Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers’ Perception, Experiences, and Challenges about Teens of Ages 12-14 Years who have Reported Online Digital Insecurity in Kenya

This study explores teachers’ perceptions, experiences, and challenges on teens ages 12-14 years old who have reported online insecurity issues in Kenya. The study recognizes that teachers have increased pressure to teach learners digital skills and manage their online and offline lives. Although much of the online insecurity discourse is children centric, little is known about the teacher’s experiences when tackling teen’s online safety issues. Outside the minimal discussion, an inferential approach towards the demarcation of online insecurity is reached that is coined on the professed notion of internet safety. Data was generated using an in-depth interview with respective teachers, Guidance & counseling, ICT teachers, and school principal. A total of twenty-three participants were recruited who qualified for the study inclusion criteria. A qualitative intrinsic case study was employed, and data analyzed thematically. The findings from the study revealed that teachers lack online safety competency skills to protect teens. Therefore, they depend on the very learners they are supposed to protect to teach them to navigate digital platforms. It is imperative to integrate online digital insecurity in the Kenya curriculum to develop workable frameworks to support teens who have encountered troubles on the internet. The study recommends effective digital literacy instruction intervention for teachers to support the children in the online space effectively. Additionally, in-depth research exploring parents’ responses to online challenges while protecting children is a gap that needs to be filled.
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning

Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset BIMrAI, we used an existing BIM model and a corresponding photo-realistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data; i.e., photorealistic renderings and BIM-based semantic labels, can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
Efficient Error-Correcting-Code Mechanism for High-Throughput Memristive Processing-in-Memory

Inefficient data transfer between computation and memory inspired emerging processing-in-memory (PIM) technologies. Many PIM solutions enable storage and processing using memristors in a crossbar-array structure, with techniques such as memristor-aided logic (MAGIC) used for computation. This approach provides highly-paralleled logic computation with minimal data movement. However, memristors are vulnerable to soft errors and standard error-correcting-code (ECC) techniques are difficult to implement without moving data outside the memory. We propose a novel technique for efficient ECC implementation along diagonals to support reliable computation inside the memory without explicitly reading the data. Our evaluation demonstrates an improvement of over eight orders of magnitude in reliability (mean time to failure) for an increase of about 26% in computation latency.
Formic acid dehydrogenation over Pd NPs supported on amine-functionalized SBA-15 catalysts: structure–activity relationships

Palladium nanoparticles (Pd NPs) supported on organoamine-functionalized SBA-15 mesoporous silica are known to have higher catalytic activity for formic acid dehydrogenation than Pd NPs supported on plain, unmodified SBA-15 mesoporous silica. The organoamine groups present around the Pd NPs play important roles in stabilizing the Pd NPs, providing strong metal-molecular support interaction (SMMSI) to the Pd NPs, and rendering enhanced catalytic activities to the materials toward formic acid dehydrogenation. However, detailed studies on the effects of different types of amine groups on the catalytic activities of mesoporous silica-supported Pd NPs toward formic acid dehydrogenation have not been reported. Herein, we describe the synthesis of SBA-15 mesoporous silica grafted with three types of organoamine groups (primary, secondary, and tertiary amine), which are further decorated with tetraamminepalladium(II) complexes that are then reduced to Pd NPs in situ with 10% of H2/N2 at 250 °C. The effects of the different compositional and steric properties associated with the amine groups on (i) the electronic properties of the Pd NPs, (ii) the size of the Pd NPs, and (iii) the structure–property relationships of the materials were closely examined. Based on the results, the materials' catalytic activities toward dehydrogenation of formic acid to generate hydrogen (H2) for fuel cells were determined.
Translation studies and Bible translation: Interview on BT List Live

