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The dark web, often linked with illegal activities, can be monitored with different solutions. However, these tools are typically purpose-specific and designed for unique use cases. In this study, we propose a flexible and scalable framework that facilitates the easy integration of new workflows for dark web analysis. The design is based on the control, logic and operations layers, supplemented by a tools module, logs management, asynchronous message-based communication and a database. The implementation maps the features into a microservice approach, utilizing the open-source technologies Docker Swarm, Kafka, ELK Stack (Elastic Search, Logstash and Kibana), and PostgreSQL. A workflow to scrape web elements of Tor onion services is deployed and validated, demonstrating considerable framework performance despite the time-consuming task of navigating the dark web. Over 16 h, the framework collected over half million onion domains (84,371 unique ones) and made 78,555 accesses to them. [ABSTRACT FROM AUTHOR]

The IGT gene family is involved in the regulation of crop plant type, and LAZY belongs to the IGT subfamily. By comparing the amino acid sequences of six Arabidopsis LAZY members as 'seeds' in the cassava genome, a total of 8 LAZY genes were identified in cassava, among which MeLAZY1c is highly homologous to AtLAZY1, which regulates branching angles. Based on this, MeLAZY1c had the highest transcription level in the stem by qRT-PCR and pMeLAZY1c was deeply stained in the vascular bundle by GUS staining, using MeLAZY1c as the experimental materials in this study. Eight photoresponsive/regulatory elements were found in the MeLAZY1c promoter, and we subsequently found that darkness significantly inhibited the relative expression level of MeLAZY1c. At the same time, gene editing of MeLAZY1c was performed and 19 homozygous edited lineages. The phenotype of MeLAZY1c mutants was observed after seedling transplantation, compared to the SC8 wild-type, the main stems of MeLAZY1c mutants had crawing growth, and the stem skin cells at the curved part were distorted and deformed with different sizes. The number of cells at 1 mm near the ground was about 1.5 times that of cells at the far ground. In conclusion, these results indicates that MeLAZY1c plays an important role in the establishment of upright/creeping growth of cassava. [ABSTRACT FROM AUTHOR]

Marketing scholars increasingly use web scraping and application programming interfaces (APIs) to collect data from the internet. Yet, despite the widespread use of such web data, the idiosyncratic and sometimes insidious challenges in its collection have received limited attention. How can researchers ensure that the data sets generated via web scraping and APIs are valid? While existing resources emphasize technical details of extracting web data, the authors propose a novel methodological framework focused on enhancing its validity. In particular, the framework highlights how addressing validity concerns requires the joint consideration of idiosyncratic technical and legal/ethical questions along the three stages of collecting web data: selecting data sources, designing the data collection, and extracting the data. The authors further review more than 300 articles using web data published in the top five marketing journals and offer a typology of how web data have advanced marketing thought. The article concludes with directions for future research to identify promising web data sources and embrace novel approaches for using web data to capture and describe evolving marketplace realities. [ABSTRACT FROM AUTHOR]

Generative Pre-Trained Transformer (GPT) models excel in text generation, text compilation, and language-related tasks. ChatGPT is based on the GPT model that responds to queries and has human-like conversational capabilities. This includes writing posts for social media, software codes, emails and essays, etc. We tried to use ChatGPT for creating a research paper summary. During our experimentation using ChatGPT, we found that ChatGPT cannot directly read links or URLs, as it is trained on a large amount of text data. Research paper summarization is essential in academics. Hence to solve this problem of research paper summarization using ChatGPT, a software system was designed. In this paper, we present how the capability of ChatGPT could be enhanced to summarize pdfs when a URL is provided. Furthermore, the generated summary can be modified in different styles of writing such as creative, expanded, shortened, and professional. [ABSTRACT FROM AUTHOR]

