Reviewerly/exHarmony huggingface dataset:
TEM data completion method based on DCGAN model

In the transient electromagnetic method (TEM) exploration, due to the complex acquisition environment, the original data has serious interference, and the entire data segment and the entire point are missing. During data processing, the missing and cut-off data due to interference are difficult to complete based on reliable geophysical models. In response to this problem, this paper establishes a deep convolutional generative countermeasure network model (DCGAN) for TEM data completion based on the generative countermeasure network (GAN). The model uses GAN network as the framework, embedding deep convolutional neural network modules, that is, using deep deconvolution and convolution layers to replace the fully connected layer in the GAN model to fully extract the spatial features of TEM data. In order to test the performance of the model, the test set is divided into two categories: random missing and overall missing. On this basis, increase the number of deleted data, control the missing rate in the test data set to 10%, 20%, 30%, 40%, and 50%, and gradually carry out the test. Finally, the test results are compared with the current mainstream data completion method GAN. The results show that the overall effect of the DCGAN model in TEM data completion is higher than that of GAN. At the same time, when the randomly missing data is less than or equal to 40% and the continuous missing data is less than or equal to 30%, the R2 of the DCGAN model is above 0.85, which can maintain good performance. Through random missing, continuous missing data completion and step-by-step experiments with different missing rates, the model can ensure the accuracy of the completion while clarifying the scope of application, which has certain practical application value.
Practice of geological survey website group construction in cloud environment

In order to realize the effective integration of scattered resources and the exchange and sharing of information, provide one-stop service for the public, and improve the application and management level of e-government, website group should be built. On the basis of analyzing the construction requirements of the website group, the positioning of the geological survey website group is clarified, and three core problems has been resolved in the geological survey website group construction: website information sharing, MVC-based page display, system and information security management, "three intelligent" processes of website group construction are described, and development suggestions for the future work is provided.
Understanding the domain of driving distraction with knowledge graphs

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including "objective factors", "human factors", "research methods", "data" and "data science". The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.
Image Compression for Machines Using Boundary-Enhanced Saliency

With the rapid development of deep learning, more and more images and videos are used for machine analysis. The amount of images and video content consumed by machines has exceeded that of humans. However, the traditional image and video coding schemes are designed for human vision system, where information that is vital to machine vision, e.g., boundary of a salient object, may not be preserved during compression. In this paper, based on high efficiency video coding (HEVC) intra coding, we propose an image compression scheme for machines using boundary-enhanced saliency. Using image classification as an example task, Grad-CAM, a deep learning visualization method, is used to interpret classification results to generate a pixel-level saliency map for each image. Object segmentation and edge detection are then performed to generate boundary map of the salient object. With boundary-enhanced saliency map, we derive a coding tree unit (CTU)-level QP adjustment scheme, where more bits are allocated to salient regions of image concerning machine vision. Experimental results show that, compared with HEVC, our proposed scheme could achieve up to 29.94% and 31.53% bitrate saving with the same TOP 1 and TOP5 accuracy performance in image classification, respectively.
Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization (EE-PSO) algorithm to improve resource utilization and reduce energy consumption. We carried out experimental evaluations with the Container CloudSim toolkit to demonstrate the effectiveness of the proposed EE-PSO algorithm in terms of energy consumption, quality of service guarantees, the number of newly created VMs, and container migrations.
DeepHair

In this paper, a deepfake-based hairstyle preview system is proposed. Most of the existing hairstyle preview systems have limitations for hairstyle transferring from still images. To provide a reliable hairstyle preview experience from different angle of views in a video, we propose a prototype system based on deepfake. A user's face can be appropriately transplanted into a hairstyle target model's face part. Additionally, users can preview various hairstyles from different angle of views in the generated hairstyle preview video. In this prototype, we provide 8 previewing hairstyles for female users and male users, including: central parting, ponytail, wavy bob, and curls for female; man bun, middle part, bangs, and side part for male.
Code switching in Grade 11 students of UM Peñaplata College

