Features Of Photos Research Articles

Persepsi Mahasiswa Terhadap Penggunaan Fitur Foto Dalam Google Translate Untuk Pembelajaran Bahasa Jepang: Case Study

Google Translate is one of the most popular translation applications today. This application is widely used by the community, one of which is among students who study foreign languages. One of the most frequently used features is the photo feature on Google Translate. In order to improve the quality of Japanese language learning at Semarang State University, this research will be carried out to explore the use of modern technology, especially the photo feature in Google Translate, as a potential tool in Japanese language education. The purpose of this study is to identify the extent to which students use this technology in the environment of Japanese Language Education students at this university. In addition, this study aims to analyze students' views on the effectiveness and usefulness of this feature in assisting their understanding and mastery of Japanese. The research method used is descriptive research method with a quantitative approach. This article will also present the results of an opinion survey of the students who were the subjects of the study. The results of the survey will be interpreted to provide a better understanding of how the student uses the photo features in Google Translation in their English language learning process.

Personality of organizational social media accounts and its relationship with characteristics of their photos: analyses of startups’ Instagram photos

BackgroundOrganizational accounts of social networking sites (SNSs) are similar to individual accounts in terms of their online behaviors. Thus, they can be investigated from the perspective of personality, as individual accounts have been in the literature. Focusing on startups’ Instagram accounts, this study aimed to investigate the characteristics of Big Five personality traits and the relationships between the traits and the characteristics of photos in organizational SNS accounts.MethodsThe personality traits of 108 startups’ accounts were assessed with an online artificial intelligence service, and a correspondence analysis was performed to identify the key dimensions where the account were distributed by their personality. Photo features were extracted at the content and pixel levels, and correlational analyses between personality traits and photo features were conducted. Moreover, predictive analyses were performed using random forest regression models.ResultsThe results indicated that personality of the accounts had high openness, agreeableness, and conscientiousness and moderate extraversion and neuroticism. In addition, the two dimensions of high vs. low in neuroticism and extraversion/openness vs. conscientiousness/agreeableness in the accounts’ distribution by their personality traits were identified. Conscientiousness was the trait most associated with photo features—in particular, with content category, pixel-color, and visual features, while agreeableness was the trait least associated with photo features. Neuroticism was mainly correlated with pixel-level features, openness was correlated mainly with pixel-color features, and extraversion was correlated mainly with facial features. The personality traits, except neuroticism, were predicted from the photo features.ConclusionsThis study applied the theoretical lens of personality, which has been mainly used to examine individuals’ behaviors, to investigate the SNS communication of startups. Moreover, it focused on the visual communication of organizational accounts, which has not been actively studied in the literature. This study has implications for expanding the realm of personality research to organizational SNS accounts.

Data-driven analysis of surface roughness influence on weld quality and defect formation in laser welding of Cu–Al

The laser welding of Cu–Al alloys for battery applications in the automotive industry presents significant challenges due to the high reflectivity of copper. Inadequate bonding and low mechanical strength may occur when the laser radiation is directed toward the copper side in an overlap configuration welding. To tackle these challenges, a laser surface treatment technique is implemented to enhance the absorption characteristics and overcome the reflective nature of the copper material. However, elevating the surface roughness and heat-energy input over threshold values leads to heightened temperature and extreme weld. This phenomenon escalates the formation of detrimental intermetallic compounds (IMC), creating defects like cracks and porosity. Metallurgical analysis, which is time-consuming and expensive, is usually used in studies to detect these phases and defects. However, to comprehensively evaluate the weld quality and discern the impact of surface structure, adopting a more innovative approach that replaces conventional cross-sectional metallography is essential. This article proposes a model based on the image feature extraction of the welds to study the effect of the laser-based structure and the other laser parameters. It can detect defects and identify the weld quality by weld classification. However, due to the complexity of the photo features, the system requires image processing and a convolutional neural network (CNN). Results show that the predictive model based on trained data can detect different weld categories and recognize unstable welds. The project aims to use a monitoring model to guarantee optimized and high-quality weld series production. To achieve this, a deeper study of the parameters and the microstructure of the weld is utilized, and the CNN model analyzes the features of 1310 pieces of weld photos with different weld parameters.

