Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF CONCEPT-MAPPING INSTRUCTIONAL STRATEGY ON SENIOR SCHOOL STUDENTS’ ACHIEVEMENT IN BIOLOGY, LAGOS STATE, NIGERIA

Concept mapping instructional strategy is one of the innovations in the teaching and learning of science and biology in particular. Previous studies have reported that teachers and students experienced difficulties in the use of concept mapping. Hence, this study investigated the effects of Concept-mapping instructional strategy on senior secondary school students’ achievement and retention in biology in Lagos, Nigeria. Quasi-experimental research design was adopted for the study. The population comprised all senior secondary school two biology students. Two intact classes of SS II students were involved in the study. The research instruments used were Biology Achievement Test (BAT) and Concept-Mapping Based Lesson Plan (CMBLP). The reliability of the instruments was 0.75 and 0.82 respectively. Three research questions with corresponding three hypotheses guided the study. Analysis of Co-variance (ANCOVA) statistical tool was used tested the hypotheses at 0.05 level of significance. The results of the study showed that: (i) the experimental group performed better than the control group, that is: concept-mapping affects students' achievement and retention; and (ii) there was no gender influence on students’ achievement. Based on results, it recommended among others that teachers should be encouraged to adopt the use of concept-mapping instructional strategy in teaching senior school biology. Also, male students should be encouraged to better their achievement and retention just like their female counterparts.
Reinforcement Learning In Factory Automation

Manufacturing has always been one of the important means by which wealth is created. However, contemporary manufacturing companies face enormous challenges from uncertain markets, competitors, etc. Those challenges are driving researchers to create new manufacturing paradigms to be able to produce diversified products with high quality, to be able to meet the demand of new and highly customized products with short life cycles and fierce competition, and to be able to operate cost-effectively under various market demands. Researchers developed various manufacturing paradigms to help manufacturing companies, such as Flexible Manufacturing System (FMS), Dedicated Manufacturing System (DMS), and Reconfigurable Manufacturing System (RMS). RMS is one of the latest manufacturing paradigms, providing flexibility, scalability, convertibility, and other advantages to manufacturing companies. However, upgrading an existing manufacturing system to an RMS is extremely difficult, especially traditional manufacturing systems built with legacy techniques. Developers face various issues, such as lack of necessary sensors, high software development cost, etc. This research aims to use Digital Twin and Reinforcement Learning (RL) techniques to help traditional manufacturing systems move to RMS paradigms. To achieve this goal, we explore Digital Twin and Reinforcement Learning (RL) techniques and conduct a case study of learning a supervisory controller by utilizing those techniques. Based on the case study and experiments, we propose a set of guidelines to assist developers in utilizing the aforementioned techniques. In conclusion, this research aims to prove that Digital Twin and Reinforcement Learning (RL) techniques can effectively and cost-efficiently mitigate the risk of moving traditional manufacturing systems to RMS paradigms, in particular regarding convertibility and scalability. Reinforcement Learning In Factory Automation i
Perfect Observability is a Myth: Restraining Bolts in the Real World

In the presence of a restraining bolt that models features of the world that are distinct from those modeled by a reinforcement learning agent, a fully observable setting is assumed for the developer of the restraining speciﬁcations. However, the real world is often noisy and partially observable. We consider the setting where the generator of the speciﬁcation infers a noisy map of the features, thereby disentangling the bolt’s observa- tion from its feature space. A set of low and high probability ﬂuents is extracted from bolt’s observations which are used to specify the restrictions in linear temporal logic. We em-pirically evaluate our approach’s performance for noisy re- straining bolts with AI agents and show that the agent can still learn to effectively conform to the correct speciﬁcations through appropriate reward shaping.
Egg Hunt in Tesla Infotainment: A First Look at Reverse Engineering of Qt Binaries

As one of the most popular C++ extensions for developing graphical user interface (GUI) based applications, Qt has been widely used in desktops, mobiles, IoTs, automobiles, etc . Although existing binary analysis platforms ( e.g. , angr and Ghidra ) could help reverse engineer Qt binaries, they still need to address many fundamental challenges such as the recovery of control flow graphs and symbols. In this paper, we take a first look at understanding the unique challenges and opportunities in Qt binary analysis, developing enabling techniques, and demonstrating novel applications. In particular, although callbacks make control flow recovery challenging, we notice that Qt’s signal and slot mechanism can be used to recover function callbacks. More interestingly, Qt’s unique dynamic introspection can also be repurposed to recover semantic symbols. Based on these insights, we develop Q T RE for function callback and semantic symbol recovery for Qt binaries. We have tested Q T RE with two suites of Qt binaries: Linux KDE and the Tesla Model S firmware, where Q T RE additionally recovered 10 , 867 callback instances and 24 , 973 semantic symbols from 123 binaries, which cannot be identified by existing tools. We demonstrate a novel application of using Q T RE to extract hidden commands from a Tesla Model S firmware. Q T RE discovered 12 hidden commands including five unknown to the public, which can potentially be exploited to manipulate vehicle settings.
Making Sense of Sentiments for Aesthetic Plastic Surgery

