Click Frequency Research Articles

Research on Social Network Advertisement Delivery Platform Based on Blockchain

This paper briefly introduces the advertisement delivery model based on blockchain and the storage of key point information that can reflect the whole delivery process. After that, the simulation experiment was carried out on the advertisement delivery platform in a laboratory with the local server and the virtual server provided by Ethereum. The experiment detected the advertisement delivery function and the resistance to click fraud. The results showed that the advertisement delivery platform successfully completed the advertisement delivery and left a complete record. Facing the attack of tampering with the click frequency record of advertisements, the advertisement delivery platform using blockchain still maintained accurate records.

The predictive value of gaze behavior and mouse-clicking in testing listening proficiency: A sensor technology study

This study employed eye-tracking and mouse click frequency analysis to investigate the predictive power of gaze behaviors, mouse-clicking, and their interactive effects with linguistic backgrounds on the IELTS (International English Language Testing System) listening test scores. A total of 77 test takers (45 with English as their first language (E-L1) and 32 with English as their second language (E-L2)) participated in this study. Their eye movements and mouse click frequencies were recorded as they took a computer-based IELTS listening test. The subsequent data analysis, utilizing linear mixed models, showed that gaze patterns, mouse actions, and language background significantly predicted listening test outcomes across four listening test sections and between E-L1 and E-L2 candidates, accounting for 33.2% of the variance observed in test scores. These results indicate the effect of potential sources of construct-irrelevant variance on test scores, which are not predicted in the available construct definitions of the test used in the study. Implications for the listening construct and test validity are discussed.

PENGARUH IKLAN ONLINE PADA FACEBOOK TERHADAP KEPUTUSAN PEMBELIAN PAKAIAN (Studi pada Mahasiswa Politeknik Mandiri Bina Prestasi)

This study was conducted to determine how far the influence of consumer attitudes on online advertising, consumer memory on online advertising and the frequency of consumer clicks on Facebook media online advertisements can influence consumer purchasing decisions. The population in this study were all students at Bina Prestasi Independent Polytechnic, totaling 444 people. Sampling using the slovin formula. From a population of 444, 81 people were selected as samples in this study. Research data obtained through questionnaires distributed by google form and literature study. Data analysis using multiple linear regression analysis techniques. Some of the analytical methods used are descriptive methods, Likert scale and instrument validity testing. The results showed that consumer attitudes, consumer memory and consumer click frequency had a simultaneous and significant effect on consumer purchasing decisions. Then partially consumer attitudes and consumer memory have a significant effect on purchasing decisions. However, the variable frequency of consumer clicks has no effect on consumer purchasing decisions. This can be seen from the results of the analysis that consumers rarely click on online advertisements that appear on the Facebook media, some consumers click on online advertisements only because they have bought before, and if they have seen the same advertisement many times on the Facebook media even though they have never bought it before, they will click on it.Keywords: attitude, recall, click frequency, consumer purchasing decisions.

Characterization of sound production by the pot-bellied seahorse (Hippocampus abdominalis) during feeding.

Sound production during feeding by the pot-bellied seahorse, Hippocampus abdominalis, was quantified with an observation of clicks (acoustic signal) and snicks (visual behavior). Female, male, and juvenile seahorses had feeding sounds characterized for peak (dominant) frequency (Hz), sound pressure level (SPL), and duration (ms). Subject body size and condition was estimated by standard length (SL, cm), to determine an estimate of body condition index (BCI). An inverse correlation between mean peak frequency (Hz) of clicks and SL was found for females. A negative correlation between peak frequency (Hz) of clicks and a residual BCI was determined for both males and females, suggesting that acoustic signals may contain information regarding fitness.

A Graph Positional Attention Network for Session-Based Recommendation

The main idea of a session-based recommendation system is to model the user’s historical click sequence and then summarize user preferences and predict the items the user will interact with. The session recommendation model based on graph neural networks has attracted much attention in recent years because it can accurately obtain the local relationship between items. However, the traditional session recommendation model based on graph neural Networks lack the use of user’s higher-order features or fail to address the impact of item position information on the current session, which are both critical to the recommendation system. In addition, some models proposed the position information while neglects the click frequency information. We propose a graph network recommendation model called GPAN based on position attention in response to the abovementioned problems. Specifically, we propose a novel high-low order session perceptron that uses the perceptron to model undirected and directed graphs separately to obtain high and low order item representations in a session. For position information, we designed a position layer to calculate independently. Finally, the user’s short-term preference and long-term preference are aggregated to obtain the recommendation sequence. The results through a large number of experiments on three real datasets show that the performance of the proposed GPAN model is the best.