Photo Feature

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Curbing customer-to-customer misbehavior contagion in the sharing economy

The sharing economy has fundamentally transformed customers’ lives. Providing short-term access to resources, however, creates environments in which customers regularly become the target of other customers’ misbehavior, either personally or toward shared resources. Although this customer-to-customer (C2C) misbehavior is known to be contagious, the reasons for its spread and the effectiveness of containment measures across sharing economy markets remain unclear. Three experiments reveal the moderating role of on-site supervision: platform-provider-directed blame attributions drive C2C misbehavior contagion in settings with formal on-site supervision, while social norms underlie contagion when on-site supervision is absent. Perpetrator-directed blame attributions reverse contagion irrespective of on-site supervision. More intrusive platform and peer-provider measures (in-person reprimands, in-app messages, and photo features) are most effective at curbing contagion by reducing social norms to misbehave and shifting blame to the perpetrator. However, these measures are only effective at certain C2C misbehavior severity levels for different sharing economy market types.

Unpaired Artistic Portrait Style Transfer via Asymmetric Double-Stream GAN.

With the development of image style transfer technologies, portrait style transfer has attracted growing attention in this research community. In this article, we present an asymmetric double-stream generative adversarial network (ADS-GAN) to solve the problems that caused by cartoonization and other style transfer techniques when they are applied to portrait photos, such as facial deformation, contours missing, and stiff lines. By observing the characteristics between source and target images, we propose an edge contour retention (ECR) regularized loss to constrain the local and global contours of generated portrait images to avoid the portrait deformation. In addition, a content-style feature fusion module is introduced for further learning of the target image style, which uses a style attention mechanism to integrate features and embeds style features into content features of portrait photos according to the attention weights. Finally, a guided filter is introduced in content encoder to smooth the textures and specific details of source image, thereby eliminating its negative impact on style transfer. We conducted overall unified optimization training on all components and got an ADS-GAN for unpaired artistic portrait style transfer. Qualitative comparisons and quantitative analyses demonstrate that the proposed method generates superior results than benchmark work in preserving the overall structure and contours of portrait; ablation and parameter study demonstrate the effectiveness of each component in our framework.

In Memoriam—Gerhard Roth (June 24, 1942–February 8, 2022)

