Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential spaces: Supporting the development of relationships between classroom practitioners and children with complex needs in Belarus through music therapy consultation

ABSTRACT Introduction: Following positive outcomes of music therapy-based skills-sharing projects undertaken there in 2009, this doctoral research responded to a locally identified need by classroom practitioners at a Belarusian Development Centre seeking to develop relationships with children with complex needs. Method The study employed a convergent mixed-methods, pre- and post-intervention design. Winnicott’s theory of the holding environment provided the theoretical framework for a new evaluation instrument which underpinned the structure of a specifically designed staff development programme. Eight staff participants evaluated their usual musical interaction with a child with complex needs (pre-intervention). Following engagement with the staff development programme (intervention), they then undertook ten filmed individual music sessions with the same child (post-intervention). Sessions were filmed and two self-chosen extracts pre- and post-intervention were self-rated and peer-rated against the descriptors of the evaluation instrument. Participants then reflected on the experience with the child in interviews. Results Results focus on that part of the qualitative data set that describes ways in which Winnicott’s theory of holding was accessible and applicable in supporting optimisation of teacher-pupil relationships. Outcomes showed that the experience of Winnicott’s “holding” in the learning process supported participants to engage with a process of change, facilitating potential spaces for play and development in relationships within the research group and between staff and children. Discussion Participants required support to maintain two levels of awareness in their learning – this internal process of change in perceiving, attuning to and empathising with the child which then underpinned growth in their concrete musical interactional skills.
Civil Society Organizations and electoral credibility in Nigeria

ABSTRACT Electoral malpractices undermine the legitimacy of the electoral process, the general acceptability of electoral outcomes, and challenges the democratization process in Nigeria. This study examines the role of Civil Society Organizations (CSOs) in the electoral process and its impact on the credibility of electoral outcomes in Nigeria, with emphasis on the 2019 general election. With evidence collected from documentary sources, we argue that the role of CSOs in the democratization process (before, during, and after elections) is important in advancing democracy in Nigeria. We found that the participation of CSOs in the electoral process of 2019, as significant as it was, was undermined by the paucity of fund, insecurity, administrative bottlenecks, and limited access for monitoring movement of sensitive election materials. We recommend, among other things, that the government set aside support funds for qualified/accredited Civil Society Organisations in the electoral process to aid them in undertaking their important role in the democratization process.
Networks of SARS-CoV-2 transmission

Individual and network heterogeneity should inform respiratory pandemic responses The basic reproduction number, R0 (the number of infections caused by a case in a homogeneously susceptible population), for a particular infection is dependent on the epidemiological triad of the biological characteristics of the pathogen, the environment, and the characteristics of the population (1). Even for diseases with similar transmission characteristics, R0 varies by population owing to differential opportunities for onward transmission according to the contact patterns and the size of the transmission network of an infected individual (1). Although transmission can happen in many settings, some factors facilitate a greater risk of infection because of compounded risks often driven by network dynamics (frequent contacts, close proximity, and prolonged contact) and structural-level determinants (such as poverty, occupation, and household size) (2–4). Understanding drivers of transmission risks and heterogeneity could be used to improve modeling and guide population- and setting-specific mitigation strategies.
Impact of Dietary Fructose on the Lipid Profile in Six Macaws

Abstract: Atherosclerosis is frequently diagnosed in captive parrots. Recent studies have highlighted the potential role of high fructose intake in the development of human cardiovascular disease. The objective of this study was to investigate the effects of dietary fructose on the lipid profile in psittacine birds. Six macaws, composed of 3 green-winged macaws (Ara chloropterus, 2 females, 1 male), and 3 blue-and-gold macaws (Ara ararauna, 1 female, 2 males), ranging from 15 to 26 years of age, were used as subject animals for this dietary investigation. Initially, fruits were removed from the diet and replaced with vegetables lower in fructose for a month. Then, with a crossover study design, each bird was randomly selected to receive either 34 kcal of a fructose solution daily for 5 days then an equivalent volume of water for 5 days, or water in the first 5 days followed by the 34 kcal fructose solution. Lipid profiles were repeatedly performed over the course of the study at specific intervals. Serum cholesterol (3.01–5.55 mmol/L), triglycerides (0.27–2.70 mmol/L), high-density lipoprotein–cholesterol (1.43–4.79 mmol/L), low-density lipoprotein–cholesterol (0.02–0.85 mmol/L), and very low density lipoprotein–cholesterol (0.12–1.22 mmol/L) levels were not statistically significantly different over time and treatment. Although no significant effect could be demonstrated in this study, further studies are needed to investigate the effect of a high fructose intake in psittacine birds, because it could have a deleterious influence on their cardiovascular health if proven to be linked with dyslipidemia as reported in humans. Higher amounts of fructose and longer periods of administration should be considered for future studies.
Eutobrilus godlewskii sp. nov. and Tobrilus gigas sp. nov. (Nematoda: Triplonchida) from Lake Baikal, Russia