Behavioral flexibility-the ability to tailor motor actions to changing body-environment relations-is critical for functional movement. Navigating the everyday environment requires the ability to generate a wide repertoire of actions, select the appropriate action for the current situation, and implement it quickly and accurately. We used a new, adjustable barrier paradigm to assess flexibility of motor actions in 20 17-month-old (eight girls, 12 boys) and 14 13-month-old (seven girls, eight boys) walking infants and a comparative sample of 14 adults (eight women, six men). Most participants were White, non-Hispanic, and middle class. Participants navigated under barriers normalized to their standing height (overhead, eye, chest, hip, and knee heights). Decreases in barrier height required lower postures for passage. Every participant altered their initial walking posture according to barrier height for every trial, and all but two 13-month-olds found solutions for passage. Compared to infants, adults displayed a wider variety of strategies (squat-walking, half-kneeling, etc.), found more appropriate solutions based on barrier height (ducked at eye height and low crawled at knee height), and implemented their solutions more quickly (within 4 s) and accurately (without bumping their heads against the barrier). Infants frequently crawled even when the barrier height did not warrant a low posture, displayed multiple postural shifts prior to passage and thus took longer to go, and often bumped their heads. Infants' improvements were related to age and walking experience. Thus, development of flexibility likely involves the contributions of multiple domains-motor, perception, and cognition-that facilitate strategy selection and implementation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In recent years, transformer-based models have played a significant role in advancing language modeling for natural language processing. However, they require substantial amounts of data and there is a shortage of high-quality non-English corpora. Some recent initiatives have introduced multilingual datasets obtained through web crawling. However, there are notable limitations in the results for some languages, including Spanish. These datasets are either smaller compared to other languages or suffer from lower quality due to insufficient cleaning and deduplication. In this paper, we present esCorpius-m, a multilingual corpus extracted from around 1 petabyte of Common Crawl data. It is the most extensive corpus for some languages with such a level of high-quality content extraction, cleanliness, and deduplication. Our data curation process involves an efficient cleaning pipeline and various deduplication methods that maintain the integrity of document and paragraph boundaries. We also ensure compliance with EU regulations by retaining both the source web page URL and the WARC shared origin URL. [ABSTRACT FROM AUTHOR]

Researchers routinely infer learning and other unobservable psychological functions based on observable behavior. But what behavioral changes constitute evidence of learning? The standard approach is to infer learning based on a single behavior across individuals, including assumptions about the direction and magnitude of change (e.g., everyone should avoid falling repeatedly on a treacherous obstacle). Herewe illustrate the benefits of an alternative "multiexpression, relativist, agnostic, individualized" approach. We assessed infant learning from falling based on multiple behaviors relative to each individual's baseline, agnostic about the direction and magnitude of behavioral change. We tested infants longitudinally (10.5-15 months of age) over the transition from crawling to walking. At each session, infants were repeatedly encouraged to crawl or walk over a fall-inducing foam pit interspersed with no-fall baseline trials on a rigid platform. Our approach revealed two learning profiles. Like adults in previous work, "pit-avoid" infants consistently avoided falling. In contrast, "pit-go" infants fell repeatedly across trials and sessions. However, individualized comparisons to baseline across multiple locomotor, exploratory, and social-emotional behaviors showed that pit-go infants also learned at every session. But they treated falling as an unimpactful "pratfall" rather than an aversive "pitfall." Pit-avoid infants displayed enhanced learning across sessions and partial transfer of learning from crawling to walking, whereas pit-go infants displayed neither. Thus, reliance on a predetermined, "one-size-fits-all" behavioral expression of a psychological function can obscure different behavioral profiles and lead to erroneous inferences. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the effects of crawling exercises at home on improving young children's behavioral performance, thereby providing a reference for the role of physical activity in protecting and promoting behavioral development during the COVID-19 outbreak. In total, 106 young children were included and randomly divided into the control group (51) and the experimental group (55). A 10-week exercise intervention was implemented, during which the control group engaged in rhythmic exercises, while the experimental group performed crawling exercises. The behavioral performance of young children was assessed using the Child Behavior Checklist before and after the intervention period. Compared with before intervention, the behavior scores in the control group and the experimental group after intervention declined. In the control group, the internalizing behavior score, the externalizing behavior score, and the total behavioral score decreased by 1.12, 0.23 and 1.35 points, while those in the experimental group decreased by 1.78, 0.88 and 2.65 points, respectively. After the intervention, the experimental group behaved significantly better than the control group with regard to anxiety-depression and total behavioral performance (P < 0.05). Both crawling exercises and rhythmic exercises led to a statistically significant improvement in the behavioral performance of young children. However, owing to the inherent benefits of integrating diverse movement elements and offering adaptable levels of difficulty, crawling exercises demonstrated a more prominent influence on the behavior of young children as compared to rhythmic exercises. As such, crawling can be recommended as a form of physical exercise for young children at home when outdoor activities are not feasible due to weather changes or epidemic control measures. • Physical activities in the long-term home environment can improve young children's behavioral performance. • Crawling exercises have a stronger impact on young children's behavior than rhythmic exercises. • Crawling exercises can be recommended as a form of physical exercise for young children in the home environment. [ABSTRACT FROM AUTHOR]