A live webinar on translation studies and its implications for Bible translation was held on 20 August 2020. The goal was to answer the question: What insights can Bible translation practitioners glean from the field of translation studies? It is argued that the contribution of translation studies to Bible translation cannot be ignored; instead, translation studies is indispensable for Bible translation, especially in the planning, the establishment and the execution of a Bible translation project. After the introduction, the webinar focused on the nature of translation studies followed by the dissemination of translation knowledge for Bible translation. The conclusion listed the shifts that need to take place in Bible translation on the basis of its engagement with translation studies.
A utility piezoelectric energy harvester with low frequency and high-output voltage: Theoretical model, experimental verification and energy storage

In this paper, a utility piezoelectric energy harvester with low frequency and high-output voltage is presented. Firstly, the harvester’s three theoretical models are presented, namely the static model, the quasi static model and the dynamic vibration model. By analyzing the influence of the mass ratio of the mass block to the beam on output characteristics of the harvester, we compare the quasi static model and the dynamic vibration model and then define their applicable ranges. Secondly, simulation and experiments are done to verify the models, using the harvester with PZT-5H piezoelectric material, which are proved to be consistent with each other. The experimental results show that the output open-circuit voltage and the output power can reach up to 86.36V and 27.5mW respectively. The experiments are conducted when this harvester system is excited by the first modal frequency (58.90Hz) with the acceleration 10m/s2. In this low frequency vibration case, it is easy to capture the energy in the daily env.
Unit Ball Model for Hierarchical Embeddings in Complex Hyperbolic Space

Learning the representation of data with hierarchical structures in the hyperbolic space attracts increasing attention in recent years. Due to the constant negative curvature, the hyperbolic space resembles tree metrics and captures the tree-like properties of hierarchical graphs naturally, which enables the hyperbolic embeddings to improve over traditional Euclidean models. However, most graph data, even the data with hierarchical structures are not trees and they usually do not ubiquitously match the constant curvature property of the hyperbolic space. To address this limitation of hyperbolic embeddings, we explore the complex hyperbolic space, which has the variable negative curvature, for representation learning. Speciﬁ-cally, we propose to learn the graph embeddings in the unit ball model of the complex hyperbolic space. The unit ball model based embeddings have a more powerful representation capacity to capture a variety of hierarchical graph structures. Through experiments on synthetic and real-world data, we show that our approach improves over the hyperbolic embedding models signiﬁcantly.
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

We address the problem of causal effect estimation in the presence of unobserved confounding, but where proxies for the latent confounder(s) are observed. We propose two kernel-based methods for nonlinear causal effect estimation in this setting: (a) a two-stage regression approach, and (b) a maximum moment restriction approach. We focus on the proximal causal learning setting, but our methods can be used to solve a wider class of inverse problems characterised by a Fredholm integral equation. In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting. We provide consistency guarantees for each algorithm, and we demonstrate these approaches achieve competitive results on synthetic data and data simulating a real-world task. In particular, our approach outperforms earlier methods that are not suited to leveraging proxy variables.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes a dialogue context and a slot with a pre-trained self-attentive encoder, and generates slot value in auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information of different slots to facilitates the cross-domain knowledge transfer. Experimental results on MultiWOZ shows that our model significantly improve existing state-of-the-art results in zero-shot cross-domain setting.
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking

In many applications, it is desirable that a classifier not only makes accurate predictions, but also outputs calibrated posterior probabilities. However, many existing classifiers, especially deep neural network classifiers, tend to be uncalibrated. Post-hoc calibration is a technique to recalibrate a model by learning a calibration map. Existing approaches mostly focus on constructing calibration maps with low calibration errors, however, this quality is inadequate for a calibrator being useful. In this paper, we introduce two constraints that are worth consideration in designing a calibration map for post-hoc calibration. Then we present Meta-Cal, which is built from a base calibrator and a ranking model. Under some mild assumptions, two high-probability bounds are given with respect to these constraints. Empirical results on CIFAR-10, CIFAR-100 and ImageNet and a range of popular network architectures show our proposed method significantly outperforms the current state of the art for post-hoc multi-class classification calibration.
DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks

Data movement between the CPU and main memory is a first-order obstacle against improv ing performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarch ies, aggressive hardware prefetcher s) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques (e.g., cach ing and prefetch ing) to more memory-centric techniques (e.g., NDP), thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement. With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.
Brain-derived neurotrophic factor as a potential diagnostic marker in minimal hepatic encephalopathy

Introduction Minimal hepatic encephalopathy (MHE) is a common complication of liver cirrhosis not only leading to a decrease in the quality of life, but also predicting development of overt encephalopathy. The diagnosis of MHE usually relies on a combination of neuropsychological tests, while robust serum biomarkers are lacking. We aimed to assess serum concentrations of brain-derived neurotrophic factor (BDNF) in MHE patients. Material and methods Serum BDNF was assessed in 78 patients with liver cirrhosis (53 male, median age 55 years) and 40 healthy individuals. 43 subjects underwent extensive evaluation for MHE by psychometric hepatic encephalopathy score (PHES) and inhibitory control test (ICT) or critical flicker frequency (CFF). Results Serum BDNF was twofold lower in liver cirrhosis compared to healthy subjects [13.6 (7.8-22.6) vs. 33.0 (24.1-40.7) ng/ml, p < 0.001] and its decrease reflected a degree of liver insufficiency assessed by model for end-stage liver disease (MELD). BDNF showed a negative correlation with bilirubin (R = –0.35, p = 0.005) and international normalized ratio (INR) (R = –0.37, p = 0.003), and positive with platelets (PLT) (R = 0.36, p = 0.004), while no associations with age, sex, body mass index (BMI), waist-hip ratio (WHR), creatinine and ammonia were noted. Importantly, subjects with a diagnosis of MHE by at least two modalities showed the lowest levels of BDNF [10.9 (2.5-14.4) vs. 19.9 (9.3-29.4) ng/ml, p < 0.01]. Patients with self-reported sleep disturbances had significantly lower serum BDNF [13.0 (2.5-23.4) vs. 20.0 (8.4-31.3) ng/ml, p = 0.04]. Conclusions The lowest serum BDNF concentration was noted in patients with MHE and sleep disturbances, which suggests a role in pathophysiology of hepatic encephalopathy but also as a potential biomarker.
Genetic variability, heritability and genetic advance of yield attributing traits in maize

The present experiment was conducted at Agricultural College Farm, Naira during kharif 2013-14 to estimate the genetic variability, heritability and genetic advance in 30 inbred lines. The genotypic coefficients of variation (GCV) for all the characters studied were lesser than the phenotypic coefficients of variation (PCV) indicating the effect of the environment. The high estimates of heritability with high genetic advance as per cent of mean and high PCV and GCV were recorded for grain yield per plant and 100 kernel weight followed by LAI 90 DAS. Whereas, low estimates of heritability with low genetic advance as per cent of mean and low PCV and GCV were recorded for RGR 60, 90 DAS followed by harvest index.
Entropy for spherically symmetric, dynamical black holes from the relative entropy between coherent states of a scalar quantum field

The goal of this paper is to prove an area law for the entropy of dynamical, spherically symmetric black holes from the relative entropy between coherent states of the quantum matter, generalising the results by Hollands and Ishibashi (2019 Class. Quantum Grav. 36 195001). We consider the relative entropy between a coherent state and a suitably chosen asymptotically vacuum state for a scalar quantum field theory propagating over a dynamical black hole. We use the conservation law associated to the Kodama vector field in spherically symmetric spacetimes, and the recent results on the relative entropy between coherent states found in Longo (2019 Lett. Math. Phys. 109 2587) and Casini et al (2019 Phys. Rev. D 99 125020). We consider the back-reaction of the quantum matter on the metric. From the conservation law associated with the Kodama vector field, we obtain an equation for the derivative along the outgoing lightrays of the generalised entropy, defined as the sum of the relative entropy between coherent states of the scalar field and one quarter of the horizon area.
Earnings Quality and Accruals over Company´S Life Cycle

