where sensors' data of indefinite length are obtained.
Virtual reality for law enforcement training: a demonstration and implication for dispatch priming

We conducted an experimental evaluation of virtual training experiences (VTE) for law-enforcement training during an international policing conference (American Society of Evidence Based Policing). Among the volunteers (N = 77), 30% were female and 70% males with 49% employed in law enforcement and 51% in other occupations. Participants were outfitted with a controller gun, holster, and a virtual-reality headset. Each of four scenarios used involved a call for service reporting the presence of an unknown man acting strangely, with random assignment to one of four conditions: priming message (yes, no) and instrument held by suspect (gun, cellphone). Results show no significant effect of dispatch priming on participants’ responses but do show that law-enforcement officers made more accurate decisions than did non-law-enforcement participants. The feasibility and potential usefulness of VTE as a training tool is discussed along with recommendations for future policing studies involving VR.
Non-Smooth Control Barrier Navigation Functions for STL Motion Planning

This paper reports on a new approach to Signal Temporal Logic (STL) control synthesis, that 1) utilizes a navigation function as the basis to construct a Control Barrier Function (CBF), and 2) composes navigation function-based barrier functions using nonsmooth mappings to encode Boolean operations between the predicates that those barrier functions encode. Because of these two key features, the reported approach 1) covers a larger fragment of STL compared to existing approaches, 2) alleviates the computational cost associated with evaluation of the control law for the system in existing STL control barrier function methodologies, and 3) simultaneously relaxes some of the conservativeness of smooth combinations of barrier functions as a means of implementing Boolean operators. The paper demonstrates the efficacy of this new approach with three simulation case studies, one aiming at illustrating how complex STL motion planning specification can be realized, the second highlights the less-conservativeness of the approach in comparison to the existing methods, and another that shows how this technology can be brought to bear to push the envelope in the context of human-robot social interaction.
Demonstrating Interaction: The Case of Assistive Technology

Technology “demos” have become a staple in technology design practice, especially for showcasing prototypes or systems. However, demonstrations are also commonplace and multifaceted phenomena in everyday life, and thus have found their way into empirical research of technology use. In spite of their presence in HCI, their methodical character as a research tool has so far received little attention in our community. We analysed 102 video-recorded demonstrations performed by visually impaired people, captured in the context of a larger ethnographic study investigating their technology use. In doing so, we exhibit core features of demonstrational work and discuss the relevance of the meta-activities occurring around and within demonstrations. We reflect on their value as an approach to doing HCI research on assistive technologies (AT), for enabling shared understanding and letting us identify opportunities for design. Lastly, we discuss their implications as a research instrument for accessibility and HCI research more broadly.
Adaptation of a Pronunciation Dictionary for Dysarthric Speech Recognition

In the general framework of an automatic speech recognition system, a pronunciation dictionary, that is a mapping table from a phoneme sequence to a word, is used both in the processes of training and recognition. However, this pronunciation dictionary is not always adequate in the case of dysarthric speech recognition because dysarthric people often have difficulty pronouncing words in the same way they are pronounced in the dictionary. In this paper, we investigate the adaptation of a pronunciation dictionary to an individual dysarthric person and evaluate the effectiveness of adapting the dictionary using a dysarthric speech recognition task. In the proposed method, in order to find rules we can use to modify a dictionary, we analyze the pattern of mis-recognition in the phoneme recognition results. By following the extracted rules, we add pronunciations to the dictionary for the target dysarthric person. We evaluate the effectiveness of the adapted pronunciation dictionary on a continuous speech recognition task and demonstrate that the adapted dictionary can decrease the word error rate.
A flexible method for parameterizing ranked nodes in Bayesian networks using Beta distributions

When novice modelers first attempt to build a Bayesian network, they are often impressed with the intuitive graphical structures that capture their causal understanding. This favorable impression evaporates on proceeding to parameterization. Conditional probability tables (CPT) require parameters for often hundreds of very similar scenarios and specifying them in the absence of data can be overwhelming. The problem is even more severe when eliciting parameters from experts with limited time. Often, there is local structure with fewer parameters that better describes the relationship. Such structures include the Noisy OR, decision trees, and equations. These work well for modelers, but can be an issue for experts and particularly groups of experts. An alternative approach is to elicit only a few CPT rows and interpolate the remainder. This is a promising approach, as it can handle unknown structures and multiple experts, but existing techniques can be limited. Here, we present a flexible approach called InterBeta for performing CPT interpolation with ordered nodes. In the simplest case, just two CPT rows are needed, but this can be easily augmented with further information. The basic approach assumes input independence, but allows dependencies to be reintroduced as required, and can also be combined with other local structures such as decision trees or equations, leaving the interpolator to fill in the gaps. We explain the InterBeta method, describe its capabilities and limitations and how it compares to similar approaches and show how it can trade-off elicitation effort against faithfully representing expert understanding.
PV system site selection using PVGIS and Fuzzy AHP