Limbless crawling on land requires breaking symmetry of the friction with the ground and exploiting an actuation mechanism to generate propulsive forces. Here, kirigami cuts are introduced into a soft magnetic sheet that allow to achieve effective crawling of untethered soft robots upon application of a rotating magnetic field. Bidirectional locomotion is achieved under clockwise and counterclockwise rotating magnetic fields with distinct locomotion patterns and crawling speed in forward and backward propulsions. The crawling and deformation profiles of the robot are experimentally characterized and combined with detailed multiphysics numerical simulations to extract locomotion mechanisms in both directions. It is shown that by changing the shape of the cuts and orientation of the magnet the robot can be steered, and if combined with translational motion of the magnet, complex crawling paths are programed. The proposed magnetic kirigami robot offers a simple approach to developing untethered soft robots with programmable motion. [ABSTRACT FROM AUTHOR]

Generative Pre-Trained Transformer (GPT) models excel in text generation, text compilation, and language-related tasks. ChatGPT is based on the GPT model that responds to queries and has human-like conversational capabilities. This includes writing posts for social media, software codes, emails and essays, etc. We tried to use ChatGPT for creating a research paper summary. During our experimentation using ChatGPT, we found that ChatGPT cannot directly read links or URLs, as it is trained on a large amount of text data. Research paper summarization is essential in academics. Hence to solve this problem of research paper summarization using ChatGPT, a software system was designed. In this paper, we present how the capability of ChatGPT could be enhanced to summarize pdfs when a URL is provided. Furthermore, the generated summary can be modified in different styles of writing such as creative, expanded, shortened, and professional.

IntroductionThe characteristic feature of primitive reflexes is that they occur early in development and must expire at a well-defined age. The study was conducted prospectively on a group of 107 children (74 boys). The study population included 83 infants born on time (weight 3,465 ± 395 g) and 24 born prematurely (weight 2,225 ± 793 g).MethodsAn analysis of motor development at 3 months of age consisting of a qualitative assessment (motor performance) and a check of reflexes was performed; at 9 months, the child was checked for crawling and sitting down, and at 16 months for walking.ResultsThe more abnormal reflexes, the less likely it was to achieve the assessed milestones in time. It is possible to notice that the qualitative assessment is, in each case, a better predictor of the milestones than any of the reflexes, except for walking, where the Babkin reflex was strongly predictive. However, the qualitative assessment sum still occupies the second and third positions in prone and supine positions.DiscussionThe occurrence of Babkin’s reflex at 3 months of age impacts the achievement of sitting down and walking functions. An abnormal Galant reflex was strongly associated with the lack of occurrence of crawling on time. At the same time, a high-quality score at 3 months of age guarantees the development of crawling on time, sitting down, and walking.

Abstract Limbless crawling on land requires breaking symmetry of the friction with the ground and exploiting an actuation mechanism to generate propulsive forces. Here, kirigami cuts are introduced into a soft magnetic sheet that allow to achieve effective crawling of untethered soft robots upon application of a rotating magnetic field. Bidirectional locomotion is achieved under clockwise and counterclockwise rotating magnetic fields with distinct locomotion patterns and crawling speed in forward and backward propulsions. The crawling and deformation profiles of the robot are experimentally characterized and combined with detailed multiphysics numerical simulations to extract locomotion mechanisms in both directions. It is shown that by changing the shape of the cuts and orientation of the magnet the robot can be steered, and if combined with translational motion of the magnet, complex crawling paths are programed. The proposed magnetic kirigami robot offers a simple approach to developing untethered soft robots with programmable motion.

This study reports on crawling variability and differences in variability between infants with typical development (TD) and those with developmental delays. This longitudinal study included 0-year-old infants with no apparent dysfunction. The crawling movement in the video was coded objectively with reference to previous research. The infants were screened after 2 years of age, and the TD and suspected developmental delay (SDD) crawling codes were analysed and compared. Among TD infants, variation in hands-and-knees crawling decreased more in the group with belly-crawling experience than in the group without. However, this tendency was not observed in infants with SDD. Furthermore, TD infants showed more variable patterns of limb movement timing during belly-crawling than those with SDD. This study suggests variability during the entire crawling period. Moreover, exploration of an efficient diagonal gait was critical for crawling variability and subsequent development, which tends to be deficient in infants at risk of developmental delay. [ABSTRACT FROM AUTHOR]

