Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Prototype Filter With Constrained Frequency Spreading for OQAM/FBMC Systems

In this letter, we propose a short filter (i.e., the overlapping factor is one) with constrained frequency spreading (CFS) for offset quadrature amplitude modulation-based filter bank multicarrier (OQAM/FBMC) systems. In detail, a system of equations in the filter’s frequency coefficients is formulated and solved in the sense of least squares, then the filter is obtained through an inverse discrete Fourier transform. Simulation results reveal that the proposed CFS filter is able to achieve comparable performance to existing short filters in the presence of different channel impairments with less frequency spreading, which leads to lower implementation complexity of OQAM/FBMC using frequency spreading structure, thus it is a valuable choice when considering both the CFS and a near perfect reconstruction of the OQAM/FBMC signal.
effect of mindfulness meditation on blood pressure on elderly with hypertension at the elderly Posyandu

Hypertension is a disease that many people suffer from that can cause other, more dangerous health problems such as rupture of blood vessels in the brain and heart, resulting in death. According to WHO data, the coverage of hypertension reaches 1.13 billion. The management of hypertension is still dominated by pharmacological therapy. As for reducing this complexity, experts apply complementary therapies. Complementary therapy is a complementary therapy of conventional therapy for healing. One of the complementary nursing therapies that is easy to do to lower blood pressure is through meditation. Of the several types of meditation, there is one type of meditation that is closely related to human psychology, namely Mindfulness Meditation. So this study aims to prove the effect of Mindfulness Meditation on Blood Pressure in Elderly With Hypertension (In the Elderly Posyandu, Dukuh Village, Gondang District, Tulungagung Regency). The research design used was One Group Pre Test-Post Test with a cross sectional approach where the research subjects were only observed once and measurements were made on the subject variables at the time of examination.
Analysis Of Financial Distress Prediction In Cooperative Financial Institutions

Abstrack: This study has two objectives, namely, first aims to develop a discriminant model from the components of the cooperative assessment aspect in order to predict the occurrence of financial financial distress in legal entities in West Jakarta. Second, aims to find out the most dominant factors in predicting financial financial distress in cooperatives.This study uses as many samples as savings and loan cooperatives that provide financial statements to researchers for two years, from 2015 to 2016, both those which are still active and those who are not active. Data collection method is done by purposive sampling. While the analysis used is logistic regression. The results of data analysis obtained that from 17 financial ratios to assess the level of health of cooperatives, only 2 ratios that predominantly affect the level of health of cooperatives, namely the ratio of fixed assets to total assets (X9) and the ratio of the volume of loans to funds received (X12).
Análisis de riesgos de ciberseguridad en arquitectura de vehículos automatizados

espanolLos vehiculos conectados y automatizados han sido recientemente concebidos como entes ciberfisicos, estrechamente relacionados con la red del Internet de las cosas (IoT). Este hecho supone un incremento en la superficie de ataque del vehiculo, que junto a la creciente tendencia hacia vehiculos automatizados, hacen que estos riesgos de ciberseguridad puedan tener conse- cuencias catastroficas en seguridad vial. En el presente trabajo se expone un analisis de riesgos de ciberseguridad en el marco de una arquitectura de vehiculos automatizados. Este analisis previo se realiza en el contexto de dos escenarios de estudio en maniobras cooperativas. Inicialmente se presenta un estado del arte de la ciberseguridad en automocion, asi como tambien su repercusion en entornos automatizados, haciendo especial enfasis en las comunicaciones entre vehiculos y con infraestructura. Ademas, se analizan dos maniobras cooperativas, y se ilustran una serie de posibles ataques en la plataforma. EnglishConnected and automated vehicles have been recently categorized as cybephysical entities, tightly related with a part of the Internet of Things (IoT) network. As a consequence the attack surface of a modern vehicle is increased, which added to the automation trend, makes cibersecurity risks a higher threat in the road. In this work a framework for automated vehicles is described, with the objective of validating security strategies when performing cooperative maneuvers. A review of the state of the art in automotive cibersecurity is presented, along its e ect in automated vehicles, making special emphasis in inter-vehicle (V2V) communication and to the infrastructure (V2I). Moreover, two maneuvers are studied and a series of safety factors, taking into consideration the possible intervention of external malicious agents.
“Those in the know”: primary school literacy as a corporate field of knowledge?