Abstract. Eutobrilus godlewskii sp. nov. and Tobrilus gigas sp. nov. are described and illustrated based on the material collected from Lake Baikal, Siberia, Russia. The species Eutobrilus godlewskii sp. nov. morphologically is close to E. fortis (Tsalolikhin, 1972) and E. prodigiosus Shoshin, 1988. From the former species it differs in the narrower labial region, longer outer labial setae, longer spicules and the absence of crystalloids. It differs from E. prodigiosus in the comparatively longer pharynx, another structure of stoma, longer spicules, another arrangement of precloacal supplements and the absence of crystalloids. Another new species, Tobrilus gigas sp. nov. is the largest among valid species of the genus Tobrilus. Morphologically it is close to T. undophylus Shoshin, 1988 and T. kamtschaticus Gagarin, 2004. From the former species it differs by having a narrowed male body, longer outer labial setae and spicules. From T. kamtschaticus it differs in wider labial region, longer outer labial setae and spicules. This article presents two tables showing the morphological characteristics of males among valid species of the genus Eutobrilus and Tobrilus from Lake Baikal.
Exploring the experiences of social workers in working with children suspected to have fetal alcohol spectrum disorders

Fetal alcohol spectrum disorder (FASD) is one outcome from prenatal alcohol exposure. Social workers are likely to encounter children with the condition, due to the greater likelihood of prenatal alcohol exposure among children in social services settings. This study explores the experiences of social workers in working with children suspected of having FASD and the support offered to social workers, the children and their families. Semi-structured interviews followed by qualitative framework analysis were conducted with seven child and family social workers along with one child protection solicitor who had experience of handling FASD cases. The two main themes that emerged from the data were a lack of knowledge about FASD and the paucity of diagnosis. Lack of knowledge among the social workers was linked to difficulty in managing children suspected to have the condition, feelings of frustration and normalisation of challenging behaviours. The paucity of diagnosis led to an under-emphasis of FASD in assessments, a dearth of specialist services and confusion about its specific effects in contexts of multiple substance misuse and harmful socio-environmental factors. The need for increased FASD awareness within social services and the development of FASD-targeted support for children and families is highlighted. Social workers would benefit from the inclusion of FASD-focused training in their curricula and professional development plans. Improving the diagnostic capacities of health institutions would address the paucity of diagnosis and raise the profile of FASD, especially in the social services setting.
‘What about me?’ Stories of the educational experiences of care-experienced children and young people in a Scottish local authority

This qualitative study explores the educational experiences of looked after children and young people in one Scottish local authority. The preoccupations of government are academic achievement and school attendance, but these are not the prime concerns of the children, carers and professionals involved. Moreover, they can be both enhanced and restricted by the background characteristics and care situations of the young people and the responses of schools to their needs and behaviour. Five influential factors emerged from interviews and focus groups with professionals, carers and young people: behaviour; school attendance; carers as educators; friendships; and communication between home and school. Each of them is discussed with extended quotations that convey the voices of participants.
Evolutionary algorithms in high-dimensional radio access network optimization

This article describes the project result of modeling and optimizing Radio Access Network. We have proposed a solution for controlling a large number of antennas in the conditions of engineering constraints and a large search space dimension. For estimating the performance, a virtual environment has been developed, that allows changing the parameters of Radio Access antennas to control the coverage and signal quality for all User Equipments. To optimize the Radio Access network, we have analyzed DE, CMA-ES, MOS, self-adaptive surrogate CMA-ES, lq-CMA-ES, BIPOP CMA-ES, sep-CMA-ES, lm-CMA-ES, HMO-CMA-ES, JADE, PSO, which have been adapted to the constraints of the task. To reduce dimension, graph clustering methods - Spectral clustering, Label propagation, Markov Clustering - are compared in dividing the network into groups. The experiments illustrate the efficiency of optimizing a large Radio Access network by the cluster approach.
Numerical solution of two-dimensional nonlinear fractional order reaction-advection-diffusion equation by using collocation method