This study aimed to discover the code switching found in impromptu speeches of the Grade 11 students of UM Peñaplata College. Specifically, the study sought to determine and describe the common words used in code switching in the impromptu speeches of the Grade 11 students and to analyze the types and functions of the common words they code switched. Since there have been no studies related to CS in the Island Garden City of Samal, the researchers are urged to conduct this kind of research. In addition to this, the researchers also want to know the role of CS in the academe since English language proficiency is highly necessary especially in an English classroom. The participants of this study are from UM Peñaplata College, specifically the Grade 11 Senior High School students. Among the 78 respondents, there were 67 purposively selected which were subjected to Textual Analysis. Analysis revealed four most common code-switching words committed in impromptu speeches. These are ahm, ah, kanang, and ay. The types of these code-switching words are intersentential, intra-sentential, intra-word, and tag-switching. Also, the functions of these code-switched words are fillers, hesitations, discourse markers, literary translation, and repetition of words. With these, language educators should continuously find strategies to suit the ever-changing nature of the curriculum and handle the weakness evident in the teaching of the English language.
Detecting changes in visual scenes during saccades: Replicating and extending John Grimes's experiments

In the mid-1990’s, John Grimes’s work on how saccades influence visual perception revealed unexpected results: subjects failed to detect retrospectively obvious scene changes that occurred during saccades, despite having fixated before and after on the changed stimulus (Grimes, 1996). Since his original work, many studies have demonstrated other ways of inducing change-blindness, supporting the general finding that fixations upon changed stimuli can fail to yield detection of those changes. However, to date, no attempt has been made to replicate the original work by Grimes. In this investigation, we replicated, refined, and extended the original Grimes paradigm in two laboratories (at the University of Florida and Lingnan University) to evaluate the relationship between saccades and visual perception. In our task, the first of several planned experiments, 21 participants at each institution (42 total) viewed a newly-developed set of 100 change-blindness images. Participants were given instructions that their primary task was to remember as many details as possible, but they needed to report whenever they noticed that something changed in the image. For half of the images, one object in the image changed during a pseudo-randomly determined saccade. For the other half of the images, nothing changed. Results revealed that the “miss” rate for subjects across both sites was significantly above zero. Follow-up analyses showed that the size of the image redraw area, the amplitude of the saccade, the redraw time during the saccade, and the distance between gaze position and change center were all factors that influenced change-detection performance. Together, our results (1) replicate Grimes’s work, and (2) reveal important eye-movement and image-based factors that contribute to change-detection failures. We will discuss how these results can inform current theories of visual awareness (e.g., Higher-Order Theory and Integrated Information Theory), and how this paradigm can incorporate metacognitive measures to provide additional insights.
Mobile seamless learning in primary education: a case study on second grade students in Greece

The paper presents an experimental study aiming to explore primary school students’ response to Mobile Seamless Learning activities. The educational intervention and the consequent investigation were conducted in a suburban primary school in Greece, with second grade pupils, in the context of a learning subject entitled “Studying our Living Environment”. The participant students (n = 14) engaged in both face-to-face (in-class, outdoor) and online (home) collaborative and individualistic activities supported by a variety of digital applications. Primary research data were gathered upon completion of the intervention via 5 group interviews and 12 individual interviews. The findings have shown that Mobile Seamless Learning can facilitate pupils’ active engagement and improve their attitudes toward collaboration. Moreover, this approach can lead to the development of a learning community that promotes learners’ motivation, enables them to construct new knowledge, and develop essential skills.
Forecast of Photovoltaic Plant Output Using Long Short-Term Memory (LSTM) Network: North Morocco Case Study