ABSTRACT Using our re-readings of Margaret Meek Spencer’s work and career, we connect her scholarship to our mapping of contemporary changes in knowledge and our investigations of so-called “knowers”: those mobilising knowledge to underpin and control professionals’ enactment of policy in primary school literacy. Working from the theoretical resources of Basil Bernstein, we develop an analysis of these knowers’ production of knowledge, navigating from his conceptualisations of “official” and “pedagogical” fields into what we define as a “corporate” field of knowledge. Presenting data from our research into two groups - (i) commercial consultants, retailing policy solutions to professionals; and (ii) a Chief Executive officer and leaders within multi-academy trusts - we argue that working in a corporate field, these knowers have influence on professionals’ knowledge and positioning. We draw out speculative implications for readers of this journal concerning potential contributions to resistance as well as developing a reflexive approach to our own work.
Quantifying the impact of building envelope condition on energy use

ABSTRACT The gap between the architectural information and the as-is building condition has been known as one of the pivotal factors influencing deviations between actual and predicted building energy consumption. Despite such significance, quantifying the impact of deviated building information on energy use has not been fully investigated. This paper explores building information modelling (BIM)-driven experimental simulation to quantify the impact of building envelope condition on energy use, which can infer the impact of reflecting the as-is building conditions in as-designed BIMs on the reliability of energy analysis. First, BIM-driven energy simulations are conducted with varied thermo-physical properties of building envelope elements in gbXML-based BIMs under different climate conditions. Building upon the impacting factor for energy analysis (IFEA), the simulation results are then used to infer the impact of the deviated building condition on energy consumption. Through case studies, it is observed that the annual energy consumption of a residential building can deviate by 18–20%, whereas thermal resistances of exterior walls can deviate by 1 m2K/W. This paper validates quantitatively the potential benefits of reflecting the as-is building condition in BIM-based energy performance analysis. This provides practitioners with insights into how to improve the reliability of energy analysis of existing buildings.
On the use minor and non-destructive methods for the safety evaluation of an historic RC bridge: the Bôco Bridge

The authors would like to express their gratitude to Tezin Nyandak and Goncalo Escusa for their help during the experimental campaign. The work was also financed by FEDER funds through the Competitiveness Factors Operational Programme - COMPETE and by national funds through FCT – Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633.
Problems, Coping Strategies, and Online Teaching Readiness of English Teachers Amid COVID-19 Pandemic

The unexpected shift of English language teaching from face-to-face classroom interaction to online learning activities using digital platforms has resulted in several challenges for English teachers and students. Hence, this qualitative study explored the challenges encountered, coping mechanisms, and online teaching readiness of English teachers in selected private schools in Iligan City for the school year 2020-2021. Interviews were conducted with the 30 English teachers from private higher education institutions in Iligan City to elicit free-formatted views. Based on the interview, the common problems encountered by the English teachers were time management and workload, students’ b ehavior, stress and mental health issues, and internet connection. The coping strategies utilized by the English teachers included passive, active, and avoidant coping. Moreover, English teachers believed that they have adequate technical skills, although they have limited experience with online teaching and learning. The findings suggest that English teachers who are teaching online classes during the COVID-19 pandemic were facing several challenges in their teaching. On the other hand, it is a challenge for institutions to engage students in the teaching-learning process. Also, it is a challenge for teachers to move from offline mode to online mode, change their teaching methodologies, and manage their time. The findings underscore that teacher quality and quality teaching go hand in hand. of English teachers in selected private schools in Iligan City for the school year 2020-2021. Descriptive studies are designed to describe something present through fact-finding and interpretation of data gathered. communication and discussion. Research has identified that social and communication competencies are critical competencies and influential predictors for learning outcomes and learners’ satisfaction in online learning environments .
Media Legitimacy Detection: A Data Science Approach to Locate Falsehoods and Bias using Supervised Machine Learning and Natural-Language Processing

Media sources, primarily of the political variation, have a hastening grip on narratives that can easily be constructed using biased views and false information. Unfortunately, many people in modern society are unable to differentiate these false narratives from real events. Utilizing natural language processing, sentiment analysis, and various other computer science techniques, models can be generated to help users immediately detect bias and falsehoods in political media. The models created in this experiment were able to detect up to 70% accuracy on political bias and 73% accuracy on falsehoods by utilizing datasets from a variety of collections of both political media and other mediums of information. Overall, the models were successful as the standard for most natural language processing models achieved only about 75% accuracy.
A Review on the Role of Entomopathoenic Nematodes (EPNs) in Controlling Agricultural Insect Pest

