Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Children and Media

The purpose of the present paper is to validate a short version of the MMO Player Motivation scale. Two studies were conducted using adolescent gamers from Singapore secondary schools. Study 1 used a cross-sectional design for item deduction. Study 2 used a 2-year longitudinal design for scale validation. The results showed that the brief scale conformed to the three-factor motivation model, including achievement, socialization, and immersion. The model demonstrated acceptable model ﬁt and construct validity. The study also showed that different types of gamer motivation predicted different gaming habits and behaviors 1 year later. With its predictive validity, the scale could be used to identify students who are likely to develop pathological gaming patterns and other at-risk behaviors based on their current playing motivations.
How to Estimate Statistically Detectable Trends in a Time Series: A Study of Soil Carbon and Nutrient Concentrations at the Calhoun LTSE

Soil Sci. Soc. Am. J. 83:S133–S140 doi:10.2136/sssaj2018.09. 0335 Received 16 Sept. 2018. Accepted 12 Dec. 2018. *Corresponding author (yangy48@gmail.com). © 2019 The Author(s). This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). How to Estimate Statistically Detectable Trends in a Time Series: A Study of Soil Carbon and Nutrient Concentrations at the Calhoun LTSE
A Survey of Self-supervised Learning from Multiple Perspectives: Algorithms, Applications and Future Trends

—Deep supervised learning algorithms generally require large numbers of labeled examples to achieve satisfactory performance. However, collecting and labeling too many examples can be costly and time-consuming. As a subset of unsupervised learning, self-supervised learning (SSL) aims to learn useful features from unlabeled examples without any human-annotated labels. SSL has recently attracted much attention and many related algorithms have been developed. However, there are few comprehensive studies that explain the connections and evolution of different SSL variants. In this paper, we provide a review of various SSL methods from the perspectives of algorithms, applications, three main trends, and open questions. First, the motivations of most SSL algorithms are introduced in detail, and their commonalities and differences are compared. Second, typical applications of SSL in domains such as image processing and computer vision (CV), as well as natural language processing (NLP), are discussed. Finally, the three main trends of SSL and the open research questions are discussed. A collection of useful materials is available at https://github.com/guijiejie/SSL.
Organic amendments and mulches modify soil porosity and infiltration in semiarid mine soils

The restoration of soils affected by mining activities depends on the improvement of soil physical quality. This study aimed to determine the medium‐term effects of organic amendments and mulches on soil physical properties and soil hydrological response. The experimental restoration was conducted in a homogeneous slope using a factorial design (organic amendments × mulches). The organic amendments consisted of compost from domestic organic residues, sewage sludge from an urban water treatment facility, and a control without amendment. The mulches consisted of gravel, woodchips, and a control without mulch. Modifications of the soil structure induced by the combinations of techniques were quantified by micromorphometric analysis of 2D porosity. Undisturbed soil cores were collected to determine the soil bulk density. Soil hydrological behaviour was determined by rainfall simulations. Samples from natural undisturbed soils surrounding the quarry were also taken as a reference. Restored soils with organic amendments have a higher total porosity than did nonamended substrates. Each restoration treatment combination resulted in a different pore system with very diverse hydrological responses. Organic amendments resulted in infiltration improvement and water erosion reduction. The woodchip mulch was more effective at trapping runoff and sediment, although this type of mulch did not favour vertical water movement towards deeper horizons. Therefore, the use of organic amendments should be encouraged in mine soil restoration and should be strongly recommended for its proven environmental benefits (enhancement of soil structure and infiltration and reduction of soil losses). However, the mulches did not produce the expected results in the tested arid–semiarid area.
AIS Electronic

Students entering into their first year of university studies are subject to a jarring difference between the learning experiences of high school and university. High schools typically take a very structured approach to teaching whereas university expects students to take ownership of their learning. This dramatic shift often means students are not adequately prepared to form positive study habits on their own, or existing habits are likely to break down. Further complicating this issue is the rising popularity of video games and social networking amongst students. These two areas provide fun and engaging experiences for their users where traditional learning environments struggle to do so. In this research-in-progress paper, we propose a framework that can be used by instructors to improve learning environments so that their students are better engaged and encouraged to form positive study habits. The framework utilises a hybrid of the Transtheoretical model of behaviour change (TTM) and the SNAP model of motivation. Social networking and gamification are used as triggers that enable the process to occur. It is envisaged that this research will lead to instructors creating more effective learning environments with less effort, and making academic learning a more enjoyable pursuit for students.
AIS Electronic