In Memoriam—Gerhard Roth (June 24, 1942–February 8, 2022) Anita McChesney, Co-Editor On February 8, 2022, Austria lost one of its most significant cultural voices with the death of Gerhard Roth. The preeminent contemporary Austrian author earned widespread acclaim with his masterful use of unique perspectives to unflinchingly investigate the forgotten, silenced, and overlooked realms of history, of society, and of human consciousness. Austria was often at the center of those revelations, which earned Roth a reputation as a "Nestbeschmutzer." Gerhard Roth was born in Graz, Austria, on June 24, 1942. He enrolled in medical school at the University of Graz but broke off his studies in 1967 to work at the Graz computer center, where he later became manager. These experiences trained his eye for observing details and honed his skill in identifying minutiae in nature as well as in the human psyche. Roth entered the literary scene in the 1970s as an active member of the Forum Stadtpark literary circle in Graz and he published his first novel, the autobiography of albert einstein, in 1972. He subsequently became a contributor to the circle's magazine manuskripte and was a leading member of the Graz Autorenversammlung. After 1977 he devoted his time exclusively to his craft and, after 1986, spent his time writing in alternating residences in Vienna and Southern Styria. The "Augenmensch," as Roth would come to be known, was a prolific writer, amassing over forty publications that include novels, short stories, essays, plays, radio dramas, screenplays, and photo features. Roth secured literary acclaim with his seven-volume cycle Archive des Schweigens (1980–1991). This magnum opus combines photographs and a variety of narrative perspectives and styles to unearth silenced stories in twentieth-century Austrian history, particularly those relating to the country's monarchical and National-Socialist past. The subsequent eight-volume cycle Orkus (1995–2011) complements the previous series in its exploration into hidden realms of human experience. For Roth, the two series, which together encompass [End Page 55] over 5,500 pages and to which he devoted over thirty-one years of his life, are intertwined. Indeed, he describes the two series as a double helix: elements, themes, and characters recur throughout the works, and both cycles are devoted to exploring the origins of National Socialism in Austria as well as literary possibilities and forms. Gerhard Roth's work was recognized by numerous awards, including the Literaturpreis des Landes Steiermark, the Alfred-Döblin Prize, the Peter Rosegger-Prize, the Preis des Österreichischen Buchhandels, the Bruno Kreisky Prize, the Großes Goldenes Ehrenzeichen from the city of Vienna, the Jean Paul Preis, and, in 2016, the Großer Österreichischer Staatspreis. Austria's federal president Alexander Van der Bellen acknowledged the author's importance to Austria: "Klar und konsequent setzte er sich mit österreichischer Geschichte, mit den hellen, oft auch dunklen Seiten unseres Landes auseinander." For Roth writing was very personal, indeed central to existence. In an interview I conducted with him in 2010 for a literary event at the Austrian Cultural Forum in New York, he explained, "Ich schreibe über das, was mich bewegt, was mich betrifft. Ich bin süchtig nach dem Schreiben. Mein Schreiben ist sehr persönlich." I had the pleasure of hearing some of these personal stories when I visited him at his country home in southern Styria in June 2011. Over the course of two days, Roth selflessly gave of his time, relaying stories of his upbringing, his experiences in the aftermath of World War II, and the significance of writing and photography in his quest to reveal the hidden strengths and foibles of humanity. Gerhard Roth was prolific like few others, and he dedicated himself unremittingly to unearthing the silenced realms of Austrian history and society and of human consciousness. When he passed away on February 8, Roth had already completed several hundred pages of a text fittingly entitled Die Jenseitsreise. [End Page 56] Copyright © 2023 Austrian Studies Association

DLI-Net: Dual Local Interaction Network for Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) is considered an ideal method of image retrieval due to the rich and easily accessible characteristics of sketches. It aims to find the most similar photo from the photo gallery based on the input sketch. Most previous works follow the paradigm that extracting global feature first and then projecting the features of sketch and photo to unified embedding feature space using triplet loss. However, the global feature is not appropriate for extracting the crucial fine-grained information. Based on this principle, we propose a Dual Local Interaction Network (DLI-Net). DLI-Net explores an effective and efficient way to utilize local features for FG-SBIR. Specifically, we first propose a Local Feature Extractor to extract mid-level local features. Then, in response to the problems brought by local features, we propose a Dual Interaction Module, which contains Self Interaction Module and Cross Interaction Module. Self Interaction Module speeds up retrieval by eliminating the redundant local features of background. Cross Interaction Module solves the spatial misalignment by making the sketches interact with photos. Extensive experiments on six commonly used datasets show that our DLI-Net outperforms state-of-the-art competitors by a significant margin with a reasonable retrieval speed. Moreover, to the best of our knowledge, DLI-Net is the first model that beats humans on all six datasets. Besides, DLI-Net also performs best on cross-category fine-grained sketch-based image retrieval task, which further demonstrates local features are more appropriate for FG-SBIR.

Personality of nonprofit organizations' Instagram accounts and its relationship with their photos' characteristics at content and pixel levels.