Abstract In this article, two-dimensional nonlinear and multi-term time fractional diffusion equations are solved numerically by collocation method, which is used with the help of Lucas operational matrix. In the proposed method solutions of the problems are expressed in terms of Lucas polynomial as basis function. To determine the unknowns, the residual, initial and boundary conditions are collocated at the chosen points, which produce a system of nonlinear algebraic equations those have been solved numerically. The concerned method provides the highly accurate numerical solution. The accuracy of the approximate solution of the problem can be increased by expanding the terms of the polynomial. The accuracy and efficiency of the concerned method have been authenticated through the error analyses with some existing problems whose solutions are already known.
Optimisation algorithms for parallel machine scheduling problems with setup times

Parallel machine scheduling is a problem of high practical relevance for the manufacturing industry. In this paper, we address a variant in which an unweighted combination of earliness, tardiness and setup times aggregated in a single objective function is minimised. We compare an Evolutionary Algorithm (EA) approach with a variant of local search implementing a probabilistic Best Response Dynamic algorithm (p-BRD) inspired by game theoretic considerations. Our p-BRD algorithm achieved promising results outperforming the EA on a series of test sets.
Ageing in European post-communist countries – is it a threat to the welfare system?

Abstract The aim of the study was to diagnose the current and projected pace of ageing in the societies of European post-communist countries from the point of view of its being an increasing economic burden on the economically active population. The analysis covered the years 1990–2050. Ageing is already visible and, according to the forecast, the process will accelerate even more. The old-age dependency ratio (OADR) is expected to exceed 50 in half of the countries in the study by 2050. This will result in major threats to the further development of these countries and will lead to poverty and social exclusion of the elderly. In order to slow down the ageing process, the authorities of these countries should take a number of actions, the most important of which are the implementation of an effective and efficient pro-natal policy (so that the total fertility rate would increase above 2.1), and a well-thought-out migration policy.
Benchmark generator for TD Mk landscapes

We introduce a publicly available benchmark generator for Tree Decomposition (TD) Mk Landscapes. TD Mk Landscapes were introduced by Whitley et al. to get rid of unnecessary restrictions of Adjacent NK Landscapes while still allowing for the calculation of the global optimum in polynomial time. This makes TD Mk Landscapes more lenient while still being as convenient as Adjacent NK Landscapes. Together, these properties make it very suitable for benchmarking blackbox algorithms. Whitley et al., however, introduced a construction algorithm that only constructs Adjacent NK Landscapes. Recently, Thierens et al. introduced an algorithm, CliqueTreeMk, to construct any TD Mk Landscape and find its optimum. In this work, we introduce CliqueTreeMk in more detail, implement it for public use, and show some results for LT-GOMEA on an example TD Mk Landscape problem. The results show that deceptive trap problems with higher overlap do not necessarily decrease performance and effectiveness for LT-GOMEA.
Challenges and opportunities: an in-depth empirical study on configuration error injection testing

Configuration error injection testing (CEIT) could systematically evaluate software reliability and diagnosability to runtime configuration errors. This paper explores the challenges and opportunities of applying CEIT technique. We build an extensible, highly-modularized CEIT framework named CeitInspector to experiment with various CEIT techniques. Using CeitInspector, we quantitatively measure the effectiveness and efficiency of CEIT using six mature and widely-used server applications. During this process, we find a fair number of test cases are left unstudied by the prior research work. The injected configuration errors in these cases often indicate latent misconfigurations, which might be ticking time bombs in the system and lead to severe damage. We conduct an in-depth study regarding these cases to reveal the root causes, and explore possible remedies. Finally, we come up with actionable suggestions guided by our study to improve the effectiveness and efficiency of the existing CEIT techniques.
HomDroid: detecting Android covert malware by social-network homophily analysis

Android has become the most popular mobile operating system. Correspondingly, an increasing number of Android malware has been developed and spread to steal users’ private information. There exists one type of malware whose benign behaviors are developed to camouflage malicious behaviors. The malicious component occupies a small part of the entire code of the application (app for short), and the malicious part is strongly coupled with the benign part. In this case, the malware may cause false negatives when malware detectors extract features from the entire apps to conduct classification because the malicious features of these apps may be hidden among benign features. Moreover, some previous work aims to divide the entire app into several parts to discover the malicious part. However, the premise of these methods to commence app partition is that the connections between the normal part and the malicious part are weak (repackaged malware). In this paper, we call this type of malware as Android covert malware and generate the first dataset of covert malware. To detect covert malware samples, we first conduct static analysis to extract the function call graphs. Through the deep analysis on call graphs, we observe that although the correlations between the normal part and the malicious part in these graphs are high, the degree of these correlations has a unique range of distribution. Based on the observation, we design a novel system, HomDroid, to detect covert malware by analyzing the homophily of call graphs. We identify the ideal threshold of correlation to distinguish the normal part and the malicious part based on the evaluation results on a dataset of 4,840 benign apps and 3,385 covert malicious apps. According to our evaluation results, HomDroid is capable of detecting 96.8% of covert malware while the False Negative Rates of another four state-of-the-art systems (PerDroid, Drebin, MaMaDroid, and IntDroid) are 30.7%, 16.3%, 15.2%, and 10.4%, respectively.
Deceiving neural source code classifiers: finding adversarial examples with grammatical evolution