Characteristics of auditory steady-state responses to different click frequencies in awake intact macaques

BackgroundAuditory steady-state responses (ASSRs) are periodic evoked responses to constant periodic auditory stimuli, such as click trains, and are suggested to be associated with higher cognitive functions in humans. Since ASSRs are disturbed in human psychiatric disorders, recording ASSRs from awake intact macaques would be beneficial to translational research as well as an understanding of human brain function and its pathology. However, ASSR has not been reported in awake macaques.ResultsElectroencephalograms (EEGs) were recorded from awake intact macaques, while click trains at 20–83.3 Hz were binaurally presented. EEGs were quantified based on event-related spectral perturbation (ERSP) and inter-trial coherence (ITC), and ASSRs were significantly demonstrated in terms of ERSP and ITC in awake intact macaques. A comparison of ASSRs among different click train frequencies indicated that ASSRs were maximal at 83.3 Hz. Furthermore, analyses of laterality indices of ASSRs showed that no laterality dominance of ASSRs was observed.ConclusionsThe present results demonstrated ASSRs, comparable to those in humans, in awake intact macaques. However, there were some differences in ASSRs between macaques and humans: macaques showed maximal ASSR responses to click frequencies higher than 40 Hz that has been reported to elicit maximal responses in humans, and showed no dominant laterality of ASSRs under the electrode montage in this study compared with humans with right hemisphere dominance. The future ASSR studies using awake intact macaques should be aware of these differences, and possible factors, to which these differences were ascribed, are discussed.

Echovis – A collection of human echolocation tests performed by blind and sighted individuals: A pilot study

The article presents research on the echolocation skills of blind and sighted individuals gathered for the purpose of development of an echolocation training app. The goal of the research was to determine the influence of the environment type, reverberation, clicking patterns, and the average differences between the two tested groups. Ten blind and 10 sighted subjects were tested in various echolocation tasks – stationary, moving, indoor and outdoor, using mechanical clickers, and artificially generated clicks. Ten blind children also took part in the static indoor tests. The tests were repeated using binaural recordings and spatially rendered virtual audio. The following parameters and dependencies between them were analyzed: correctness of the obstacle localization, certainty of answer, type of environment and clicker sound, and frequency and number of clicks. It was found that the number of clicks influenced the correctness and certainty of the answer in case of obstacle direction determination, but not the distance. Better results were obtained in outdoor environments and in an empty room, what implies that reverberation has a positive influence on echolocation. The expected success rates in tested echolocation tasks provided a comparison of the echolocation abilities of blind and sighted subjects and set a benchmark for future tests.

Driven by notifications - exploring the effects of badge notifications on user experience.

Notification badges are an unexplored category of visual feedback to which we are continuously exposed. This study aims to deepen knowledge on the topic by measuring the behavioral effects of notification badges on a large sample of smartphone users. More precisely, the goal of the study is to observe if the presence of notification badges increases the frequency of clicks on apps. More than 1000 participants were involved in a remote between-subjects experiment, allocated into fifteen equinumerous groups of comparison. Each participant was presented with a smartphone screen displaying fifteen app icons and just one badge notification. Participants were asked to perform a remote user test called First Impression Click Test: a methodology that indicates where they would click first to accomplish a given task (i.e., Where would you click first on this screen?). Our results show a large increase in the number of clicks on apps with notification badges compared to those without notification badges and suggest the important ability of these small affordances to attract attention and stimulate action. Based on the evidence provided, our findings have practical implications for user experience design.

Bio-Inspired Covert Active Sonar Detection Method Based on the Encoding of Sperm Whale Clicks

The covertness of an active sonar is a very important issue and the sonar signal waveform design was studied to improve covertness of the system. Click is one of the most common cetacean sounds, and has excellent detection performance. Meanwhile, existing interception systems normally classify cetacean sounds as ocean noise and filter them out. Based on this, a bio-inspired covert active sonar detection method is proposed by encoding the clicks produced by sperm whales. First, based on the frequency distribution characteristic of sperm whale clicks and frequency-hopping sequence, a set of bionic sub-pulses are constructed by encoding the centroid frequency of the real clicks. Based on the inter-click interval characteristic of the clicks and time-hopping sequence, the bionic sonar signal is constructed by combining the constructed bionic sub-pulses and encoding the inter-pulse interval between two adjacent pulses. Then, a single bionic sonar signal is utilized to estimate the speed and range of the target. Based on the characteristics of sperm whale click trains, a camouflage application method is designed to improve the camouflage ability of the bionic sonar signal. Finally, experiment results show that when the target speed is 5m/s and 10m/s and the signal to noise ratio varies from −20dB to 20dB, the root-mean-square error of estimated speed and range are lower than 0.1m/s and 0.1m, respectively. In addition, the constructed bionic sub-pulses have a 99% probability of being classified into sperm whale clicks. Experimental results demonstrate the effectiveness and covertness of the proposed method.