Nonprofit organizations (NPO) can utilize social networking sites (SNSs) for their activities. Like individual users, they can create SNS accounts, upload posts to show what they are doing, and communicate with other users. Thus, their accounts can be investigated from the same perspective of personality which has been one of the key lenses through which SNS posts of individual users was investigated. In the line of literature that analyzed the personality of non-human objects such as products, stores, brands, and websites, the present research analyzed the personality of NPOs’ Instagram accounts using an online AI service. Also, it investigated how their personality traits were related to the characteristics of the uploaded photos at content and pixel levels. The results of analysis of 223,446 photos on 177 Instagram accounts suggested that the personality of NPOs’ Instagram accounts can be summarized as being high in openness and agreeableness but low in extraversion and neuroticism. And it was found that openness and agreeableness were the personality traits that associated the most with the photo features. Also, the personality traits of NPOs’ Instagram accounts, except neuroticism, were predicted from the photo features with an acceptable level of accuracy. Implications of this research and suggestions for further research were presented.

Understanding Active Sites in Molecular (Photo)Electrocatalysis through Vibrational Spectroelectrochemistry

Molecularly defined (photo)electrocatalysts have great potential to perform important energy conversion reactions with high efficiency and selectivity. A particular advantage is their modular design, which allows their reactivity to be tailored to a specific reaction. However, to advance their rational development, a deep mechanistic understanding of the catalytic reactions is required. In situ vibrational spectrochemistry, in particular confocal Raman and infrared absorption spectroscopy, is an extremely powerful method to gain deep insights into the structure and reactivity of electrode-immobilized molecular catalytic systems under operando conditions [1]. Since both vibrational spectroscopic techniques have different selection rules, complementary information about the system under operando conditions can be obtained. In my talk, selected research examples will be used to present the different aspects and features of (photo)electrocatalysis that can be visualized using vibrational spectrochemistry. The target systems presented include, on the one hand, porphyrin- and phthalocyanine-based systems for electrocatalytic oxygen and CO2 reduction [2,3] and, on the other hand, conjugated acetylene polymers for photoelectrocatalytic hydrogen evolution [4]. Spectra evaluation under operando conditions allowed us to demonstrate charge separation and ligand binding within these catalysts, identify the active sites during catalysis, and gain insight into the redox properties, reaction intermediates as well as synergistic effects of dual active sites.[1] H. K. Ly, I. M. Weidinger, Chem. Commun. 2021, 57, 2328-2342[2] T. R. Ramuglia, V. Budhija, K. H. Ly, M. Marquardt, M. Schwalbe, I. M. Weidinger, ChemCatChem, 2021, 13, 3934.[3] H. Zhong, M. Ghorbani-Asl, K. H. Ly, J. Zhang, J. Ge, M. Wang, Z. Liao, D. Makarov, E. Zschech, E. Brunner, I.M. Weidinger, J. Zhang, A. V. Krasheninnikov, S. Kaskel, R. Dong, X. Feng, Nat. Comm. 2020, 11, 1409.[4] M. Borelli, C. J. Querebillo, D. L. Pastoetter, T. Wang, A. Milani, C. Casari, H. K. Ly, F. He, Y. Hou, C. Neumann, A. Turchanin, H. Sun, I. M. Weidinger, X. Feng, Angew. Chemie Int. Ed. 2021, 60 (34), 18876-18881.

Judging an Airbnb booking by its cover: how profile photos affect guest ratings

PurposeThis research aims to examine whether the facial appearances and expressions of Airbnb host photos influence guest star ratings.Design/methodology/approachThis research analyzed the profile photos of over 20,000 Airbnb hosts and the guest star ratings of over 30,000 Airbnb listings in New York City, using machine learning techniques.FindingsFirst, hosts who provided profile photos received higher guest ratings than those who did not provide photos. When facial features of profile photos were recognizable, guest ratings were higher than when they were not recognizable (e.g. faces too small, faces looking backward or faces blocked by some objects). Second, a happy facial expression, blond hair and brown hair positively affected guest ratings, whereas heads tilted back negatively affected guest ratings.Originality/valueThis research is the first, to the best of the authors’ knowledge, to analyze the facial appearances and expressions of profile photos using machine learning techniques and examine the influence of Airbnb host photos on guest star ratings.