Research background: The increasing number of bankruptcies and the growing risk of financial distress highlight the need for quality financial statements, conservative accounting or increase the need for quality tools to detect the occurrence of earnings management. However, the life cycle of a company affects financial performance and key aspects of earnings management, which examined in the international context only to a small extent. Purpose of the article: The paper examines the impact of the life cycle and country-specific factor on the value of discretionary accruals in the tourism sector in the Visegrad countries, which are among the most vulnerable sectors in the coming economic crisis. Methods: This study uses the method of two-way analysis of variance with interaction, while also testing the assumptions of the model by normality tests, homogeneity test and post hoc tests (Scheffé and Tukey methods). Findings & Value added: Earnings quality changes during the life cycle of a business; whereas in the first stages (introduction, growth) and in decline they use downward earnings management. On the contrary, mature and shake-out companies have enough positive earnings before taxes, which is a prerequisite for tax profit optimization. The level of earnings management in tourism varies significantly at different stages of the life cycle, but also in different countries. The results imply that the qualitative variable corporate life cycle in interaction with the country is an important explanatory variable, the implementation of which can improve the explanatory power of earnings management models in Central European developing countries.
Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics

Yang (2020a) recently showed that the Neural Tangent Kernel (NTK) at initialization has an infinite-width limit for a large class of architectures including modern staples such as ResNet and Transformers. However, their analysis does not apply to training. Here, we show the same neural networks (in the so-called NTK parametrization) during training follow a kernel gradient descent dynamics in function space, where the kernel is the infinite-width NTK. This completes the proof of the *architectural universality* of NTK behavior. To achieve this result, we apply the Tensor Programs technique: Write the entire SGD dynamics inside a Tensor Program and analyze it via the Master Theorem. To facilitate this proof, we develop a graphical notation for Tensor Programs.
Galois/monodromy groups for decomposing minimal problems in 3D reconstruction

We consider Galois/monodromy groups arising in computer vision applications, with a view towards building more efficient polynomial solvers. The Galois/monodromy group allows us to decide when a given problem decomposes into algebraic subproblems, and whether or not it has any symmetries. Tools from numerical algebraic geometry and computational group theory allow us to apply this framework to classical and novel reconstruction problems. We consider three classical cases--3-point absolute pose, 5-point relative pose, and 4-point homography estimation for calibrated cameras--where the decomposition and symmetries may be naturally understood in terms of the Galois/monodromy group. We then show how our framework can be applied to novel problems from absolute and relative pose estimation. For instance, we discover new symmetries for absolute pose problems involving mixtures of point and line features. We also describe a problem of estimating a pair of calibrated homographies between three images. For this problem of degree 64, we can reduce the degree to 16; the latter better reflecting the intrinsic difficulty of algebraically solving the problem. As a byproduct, we obtain new constraints on compatible homographies, which may be of independent interest.
Cultivation temperature and length affect the antioxidant activity and total phenolic content of soybean Koji prepared with Aspergillus awamori

In this study, soybean koji was first fermented with Aspergillus awamori, a starter organism commonly used for the fermentation of sake, at 25, 30 or 35℃ for a period of 1~5 days. Antioxidant activity, including DPPH radical-scavenging effect and Fe(superscript 2+)-chelating ability, and total phenolic content of the methanol extract of these fermented soybean koji as well as mycelial propagation of starter organism were compared. Results revealed that fermented for a same period of 3 days, the DPPH radical-scavenging effect exerted by the 30℃-koji extract was the highest showing 2.5~3.5 folds of that fermented at 25 or 35℃. The Fe(superscript 2+)-chelating ability exerted by the 30℃-koji extract was also found to be 3.6~3.8 times higher than those fermented at other temperatures. Furthermore, it was also noted that increasing or decreasing cultivation time from 3 days resulted in a decreased antioxidant activity of the koji extract. Cultivation temperature and length were also observed to affect the mycelial propagation and the total phenolic content of the koji extract. Maximum mycelial propagation of A. awamori was achieved after cultivation at 30℃ for 3 days. Finally, the highest phenolic content noted in 30℃-koji extract was closely correlated to the highest antioxidant activity observed.