Billions of dollars are spent annually throughout the world to control and prevent Agricultural insect pest. Many bacteria, fungi, and nematodes occurring naturally in soils are known to suppress Agricultural insect pest activity. Entomopathogenic nematodes (EPNs) and their associated bacterial symbionts are highly specific in their host range and compatible with many pesticides. EPNs, also called beneficial nematodes, are commercially used to control insect pests. These nematodes offer an environmentally safe alternative to chemical insecticides, and a wide range of EPNs are effective against various Agricultural insect pests. Are soil parasites that infect different types of arthropods e.g. termites, larva of butterflies, moth, beetles and grasshopper thus affecting them in various ways. They have been utilized in classical, conservation, and augmentative biological control programs. The vast majority of applied research has focused on their potential as inundatively applied augmentative biological control agents. This is through reducing their fertility or causing sterility, delaying development and shortening longevity of the arthropods. In this review, the role of EPNs in controlling Agricultural insect pest, the current merits and limitations is summarized.
Security Analysis of Smart connected Cameras: Case Study on the Eufy Doorbell

—In this project we aim to determine if and when manufacturers patch their IoT devices when vulnerabilities for similar products are discovered. To approach this on a small scale, we will attempt to examine and potentially expose vulnerabilities in either the Device, Mobile Application, Cloud Endpoints, or the Network Communication of the smart-home security system which incorporates smart doorbell cameras from Eufy. Our threat model will look at three types of attackers, an Off-Path Attacker who can exploit vulnerabilities in the mobile application or the cloud endpoints, an On-Path attacker who is already on the user’s local network, or an attacker who is geographically close to the victim but not on the actual network [1]. To streamline our approach, we will ﬁrst examine inter-company vulnerabilities which exist, from vulnerabilities found in other similar/competing products such as the Ring Doorbell camera, or similar smart home devices such as the Nest indoor security cameras. We believe that this will provide a good framework to look at existing vulnerabilities, and if they have indeed been patched by these manufacturers. We will look at the Common Vulnerabilities and Exposures (CVE’s) list, which provides a list of publicly disclosed vulnerabilities and exposures. An example of the same is CVE-2019-9483 which allowed attackers to obtain video and audio data, or insert spoofed video that did not correspond to the actual person at the door [2].
A Cueing Strategy for Prompt Tuning in Relation Extraction

Traditional relation extraction models predic-001 t conﬁdence scores for each relation type 002 based on a condensed sentence representation. 003 In prompt tuning, prompt templates is used 004 to tune pre-trained language models (PLMs), 005 which outputs relation types as verbalized type 006 tokens. This strategy shows great potential 007 to support relation extraction because it is ef-008 fective to take full use of rich knowledge in 009 PLMs. However, current prompt tuning mod-010 els are directly implemented on a raw input. 011 It is weak to encode contextual features and 012 semantic dependencies of a relation instance. 013 In this paper, we designed a cueing strategy 014 which implants task speciﬁc cues into the in-015 put. It controls the attention of prompt tun-016 ing, which enable PLMs to learn task specif-017 ic contextual features and semantic dependen-018 cies of a relation instance. We evaluated our 019 method on two public datasets. Experiments 020 show great improvement. It exceeds state-of-021 the-art performance by more than 4.8% and 022 1.4% in terms of F1-score on the SemEval cor-023 pus and the ReTACRED corpus 1 . 024
Machine Learning-Based Depression Classification Model

: Depression is a serious mental condition that affects people of all ages and genders. Depression is caused by work culture, demanding living, emotional imbalance, family troubles, and social life. As a result, according to the World Health Organization's predictions, depression will become the second greatest cause of sickness (WHO). Despite the availability of well-trained physicians, medical and psychological treatments for depression, individuals and families are reluctant to speak out or contact doctors about the disease for a variety of social reasons. The diagnosis of depression disorder entails multiple interviews with patients and their families, clinical analysis, and questionnaires, all of which take time and require well-trained physicians. The detection of depression is automated using a machine learning approach. Extraction of acoustic and facial features becomes more efficient and accurate with the use of improved machine learning techniques such as the MLP algorithm and the DeepFace library.
Comparative Analysis of Monitoring and Evaluation Systems for Zambia’s Health and Agriculture Sectors

: Monitoring and Evaluation (M&E) has increasingly become the buzz word in the implementation of poverty reduction strategies. Because of the anticipated benefits which are associated to it, M&E has fundamentally become a global phenomenon whereby national and international stakeholders in the development arena have continuously sought for enhanced public governance based on results. The aim of the paper was to investigative the sector monitoring and evaluation systems in the context of poverty reduction strategies focusing on a comparative case study of Zambia‘s Health and Agriculture sectors. A diagnostic assessment and analysis was employed to undertake the investigative study using existing literature. Consequently, results have shown that out of the six assessment criteria, the agriculture sector M&E performs better than the health M&E on four components—policy, organization, capacity and the use of M&E information. The only criterion where the health sector M&E is more developed than that of agriculture is ‗methodology‘ while the two sectors scored same for the ‗participation of actors outside of government‘ criterion. Nevertheless, there is need for accelerated development and strengthening of sector M&E systems for health and agriculture. Although the agriculture sector compares well against the health sector M&E, there are more gaps that require attention if both sectors were to enjoy the benefits that go with a successfully implemented mechanism. For both sectors, there is need for instance, to step up efforts of ensuring that the roles of the Zambia Statistics Agency, parliament, Civil Society and the Ministry of Finance and National Planning are well defined and enforced. The weaknesses that exist regarding the rationalization and coordination of donor M&E and sector M&E undermine capacity to have strong supply and demand sides.
Predicting Length of Stay After Thoracolumbar Trauma: A Single-Center, Retrospective Analysis