HEPre: Click frequency prediction of applications based on heterogeneous information network embedding

Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users’ mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of users’ mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users.

Echolocation variability of franciscana dolphins (Pontoporia blainvillei) between estuarine and open-sea habitats, with insights into foraging patterns.

Environmental and ecological factors can trigger changes in the acoustic repertoire of cetaceans. This study documents the first use of a well-established passive acoustic monitoring device (C-POD) to analyze echolocation sounds and behavior of franciscana dolphins in different habitats: estuary [Babitonga Bay (BB)] and open sea [Itapirubá Beach (IB)]. A total of 10 924 click trains were recorded in BB and 6 093 in IB. An inter-click interval < 10 ms (so called "feeding buzzes") was used as a proxy for foraging activity. The main difference in the acoustic parameters between the two habitats was related to the frequency spectrum, with higher maximum and lower modal and minimum click frequencies in BB, and a train frequency range of 17 kHz, against 10 kHz in IB. Also, the click emission rate (clicks/s) was almost 20% higher in BB. Both studied habitats showed a high proportion of feeding buzzes (BB = 68%; IB = 58%), but with a higher probability of occurrence in BB (p < 0.001) and at night (p < 0.001) in both habitats. The C-PODs showed great potential to monitor occurrence, bioacoustics parameters, and echolocation behavior of franciscana dolphins. Longer-term temporal and spatial monitoring are necessary for elucidating several issues raised in this study.

Applicability analysis of prediction methods in the system for selection personalized offers by analytical modeling

The relevance of the work is justified by the frequent occurrence of the need to solve the problems of choosing personalized offers in information systems and the many possible methods of machine learning, among which it is necessary to choose the most suitable one. The purpose of this study is to simulate a system for selecting personalized offers as a queuing system for estimating equipment costs when using each of the methods necessary to service the required part of requests for a given time limit. This solves the problem of assessing the minimum number of servicing devices (backend servers) required to ensure the operation of the system at a given level. The paper shows that the system can be described by a multichannel queuing system without losses. The distribution function of the spent time of the request in the system (the service time plus the waiting time in the queue) is calculated, since in the literature for such systems only the distribution function of the waiting time in the queue is described. Transformations of the expression for the probability of waiting are given, which solve the overflow problem in the software implementation. In the final part, as an example, the system was modeled according to the given parameters, and the minimum number of servicing devices was estimated to ensure a given system response time. Based on the data obtained, it is possible to make a decision on the advisability of using one or another method for predicting the frequency of user clicks or ranking.

Interdisciplinary approach to the rehabilitation of patients with temporoman-dibular joint abnormality, and a need for complete dental reconstruction: out-comes

Background. This study aimed to improve the clinical effect of dental orthopedic treatment for patients who need a complete dental reconstruction, by making use of the temporomandibular joint characteristics.&#x0D; Materials and Methods. A total of 647 patients with adentia who needed a complete dental reconstruction were exposed to a comprehensive examination and treatment. Patients were divided into 3 groups: orthopedic rehabilitation group (218 patients), interdisciplinary rehabilitation group (195 patients), and multidisciplinary rehabilitation group (234 patients). The follow-up period was 7 years.&#x0D; Results. After the proposed rehabilitation approach, patients in the main group demonstrated an improvement in the VAS (Visual Analogue Scale) score, which was significantly smaller with respect to that in the comparison groups. The frequency of clicking and pain during chewing as well as soreness sensations in this group decreased at a significantly lower pace compared to that in the comparison groups.&#x0D; Conclusions. Dental reconstruction procedures aimed at restoring the shape of the teeth should focus on pain relief and temporomandibular joint function correction. The most important stage in implementing this approach is a complete and consistent examination of patients who need dental reconstruction.