Self-management programs have been introduced in order to help chronic patients better manage their symptoms, treatments, and the physical, as well as the psychosocial consequences and lifestyle changes inherent in living with a chronic condition. As many chronic patients tend to also suffer from mental health issues, such as depression and anxiety, coping resources and constructive coping strategies can also help them improve their psychological health, which in turn can help them improve their quality of life. Information technologies can provide useful opportunities for improving the self-management support that are provided to chronic illnesses such as asthma, especially when they can be integrated with patients’ ongoing medical care and by enabling the patients to also better cope with mental health issues. The present paper describes an exploratory study that studied 17 asthma patients who used a self-management system and examined if and how the system also helped them cope with the negative emotions evoked by their disease. Adopting a coping theory perspective, the paper identified several coping responses that the studied self-management system seemed to support, and developing future self-management systems so that they can also support these coping responses can be useful for improving the health of chronically ill patients.
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a fading multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(1/K^{1/2})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(1/K^{3/4})$ </tex-math></inline-formula>. In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are <inline-formula> <tex-math notation="LaTeX">$(a)$ </tex-math></inline-formula> known, or <inline-formula> <tex-math notation="LaTeX">$(b)$ </tex-math></inline-formula> unknown at the parameter server.
One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.
Concurrent learning based finite time parameter estimation in adaptive control of uncertain switched systems

In this paper, We propose concurrent learning adaptive controller, which uses recorded and current data concurrently for adaptation, to model reference adaptive control (MRAC) of uncertain switched systems. In standard MRAC architecture for switched systems, the adaptive update laws are derived based on the gradient descent scheme, but here we developed two novel parameter estimation schemes by using modification terms in adaptation laws in which recorded data is used simultaneously with current data and a triggering time is considered in which a sufficient condition on the linear independence of the recorded data is obtained to guarantee the exponential convergence of tracking error and parameter estimation error to zero for the uncertain switched system under all admissible switching strategy. The convergence of the parameters to the ideal values makes an on-line learned model of the system available. This sufficient condition is easily verifiable in comparison to the restrictive persistence of excitation (PE) condition of the standard MRAC structures in practical applications. Finally a simulation example is given to illustrate the efficacy of the proposed method.
Assumption University-eJournal of Interdisciplinary Research (AU-eJIR): Vol. 4. Issue.2, 2019

This study sought to find out effectiveness of Assessment-Supported Instructional Model (ASIM) on achievement and ability of English language students in Oke Ero Local Government Area of Kwara State. Quasi-experimental design was adopted in the study. Two research questions and two null hypotheses were formulated to guide the study. Two hundred students constituted the sample for the study. The experimental and control groups were taught topics in English language using ASIM and conventional methods respectively. English Language Achievement Test (ELAT) of 24 items was used for data collection. Mean and Standard Deviation were used to answer the research questions while ANCOVA was used to test null hypotheses at p < 0.05. The findings showed, among others, that ASIM was more effective in enhancing students’ achievement in English language than the conventional methods of teaching. The high ability students also achieved more than the low ability students in the achievement. There was significant interaction effect between instructions and ability group in achievement as shown by the study. Consequently, recommendations such as adoption of the model and sensitization and training of the teachers on its importance and usage were forwarded.
Geographical variations in the effectiveness and side effects of deep ocean carbon sequestration