Background Length of stay (LOS) is a meaningful outcome measure for more efficient and effective quality of care. However, algorithms to predict LOS have yet to be created for patients who undergo surgical management for traumatic spinal fractures. Objectives The objectives of this study were to (1) identify preoperative, perioperative, and postoperative factors associated with increased LOS and (2) create predictive formulas to estimate LOS in thoracolumbar trauma patients who undergo surgical correction. Methods This is a retrospective case series of 196 patients operated for thoracolumbar spine trauma from January 2012 to December 2017 at a level 1 trauma and academic institution. Bivariate analysis between LOS and various preoperative, perioperative, and postoperative factors was conducted to identify significant associations. Multivariate analysis was conducted to create models capable of predicting LOS. Results LOS was significantly associated with various preoperative (eg, Charlson Comorbidity Index, Glasgow Coma Scale [GCS], injury severity score), operative (eg, length of surgery, number of instrumented segments, surgical technique), and postoperative variables (eg, complications, discharge location). Multivariate analysis of preoperative variables identified 5 significant independent predictors that could predict LOS with strong correlation with observed LOS (ρ = 0.63). With all variables considered, multivariate analysis identified 8 variables (GCS, American Society of Anesthesiologists score, neurological status, polytrauma, packed red blood cell transfusion, number of unique postoperative complications, skin complications, and discharge facility) that could predict LOS with strong correlation (ρ = 0.80). Conclusions Various preoperative, perioperative, and postoperative factors are significantly associated with LOS in traumatic thoracolumbar spine patients. We developed models with good predictive capacity for LOS. If validated, these models should help in risk stratifying patients for increased LOS and consequently improve perioperative patient counseling. Clinical Relevance This article contributes to identifying and predicting patients who are high risk for extended LOS after traumatic thoracolumbar injuries. Level of Evidence 4.
Intellectual Interactive System (a case study of NMIT)

Many individuals nowadays use smart phones with a plethora of new applications, demonstrating that technology is rapidly evolving. A chat bot contains information in its database that allows it to recognize phrases and make decisions on its own in response to a question. The college inquiry chat bot will be created using an algorithm that analyses questions and comprehends the messages of users. RASA is used to create this conversation bot. Rasa is a two-part open-source platform for creating AI bots that consists of Rasa NLU and Rasa core. Rasa core is the component that manages the framework's conversation engine and aids in the creation of more complicated chatbots with customization. Rasa’s NLU provides developers with the technology and tools they need to capture and analyse user input, as well as determine intent and entities. To create a College Enquiry Chatbot that can answer students' questions in a matter of minutes. Rasa and Python 3.6 or above are software prerequisites. The goal is to create a chatbot that can answer questions from students, search for answers, and provide solutions. The enquiries will be handled by the Chabot, which will save time and effort for humans.
Enabling and Integrating Technology With Personalized Learning

Personalized learning refers to a variety of instructional methods, academic support strategies, and educational programs that are proposed to address specific interests and learning needs of the students. The key goal of integrating technology with personalized learning is to have students progress from high school to college or a career. But currently, most of the students leave high school without the knowledge and technical skills that they need for success in further education and workplace. In order to improve K-12 education (i.e., kindergarten [K] and the 1st through the 12th grade) fundamental and systemic changes in middle and high school education are required. Integration of technology can help the students to improve knowledge, advance skills, and to gain the competencies to work well in the society and workforce. This chapter focuses on applicability of technology, implementation, and feasibility issues that play a key role in personalized learning.
Research on Pollution Status and Resource Utilization of Livestock Manure

The rapid development of large ‐ scale and intensive aquaculture has led to a large increase in the amount of livestock and poultry manure, resulting in environmental problems of varying degrees in some areas of our country. Research on the resource utilization potential of livestock and poultry manure is essential to realize the resource utilization of livestock manure and realize the sustainable development of the livestock and poultry breeding industry. This article summarizes the amount of livestock and poultry manure produced, pollution status and different resource treatment methods, in order to provide theoretical guidance for the resource utilization and low ‐ cost recycling of livestock and poultry manure.