Utilizing Student Time Series Behaviour in Learning Management Systems for Early Prediction of Course Performance

Predictive analytics in higher education has become increasingly popular in recent years with the growing availability of educational big data. Particularly, a wealth of student activity data is available from learning management systems (LMSs) in most academic institutions. However, previous investigations into predictive analytics in higher education using LMS activity data did not adequately accommodate student behaviours in the form of time series. In this study, we have applied a deep learning approach — long short-term memory (LSTM) networks — to analyze student online temporal behaviours using their LMS data for the early prediction of course performance. To reveal the potential of the deep learning approach in predictive analytics, we compared LSTM networks with eight conventional machine learning classifiers in terms of the prediction performance as measured by the area under the ROC (receiver operating characteristic) curve (AUC) scores. Results indicate that using the deep learning approach, time series information about click frequencies successfully provided early detection of at-risk students with moderate prediction accuracy. In addition, the deep learning approach showed higher prediction performance and stronger generalizability than the machine learning classifiers.

Determinants of echolocation click frequency characteristics in small toothed whales: recent advances from anatomical information

Abstract The pulse‐like clicking sounds made by odontocetes for echolocation (biosonar) can be roughly classified by their frequency characteristics into narrow‐band high‐frequency (NBHF) clicks with a sharp peak at around 130 kHz and wide‐band (WB) clicks with a moderate peak at 30–100 kHz. Structural differences in the sound‐producing organs between NBHF species and WB species have not been comprehensively discussed, nor has the formation of NBHF and WB clicks. A review of the sound‐producing organs, including the latest findings, could lead to a new hypothesis about the sound production mechanisms. In the current review, data on echolocation click characteristics and on the anatomical structure of the sound‐producing organs were compared in 33 species (14 NBHF species and 19 WB species). We review interspecific information on the characteristics of click frequencies and data from computed tomography scans and morphology of the sound‐producing organs, accumulated in conventional studies. The morphology of several characteristic structures, such as the melon, the dense connective tissue over the melon (the ‘porpoise capsule’), and the vestibular sacs, was compared interspecifically. Interspecific comparisons suggest that the presence or absence of the porpoise capsule is unlikely to affect echolocation frequency. Folded structures in the vestibular sacs, features that have been overlooked until now, are present in most species with NBHF sound production and not in WB species; the vestibular sacs are therefore likely to be important in determining echolocation click frequency characteristics. The acoustical properties of the shape of the melon and vestibular sacs are important topics for future investigations about the relationship between anatomical structure and sound‐producing mechanisms for echolocation clicks.

Fine-grained image classification with factorized deep user click feature

The advantages of user click data greatly inspire its wide application in fine-grained image classification tasks. In previous click data based image classification approaches, each image is represented as a click frequency vector on a pre-defined query/word dictionary. However, this approach not only introduces high-dimensional issues, but also ignores the part of speech (POS) of a specific word as well as the word correlations. To address these issues, we devise the factorized deep click features to represent images. We first represent images as the factorized TF-IDF click feature vectors to discover word correlation, wherein several word dictionaries of different POS are constructed. Afterwards, we learn an end-to-end deep neural network on click feature tensors built on these factorized TF-IDF vectors. We evaluate our approach on the public Clickture-Dog dataset. It shows that: 1) the deep click feature learned on click tensor performs much better than traditional click frequency vectors; and 2) compared with many state-of-the-art textual representations, the proposed deep click feature is more discriminative and with higher classification accuracies.

Long-term foraging dive characteristics of Cross Seamount beaked whales at the Pacific Missile Range Facility

Beaked whale foraging dive clicks similar to those detected at Cross Seamount were detected during passive acoustic monitoring of marine mammals at the Pacific Missile Range Facility (PMRF) off Kauai, Hawaii using bottom-mounted hydrophones from January 2007 through August 2018. The Cross Seamount beaked whale (BWC) foraging dive click frequency sweeps from approximately 18 kHz to above the recording bandwidth of the PMRF sensors of 48 kHz at inter-click-intervals of 0.14 s (std 0.06 s). These unique clicks have only been detected at Cross Seamount and around the Hawaiian Islands, and because they have only been detected at night there has been no visual detections or species confirmation. However, they are distinctive enough to automatically detect, and can be used to describe basic presence at PMRF. Detections averaged 0.047 dives/h (in a 24-h cycle), and occurred almost entirely on the southern part of the range (water depth <1000 m). 702 BWC foraging dives were used to determine a foraging dive baseline over a 12-year period, to compare foraging dive rates during US Naval exercise events and baseline periods, and to contrast BWC foraging dive behavior from Blainville’s beaked whale foraging dive behavior at PMRF.Beaked whale foraging dive clicks similar to those detected at Cross Seamount were detected during passive acoustic monitoring of marine mammals at the Pacific Missile Range Facility (PMRF) off Kauai, Hawaii using bottom-mounted hydrophones from January 2007 through August 2018. The Cross Seamount beaked whale (BWC) foraging dive click frequency sweeps from approximately 18 kHz to above the recording bandwidth of the PMRF sensors of 48 kHz at inter-click-intervals of 0.14 s (std 0.06 s). These unique clicks have only been detected at Cross Seamount and around the Hawaiian Islands, and because they have only been detected at night there has been no visual detections or species confirmation. However, they are distinctive enough to automatically detect, and can be used to describe basic presence at PMRF. Detections averaged 0.047 dives/h (in a 24-h cycle), and occurred almost entirely on the southern part of the range (water depth <1000 m). 702 BWC foraging dives were used to determine a foraging dive baseli...

