Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Aided Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments

—Navigating a large-scaled robot in unknown and cluttered height-constrained environments is challenging. Not only is a fast and reliable planning algorithm required to go around obstacles, the robot should also be able to change its intrinsic dimension by crouching in order to travel underneath height-constrained regions. There are few mobile robots that are capable of handling such a challenge, and bipedal robots provide a solution. However, as bipedal robots have nonlinear and hybrid dynamics, trajectory planning while ensuring dynamic feasibility and safety on these robots is challenging. This paper presents an end-to-end vision-aided autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore height-constrained environments. A vertically-actuated Spring-Loaded Inverted Pendulum (vSLIP) model is introduced to capture the robot’s coupled dynamics of planar walking and vertical walking height. This reduced-order model is utilized to optimize for long-term and short-term safe trajectory plans. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory. The entire framework is tested and experimentally validated using a bipedal robot Cassie. This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments. (Video 1 )
University of Birmingham Ensuring the Reliability of Your Model Checker:

Probabilistic model checking provides formal guarantees on quantitative properties such as reliability, performance or risk, so the accuracy of the numerical results that it returns is critical. However, recent results have shown that implementations of value iteration, a widely used iterative numerical method for computing reachability probabilities, can return results that are incorrect by several orders of magnitude. To remedy this, interval iteration, which instead converges simultaneously from both above and below, has been proposed. In this paper, we present interval iteration techniques for computing expected accumulated weights (or costs), a considerably broader class of properties. This relies on an eﬃcient, mainly graph-based method to determine lower and upper bounds for extremal expected accumulated weights. To oﬀset the additional eﬀort of dual convergence, we also propose topological interval iteration, which increases eﬃciency using a model decomposition into strongly connected components. Finally, we present a detailed experimental evaluation, which highlights inaccuracies in standard benchmarks, rather than just artiﬁcial examples, and illustrates the feasibility of our techniques.
Prevalence and Risk Factors of Low Back Pain among the Clinical Students of the College of Health Sciences in Bayero University Kano

Background: Low Back Pain (LBP) is a common disorder that is identified to be the major cause of disability, affecting performance at work and general well-being. The aim of this study was to determine the prevalence and risk factors of low back pain among clinical students of the College of Health Sciences at Bayero University Kano. Method and Materials: A total of 191 clinical students were recruited in this study, the subjects were drawn from the College of Health Sciences at Bayero University Kano. A self-administered questionnaire was administered after which the physical characteristics of the participants were measured and recorded. Data collected was summarized using descriptive statistics and analyzed using inferential statistics of chi-square to test the association between the risk factor variables and low back pain. Results: The study determined the 12months prevalence, 1month prevalence, 7days prevalence, and point prevalence to be 44.4%, 28.9%, 22.5% and 48.1% respectively among the clinical students and a significant association (p< 0.05) was found between Low back pain, age and course of study. Conclusion: Low back pain appears to be a common problem among college students. Therefore, the students should be aware of the risk factors, appropriate preventive measures and treatment of low back pain to reduce the risks of getting it.
TEAM BUILDING BASED ON A RELATIONAL ANALYSIS ON A PROFILING SYSTEM

In this article, we will show the application of machine learning techniques to develop a recommendation system for employee profiles in order to assign them to specific projects. The aim of this system is to help managers to have a clear idea about the profiles of their collaborators in order to build up a productive team. Our work presents a recommendation system based on a profiling system and relational profile which is based on the publications posted in a professional network. It is made up of two parts. The first part is dedicated to the profiling system based on the extraction of information from internal data and the extraction of interests, psychological profile, and relational profile from the publications exchanged in the professional platform, and the second part is dedicated to the recommendation of profiles corresponds to the requirements of each project.
Survival and cause-specic mortality in adult females of a northern migratory ungulate

Survival of migratory species, such as ungulates, can vary seasonally due to changing environmental conditions, e.g., weather or predation pressure. Female wild forest reindeer (Rangifer tarandus fennicus, ‘WFR’) migrate between calving and wintering grounds. Their annual cycle includes four main seasons (wintering, calving, rut and autumn migration) during which they are subject to different conditions, but nothing is known about their survival or mortality patterns. We tracked two separated WFR subpopulations in Kainuu and Suomenselkä in Finland with GPS-collars. Using histories from 271 WFR females followed during 2009–2020, we examined annual and seasonal patterns in survival and cause-specic mortality (predation, tra�c and unknown causes) with known-fate models. Annual, seasonal and population variation in survival were negligible (annual mean = 0.85). Predation was the primary cause of mortality (56% of all cases) and wolves (Canis lupus) killed the majority of predated individuals (59% of predation mortality). Importantly, predation rates were higher in Kainuu where the wolf density was 3.7 times higher. In line with this, seasonal variation was not clear in Suomenselkä whereas predation varied across the annual cycle in Kainuu being lower during autumn migration (1.4% / 2 months) and calving (1.7% / 4 months) but higher during rutting (2.8% / 2 months) and wintering (5.3% / 4 months) season. Our results warrant research on the mechanisms (climate, habitat, movement patterns etc.) affecting predation rates during these periods of higher mortality.
The role of antioxidant response and nonphotochemical quenching of chlorophyll �uorescence in long-term adaptation to Cu-induced stress in Chlamydomonas reinhardtii