Apply It!: • Evidence suggests quadrupedal motor control mechanisms aid in balance and coordination during bipedal tasks. • Quadrupedal movement training provides participants with opportunities to improve total body joint stability patterns by altering the base of support and center of mass through stationary and traveling variations. • Electromyographic studies on crawling movements in adults have shown substantial muscle activity in the trunk stabilizers and other supporting muscles such as the shoulders, triceps, quadriceps, calves, and hamstrings. The center of mass location changes the pattern of muscle activation in relation to the distribution of the load. • Promising evidence suggests that quadrupedal movement training may improve joint proprioception and range of motion. [ABSTRACT FROM AUTHOR]

Simple Summary: The fall armyworm (FAW), Spodoptera frugiperda, is one of the world's most important corn pests. FAW larval dispersal is a major factor contributing to local spread and damage by this pest. In this study, we investigated the crawling and ballooning dispersal characteristics of FAW larvae in the laboratory and found that airflow was an important factor influencing larval ballooning and, thereby, the long-distance dispersal of FAW larvae. These results provide scientific information for field monitoring and control strategies against FAW. The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a major pest of corn worldwide. FAW larval dispersal is an important life strategy that influences FAW population distribution in corn fields and subsequent plant damage. We studied FAW larval dispersal in the laboratory with sticky plates placed around the test plant and a unidirectional airflow source. Crawling and ballooning were the main dispersal means of FAW larvae both within and between corn plants. All larval instars (1st–6th) could disperse by crawling, with crawling being the only dispersal mechanism for 4th–6th instars. By crawling, FAW larvae could reach all aboveground parts of a corn plant as well as adjacent corn plants where leaves overlapped. Ballooning was used primarily by 1st-3rd instar larvae, and the proportion of these larvae that used ballooning decreased with age. Ballooning was largely governed by the larva's interaction with airflow. Airflow influenced the direction and distance of larval ballooning. With an airflow speed of about 0.05 m/s, 1st instars could travel up to 196 cm from the test plant, indicating that long-distance FAW larval dispersal depends on ballooning. These results increase our understanding of FAW larval dispersal and provide scientific information for the development of FAW monitoring and control strategies. [ABSTRACT FROM AUTHOR]

The Web of Things (WoT) is an enhanced form of the Internet of Things (IoT) that has changed the trend of life nowadays. Due to IoT, life is transformed into smart life, such as smart buildings, smart vehicles, smart agriculture, smart businesses, etc., by connecting a certain number of things to the internet. Many people are now working on ways to locate indoor things to interact and exchange data between smart things and web services and apps, which is called “WoT,” or “Web of Things.” To interact and exchange the data, researchers need a search engine on WoT. However, locating indoor things in the Web of Things (WoT) remains a challenge due to the lack of a unified indexing system. In this research, we propose a novel approach to index indoor things in the WoT by leveraging machine learning and web technologies. Our approach includes a data preprocessing step, where we extract relevant features from the sensor data, followed by a clustering algorithm to group similar devices. We then use a semantic model to assign meaning to the clusters and develop a search engine to enable efficient searching of indoor things. Our proposed approach improves the accuracy and efficiency of locating indoor things in the WoT, paving the way for new applications in smart homes, healthcare, and industrial automation.

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Many different pathways can lead to the same result or developmental outcome. What are the developmental routes that result in the onset of walking? In this longitudinal study, we documented patterns of infant locomotion during everyday activities at home for 30 prewalking infants. Using a milestone‐based design, we focused on observations spanning the two months before the onset of walking (M age at walk onset = 11.98 months, SD = 1.27). We examined how much time infants spent in motion and when they moved, whether they were more likely to do so while prone (crawling) or upright with support (cruising or supported walking). Results showed immense variability in infants' practice regimes en route to walking—some infants spent relatively similar amounts of time crawling, cruising, and supported walking at each session, others preferred one method of travel over the alternatives, and some switched between different types of locomotion from session to session. In general, however, infants spent a larger share of their movement time in upright positions compared to prone. Finally, our densely sampled dataset revealed a clear feature of locomotor development: infants follow many distinct and variable paths to walk onset, regardless of the age at which it is attained. [ABSTRACT FROM AUTHOR]

