Miracle-dz/UniFAR-FTUs huggingface dataset:
The Phonology of the Definite Determiners of Tihami Arabic

Unlike in Standard Arabic and other Arabic dialects, the definite determiner in Yemeni Arabic dialects occurs in different forms and shows much more variety than its counterparts in other Arabic dialects. This paper is about the phonology of the definite determiners in three Yemeni Arabic dialects spoken within the governorate of Hajjah. The theme of this article was inspired by an atlas of the dialects of north Yemen created by Peter Behnstedt (2016), which briefly presents a few examples of the different definite determiners attested across some northern Yemeni dialects. This paper focuses on the definite determiner of a specific region of the north of Yemen and brings more data to investigate further the phonology of the definite determiner (DET) used in this region using an OT analysis. The dialects addressed in this paper are spoken in Hajjah governorate, a north-western city part of the Tihama coastal line of Yemen. Its population is around 2 million. This paper analyzes data from three dialects in this province. The dialects are collectively known as Tihami Arabic. In two of these dialects, the determiner is /m-/, and in the third, it is /b-/, but I will analyze one of the /m-/ dialects and the /b-/ dialect. This paper mainly investigates two of these dialects, which show some interesting phonological alternations of the definite determiner. The definite determiner of one of these dialects is /m-/, and it will be referred to as the OCP m-dialect. I use the term OCP with this dialect because the Obligatory Contour Principle (OCP, Will Leben 1973) plays a major role in the analysis of this dialect. The OCP m-dialect is spoken in the mountains of Hajjah, and data was collected from two participants who live in the Kashar area. The definite determiner of the other dialect is /b-/, so this dialect will be referred to as the b-dialect. The data for the b-dialect was collected from two speakers from WashHa, particularly the Bani Hani tribe. The 4 informants for these dialects are between the age of 30-40 years old. They are all males. They all have a bachelor’s degree at least. This paper is organized as follows. Section 2 introduces geminates and the approach I use to analyze initial geminates in Tihami dialects. Word-initial gemination is a common property between these dialects that results from the assimilation of the determiner into the following word-initial labial. Section 3 presents data from the OCP m-dialect and the b-dialect and analyzes the phonology of their determiners using optionality within OT. It also goes over interesting cases of DET in the b-dialect. Section 4 discusses the findings and concludes.
Teacher Educators’ Understanding and Experiences with Implementing the Initial Primary Teacher Education Policy in Malawi

The initial primary teacher education (IPTE) curriculum policy was revised in 2016 and implemented in 2017 in Malawi (Ministry of Education, 2021). This study investigates teacher educators’ (TE) understanding and experiences in two teacher training colleges. The study is informed by the cognitivist theory of Spillane et al. (2002). Following a qualitative research design, data were collected through face-to-face interviews with teacher educators and principals. Classroom lesson observations were used to triangulate interview data. Data analysis showed that teacher educators’ superficial understanding of the espoused goals, content, and pedagogy of the IPTE impedes effective teacher education. The study found that the implementation of the revised IPTE curriculum is constrained by ineffective re-education and orientation of lecturers to the revised curriculum; unavailability of qualified lecturers; the duration and mode of training; the lack of teaching and learning resources; and TEs that are not being supported by the Ministry of Education officials. The study also shows that superficial understanding and ineffective implementation of the IPTE policy by educational stakeholders, such as lecturers, also comprises the quality of primary teacher education in Malawi. Based on the findings, several recommendations are made to improve the implementation of the IPTE in the country. These recommendations have implications both in Malawi and beyond.
Construction Image Synthetization to Overcome a Small, Biased Real Training Dataset for DNN-Powered Visual Scene Understanding

Deep neural networks (DNNs) have become a driving factor of visual scene understanding. However, the shortage of construction training images has been a major barrier to fully leverage its maximum performance potential. To address this issue, we investigate the effectiveness of synthetic images on DNN training in a common real-world scenario where only a small, biased real training image dataset is available. To this end, we synthetize numerous construction training images and conduct a DNN training experiment in real construction settings. Results show that the combined dataset-trained model always outperforms the one trained with only a small, biased real dataset. This finding indicates that an image synthetization approach has promising potential to enhance a given real training dataset in terms of data quantity and diversity. Image synthetization with automated labeling will mitigate the training image shortage, contributing to the development of more accurate and scalable DNNs for construction scene understanding.
Double-population genetic algorithm for multi-objective Job Shop scheduling problem