With the decreasing reserves of conventional sources and the high emission of harmful gases caused by them, the inclusion of renewable energy sources in power system is increasing. However, to best utilize them, different site location criteria for PV generator installment need to be considered in the decision-making process. This paper presents Fuzzy Analytical Hierarchical Process (AHP) method used in energy planning to find the best Photovoltaic (PV) system site location for the established criteria and factors. Eight criteria were identified and evaluated. These include the solar energy potential, distance to the transmission line, PV surface slope, sunshine duration, the total amount of energy/PV, the temperature ratio, site survey, and performing shading analysis. PVGIS software tool is used to collect necessary data. Evaluation criteria are prioritized by applying fuzzy AHP, fuzzifying the inputs of the decision matrix using triangular fuzzy numbers. The obtained results and the methodology show potential in finding the best location where the PV system can be best utilized.
Gate Access Control using Face Mask Detection and Temperature

For the prevention and spread of COVID-19, people are currently used to screen temperature and identify masks in public spaces. All scanning entries include temperature testing equipment; however, manual temperature scanning has some limitations. Temperature scanners aren't well-known among personnel. Despite higher temperature readings or the lack of masks, people are frequently given admission. Manual scanning equipment is useless in huge groups. As a result, an automated system that monitors temperature and mask is required. To address this problem, a completely automated temperature scanner and entry provider system has been presented. A contactless temperature scanning and a camera are used in the system to take images. The scanner is attached to a gate-like mechanism that blocks admission if a feverishness or no mask is detected. The gadget employs a temperature sensor and camera attached to a Raspberry Pi system to monitor the entire operation. The major goal of this article is to automate the entire COVID scanning procedure to reduce the possibility of COVID-19 spreading in densely populated areas like malls, schools, and universities
Rapid Model Transfer for Medical Image Segmentation Via Iterative Human-in-the-Loop Update: from Labelled Public to Unlabelled Clinical Datasets for Multi-Organ Segmentation in CT

Despite the remarkable success on medical image analysis with deep learning, it is still under exploration regarding how to rapidly transfer AI models from one dataset to another for clinical applications. This paper presents a novel and generic human-in-the-loop scheme for efficiently transferring a segmentation model from a small-scale labelled dataset to a larger-scale unlabelled dataset for multi-organ segmentation in CT. To achieve this, we propose to use an igniter network which can learn from a small-scale labelled dataset and generate coarse annotations to start the process of human-machine interaction. Then, we use a sustainer network for our larger-scale dataset, and iteratively updated it on the new annotated data. Moreover, we propose a flexible labelling strategy for the annotator to reduce the initial annotation workload. The model performance and the time cost of annotation in each subject evaluated on our private dataset are reported and analysed. The results show that our scheme can not only improve the performance by 19.7% on Dice, but also expedite the cost time of manuallabelling from 13.87 min to 1.51 min per CT volume during the model transfer, demonstrating the clinical usefulness with promising potentials.
A Gesture Recognition System for Cranes Using Deep Learning with a Self-attention Mechanism

This research is aimed at recognizing the gesture of a lifting coordinator and automating the operation of a crane by introducing a system with deep learning. This paper first explains the outline of a gesture recognition system, and describes skeletal detection and its accuracy improvement technique. Furthermore, a gesture recognition system is constructed using a 1DCNN, and the recognition accuracy is verified to be improved by introducing a self-attention mechanism.
Ghostwriting: Simulacra of Academic Performance

The article is a book review on “Ethnography of Bogus. Who and how writes customized academic essays in Russia” by A. Davydov and P. Abramov (Moscow: Khamovniki Foundation for Social Research: Common Place, 2021, 176 p.). This book describes the practices of contract cheating in Russian higher education as an established social institution involving undergraduate and graduate students. “Ethnography of Bogus” presents the faculty staff only as a resource for the market of ghostwriting: an explicit resource when teachers of higher education become ghostwriters, or an implicit resource, when formalist assessment mechanisms of student work contribute to the development of ghostwriting practices. The description of ghostwriting practices in the study reveals the decline of the concept of research advisor for undergraduate and graduate students, due to the precarity of teachers in higher education. The decline is incited by mass student entry policy and efficient management mechanisms of higher education institutions. As a result, the students who turn to contract cheating in a regular manner do not develop professional and academic skills appropriate to an undergraduate or graduate degree. The students develop their soft skills required in interactions with ghost-writers when they negotiate the terms of contract cheating as customers of writing services.
A hybrid <scp>HXPLS‐TMFCC</scp> parameterization and <scp>DCNN‐SFO</scp> clustering based speaker diarization system