Confined space searches such as pipeline inspections are widely demanded in various scenarios, where lightweight soft robots with inherent compliance to adapt to unstructured environments exhibit good potential. We proposed a tubular soft robot with a simple structure of a spring-rolled dielectric elastomer (SRDE) and compliant passive bristles. Due to the compliance of the bristles, the proposed robots can work in pipelines with inner diameters both larger and smaller than the one of the bristles. Firstly, the nonlinear dynamic behaviors of the SRDE were investigated experimentally. Then, we fabricated the proposed robot with a bristle diameter of 19 mm and then studied its performance in pipelines on the ground with inner diameters of 18 mm and 20 mm. When the pipeline's inner diameter was less than the outer diameter of the bristles, the bristles remained in the state of bending and the robot locomotion is mainly due to anisotropic friction (1.88 and 0.88 body lengths per second horizontally and vertically, respectively, in inner diameter of 18 mm and 0.06 body length per second in that of 16 mm). In the case of the pipeline with the larger inner diameter, the bristles were not fully constrained, and a small bending moment applied on the lower bristle legs contributed to the robot's locomotion, leading to a high velocity (2.78 body lengths per second in 20 mm diameter acrylic pipe). In addition, the robot can work in varying geometries, such as curving pipes (curve radius ranges from 0.11 m to 0.31 m) at around two body lengths per second horizontally and on the ground at 3.52 body lengths per second, showing promise for pipeline or narrow space inspections. [ABSTRACT FROM AUTHOR]

Due to the availability of a vast amount of unstructured data in various forms (e.g., the web, social networks, etc.), the clustering of text documents has become increasingly important. Traditional clustering algorithms have not been able to solve this problem because the semantic relationships between words could not accurately represent the meaning of the documents. Thus, semantic document clustering has been extensively utilized to enhance the quality of text clustering. This method is called unsupervised learning and it involves grouping documents based on their meaning, not on common keywords. This paper introduces a new method that groups documents from online laboratory repositories based on the semantic similarity approach. In this work, the dataset is collected first by crawling the short real-time descriptions of the online laboratories' repositories from the Web. A vector space is created using frequency-inverse document frequency (TF-IDF) and clustering is done using the K-Means and Hierarchical Agglomerative Clustering (HAC) algorithms with different linkages. Three scenarios are considered: without preprocessing (WoPP); preprocessing with steaming (PPwS); and preprocessing without steaming (PPWoS). Several metrics have been used for evaluating experiments: Silhouette average, purity, V-measure, F1-measure, accuracy score, homogeneity score, completeness and NMI score (consisting of five datasets: online labs, 20 NewsGroups, Txt_sentoken, NLTK_Brown and NLTK_Reuters). Finally, by creating an interactive webpage, the results of the proposed work are contrasted and visualized. [ABSTRACT FROM AUTHOR]

Free and open-source media centers are experiencing a boom in popularity for the convenience they offer users seeking to remotely consume digital content. Kodi is today's most popular home media center, with millions of users worldwide. Kodi's popularity derives from its ability to centralize the sheer amount of media content available on the Web, both free and copyrighted. Researchers have been hinting at potential security concerns around Kodi, due to add-ons injecting unwanted content as well as user settings linked with security holes. Motivated by these observations, this article conducts the first comprehensive analysis of the Kodi ecosystem: 15,000 Kodi users from 104 countries, 11,000 unique add-ons, and data collected over 9 months. Our work makes three important contributions. Our first contribution is that we build "crawling" software (de-Kodi) which can automatically install a Kodi add-on, explore its menu, and locate (video) content. This is challenging for two main reasons. First, Kodi largely relies on visual information and user input which intrinsically complicates automation. Second, the potential sheer size of this ecosystem (i.e., the number of available add-ons) requires a highly scalable crawling solution. Our second contribution is that we develop a solution to discover Kodi add-ons. Our solution combinesWeb crawling of popular websites where Kodi addons are published (LazyKodi and GitHub) and SafeKodi, a Kodi add-on we have developed which leverages the help of Kodi users to learn which add-ons are used in the wild and, in return, offers information about how safe these add-ons are, e.g., do they track user activity or contact sketchy URLs/IP addresses. Our third contribution is a classifier to passively detect Kodi traffic and add-on usage in the wild. Our analysis of the Kodi ecosystem reveals the following findings. We find that most installed add-ons are unofficial but safe to use. Still, 78% of the users have installed at least one unsafe add-on, and even worse, such add-ons are among the most popular. In response to the information offered by SafeKodi, one-third of the users reacted by disabling some of their add-ons. However, the majority of users ignored our warnings for several months attracted by the content such unsafe add-ons have to offer. Last but not least, we show that Kodi's auto-update, a feature active for 97.6% of SafeKodi users, makes Kodi users easily identifiable by their ISPs. While passively identifying which Kodi add-on is in use is, as expected, much harder, we also find that many unofficial add-ons do not use HTTPS yet, making their passive detection straightforward. [ABSTRACT FROM AUTHOR]