The aim of present study was to analyse selected aspects of the mechanism of protection of the photosynthetic apparatus and antioxidant activity in response to excessive copper concentrations in wall-less strains of Chlamydomonas reinhardtii not adapted and adapted for growth in the presence of elevated copper level. The measured parameters were photosynthetic pigment content, prenyllipid antioxidant ( α -tocopherol, plastoquinone pool) content, peroxidase activity, and nonphotochemical quenching e�ciency. The results obtained suggest that the increased content of tocopherol and plastoquinone, as well as the increased e�ciency of nonphotochemical quenching of chlorophyll �uorescence, play a role in the acquisition of tolerance to copper. The role of light in the enhancement of copper toxicity and the role of POX in response to elevated copper have also been shown.
STUDY ON ELASTOPLASTIC DAMAGE MODEL OF UNSATURATED ROCK MASS AND ITS APPLICATION

Considering rock as porous media,the relation between rock porosity and volumetric strain is established. Then the rock permeability coefficient,damage,saturation′s evolution equations are defined;and the unsaturated elastoplastic damage coupling model is proposed based on the effective stress law of porous media. The proposed model is implemented with user subroutines into the nonlinear FE software ABAQUS;then the stability evaluation of rock mass for the diversion tunnel of Jinping Hydropower Station is conducted. The numerical results show that the proposed model is reasonable and it can explain the rock mass′s saturated-unsaturated-resaturated process.
Ensuring Privacy Policy Compliance of Wearables with IoT Regulations

In an era where wearables, particularly those in non-hospital settings, collect and transmit sensitive personal data, it is imperative to implement stringent privacy safeguards. The National Institute of Standards and Technology (NIST) Internal Report 8228 provides regulations for securing Internet of Things (IoT) devices, data, and the privacy of individuals. We have developed a novel framework for examining the privacy policies governing the data and information utilized by wearable devices to ensure that these IoT devices work in adherence to the NIST controls. Our approach entails constructing an ontology of the pertinent NIST regulations, extracting key regulation terms, establishing clear annotation guidelines, and reasoning over the developed ontology. Our primary contribution is developing a novel method to accurately retrieve the expectations, privacy risk mitigation areas, and the associated regulations using Natural Language Processing and Semantic Web concepts. Ultimately, vendors and users can use our publicly available ontology to semi-automate the privacy compliance process for wearables, ensuring that the data collected and transmitted through the devices are secure, thereby protecting both the devices and the individuals who use them.
Big Data Research

The social Internet of Things (SIoT) shares large amounts of data that are then processed by other Internet of Thing (IoT) devices, which results in the generation, collection, and treatment of databases to be analyzed afterwards with Big Data techniques. This paradigm has given rise to users’ concerns about their privacy, particularly with regard to whether users have to use a smart handling (self-establishment and self-management) in order to correctly install the SIoT, ensuring the privacy of the SIot-generated content and data. In this context, the present study aims to identify and explore the main perspectives that deﬁne user privacy in the SIoT; our ultimate goal is to accumulate new knowledge on the adoption and use of the concept of privacy “by default” in the scientiﬁc literature. To this end, we undertake a literature review of the main contributions on the topic of privacy in SIoT and Big Data processing. Based on the results, we formulate the following ﬁve areas of application of SIoT, including 29 key points relative to the concept of privacy “by default”: (i) SIoT data collection and privacy; (ii) SIoT security; (iii) threats for SIoT devices; (iv) SIoT devices mandatory functions; and (v) SIoT and Big Data processing and analytics. In addition, we outline six research propositions and discuss six challenges for the SIoT industry. The results are theorized for the future development of research on SIoT privacy by “default” and Big Data processing. © 2021 The Authors. Published by Elsevier
PREDICTION OF SENSOR DEVICES’ FAILURE IN UNMANNED AERIAL VEHICLES USING KALMAN FILTER & PARTICLE FILTER