With social media pervading all aspects of our life, the opinions expressed by netizens are a gold mine ready to be exploited in a meaningful way to influence all major public do-mains. Sentiment analysis is a way to interpret this unstructured data using AI tools. It is a well-known fact that there has been a 'Zoom Boom’ in the field of aesthetic plastic surgery due to the COVID-19 pandemic and the same has put the focus of attention sharply on our appearance. Polarity detection of tweets published on popular aesthetic plastic surgery procedures before and after the onset of COVID can provide great insights for aesthetic plastic surgeons and the health industry at large. In this work, we develop an end-to-end system for the sentiment analysis of such tweets incorporating a state-of-the-art fine-tuned deep learning model, an ingenious 'keyword search and filter approach’ and SenticNet. Our system was tested on a large database of 196,900 tweets and the results were visualized using affectively correct word clouds and also subjected to rigorous statistical hypothesis testing to draw meaningful inferences. The results showed a high level of statistical significance.
SPPACK: Stata module for cross-section spatial-autoregressive models

The spmat, spreg and spivreg commands create spatial-weighting matrices, manage spatial-weighting matrices, and estimate the parameters of cross-sectional spatial-autoregressive models with spatial-autoregressive disturbances that may contain additional endogenous covariates. The estimation method is either maximum likelihood or generalized spatial two-stage least squares. The working papers that document these commands are available at http://econweb.umd. edu/~prucha/Papers/WP_spmat_2011. pdf, http://econweb.umd. edu/~prucha/Papers/WP_spreg_2011. pdf, and http://econweb.umd. edu/~prucha/Papers/WP_spivreg_2011. pdf .
The machine-assisted proof of programming language properties

The Machine-Assisted Proof of Programming Language Properties Myra VanInwegen Advisor: Carl Gunter The goals of the project described in this thesis are twofold. First, we wanted to demonstrate that if a programming language has a semantics that is complete and rigorous (mathematical), but not too complex, then substantial theorems can be proved about it. Second, we wanted to assess the utility of using an automated theorem prover to aid in such proofs. We chose SML as the language about which to prove theorems: it has a published semantics that is complete and rigorous, and while not exactly simple, is comprehensible. We encoded the semantics of Core SML into the theorem prover HOL (creating new de nitional packages for HOL in the process). We proved important theorems about evaluation and about the type system. We also proved the type preservation theorem, which relates evaluation and typing, for a good portion of the language. We were not able to complete the proof of type preservation because it is not true: we found counterexamples. These proofs demonstrate that a good semantics will allow the proof of programming language properties and allow the identi cation of trouble spots in the language. The use of HOL had its plusses and minuses. On the whole the bene ts greatly outweigh the drawbacks, enough so that we believe that these theorems could not been proved in amount of time taken by this project had we not used automated help. ii
Tangible Privacy for Smart Voice Assistants: Bystanders' Perceptions of Physical Device Controls

Smart voice assistants such as Amazon Alexa and Google Home are becoming increasingly pervasive in our everyday environments. Despite their benefits, their miniaturized and embedded cameras and microphones raise important privacy concerns related to surveillance and eavesdropping. Recent work on the privacy concerns of people in the vicinity of these devices has highlighted the need for 'tangible privacy', where control and feedback mechanisms can provide a more assured sense of whether the camera or microphone is 'on' or 'off'. However, current designs of these devices lack adequate mechanisms to provide such assurances. To address this gap in the design of smart voice assistants, especially in the case of disabling microphones, we evaluate several designs that incorporate (or not) tangible control and feedback mechanisms. By comparing people's perceptions of risk, trust, reliability, usability, and control for these designs in a between-subjects online experiment (N=261), we find that devices with tangible built-in physical controls are perceived as more trustworthy and usable than those with non-tangible mechanisms. Our findings present an approach for tangible, assured privacy especially in the context of embedded microphones.
S 4 . Testing phase S 4 . 1 Test-time augmentation