Enhanced richness and size of the data on the web paves the path for increased online services, supporting the sophisticated usage of heterogeneous complex tasks by users. As deep web online services are increasing, several efficient techniques to explore the location of deep web interfaces are also leveraged thereby providing better support in user information exploration on the web concerning queries and the user clicks on web-related search data. This searching process provides an improved experience to users using crawling websites and searches rank with titles and links. The main problem behind search data on the web is organizing user search information based on their preferences into dynamic query formations and data coming from associated web links in an automated way on web services on high-quality sparse data. Vast amounts of data with the Dynamic nature of web interfaces explore or achieve high efficiency and coverage of all deep web interfaces that turn out to be a challenging issue. To handle such issues, we propose and introduce a novel and efficient two-phase deep learning data crawler framework (NTPDCF). The first phase initiates in gathering accurate and highly relevant links using the search engine, and the second phase explores fast and in-site relevant website links using adaptive site ranking. The approach focuses on drilling relevant site data and top-k ranking with different relations based on dynamic features with user preferences in single- and multi-query formation with adaptive weight features. The method promises to visualize improved results with efficient data exploration over the traditional approach concerning real-time defense and e-commerce related to web-based services. [ABSTRACT FROM AUTHOR]

In recent years, transformer-based models have played a significant role in advancing language modeling for natural language processing. However, they require substantial amounts of data and there is a shortage of high-quality non-English corpora. Some recent initiatives have introduced multilingual datasets obtained through web crawling. However, there are notable limitations in the results for some languages, including Spanish. These datasets are either smaller compared to other languages or suffer from lower quality due to insufficient cleaning and deduplication. In this paper, we present esCorpius-m, a multilingual corpus extracted from around 1 petabyte of Common Crawl data. It is the most extensive corpus for some languages with such a level of high-quality content extraction, cleanliness, and deduplication. Our data curation process involves an efficient cleaning pipeline and various deduplication methods that maintain the integrity of document and paragraph boundaries. We also ensure compliance with EU regulations by retaining both the source web page URL and the WARC shared origin URL.

Wearable robots, such as exoskeletons, can potentially reduce the load at targeted muscles of the human body during fatiguing tasks. It is common, however, that use of a wearable robot causes increased load at untargeted muscles, leading to minimal net improvement. Here, musculoskeletal impacts of a wearable robotic device are examined to establish a foundation for the design and control of a robot based on a musculoskeletal model and experimental data. The model predicts the effect of the device, called Supernumerary Robotic Limbs (SuperLimbs), on the wearer’s whole body muscular effort. SuperLimbs brace the upper body of a human while they work near floor-level. Its effectiveness varies depending on how it is attached to the human (harness design), how it is coupled to the floor (wrist and hand design), and how it is controlled (actuation policy). These behaviors and their interplay are analyzed and used to inform the design and control of the robot. First, body movements are measured with a motion capture system while a human subject crawls on the floor. Their muscular activity and the floor reaction forces are then estimated based on a musculoskeletal model’s inverse dynamics optimization. The effect of the SuperLimbs is assessed by replacing both human arms in the model with robotic limbs. The analysis reveals that the human muscle load is minimized with a particular combination of SuperLimbs joint torques that can be used as feedforward commands to the SuperLimbs controller. Desirable harness and wrist properties are obtained by varying the parameters of the Human+Robot model, and tracking the effect of these changes on the distribution of muscles forces in the human’s back. It is found that a harness chest plate of the SuperLimbs attached at its anterior edge minimizes muscle activity in the back’s vulnerable lower lumbar region. The model is verified with ground reaction force experiments, and its validity is examined for every simulation experiment.

Aims: This study sought to determine whether not sleeping soundly in early infancy predicted poor development. This complemented earlier studies of children aged 12 months or older. Methods: Sleep/wake patterns of 35 Japanese infants (23 males) with a gestational age of 37< weeks were recorded at home using actigraphy. Families were registered with a laboratory-based database for infant research. Follow-up recordings were conducted at 3, 4, 6, 12, and 24 months. Crawling was rated by mothers at 12 months and used to create regular and irregular crawling groups. Temperament was scaled using the Japanese Infant Behaviour Questionnaire-Revised Questionnaire and the Japanese Early Childhood Behaviour Questionnaire. Results: At 4 months, infants with regular crawling style had shorter night-time sleep than infants with irregular crawling style. However, at 12 months, the former had longer motionless sleep at night compared to the latter. Before 6 months, infants with regular crawling style showed lower sleep efficiency, especially during the day, compared to those with irregular crawling style. In addition, the amount of night-time active sleep at 3 and 4 months were positively correlated with day-time activity, but not at 6 months. Conclusion: Short fragmental sleep in early infancy did not always predict poor development. [ABSTRACT FROM AUTHOR]