Unmanned aerial vehicles like drones or certain type of helicopters are having rising importance over the past few decades due to utilizing them in various applications. Since there is no human pilot to respond to any aberrant event, reliability is an important concern in UAV operation. The numerous sensing systems and aerospace navigational instruments used in large aero planes is not applicable to be installed in small UAVs because of the size and budget limitations. As a result, various measures such as analysis redundancies should indeed be used to detect and boost the dependability of positioning instruments. Based on cognitive redundancies, this work provides a sensory fault diagnosis and diagnostic system for tiny unmanned drones. The defect is detected by comparing any significant change in the aircraft’s behavior to the mistake performance, which is approximated with the help of an eyewitness. With the Extended Kalman filter Identification, the observation is derived from input-output observational data. Utilizing feedback experimental observations, the Kalman methodology may recognize the systems and an observers with qualities comparable to a Kalman filter and also provide an extra care with incorporating particle filter mechanism to minimize the noise in the equipment to the least value possible. The suggested technique yields comparable findings to the Kalman filter and in addition to particle filter, and there’s no need to calculate systems matrix or sensors and processing noise covariance. The technology was solely tested with actual drones aircraft performance, and the findings were matched to those obtained using alternative methods.
the Arts The Structure of Musical Dislikes

The current study explored the structure of rationales for musical dislikes. In an online survey, participants ( N = 627) evaluated self-selected styles and artists in a slight and strong degree of dislike condition with respect to 41 reasons for musical dislikes distilled from a previous interview study. After constructing ninesubscalesofreasons,alatentpro ﬁ leanalysisidenti ﬁ edtwopro ﬁ lesofexplanatorystrategies fordisliked music.The highbrow pro ﬁ leincludedreasons suchasthe music being TooSimple, or NotAuthentic,having No Impact on the listener, and a perceived Social Incongruence, and was mainlyassociated with a dislike of German schlager, traditional music, and pop. The lowbrow pro ﬁ le included reasons such as the music being Too Niche and Too Complex and was associated with a dislike of jazz, classical music, heavy metal, and techno. A correlational network revealed that Displeasure can occur in relation to Social Incongruence, or in relation to Too Niche music. No Impact occurs in response to music regarded as Too Simple or Not Authentic. A strong dislike is consistently characterized by higher Displeasure, while Social Incongruence and Not Authentic were reasons to strongly dislike artists from mainstream styles. Hence, investigating fundamental musical value judgments, the current studyshowsthat musical dislikes are acom-plex, multidimensional component of musical taste. The results have implications for the psychology and sociology of music, widening our understanding of people ’ s attitudes toward music and its role in everyday life.
LSU Scholarly

Inclusive jet cross sections for events with a large rapidity gap with respect to the proton direction from the reaction ep → jet + X with quasi-real photons have been measured with the ZEUS detector. The cross sections refer to jets with transverse energies E jetT > 8 GeV. The data show the characteristics of a diﬀractive process mediated by pomeron exchange. Assuming that the events are due to the exchange of a pomeron with partonic structure, the quark and gluon content of the pomeron is probed at a scale ∼ ( E jetT ) 2 . A comparison of the measurements with model predictions based on QCD plus Regge phenomenology requires a contribution of partons with a hard momentum density in the pomeron. A combined analysis of the jet cross sections and recent ZEUS measurements of the diﬀractive structure function in deep inelastic scattering gives the ﬁrst experimental evidence for the gluon content of the pomeron in diﬀractive hard scattering processes. The data indicate that between 30% and 80% of the momentum of the pomeron carried by partons is due to hard gluons.
Modiﬁed control variates method based on second-order saddle-point approximation for practical reliability analysis

A novel method is presented for efﬁciently analyzing the reliability of engineering components and systems with highly nonlinear complex limit state functions. The proposed method begins by transforming the integral of the limit state function into an integral of a highly correlated limit state function using the control variates method. The second-order reliability method is then employed within the control variates framework to approximate the highly correlated limit state function as a quadratic polynomial. Subsequently, the probability of failure is obtained through the estimation of the saddle-point approximation and a small number of samples generated by Latin hypercube sampling. To demonstrate the effectiveness of the proposed method, four examples involving mathematical functions and mechanical problems are solved. The results are compared with those obtained using the second-order reliability method (SORM), control variates based on Monte Carlo simulation (CVMCS) with second-order saddle-point approximation (SOSPA), importance sampling (IS) and Monte Carlo simulation (MCS). The ﬁndings demonstrate that, while maintaining high-precision reliability results,
HYBRID ENSEMBLE MODEL FOR FAKE NEWS DETECTION