The capture and injection of carbon dioxide (CO2) into the deep ocean could provide a relatively long‐term mitigation of climate change, but would come at the expense of enhancing acidification at the seafloor. We employ an Earth system model to survey the regional differences in the effectiveness and side effects of CO2 injection. Sequestration efficiency, as calculated relative to the ‘natural’ invasion from the atmosphere that would occur in the absence of mitigation, is highest for injection in the deep NW Pacific, but can be negative for shallow sites. For higher climate sensitivities and greater total emissions, sequestration efficiency is enhanced, decreasing the relative cost and increasing its potential value as a form of mitigation. However, CO2 injection increases the total area of seafloor bathed in under‐saturated waters, with Atlantic sites inducing particularly large increases in seafloor undersaturation as well as having less favorable sequestration efficiency.
The Hidden Subgroup Problem

We give an overview of the Hidden Subgroup Problem (HSP) as of July 2010, including new results discovered since the survey of arXiv:quant-ph/0411037v1. We recall how the problem provides a framework for efficient quantum algorithms and present the standard methods based on coset sampling. We study the Dihedral and Symmetric HSPs and how they relate to hard problems on lattices and graphs. Finally, we conclude with the known solutions and techniques, describe connections with efficient algorithms as well as miscellaneous variants of HSP. We also bring various contributions to the topic. We show that in theory, we can solve HSP over a given group inductively: the base case is solving HSP over its simple factor groups and the inductive step is building efficient oracles over a normal subgroup N and over the factor group G/N. We apply this analysis to the Dedekindian HSP to get an alternative abelian HSP algorithm based on a change of the underlying group. We also propose a quotient reduction by the normal group obtained via Weak Fourier Sampling. We compute the exact expression of Strong Fourier Sampling over the dihedral group, showing how the previous reduction is natural and matches the standard one. We also give a reduction of rigid graph isomorphism problem to HSP over the alternating group. For this group and other simple groups, we propose maximal subgroup reduction as a possible approach. We also analyse Regev's algorithm for the poly(n)-uniqueSVP, prove how the degree of the polynomial is related to the oracle complexity used and we suggest several variants.
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks

From the Publisher: Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptions (MLP). These are the most widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
Aging and Molecular Changes of Dissolved Organic Matter Between Two Deep Oceanic End‐Members

The global ocean contains a massive reservoir of dissolved organic carbon (DOC), rivaling the atmosphere's pool of CO2. The most recalcitrant fractions have mean radiocarbon ages of ~4,000 years in the Atlantic to ~6,000 years in the Pacific. Knowing the radiocarbon signatures of DOC and the molecular composition of dissolved organic matter (DOM) is crucial to develop understanding of the persistence and lifetime of the DOC pool. In this research, we collected samples from the deep North Pacific in August 2013 (aboard the RV Melville) to couple the Δ14C content of solid‐phase‐extracted DOM (Δ14C‐SPE‐DOM) with its molecular composition in the ocean's oldest deep waters. We find that deep waters in this region held a mean Δ14C‐SPE‐DOM value of −554 ± 9‰ (~6,400 14C years), substantially more depleted than that in the deep Atlantic, which held a mean Δ14C‐SPE‐DOM value of −445 ± 5‰. While we find a more degraded molecular composition of DOM in the deep Pacific than the deep Atlantic, the molecular formulae within the Island of Stability (Lechtenfeld et al., 2014, https://doi.org/10.1016/j.gca.2013.11.009), are largely retained. These results imply that a fraction of deep DOM is resistant to removal and present in both the deep Atlantic and Pacific Oceans.
Unslotted deflection routing: a practical and efficient protocol for multihop optical networks

This paper is concerned with all-optical networks using deflection routing and time division multiplexing. Slotted networks make use of the synchronous arrival of the packets to the routers to minimize locally the number of deflections. We show that the difference in performance between slotted and unslotted networks is mainly due to the fact that unslotted networks cannot easily perform such local optimization. We also show that minimizing locally the number of deflections in unslotted networks gives rise to an NP-complete problem. To overcome this problem, we have designed a heuristic whose aim is to limit locally the number of deflections. We experimentally demonstrate that this heuristic enhances unslotted routing almost at the same performance level as slotted routing. As a consequence, we have shown that unslotted deflection routing can be implemented is a way which makes it a competitive alternative to slotted deflection routing for optical time division multiplexing deflection networks.
Improved transcriptome annotation and read-through transcript identification with TIF-Seq2