Due to the expansion of data, search engines encounter different obstacles for retrieving better relevant content to user's search queries. Consequently, various retrieval and ranking algorithms have been applied to satisfy the result's relevancy according to user's needs. Unfortunately, indexing and ranking processes face several challenges to achieve highly accurate results, since most of the existing indexes and ranking algorithms crawl documents and web pages based on limited number of criteria that satisfy user needs. So, this research studies and observes how search engines work and which factors contribute to higher rankings results. The research also proposes a Multi Criteria Indexing and Ranking Model (MCIR) based on weighted documents and pages which depend on one or more ranking factors, aiming at building a model that achieves high performance, better relevant pages, and the ability to index and rank both online/offline pages and documents. The MCIR model was applied on three different experiments to compare documents and pages results in terms of ranking scores, based on one or more criteria of user's preferences. The results of applying MCIR model proved that final pages ranking results depending on multi-criteria are better than using only one criterion, and some criteria have great effect on ranking results than other criteria. It was also observed that the MCIR model achieved high performance on indexing and ranking dataset up to 100 gigabytes. [ABSTRACT FROM AUTHOR]

Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations. Our study of three popular messengers (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram, we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings. Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool "JTR," we can iterate through the entire worldwide mobile phone number space in <150 s on a consumer-grade GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest. Regarding mitigations, we most notably propose two novel rate-limiting schemes: our incremental contact discovery for services without server-side contact storage strictly improves over Signal's current approach while being compatible with private set intersection, whereas our differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information. [ABSTRACT FROM AUTHOR]

Climbing robots that use bionic claws can climb vertical or even inverted rough surfaces. However, wall-climbing robots with unidirectional spiny feet cannot crawl horizontally or downward on vertical rough surfaces. In this paper, a pair of gripping spiny feet is used to give a robot the capacity to crawl in any direction on a rough wall. On the basis of observations of the gecko's method for grasping onto a vertical rough surface, a multilevel interlocking structure is proposed. A spherical contact model of the claw tip on a vertical rough surface is established, and the influences of the contact angle, friction coefficient, and other factors on the grappling claw action are analyzed. Moreover, the optimal structure of the grappling claws is proposed. The force during the grasping and detachment of the mechanism and the influence of the number of feet on grasping performance are determined through experiments. Furthermore, a six-legged wall-climbing robot is designed and evaluated in terms of crawling on a vertical rough surface at various angles. The feasibility of using an opposed gripping mechanism to allow a robot to crawl in any direction on a vertical rough surface is also verified. [ABSTRACT FROM AUTHOR]

With the freedom offered by the Deep Web, people have the opportunity to express themselves freely and discretely, and sadly, this is one of the reasons why people carry out illicit activities there. In this work, a novel dataset for Dark Web active domains known as crawler-DB is presented. To build the crawler-DB, the Onion Routing Network (Tor) was sampled, and then a web crawler capable of crawling into links was built. The link addresses that are gathered by the crawler are then classified automatically into five classes. The algorithm built in this study demonstrated good performance as it achieved an accuracy of 85%. A popular text representation method was used with the proposed crawler-DB crossed by two different supervised classifiers to facilitate the categorization of the Tor concealed services. The results of the experiments conducted in this study show that using the Term Frequency-Inverse Document Frequency (TF-IDF) word representation with a linear support vector classifier achieves 91% of 5 folds cross-validation accuracy when classifying a subset of illegal activities from crawler-DB, while the accuracy of Naïve Bayes was 80.6%. The good performance of the linear SVC might support potential tools to help the authorities in the detection of these activities. Moreover, outcomes are expected to be significant in both practical and theoretical aspects, and they may pave the way for further research. [ABSTRACT FROM AUTHOR]