The increasing number of fake news spread to give a negative impact to many parties including society. Hence, to counter the high volume of fake news dissemination problems, numerous machine learning techniques have been used for automated fake news detection. Thus, this study implements the machine learning algorithms by developing a hybrid ensemble model for the classification of fake news. The primary objective of this work is to increase the performance of the machine learning model by developing the hybrid ensemble model to assist in the automatic detection of fake news in classifying the labelled news. This paper focuses on implementing the model with unorganized data in textual forms of Malay language news. A few performance metrics such as recall, accuracy, precision, and f-score will be used to measure the performance of the proposed model. The model has successfully increased the accuracy of fake news detection with 75% score.
Automatic Collation for Diversifying Corpora: Commonly Copied Texts as Distant Supervision for Handwritten Text Recognition

Abstract Handwritten text recognition (HTR) has enabled many researchers to gather textual evidence from the human record. One common training paradigm for HTR is to identify an individual manuscript or coherent collection and to transcribe enough data to achieve acceptable performance on that collection. To build generalized models for Arabic-script manuscripts, perhaps one of the largest textual traditions in the pre-modern world, we need an approach that can improve its accuracy on unseen manuscripts and hands without linear growth in the amount of manually annotated data. We propose Automatic Collation for Diversifying Corpora (ACDC), taking advantage of the existence of multiple manuscripts of popular texts. Starting from an initial HTR model, ACDC automatically detects matching passages of popular texts in noisy HTR output and selects high-quality lines for retraining HTR without any manually annotated data. We demonstrate the effectiveness of this approach to distant supervision by annotating a test set drawn from a diverse collection of 59 Arabic-script manuscripts and a training set of 81 manuscripts of popular texts embedded within a larger corpus. After a few rounds of ACDC retraining, character accuracy rates on the test set increased by 19.6% absolute percentage, while a supervised model trained on manually annotated data from the same collection increased accuracy by 15.9%. We analyze the variation in ACDC’s performance across books and languages and discuss further applications to collating manuscript families.
Effect Of Leadership Capability On The Performance Of Level Four Public Hospitals In Kenya

Organizations and more specifically public hospitals are rethinking on strategic management capabilities in order to improve performance. For the government of Kenya to achieve the social pillar of Vision 2030, public health facilities should embrace strategic management capabilities. The study sought to evaluate the effect of leadership capabilities on performance of level four public hospitals in Kenya. The study was anchored on the dynamic capability and transformational leadership theories. The target population was 257 level four public hospitals in Kenya. A sample of 157 level four public hospitals in Kenya was determined using Israel formula. Systematic sampling technique was used to select the respondents. Primary data was collected using questionnaires. Data was analyzed using descriptive and inferential statistics. The coefficient for leadership capability was significant (.185, p<.05) an implication that leadership capability significantly affects the performance of level four public hospitals in Kenya. The study concluded that leadership capability significantly affects the performance of level four public hospitals in Kenya. The study recommends hospitals to engage capable leaders who can be able to formulate objectives that reflect vision and mission of the organization. The leaders should also be able to always engage key stakeholders whenever they make strategic decisions.
GAMIFICATION TO IMPROVE SCRUM ADOPTION: A CASE STUDY AT POULTRY STARTUP IN INDONESIA

Agile methodology offers software development flexibility to respond to changes quickly. Agile methods are better than traditional ones, especially in terms of reducing costs much more efficiently. One of the Agile approaches is Scrum which involves a small team to make a Sprint in the process. Agile and Scrum are not without gaps; there are challenges in adopting them. Among the challenges are poor communication, lack of team motivation, and lack of support from the senior development team. All of Scrum’s adaptation challenges stem from the human factor running it. Applying the concept of gamification in Scrum can address these challenges. Gamification can enrich non-gaming processes by making them more enjoyable and increasing the productivity of the development team. This study focuses on investigating the Scrum team at the poultry startup that applies the Scrum approach to adapting to changes quickly. Jira application is used to manage scrum within the company. Jira Software has “Trophies for Jira Dashboard” application for gamification implementation on the development team. The application of gamification will make the development team feel happy, like they are running a game in a work environment with healthy and fun competition. By implementing gamification, development team experiences personal growth, more motivation at work, increased efficiency, and early issue resolution, leading to improved productivity and enhancing agile success rate. As a result of this research, gamification process impacts 69% of Agile success, indicating that the company will have benefit by applying gamification without significantly increasing its costs.