Ethical Dimensions of App Designs: A Case Study of Photo- and Video-Editing Apps

This article presents an ethnographic study on the user experience (UX) design of the photo- and video-editing apps of millennial and Generation Z participants from different cultural groups. The case study calls attention to the implications of rhetorical misrepresentations of reality that photo- and video-editing apps afford and encourages future large-scale studies on the negative psychological and behavioral impacts such apps can have on users’ psychology, behaviors, and well-being. The authors use frameworks in virtue ethics to argue that despite slight variations, photo and video app UX has ethical implications that can negatively impact young adult users. For example, the study suggests that the photo and video app features tend to subvert the traditional Chinese virtues of modesty, honesty, and the middle way and that hyperbolic and playful designs can cause addictive behaviors.

DOG-ADTCP: A new feature descriptor for protection of face identification system

Nowadays the face biometric system is gaining popularity for authentication in access control applications. At the same time, the threats to authentication systems are also increased in terms of spoofing or presentation attacks where an intruder attempts to spoof the face recognition system by using genuine user photos or video to gain access. In order to effectively secure the secrecy of a genuine user, there is an urgent need for building a face authentication system with anti-spoofing countermeasures. In this paper, we introduce a novel face anti-spoofing approach, which is mainly based on contrast and texture features of both real and spoof photos. A modified Difference of Gaussian (DOG) filtering method and Angle Directional Ternary Co-relation pattern (ADTCP) are used. The publicly available NUAA, MSU-MFSD and REPLAY-ATTACK photo-impostor datasets are tested on the approach, which includes images with different illumination and area of the face. The accuracy of the proposed approach is evaluated using different metrics. The results show that our proposed method is superior to other state-of-the-art (SOTA) practices when tested on three photo-impostor datasets.

#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses

The goal of the current study was to explore the visual representation of #ShoutYourAbortion hashtag movement on Instagram. The photos’ content and embedded texts in the photos were examined. And the photos were clustered using k-means clustering algorithm, and the resulting clustered were compared using the same criteria above. Photo features which shows the content- and pixel-level characteristics were extracted and used for comparison between clusters. The photo features were also used to examine their relationships with the public’s responses. It was found that text was the main type of content, and the texts presented in photos were mainly about stories told in first person point of view as a woman. The photos were grouped into two clusters, which differed in terms of content and photo features. And the public’s responses were found to be related to photo features. The results are expected to contribute to the understanding of hashtag movements via photos and making photos in hashtag movements more appealing to the public.

Predicting comments on Facebook photos: Who posts might matter more than what type of photo is posted

The number of likes and comments received to social media posts and images are influential for users’ self-presentation and problematic Facebook use. The aim of this study was to highlight the most relevant factors predicting the popularity (i.e., the probability to receive at a least a comment) of Facebook photos based on: (i) Facebook user-related features; (ii) Facebook photo-related features; and (iii) and psychological variables. A mixed approach was used, including objective data extracted from Facebook (regarding users’ characteristics and photo features) as well as answers to a questionnaire. Participants were 227 Facebook users (M = 25.01(1.05) years). They were asked to answer a questionnaire and provide a copy of their Facebook profile data. A total of 180,547 photos receiving a total of 122,689 comments were extracted. Results showed that user-related features (Facebook network and activities) were the most relevant in predicting image popularity accurately. It seems that who posts a Facebook photo matters more than the type of photo posted and the psychological profile of the user. Results are discussed within a psychological perspective.Future research should look at the sentiment (positive vs. negative) of the comments received by different types of photos. This is the first study exploring what makes a Facebook photo popular using objective data rather than self-reported frequency of Facebook activity only. Results might advance current methods and knowledge about potential problematic behaviors on social media.