To protect user privacy, legislators have regulated the use of tracking technologies, mandating the acquisition of users' consent before collecting data. As a result, websites started showing more and more consent management modules--i.e., Consent Banners--the visitors have to interact with to access the website content. Since these banners change the content the browser loads, they challenge web measurement collection, primarily to monitor the extent of tracking technologies, but also to measure web performance. If not correctly handled, Consent Banners prevent crawlers from observing the actual content of the websites. In this paper, we present a comprehensive measurement campaign focusing on popular websites in Europe and the US, visiting both landing and internal pages from different countries around the world. We engineer Priv-Accept, a Web crawler able to accept the Consent Banners, as most users would do in practice. It lets us compare how webpages change before and after accepting such policies, if present. Our results show that all measurements performed ignoring the Consent Banners offer a biased and partial view of the Web. After accepting the privacy policies, web tracking is far more pervasive, and webpages are larger and slower to load. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hydraulic model is a set of closed-form, deterministic equations used to predict average crowd speeds and movement times of evacuees exiting a building. The standard SFPE hydraulic model assumes that crowd speed is only influenced by crowd congestion and type of terrain, when in fact, it is also affected heavily influenced by smoke, which not only causes evacuees to slow down due to poorer visibility but may also force them to switch a different movement mode: crawling. This paper gathers and refines empirical data from various published experiments involving individuals and groups moving through smoke by walking upright and crawling, to establish two new variants of the hydraulic model describing the movement of crowds through smoke by the two movement modes. [ABSTRACT FROM AUTHOR]

Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service's website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker's limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dil Araştırmaları is the property of Journal of Dil Arastirmalari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service's website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker's limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice. [ABSTRACT FROM AUTHOR]

In this paper, a multi-functional soft robot module that can be used to constitute a variety of soft robots is proposed. The body of the soft robot module made of rubber is in the shape of a long strip, with cylindrical chambers at both the top end and bottom end of the module for the function of actuators and sensors. The soft robot module is driven by supercoiled polymer artificial muscle (SCPAM) strings, which are made from conductive nylon sewing threads. Artificial muscle strings are embedded in the chambers of the module to control its deformation. In addition, SCPAM strings are also used for the robot module's sensing based on the linear relationship between the string's length and their resistance. The bending deformation of the robot is measured by the continuous change of the sensor's resistance during the deformation of the module. Prototypes of an inchworm-like crawling robot and a soft robotic gripper are made, whose crawling ability and grasping ability are tested, respectively. We envision that the proposed proprioceptive soft robot module could potentially be used in other robotic applications, such as continuum robotic arm or underwater robot. [ABSTRACT FROM AUTHOR]

The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a major pest of corn worldwide. FAW larval dispersal is an important life strategy that influences FAW population distribution in corn fields and subsequent plant damage. We studied FAW larval dispersal in the laboratory with sticky plates placed around the test plant and a unidirectional airflow source. Crawling and ballooning were the main dispersal means of FAW larvae both within and between corn plants. All larval instars (1st–6th) could disperse by crawling, with crawling being the only dispersal mechanism for 4th–6th instars. By crawling, FAW larvae could reach all aboveground parts of a corn plant as well as adjacent corn plants where leaves overlapped. Ballooning was used primarily by 1st-3rd instar larvae, and the proportion of these larvae that used ballooning decreased with age. Ballooning was largely governed by the larva’s interaction with airflow. Airflow influenced the direction and distance of larval ballooning. With an airflow speed of about 0.05 m/s, 1st instars could travel up to 196 cm from the test plant, indicating that long-distance FAW larval dispersal depends on ballooning. These results increase our understanding of FAW larval dispersal and provide scientific information for the development of FAW monitoring and control strategies.

Confined space searches such as pipeline inspections are widely demanded in various scenarios, where lightweight soft robots with inherent compliance to adapt to unstructured environments exhibit good potential. We proposed a tubular soft robot with a simple structure of a spring-rolled dielectric elastomer (SRDE) and compliant passive bristles. Due to the compliance of the bristles, the proposed robots can work in pipelines with inner diameters both larger and smaller than the one of the bristles. Firstly, the nonlinear dynamic behaviors of the SRDE were investigated experimentally. Then, we fabricated the proposed robot with a bristle diameter of 19 mm and then studied its performance in pipelines on the ground with inner diameters of 18 mm and 20 mm. When the pipeline’s inner diameter was less than the outer diameter of the bristles, the bristles remained in the state of bending and the robot locomotion is mainly due to anisotropic friction (1.88 and 0.88 body lengths per second horizontally and vertically, respectively, in inner diameter of 18 mm and 0.06 body length per second in that of 16 mm). In the case of the pipeline with the larger inner diameter, the bristles were not fully constrained, and a small bending moment applied on the lower bristle legs contributed to the robot’s locomotion, leading to a high velocity (2.78 body lengths per second in 20 mm diameter acrylic pipe). In addition, the robot can work in varying geometries, such as curving pipes (curve radius ranges from 0.11 m to 0.31 m) at around two body lengths per second horizontally and on the ground at 3.52 body lengths per second, showing promise for pipeline or narrow space inspections.