Due to the volatile and intermittent nature of solar energy, the outputs of photovoltaic (PV) power plants present an uncertainty that can disrupt the security and stability of the electricity grid. Therefore, accurate forecasting of PV output power is of great importance to solve this problem and maintain the distribution network integrity. In this paper, the long short-term memory (LSTM) network, which is the memory of recurrent neural networks (RNNs), is proposed and applied to the data collected from the PV installation of the Faculty of Sciences and Technology of Tangier (FSTT). The LSTM architecture of this study has been improved according to different network simulations, all based on the root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) performance evaluation indicators. The results showed that the LSTM network used gives good PV output power forecasting accuracy.
POJOK LITERASI UNTUK MENINGKATKAN MINAT BACA SISWA KELAS III MI ALMOURKY

In the process of teaching and learning, one aspect that cannot be separated is the ability to read. Every student has a variety of reading interests, and reading is an entry point for knowledge-based information. The capacity to understand what is written and read is clearly influenced by this interest. To increase students' interest in reading, elementary school students must get used to it. The Literacy Corner in the classroom is an important component that cannot be separated from the activities used for teaching and learning. The second grade students of Madrasah Ibtidaiyah Al Mourky became the focus of this research. The method of collecting data through direct observation and documentation is the subject of research. The information obtained was then subjectively investigated, showing that, first, the proficiency angle is a serious emphasis on adjustment exercises that have just been carried out before teaching and learning exercises begin. Second, students can be more interested in reading if they have access to a literacy corner that they can use every day.
Increasing Ability to Recognize Letters Through Animation Media in Children Aged 5-6 Years in Kindergarten

The ability to recognize letters is the first step for children to be able to read, the ability to recognize letters well then the child tends to have the ability to read well too. This research is a classroom action research, with the object of Group B TK Dharma Wanita Persatuan Plaosan totaling 12 children. The writing of this study aims to determine the ability to recognize letters and the results of their improvement. Data collection techniques were carried out by means of observation, interviews and documentation. This research was conducted in 3 cycles, namely pre-cycle, cycle I and cycle II. Based on the results of the research that has been done, it can be seen that with animation media, the ability to recognize letters in children can be improved well with a significant increase in each cycle. The percentage value obtained in the first cycle reached a percentage of 56.5% but had not reached the success target, therefore it was carried out again in the second cycle which achieved a success percentage of 82.2%. Thus, it can be concluded that there is an increase in the ability to recognize letters through animation media in Dharma Wanita Persatuan Plaosan Kindergarten with a percentage reaching 82.2%.
Are Machines more Effective than Humans for Graphical Perception Tasks?

Position and length are two dominant visual channels that underpin our ability to reason about the visual world in settings ranging from natural scenes to charts and diagrams. Despite progress in convolutional neural networks (CNNs) performing image classification, we know little about cognitive tasks where models need to understand the relationships between objects in charts. When asked, "What is the ratio between two bars?" CNNs will need to identify the bars in an image and the relationship between bars to answer correctly. In this work, we answer questions about how well can a CNN cope with new images after it has learned from a particular set of ratios or heights that were distorted to further away from each other or shifted to never seen before? Are machines more effective than humans for ratio estimate tasks? In two experiments, we sent the models to take the same human tests to compare these two experts. We controlled four train-test configurations to be the same or carry distribution shifted on two features: (1) task label (ratios) and (2) visual feature (bar heights). We found that sampling methods lowered the human errors up to three-folds when we aggregated the predictions. However, CNNs were not robust to distribution shift: accuracy dropped significantly when specific test instances fell out of the training data span. Next, we introduce a trial-by-trial comparison to measure if the two experts systematically make errors of the same inputs. We found that, unlike humans, CNNs were remarkably consistent with one another and showed marginal position preferences over length. We further test for the presence of Weber's law in CNNs and humans by selecting bar heights such that the height difference computed on pairs of images spanned a wide range. We observed the presence of Weber's law for neither CNNs nor humans.
Do the saliency features of a scene fade over time?