To solve multi-objective Job Shop scheduling problem,a double-population genetic algorithm based on forward-backward scheduling approach and Giffler-Thompson algorithm was proposed.The method reduced the solution space by means of active scheduling.Forward-backward genetic scheduling algorithm was proposed to optimize different objective functions in different populations and multi-objective problems were decomposed into various single-objective problems.In each generation of the evolving process,individual migration operator was applied to accelerate the parallel searching.An elitist arena's principle was presented to improve the efficiency of constructing the Pareto optimal solutions.The experimental results of the Benchmark instances taken from literature demonstrated the effectiveness of the algorithm proposed on solving multi-objective job shop scheduling problems.
Real Estate Price Prediction Using Machine Learning Algorithm

- Linear Regression (LR) and other Machine Learning algorithms are used to forecast the price of real estate. Therefore, it is possible to utilise the expected price to assist property/house sellers set their selling prices. The concept that housing prices are influenced by characteristics such as location, distance, and region is known as price prediction. Because of this, the prices they expect to pay are vastly different from what they really do. A regression model was constructed to forecast the price of residential houses. Entering the data into the website will get the desired outcome. Then, the input data will be subjected to the regression procedure. The user's input will be fed into the model, and the estimated sale price of the property will be shown on the website in a matter of seconds.
71 . Cognitive Human–Robot Interaction

A key research challenge in robotics is to design robotic systems with the cognitive capabilities necessary to support human–robot interaction. These systems will need to have appropriate representations of the world; the task at hand; the capabilities, expectations, and actions of their human counterparts; and how their own actions might a! ect the world, their task, and their human partners. Cognitive human–robot interaction is a research area that considers human(s), robot(s), and their joint actions as a cognitive system and seeks to create models, algorithms, and design guidelines to enable the design of such systems. Core research activities in this area include the development of representations and actions that allow robots to participate in joint activities with people; a deeper understanding of human expectations and cognitive responses to robot actions; and, models of joint activity for human–robot interaction. This chapter surveys these research activities by drawing on research questions and advances from a wide range of "elds including 71.1 Human Models of Interaction . 1908 71.1. 1 Mental Models of Robots . 1908 71.1. 2 Social Cognition . 1911
Alkali-activated concrete for the production of building blocks: research achievements and future challenges

Several global challenges identified by the UN Sustainable Development Goals are either directly or indirectly linked to the construction sector. The need for decent and affordable houses is an urgent problem for many developing countries, whereas the concerns about the carbon emissions related to the manufacture of Portland cement are growing worldwide. A number of possible solutions are currently offered by the research, which has been investigating the recycling of waste/by-products into sustainable building materials during the last decades. This paper discusses the experience gathered in the manufacture of building blocks using alkali-activated concrete produced from waste streams such as fly ash, slag, or cement kiln dust. Laboratory investigations on binder development, concrete mix proportioning, and building block sample production, as well as full size factory trials with industrial equipment, were carried out for assessing the potential and the challenges of this technology. Obtained results demonstrated the technical feasibility of manufacturing building blocks with alkali-activated concrete, and highlighted the challenges for a viable and sustainable application of this technology.
A Scientific Research Recommendation System Based on Privacy-Preserving Training Dataset

Scientific research recommendation system can provide the valuable reference for researchers to choose topics and determine research direction. However, traditional scientific research recommendation obtains the model by training the behaviour dataset of researchers stored in the centre, which may lead to the disclosure of researchers’ sensitive information. In this paper, we propose a scientific research recommendation system based on privacy-preserving training dataset. Specifically, we use the federated learning mechanism and threshold homomorphic encryption technology to make the scientific research recommendation model available without uploading the raw dataset, which can protect the privacy of the researchers’ behaviour dataset. Additionally, we also use a method to process the dataset of low-quality researchers to improve the accuracy of recommendation model. Through analysis, not only the researchers’ privacy can be protected, but also the recommendation model accuracy can be optimized. The experimental results show that the proposed scheme can satisfy the functional requirements of scientific research recommendation system.
PHYTOSOME: A MODERNISTIC APPROACH FOR EFFICIENT DELIVERY OF POLYPHENOLIC PHYTOCONSTITUENTS

Efficient delivery of phytoconstituents is almost challenging task for scientific community owing to their imbalanced hydrophilicity and lipophilicity considerations. The majority of phytoconstituents is mainly hydrophilic and has large molecular size, resulting in low absorption and bioavailability. Phytosomes and vesicular systems are a unique and revolutionary way of increasing the solubility and bioavailability of phytoconstituents and plant extracts. These are phytophospholipid complexes or herbosomes, which are complex form of phytoconstituents and phospholipid molecules. In compared to conventional dosage form, phytophospholipid complex has showed improved pharmacokinetic and pharmacodynamic properties. Solvent evaporation, anti-solvent precipitation, co-solvent lyophilization, and other methods have been used to prepare phytosomes all over the world. The current review focuses on the numerous methods for preparing phytosomes that are routinely used. Significant attributes, characterization methods, role of phospholipids in the preparation of phytosomes, various marketed and patented phytosomal products and recent investigations on phytosomes have also been highlighted.
Optimism Shapes Mindset: Understanding the Association of Optimism and Pessimism