Augmentation [1], together with transfer learning [2], is a method that often helps deep learning in achieving high levels of effectiveness when having to work with a small-sized image dataset. Augmentation aims at making a model robust to different image changes by transforming images, used as an input for training, in various ways. Representative image augmentation methods include cropping, affine transformations, and color space changes. After fine-tuning, augmentation can also be used in the testing phase. Specifically, several augmentations can be applied to the inputs used for testing, and the final prediction is then produced by combining the different predictions made. Through TTA, the chance of being over-confident towards incorrect predictions can be reduced. However, during testing, there is a drawback in terms of computational cost, given that the model under consideration needs to make predictions multiple times [1]. The use of TTA is known to have resulted in effectiveness improvements for VGG [3] and AlexNet [4], and it also helped in improving IoU values when performing segmentation [5]. In this study, we applied augmentation during both the fine-tuning and testing phase, denoting the resulting model as TTA U-Net. For model names without the prefix TTA, neither augmentation nor TTA was applied. Figure 1 illustrates the image augmentation methods used in our experiments. When TTA was applied during fine-tuning, there was a 60% chance of using augmentation. Upon using augmentation, each of the five different augmentation methods was applied randomly. One the other hand, when using augmentation during testing, only three augmentation methods were applied to a particular patch, namely brightness, contrast, and HSV, thus resulting in three more patches. As a result, a total of four patches, including the original patch, were used to generate (i.e., predict) four corresponding mask patches. These mask patches were then averaged in a pixel-wise fashion to get the final mask patch.
Mining Physical Activity Behaviours in Older Adults Name :

Our project focuses on mining physical activity behaviours by identifying and exploring physical activities (PA) between different individuals. We aim to test and develop unsupervised methods to recognise different physical activity behaviours among older adults. For this research project, we are using the data provided by the Growing Old Together Study, which consists of 164 participants whose daily activities were monitored using wearable sensors over the span of two weeks, one before and one after a lifestyle intervention. We aggregated the obtained data at various scales, i.e. we combined different window lengths and aggregation methods to obtain the best possible combination for our goals. After some initial analysis and pre-processing of the provided data, we selected 114 participants for our experiments. For every participant, we constructed frequency features per 30-second aggregation window. Afterwards, we extracted the features over all the hours and the days. Then, using the constructed feature dataset, we applied two unsupervised methods, namely the k-means clustering algorithm and the hierarchical clustering algorithm, to identify differences in activity behaviours for the concluded clusters. Based on these, we expect to identify active or less active behaviours among the participants. We also consider ways to optimise the feature dataset by reducing the dimensions of our data for our clustering tasks, such as principal component analysis. This research would help us to stimulate vital and healthy ageing among older people along with inspiration and motivation to get started and stay active for a longer, healthier and happier life.
Effects of Psychotropic Medication on Somatic Sterol Biosynthesis of Adult Mice

Polypharmacy is commonly used to treat psychiatric disorders. These combinations often include drugs with sterol biosynthesis inhibiting side effects, including the antipsychotic aripiprazole (ARI), and antidepressant trazodone (TRZ). As the effects of psychotropic medications are poorly understood across the various tissue types to date, we investigated the effects of ARI, TRZ, and ARI + TRZ polypharmacy on the post-lanosterol biosynthesis in three cell lines (Neuro2a, HepG2, and human dermal fibroblasts) and seven peripheral tissues of an adult mouse model. We found that both ARI and TRZ strongly interfere with the function of 7-dehydrocholesterol reductase enzyme (DHCR7) and lead to robust elevation in 7-dehydrocholesterol levels (7-DHC) and reduction in desmosterol (DES) across all cell lines and somatic tissues. ARI + TRZ co-administration resulted in summative or synergistic effects across the utilized in vitro and in vivo models. These findings suggest that at least some of the side effects of ARI and TRZ are not receptor mediated but arise from inhibiting DHCR7 enzyme activity. We propose that interference with sterol biosynthesis, particularly in the case of simultaneous utilization of medications with such side effects, can potentially interfere with functioning or development of multiple organ systems, warranting further investigation.
Line X Tester Analysis and Heterosis in Grain Sorghum Hybrids Under Water Stress Conditions

: Twenty five F 1 grain sorghum crosses, its parents (five female lines and five male lines) and one commercial hybrid (Shandaweel-6) as check were evaluated for yield and four other characters in 2010 and 2011 seasons at Shandaweel Res. Station under two levels of irrigation (100% and 40% ET). The obtained data showed significant or highly significant differences between years, irrigation treatments and among genotypes for all studied traits except between years for 1000-grain weight. The interactions years × genotypes, irrigation × genotypes and year × irrigation × genotypes were highly significant for all studied traits except for No. of green leaves. Also, the interaction years × irrigation was significant for plant height and 1000-grain weight. Highly significant differences among crosses, and parents for all studied traits were obtained in two seasons, except for No. of green leaves in 2011 season. Mean squares due to parents vs. crosses were highly significant differences for all studied traits. Partitioning sum of squares of crosses to their contributions (females, males and males × females interaction) showed significant or highly significant differences for all studied traits in the two seasons, except males × females interaction for No. of green leaves in 2011 season. Likewise, the interactions genotypes x irrigation, parents x irrigation, crosses × irrigation and their partitions (females × irrigation, males × irrigation and males × females × irrigation) were significant or highly significant for all studied traits in the two seasons except for males × irrigation and males × females × irrigation in the two seasons for no. of green leaves. The interaction crosses vs. parents × irrigation showed highly significant differences for all studied traits in the two years. Mean days to 50% flowering of hybrids and their parents were increased by increasing water stress, but plant height, No. of green leaves, 1000-grain weight and grain yield/plant of
Tatouage Numérique d’Images dans l’Espace Latent de Réseaux Auto-Supervisés