Research on Fast Face Retrieval Optimization Algorithm Based on Fuzzy Clustering

Aiming at the problem of long retrieval time for massive face image databases under a given threshold, a fast retrieval algorithm for massive face images based on fuzzy clustering is proposed. The algorithm builds a deep convolutional neural network model. The model can be used to extract features from face photos to obtain a high-dimensional vector to represent the high-level semantic features of face photos. On this basis, the fuzzy clustering algorithm is used to perform fuzzy clustering on the feature vectors of the face database to construct a retrieval pedigree map. When the threshold is passed in for database retrieval of the target face photos, the pedigree map can be quickly retrieved. Experiments on the LFW face dataset and self-collected face dataset show that the model is better than the commonly used K-means model in face recognition accuracy, clustering effect, and retrieval speed and has certain commercial value.

Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of images through both channel dimension and spatial dimension. In addition, a modification of the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1 score</i> , by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.

Performance evaluation of photographic measurement in the machine-learning prediction of ground PM2.5 concentration

The scarcity and sparsity of meteorological vertical observations have caused severe restrictions in the accurate forward prediction and backward analysis of weather and air pollution. In this study, we used a common camera to automatically photograph the daytime and nighttime lights at an urban site in Beijing during 2019–2020, to support or act as a possible alternative to radiosonde measurement. The photo features characterize the scattering effect of atmospheric particulate matter on visible sunlight or lamps and record cloud, fog, and precipitation processes. The probability distributions of the mean blue brightness (B) and the mean red brightness (R) are significantly different for both the sky-part and the ground-part pixels of the photo. The B/R ratio of the sky part (B/R_sky) is exponentially and negatively correlated with ground PM2.5 concentration. During the daytime, B/R_sky has a higher priority than the boundary layer height (BLH) in the determination of PM2.5 concentration by a decision tree model, whereas the BLH plays a key role at night, and the importance of B/R_sky is comparable to that of the BLH in the decision tree. The photo features were adopted as input variables into a simple multi-layer perceptron (MLP) model (3-layer neural network) predicting PM2.5 concentration, with a root mean square error of 1–3 μg/m3, indicating that the auto-shooting camera is a competitive alternative of meteorological measurement. Because of the low cost of installation, the broad application of auto-photography can make up for deficiencies of sounding observation, i.e., sparse temporal and spatial resolution, and can be accessed by the public in real time.

Content-Based Image Retrieval Techniques: A Survey

In these eras, digital day’s cell smartphones and cameras are gaining a titanic reputation, resulting in the speedy growth of virtual images that are available throughout the internet. With an exponential boom within the image sizes, databases in different media lie an extremely good project for massive scale image search. Image retrieval from databases or the internet wishes an unsuccessful and effective technique because of the explosive increase of digital snapshots. Image recovery is known to be a comprehensive research area, particularly in content-based image retrieval (CBIR). CBIR retrieves comparable photographs from a huge photograph database primarily based on photo features, which has been totally lively study vicinity recently. The content materials that may be derived from images which include color, shade, texture, shape, are used for retrieving an image from the database. This paper will provide a survey and discuss the contemporary literature of various sorts of image retrieval (IR) structures and comparisons among them.

PENGGUNAAN WHATSAPP GROUP SEBAGAI SARANA PEMBELAJARAN JARAK JAUH DI SD NEGERI TAMBAKSARI

This study aims to analyze and describe learning activities using WhatsApp Group as a means of distance learning at SD Negeri Tambaksari and its obstacles. This research was conducted using a descriptive qualitative approach conducted by interviewing participants, namely teachers, guardians of students and students. The results showed that low-class teachers opened learning with greetings and high-class teachers did not open learning, material in high and low classes covered all subjects but did not fulfil all learning outcomes, the method used by low and high-class teachers was the assignment method. , the media used by low-class teachers and high-class teachers is a photo feature, assessments conducted by low-class teachers and high-class teachers in the form of online and portfolio based tests, low-class teachers and high-class teachers do not reflect, low-class teachers close learning with giving messages to students and closing greetings while high-class teachers did not close the lesson. The obstacles for teachers in learning using WhatsApp Group include limited quota, limited cellphone storage for guardians of students, teachers find it difficult to determine the method, assignments were given by the teacher through WhatsApp Group make students lazy.