Social Media Retargeting: Opinions, Clicks and Conversion

Social media retargeting is a personalized form of online advertisement done on social media using consumers browsing history. The study aims to explore the opinions of online shoppers regarding social media retargeting along with examining the relationship between the frequency of using social media, clicks, and conversion. Data was collected from 235 respondents and 207 responses were used for the final analysis. The data was analyzed using descriptive analysis, ordinal regression, and chi-square. Results revealed that consumers have a positive attitude toward social media retargeting. But out of six opinions only relevant and motivating were statistically significant for frequency of clicks. Furthermore, the results confirmed a moderate association of a frequency of using social media with clicks and conversion, and a strong association between click and purchase. The findings of the study provide insight into the effectiveness of using social media retargeting.

Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition.

The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are usually absent in practice. It remains challenging to predict the click feature from the visual feature, because the user click frequency vector of an image is always noisy and sparse. In this paper, we devise a Hierarchical Deep Word Embedding (HDWE) model by integrating sparse constraints and an improved RELU operator to address click feature prediction from visual features. HDWE is a coarse-to-fine click feature predictor that is learned with the help of an auxiliary image dataset containing click information. It can therefore discover the hierarchy of word semantics. We evaluate HDWE on three dog and one bird image datasets, in which Clickture-Dog and Clickture-Bird are utilized as auxiliary datasets to provide click data, respectively. Our empirical studies show that HDWE has 1) higher recognition accuracy, 2) a larger compression ratio, and 3) good one-shot learning ability and scalability to unseen categories.

Between-ear sound frequency disparity modulates a brain stem biomarker of binaural hearing.

The auditory brain stem response (ABR) is an evoked potential that indexes a cascade of neural events elicited by sound. In the present study we evaluated the influence of sound frequency on a derived component of the ABR known as the binaural interaction component (BIC). Specifically, we evaluated the effect of acoustic interaural (between-ear) frequency mismatch on BIC amplitude. Goals were to 1) increase basic understanding of sound features that influence this long-studied auditory potential and 2) gain insight about the persistence of the BIC with interaural electrode mismatch in human users of bilateral cochlear implants, presently a limitation on the prospective utility of the BIC in audiological settings. Data were collected in an animal model that is audiometrically similar to humans, the chinchilla (Chinchilla lanigera; 6 females). Frequency disparities and amplitudes of acoustic stimuli were varied over broad ranges, and associated variation of BIC amplitude was quantified. Subsequently, responses were simulated with the use of established models of the brain stem pathway thought to underlie the BIC. Collectively, the data demonstrate that at high sound intensities (≥85 dB SPL), the acoustically elicited BIC persisted with interaurally disparate stimulation (click frequencies ≥1.5 octaves apart). However, sharper tuning emerged at moderate sound intensities (65 dB SPL), with the largest BIC occurring for stimulus frequencies within ~0.8 octaves, equivalent to ±1 mm in cochlear place. Such responses were consistent with simulated responses of the presumed brain stem generator of the BIC, the lateral superior olive. The data suggest that leveraging focused electrical stimulation strategies could improve BIC-based bilateral cochlear implant fitting outcomes.NEW & NOTEWORTHY Traditional hearing tests evaluate each ear independently. Diagnosis and treatment of binaural hearing dysfunction remains a basic challenge for hearing clinicians. We demonstrate in an animal model that the prospective utility of a noninvasive electrophysiological signature of binaural function, the binaural interaction component (BIC), depends strongly on the intensity of auditory stimulation. Data suggest that more informative BIC measurements could be obtained with clinical protocols leveraging stimuli restricted in effective bandwidth.