Abstract The speaker diarization is considered to be the process by which the speaker signal is segmented, and the speaker identity is grouped into homogenous regions. The central point behind this scheme is the ability to distinguish between the speaker signal and each speaker signal with the label. As mass communication and meetings grow quickly, the diarization of the speakers is burden to improve the readability of the speech transcript. To solve this problem, tangent weighted mel‐frequency cepstral coefficient (TMFCC) and the extended linear prediction with autocorrelation snapshot feature extraction and the speaker diarization approach proposes a deep convolutional neural network (DCNN) for clustering and optimization using sailfish optimizer. A new development in the HXLPS extraction method is the holoentropy with extended linear prediction with autocorrelation snapshot. TMFCC makes more efficient and improves the effectiveness of the proposed scheme using lesser energy frame and higher energy framework. When achieve this, the voice activity detection method can recognize speech and non‐speech signals. Therefore, every segmented signal is represented by the d‐vector. The label of the speaker signal is clustered according to the speaker label used in the DCNN. The evaluation methods, like tracking distance, false alarm rate, diarization error rate examine the effectiveness.
Classifying the Presence or Absence of Calcifications on Mammography using the EfficientNet: A Feasibility Study

Calcifications on mammography are important to detect malignant breast lesions. Here a deep learning-based classifier is used to differentiate the presence and absence of breast calcifications on mammography images. A total of 1471 calcification lesions included in the Digital Database for Screening Mammography were used for the dataset. High-density breasts (breast density of &#x2265;4) were excluded from the dataset. The calcification lesion dataset was created from randomly cropped patch images from each lesion that overlapped calcification lesion masks. The non-calcification dataset was created using sliding window patch images and excluded any calcification lesion mask overlap. Subsequently, the same number of patch images was created. MLO and CC binary classifiers were created using the EfficientNet(B0). The evaluation was performed via a hold-out method. Each dataset was divided based on each patient. The accuracy and AUC were calculated to evaluate model classification accuracy. The GradCAM was used to evaluate the explainability. Our EfficientNetB0-based classifiers yielded an Acc of 0.76 (left, MLO), 0.79 (right, MLO), 0.75 (left, CC), and 0.77 (right, CC) and an AUC of 0.85 (left, MLO), 0.88 (right, MLO), 0.83 (left, CC), and 0.86 (right, CC). The AUC of all models was &#x003E;0.8, and the visualization map by the GradCAM determined the calcifications globally. These results show the possibility of an explainability in the classification problem to differentiate the presence and absence of breast calcifications.
A Report on Translating Sign Language to English Language

Communication is essential to share thoughts and express ourselves, mostly communication is performed vocally. For impaired people like dumb, it is not possible to communicate vocally with normal people. A person with either hearing or speaking disorders cannot communicate with another normal person without a sign language interpreter. A sign language recognition system is used to reduce the communication gap between healthy people and impaired people. It is a system for converting sign words or sign sentences to text using deep learning. In this paper, a survey on various methods of detecting and recognizing sign language from images or video is discussed. Sign language conversion can be perform on either video or image dataset. Analysis of different methods had perform to detect sign of various language like American Sign Language, Indian Sign Language, and Indonesian Sign Language.
Comparative Evaluation of Neural Vocoders for Speech Synthesis of Operatic Singing

The voice of someone who is singing opera has different characteristics compared with someone singing nursery rhymes or pop songs, which are treated by conventional singing voice synthesis. In this study, we compared and evaluated the speech synthesis performance of recently proposed neural vocoders for an operatic singing voice. From the results of our objective evaluation experiments, it was found that the melcepstrum estimation error for Parallel WaveGAN and PeriodNet is small. Furthermore, PeriodNet has a smaller estimation error of fundamental frequency.
Demystifying Video Traffic from IoT (Spy) Camera using Undecrypted Network Traffic

Video traffic can create significant privacy and security threats to an organization or a smart home. Integration of IoT cameras has increased this problem manifold especially when there is no clear distinction among the protocols that can be used in IoT cameras and traditional video streaming or sharing applications. In this paper, we initiate a study on distinguishing video traffic in IoT cameras from that in video conferencing or sharing applications. We have used three IoT cameras, four video conferencing applications and two video sharing platforms to collect network traffic at network and above layers. We found a number of protocols like Real-time Transport Protocol, QUIC protocol, UDT protocol and TLS protocols that are used for transferring video traffic in these applications. We found that the protocols that carry IoT camera traffic have significantly different characteristics compared to that in video conferencing and sharing applications, e.g., in terms of video codec.
Conciliatory views, higher-order disagreements, and defeasible logic

Conciliatory views of disagreement say, roughly, that it's rational for you to become less confident in your take on an issue in case you find out that an epistemic peer's take on it is the opposite. Their intuitive appeal notwithstanding, there are well-known worries about the behavior of conciliatory views in scenarios involving higher-order disagreements, which include disagreements over these views themselves and disagreements over the peer status of alleged epistemic peers. This paper does two things. First, it explains how the core idea behind conciliatory views can be expressed in a defeasible logic framework. The result is a formal model that's particularly useful for thinking about the behavior of conciliatory views in cases involving higher-order disagreements. And second, the paper uses this model to resolve three paradoxes associated with disagreements over epistemic peerhood.