Résumé – Nous revisitons les techniques de tatouage numérique basées sur des transformations d’images, à la lumière des réseaux neuronaux auto-supervisées. Nous présentons un moyen d’intégrer à la fois des marques et des messages binaires dans leurs espaces latents. Notre méthode peut fonctionner à résolution variable et crée des tatouages robustes à un large éventail de transformations (rotations, recadrages, JPEG, contraste, etc.). Elle surpasse de manière significative les précédentes méthodes de tatouage zéro-bit, et ses performances en multi-bits sont comparables à celles des architectures encodeur-décodeur entraînées de bout en bout pour le tatouage numérique. Le code est disponible sur github.com/facebookresearch/ssl_watermarking. Abstract – We revisit watermarking techniques based on image transformations, in the light of self-supervised neural networks. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time. Our method can operate at any resolution and creates watermarks robust to a broad range of transformations (rotations, Recadrages, JPEG, Contraste, etc). It significantly outperforms the previous zero-bit methods, and its performance on multi-bit watermarking is on par with state-of-the-art encoder-decoder architectures trained end-to-end for watermarking. The code is available at github.com/facebookresearch/ssl_watermarking.
The black hole candidate LS I +61°0303

In recent years, fundamental relationships for the black hole X-ray binaries have been established between their X-ray luminosity $L_X$ and the photon index $\Gamma$ of their X-ray spectrum. For the moderate-luminosity regime, an anti-correlation between $\Gamma$ and $L_X$ has been observed. In this article, aimed to verify if the moderate luminous X-ray binary system LSI +61303 is a black hole, we analyse $Swift$ observations of LSI +61303. We compare the derived $L_X$ vs $\Gamma$ distribution, first with the statistical trend for black hole X-ray binaries, then with the trend of the pulsar PSR B1259-63, and finally with the individual trends of the black hole X-ray binaries Swift J1357.2-0933 and V404 Cygni. We find that the system PSR B1259-63 shows a positive correlation between $\Gamma$ and $L_X$, whereas in contrast LSI +61303 shows the same anti-correlation as for black hole X-ray binaries. Moreover, the trend of LSI +61303 in the $L_X$/$L_{Eddington} - \Gamma$ plane overlaps with that of the two black holes Swift J1357.2-0933 and V404 Cygni. All three systems, Swift J1357.2-0933, V404 Cygni and LSI +61303 well trace the last part of the evolution of accreting black holes at moderate-luminosity until their drop to quiescence.
A Large Ground-based Observing Campaign of the Disintegrating Planet K2-22b

We present 45 ground-based photometric observations of the K2-22 system collected between 2016 December and 2017 May, which we use to investigate the evolution of the transit of the disintegrating planet K2-22b. Last observed in early 2015, in these new observations we recover the transit at multiple epochs and measure a typical depth of <1.5%. We find that the distribution of our measured transit depths is comparable to the range of depths measured in observations from 2014 and 2015. These new observations also support ongoing variability in the K2-22b transit shape and time, although the overall shallowness of the transit makes a detailed analysis of these transit parameters difficult. We find no strong evidence of wavelength-dependent transit depths for epochs where we have simultaneous coverage at multiple wavelengths, although our stacked Las Cumbres Observatory data collected over days-to-months timescales are suggestive of a deeper transit at blue wavelengths. We encourage continued high-precision photometric and spectroscopic monitoring of this system in order to further constrain the evolution timescale and to aid comparative studies with the other few known disintegrating planets.
Lending and regional growth in Brazil: the development bank BNDES versus private and public banks

Abstract The present paper distinguishes how the type of bank that provides loans is related to GDP per capita in Brazilian municipalities between 2007 and 2016. A unique data set allows us to discriminate between the effects of credits operated by either private or public commercial banks, and loans provided by the national development bank BNDES either directly or indirectly through accredited financial institutions. Using the system GMM estimator, we find that credit from public commercial banks has the highest effect on economic growth but it is concentrated in the most populated municipalities. Moreover, indirect BNDES loans show a robust and significantly positive relation to local GDP given that this lending facility especially targets firms in small, credit constrained regions, and it was provided non-cyclically.