This work presents an evolutionary approach for assessing the robustness of a system trained in the detection of software vulnerabilities. By applying a Grammatical Evolution genetic algorithm, and using the output of the system being assessed as the fitness function, we show how we can easily change the classification decision (i.e. vulnerable or not vulnerable) for a given instance by simply injecting evolved features that in no wise affect the functionality of the program. Additionally, by means of the same technique, that is by simply modifying the program instances, we show how we can significantly decrease the accuracy measure of the whole system on the dataset used for the test phase. Finally we remark that these methods can be easily customized for applications in different domains and also how the underlying ideas can be exploited for different purposes, such as the exploration of the behaviour of a generic neural system.
Winner prediction for real-time strategy games through feature selection based on a genetic wrapper

We propose a method that can predict the game winner using a long short-term memory (LSTM) model that is trained using the match data of a real-time strategy game (Clash Royale). Feature selection based on a genetic wrapper is utilized to identify an excellent feature subset. Subsequently, the performance of a model trained using the data of the aforementioned subset is compared with that of an existing model. The model, which is trained using data that includes the entire features before feature selection, exhibited a winner prediction accuracy of about 60%. Conversely, the model, which is trained using the optimal subset data identified through the application of the genetic wrapper feature selection, exhibited a winner prediction accuracy of about 75%. Based on this comparison result, it was found that the proposed method increased the winner prediction accuracy by about 15% compared to the existing method. In excellent chromosomes explored through genetic wrapper feature selection, the spatial feature was always selected. Simultaneously, the temporal feature was always excluded.
Maximising hypervolume and minimising ϵ-indicators using Bayesian optimisation over sets

Bayesian optimisation methods have been widely used to solve problems with computationally expensive objective functions. In the multi-objective case, these methods have been successfully applied to maximise the expected hypervolume improvement of individual solutions. However, the hypervolume, and other unary quality indicators such as multiplicative ϵ-indicator, measure the quality of an approximation set and the overall goal is to find the set with the best indicator value. Unfortunately, the literature on Bayesian optimisation over sets is scarce. This work uses a recent set-based kernel in Gaussian processes and applies it to maximise hypervolume and minimise ϵ-indicators in Bayesian optimisation over sets. The results on benchmark problems show that maximising hypervolume using Bayesian optimisation over sets gives a similar performance than non-set based methods. The performance of using ϵ indicator in Bayesian optimisation over sets needs to be investigated further. The set-based method is computationally more expensive than the non-set-based ones, but the overall time may be still negligible in practice compared to the expensive objective functions.
A Mixed Method Approach to Evaluating eCommerce Website: Towards Socially-sensitive Guidelines for Future Design

The goal of every business website, blog and online shop is increase traffic or customer engagement with a view to improve sales. Attractive user interfaces (UI) and user experiences (UX) are essential to achieving these marketing goals. Therefore, this paper uses a mixed-method approach to evaluate a foremost customer-centric eCommerce website in Africa to uncover the usability techniques employed in designing its user-interface and how they were implemented to provide positive UX to shoppers. As a first step towards contributing to research, we conducted an expert evaluation of Jumia, using Nielsen's Heuristics to identify usability problems on its user interface. Secondly, we conducted a user-study of 78 participants to understand the effects of usability techniques on customer's activities. Findings uncover that although Jumia's website is engaging and easy-to-use, however, there are usability issues which could affect its conversion rate. In addition, we attempt to rethink UI/UX designs of such platforms with a view to offering socially-relevant guidelines and recommendations for designing or redesigning such platforms based on the unique requirements of an average customer in the Global South. The findings from our study could guide all stakeholders in the user-centered design and indeed, eCommerce loop such managers, platform and brand developers, and marketers in designing new or improving existing eCommerce websites that target customers in the Global South.