Eukaryotic transcriptomes are complex involving thousands of overlapping transcripts. The interleaved nature of the transcriptome complicates its annotation, limits our ability to identify regulatory regions and, in some cases, can lead to misinterpretation of gene expression. To improve the annotation of complex genomes, we have developed an optimized method, TIF-Seq2, able to sequence simultaneously the 5’ and 3’ end of individual mRNA molecules at single-nucleotide resolution. We investigate the transcriptome of a well characterized human cell line (K562) and identify thousands of new transcript isoforms. By focusing on RNAs challenging to investigate with RNA-seq, we accurately define boundaries of lowly expressed intergenic and read-through transcripts. We validate those novel features in cell lines and in Chronic Myeloid Leukaemia patients. Our results demonstrate that TIF-Seq2 improves the annotation of complex genomes facilitating the assignment of promoters to genes and the identification of transcriptionally fused proteins.
Influence of water soluble fertilizers on growth and yield parameters of hybrid maize ( Zea mays L.) under rainfed situation of southern transitional zone of Karnataka

A field experiment was conducted during Kharif 2014 at Agricultural and Horticultural Research Station, Bhavikere, UAHS, Shivamogga, on red clay loam soil which was low in available nitrogen, medium in available phosphorus and potassium. There were 11 treatment combination consisting of two different water soluble fertilizers along with Package of practice (viz., only RDF, 0.5 % 18:18:18, 1.0 % 18:18:18, 1.5 % 18:18:18, 0.5 % Multi-K, 1.0 % Multi-K, 1.5 % Multi-K, 1.0 % 18:18:18 + 0.5 % Multi-K, 1.0 % 18:18:18 + 1.0 % Multi-K and 1.0 % 18:18:18 + 1.5 % Multi-K). All water soluble fertilizer were given as foliar spray at two stages (30 DAS and 60 DAS) of crop growth and all the treatments received RDF as per the requirements. The experiment was laid out in RCBD with three replications. The Package of Practice + 18:18:18 @ 1.0 % + Multi-K @1.5 % recorded significantly higher plant height (218.7 cm), leaf area (62.8 cm2 plant-1), total dry weight (335 g plant-1), cob length (18.5 cm), thousand grain weight (236 g), shelling percentage (82.9 %), number of grain per cob (435), grain rows per cob (12.9), grain weight per plant (179 g), Grain yield (83.99 q ha-1), stover yield (183.38 q ha-1) and harvest index (0.46) compared to other of treatments.
Privacy in the Electronic Society: Emerging Problems and Solutions

As the global information infrastructure is becoming more ubiquitous, digital business transactions are increasingly performed using a variety of mobile devices and across multiple communication channels. This new service-oriented paradigm is making the protection of privacy an increasing concern, as it relies on rich context representations (e.g., of location and purpose) and requires users to provide a vast amount of information about themselves and their behavior. This information is likely to be protected by a privacy policy, but restrictions to be enforced may come from different input requirements, possibly under the control of different authorities. In addition, users retain little control over their personal information once it has been disclosed to third parties. Secondary usage regulations are therefore increasingly demanding attention. In this paper, we present the emerging trends in the data protection field to address the new needs and desiderata of today’s systems.
Experiments with the Negotiated Boolean Queries of the TREC 2006 Legal Discovery Track

We analyze the results of several experimental runs submitted to the Legal Discovery and Terabyte Tracks of TREC 2006. In the Legal Track, the final negotiated boolean queries produced higher mean scores in average precision and Precision@10 than a corresponding vector run of the same query terms, but the vector run usually recalled more relevant items by rank 5000, and on average the boolean query matched less than half of the relevant items. We also report the impact of query negotiation, metadata and natural language vs. keywords. We find that robust metrics (which expose the downside of blind feedback techniques) are inappropriate for legal discovery because they favour duplicate filtering. We also report the results of depth probe experiments (depth 9000 in the Legal Track and depth 5000 in the Terabyte Track) which provide a lower-bound estimate for the number of unjudged relevant items in each track.