Specific components may draw attention simply by standing out from their surroundings. Numerous research has been conducted to construct a model that considers all of the variables influencing visuomotor behavior. While most of these models work well on images, they can not predict all salient things in a video. The models, in particular, fail when a predicted but unseen incoming item directs a person's attention to an area that may be vacant. We employ video-based eye tracking with instruction-free viewing of video clips to assess the gaze pattern of the control (CTRL) subjects. We recruited 280 CTRLs from different ages (>20 years) while they sat in front of a video-based monocular eye tracker (Eyelink-1000 Plus) in a light-controlled, quiet room. A monitor-mounted camera with a 500 Hz sampling rate was used to measure eye-movements. All participants viewed 10 short videos (~1 minute), consisting of 16-17 clippets of 3-5s duration without further instructions. We then focused on one specific clippet that included multiple faces on screen and while the camera shifted to the left side, more faces entered the scene. We assessed each subject's scan paths on the screen. Simultaneously, we used a deep-gaze model to evaluate the salient locations of each frame and compared it to the participants' actual gaze location. The findings indicated that not only are faces the most prominent locations in a scene, but also that newly arrived faces aroused more attention than the recently presented faces. The deep-gaze model predicted all faces on the screen as salient objects but could not identify which face had priority. We indicated that time is critical for developing a gaze prediction model and that the arrival time of each feature would affect the attention. Additionally, the scene's scanned history and the positions of previously represented objects affect observation preferences.
Rapid Face Asset Acquisition with Recurrent Feature Alignment

We present Re current F eature A lignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603 mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.
High-order local image statistics are used in radiographic judgments of breast density

While it is well-established that human observers can use high-order image statistics to discriminate synthetic visual textures, it is unclear whether these statistics are used in real-world visual tasks. Here, we show that high-order statistics are needed to account for performance in radiologists' judgments of breast density, and we characterize the way in which they are used. To do this, we built generalized linear models of radiologists’ judgments of density (BIRADS a-b: fatty/scattered, vs. BIRADS c-d: heterogenous/dense) from 444 mammograms. Regressors consisted of statistical features extracted from interior regions of breast images: either just their spectral characteristics or also their local image statistics. The local statistics characterized the co-occurrence of pairs, triplets, and quadruplets of neighboring checks in a binarized image (Victor and Conte 2012), thus capturing information not present in the power spectrum. We computed these statistics over spatial scales ranging from the individual pixel (0.07 mm) to images downsampled by a factor of 32. Models were compared in terms of their ability to explain and predict radiologists’ judgments of low vs. high density mammograms. Inclusion of co-occurrence statistics improved performance substantially: for the explanatory model, the Akaike criterion was reduced from 346.3 to 174.2; for the predictive model, the area under the ROC curve was increased from 0.89 to 0.93 (p < 0.04). To analyze the contribution of local image statistics, we examined the performance of models that considered only a single scale. Scales corresponding to downsampling by a factor of 4 to 16 had the most impact. When local image statistics were recomputed from Gaussian surrogates that matched the original image spectra, the degree of improvement that they provided was reduced, confirming that they capture task-relevant aspects of the real images beyond the power spectrum and demonstrating their importance for real-world visual tasks.
Influences of physical image properties on image memory during naturalistic encoding

We are constantly exposed to multiple visual scenes, and while freely viewing them without an intentional effort to memorize or encode them, only some are remembered. Visual memory is assumed to rely on high-level visual perception that shows a level of cue-invariance, and therefore is not assumed to be highly dependent on physical image cues as size or contrast. However, this is typically investigated when people are instructed to perform a task (e.g. remember or make some judgement about the images), which may modulate processing at multiple levels and thus may not generalize to naturalistic visual behavior. Here I will describe a set of studies where participants (n>200) freely viewed images of different sizes or of different levels of contrast while unaware of any memory-related task that would follow. We reasoned that during naturalistic vision, free of task-related modulations, stronger physical image cues (e.g. bigger or higher contrast images) lead to higher signal-to-noise ratio from retina to cortex and would therefore be better remembered. Indeed we found that physical image cues as size and contrast influence memory such that bigger and higher contrast images are better remembered. While multiple factors affect image memory, our results suggest that low- to high-level processes may all contribute to image memory.