The present study investigated our hypothesis of an underlying relationship between optimism/pessimism and implicit theories of intelligence. We investigated the psychometric properties of optimism and mindset scales in our sample, compared confirmatory factor analysis models of the scales, examined the full measurement model to confirm quality measurement of the final structural phase of investigation, and finally conducted two competing structural equation models. We found that the direct pathway from optimism to growth mindset was significant, and the pathway from pessimism to fixed mindset was also significant. However, there were no significant direct effects of optimism on fixed mindset or pessimism on growth mindset. Measurement, research, and practice implications are discussed.
I NFORMING B ARRIERS TO C LASSROOM P RACTICES WITH AN A SSESSMENT T OOL : S TUDY ON P RIVATE S CHOOLS S ERVING C HILDREN FROM I NFORMAL S ETTLEMENTS

Classroom practices form an array of integrated teaching and learning strategies conducive to the real world. Lesser is known about the classroom practices experiences among the students belonging to the informal settlements in private schools of India. The paper presents a brief insight into barriers to classroom practices with a self-prepared assessment tool, namely Classroom Practice Position (CPP). It can aid teachers in prioritising practices for balancing participation in the classroom. The measure will help to determine the placement of classroom routine activities. The paper highlights the data from the perspectives of 58 students in Rourkela (a city in the State of Odisha, India) through a sequential exploratory method and data analysis with SPSS v.20. The CPP tool indicates that peer support from the well-performing students helps in mitigating stereotypes, in completion of classwork, identity and familiarity with teachers.
WEIGHTED INEQUALITIES FOR POSITIVE OPERATORS

A technique arising from Schur's Lemma and its converse is shown to generate weighted Lebesgue norm inequalities for a wide class of linear and non- linear positive operators. In many cases the best constants for these inequalities are determined as well. A sharp converse to Schur's Lemma is proved via a minimax principle for a class of positive operators on Banach Function Spaces. This shows that all such inequalities can be generated by this technique and establishes a structure theorem for weight pairs. Examples involving Hardy and Stieltjes operators are given as well as several Opial-type inequalities. As an illustration of the structure theorem a new proof is given of necessity in the well-known weight characterization for the Hardy operator.
The habitat uses and behaviour of Black Grouse ( Lyrurus tetrix ) in western Norwegian heathlands, in winter.

species is widely distributed globally: ranging from Brit-ain and North Eurasia to China (Lawrence, 2004; Zhang et al. 2020). Studies of Wegge & Kastdalen (2008) and Ciach (2015) showed that black grouse avoids dense woodland. The northern population is rather associated with young forest (Swenson & Angelstam, 1993; Gregersen, 2009; Ciach, 2015) or forest edge habitat (Paloc, 2004; Wegge & Kastdalen, 2008; Kurhinen et al., 2009), but coastal populations are also found in open heathlands (Baines, 1994; Starling-Westerberg, 2001). The differences in habitat use and winter behaviour between the inland and coastal populations is poorly described, but there are reasons to expect that there are important differences between these populations, in terms of habitat use and winter behavior. We expect these differences to be especially clear in winter, because of the lower temperatures, more snow cover, and higher pred-ator pressure in the inland compared to the coastal areas.
Planning with Propositional Temporally Extended Goals Using Heuristic Search

Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or ﬁnal states of a plan. The main strategy for planning with TEGs is to prune the search space during planning via goal progression. Currently, planners for TEGs prune the search space using heuristic search. In this paper we propose a method for planning with propositional TEGs using heuristic search. To this end, we translate an instance of a planning problem with TEGs into an equivalent classical planning problem. With this translation in hand, we exploit heuristic search to determine a plan. Our translation is based on the construction of a nondeterministic ﬁnite automaton for the TEG. We propose two alternative translations: the ﬁrst uses only ADL operators, and the second (more efﬁcient) uses derived predicates. We prove the correctness of our algorithm and analyze the complexity of the resulting representation. The translator is fully implemented and available. Our approach consistently outperforms existing approaches to planning with TEGs, often by orders of magnitude.
Virtual Reality: implications in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that is specially characterized by impairments in social communication and social skills. In the last decade, there has been an increase in publications on technology-based interventions for autism spectrum disorder (ASD). Virtual reality (VR) has emerged as an effective tool for intervention in the health field. Different recent papers have reviewed the VR-based treatments in ASD Virtual reality based assessments and intervention tools are promising and have shown to be acceptable amongst individuals with ASD. This scoping review reports studies utilizing virtual reality and augmented reality technology in social skills interventions for individuals with ASD. Our review suggests that there is need for studies that apply virtual and augmented realty with more rigorous designs involving established and evidenced-based intervention strategies. The studies examined suggest moderate evidence about the effectiveness of VR-based treatments in ASD. VR can add many advantages to the treatment of ASD symptomatology, but it is necessary to develop consistent validations in future studies to state that VR can effectively complement the traditional treatments.
Social skills training with video modeling in ASD (short paper)

According to the DSM-5, autism spectrum disorder (ASD) is characterized by pervasive deficits in social interaction and communication behavior, along with restricted and repetitive patterns of behavior. Social functioning is a predictor of workplace success. Deficit in this area for people with ASD, particularly high functioning mode (HF-ASD) implies difficulties managing and maintaining work. Soft skills are needed to secure and maintain adult employment. From a cognitive point of view, people with autism have a deficit in the acquisition of Theory of Mind (ToM), with the inability to recognize their own and others' mental states and lack of joint attention. According to several studies, innovative technologies can improve performance, including social performance, of people with ASD. For example, video modeling facilitates the teaching of social skills in the form of peer video modeling (PVM) and self-video modeling (SVM) in individuals with ASD. Video modeling is useful for teaching skills such as: independence, play and communication but also acquiring conversational skills, acquiring the ability to comment and compliment, acquiring pragmatic skills, and acquiring and enhancing the ability to initiate and sustain social relationships.
A partial-distributed damage method for progressive collapse of 3D steel composite buildings

The present paper studies the response of a 3D steel composite building to progressive collapse through the Partial Distributed Damage Method (PDDM). The method can assess the robustness of 3D steel and concrete composite structures, considering the introduction of partial damage to more than one element in contrast to the dominant Alternate Load Path Method (APM) which involves a complete loss of a member. Computational analysis using finite element software ABAQUS is performed, in order to account for the instability phenomena and assess the integrity of the structure. Considering a damage degree index δj, PDDM predicts lower and more critical collapse loads in comparison with APM.
The strategy of capture fisheries development in Rembang Regency

Capture fisheries have a strategic value for the socio-economic development in Rembang Regency in terms of food security, employment, poverty reduction, and economic growth. This study aims to develop a strategy of capture fisheries development in Rembang Regency. The LQ (location quotient), SWOT (Strengths, Weaknesses, Opportunities, Threats), TOWS (Threats, Opportunities, Weaknesses, Strengths) matrix, and QSPM (Quantitative Strategic Planning Matrix) were used for primary and secondary data analysis. The primary data was obtained through observations and interviews. The interviews were conducted with 200 fishermen and 10 bureaucrats. The results revealed that Rembang Regency has 17 superior commodities of capture fisheries. Strategies to be conducted for the development of capture fisheries in Rembang Regency include institutional strengthening in fisheries management, fishery resources monitoring, human resource development, fishing port development (for artisanal and industrial fisheries), fish processing industry development, attracting investment, research and development, integrated public ports and industrial estates, coastal ecosystem management, construction of toll roads and railways, and promotion (for fishery products and business investment).
An Airplane Image Target's Multi-feature Fusion Recognition Method

This paper proposes an image target's multi-feature fusion recognition method based on probabilistic neural networks(PNN) and Dezert-Smarandache theory(DSmT). To aim at multiple features extracted from an image,the information from them is fused.Firstly,the image is preprocessed with binarization and then multiple features are extracted,such as Hu moments,normalized moment of inertia,affine invariant moments,discrete outline parameters and singular values.Secondly,due to the difficulty of the construction of the basic belief assignment in DSmT,in this paper the target recognition rate matrix is constructed by PNN,that is,the basic belief assignments can be assigned to the evidence sources by PNN.Finally,the procedure of airplane target recognition can be accomplished by the DSmT combination rule at the level of decision fusion.For small distortion of target image,the multi-feature fusion method proposed in this paper is compared with the single-feature one through a series of experiments.The experimental result in this paper proves that this method greatly improves the right recognition rate,satisfies real-time requirements,and has good ability of rejection of judgement and strong insensitivity to target image size.And even for big distortion,the right recognition rate can also reach 89.3%.