Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding the Magical Minimum Sample Size: a Computer-intensive Approach to Minimize Re-aging Effort to Construct Age-length Keys for Yellowtail Flounder

Yellowtail flounder (Limanda ferruginea) is currently assessed using a stock-production model. The goal is to replace this model with an age-structured model; however validation studies show that the current method for ageing older fish is inaccurate and re-ageing the historical collection of otoliths in its entirety is not possible. Therefore our objective is to determine the minimum number of otoliths that need to be re-aged to generate a valid age-length key from a subsample of the full collection. All otoliths (n = 1747) from the 1998 spring and fall research surveys were re-aged, and used to build allyear full data age-length matrices (FDALM). These FDALM's were considered “target keys” for which we implemented a subsampling routine to reproduce them from samples of smaller sizes. Random subsamples (without replication) of different sizes were taken from the corresponding full databases, and subsampled age-length matrices (SSALM) were produced, and compared with the corresponding FDALM using a bidimensional two-sample Kolmogorov-Smirnov test. We used the obtained p-values as a measure of the similarity between the keys. To assess variability, random sampling was performed 400 times at each subsample size, and the median and 95 percentile range of the p-values were obtained. We used plots of the median p-values against subsample size to determine the size that produces the desired similarity with the full sample (i.e. a median p-value close to one with small variability). Our results indicate that small sample sizes (less than 10% of the full sample) generated non-significant differences between the SSALM and FDALM but with large variability, while subsample sizes around 60% of the full sample were similar enough to be considered adequate.
The Alignment Problem for Bayesian History-Based Reinforcement Learners∗

Value alignment is often considered a critical component of safe artificial intelligence. Meanwhile, reinforcement learning is often criticized as being inherently unsafe and misaligned, for reasons such as wireheading, delusion boxes, misspecified reward functions and distributional shifts. In this report, we categorize sources of misalignment for reinforcement learning agents, illustrating each type with numerous examples. For each type of problem, we also describe ways to remove the source of misalignment. Combined, the suggestions form high-level blueprints for how to design value aligned RL agents.
Seeing more than whitespace — Tokenisation and disambiguation in a North Sámi grammar checker

Communities of lesser resourced languages like North Sámi benefit from language tools such as spell checkers and grammar checkers to improve literacy. Accurate error feedback is dependent on well-tokenised input, but traditional tokenisation as shallow preprocessing is inadequate to solve the challenges of real-world language usage. We present an alternative where tokenisation remains ambiguous until we have linguistic context information available. This lets us accurately detect sentence boundaries, multiwords and compound error detection. We describe a North Sámi grammar checker with such a tokenisation system, and show the results of its evaluation.
Antimicrobial activities and phylogenetic study of bacteria associated with Cyperus rotundus rhizosphere from Cemoro Sewu Plateau, Indonesia

Abstract. Ambarwati A, Wahyuono S, Moeljopawiro S, Sembiring L, Yuwono T. 2019. Antimicrobial activities and phylogenetic study of bacteria associated with Cyperus rotundus rhizosphere from Cemoro Sewu Plateau, Indonesia. Biodiversitas 20: 2206-2212. A study has been conducted to investigate the activity of bacteria isolated from the rhizosphere of Purple Nut Sedge (Cyperus rotundus) from Cemoro Sewu Plateu, Indonesia as antimicrobial agent and the molecular relatedness between the isolates with other bacteria based on phylogenetic tree analysis. A total of six bacteria were obtained and tested for their capability to inhibit the growth of test organisms using agar block method and characterized molecularly using 16S rDNA sequence analysis. It was observed that the six isolates demonstrated the ability to inhibit at least one of four test microorganisms growth, with a diameter of inhibition zone ranging from 8-35 mm. The CRC32 isolate was the best isolate in inhibiting Staphylococcus aureus ATCC 25923 with a diameter inhibition zone of 26 mm (strong), while isolate CRA8 was the best anti-Candida (showing 35 mm of inhibition zone). The phylogenetic tree analysis resulted in two genera, four isolates belonging to Bacillus, while the other two isolates were known related to Paenibacillus genus. BLAST analysis on the CRC 32 isolate revealed that it is closely related with Paenibacillus peroriae strain 3763 with 99% sequence similarities and CRA8 isolates demonstrated the highest similarity with Paenibacillus sonchi strain X19-5 (97%). The results of this study demonstrated that the bacterial isolates from plant rhizosphere of Cemoro Sewu Plateau were the members of the Bacillus and Paenibacillus genera and demonstrated the potential as antimicrobial agent.
Children master the cardinal significance of counting after they learn to count

Children learn the meaning of number words by going through a systematic set of stages of knowledge that culminates in their mastery of counting. Theoretical work has long suggested that children’s acquisition of counting is not procedural, but semantic: all counters understand that counting computes cardinality. Yet, recent research has cast doubt on whether early counters truly understand the meaning of these words. Here we show that early counters also have an immature understanding of how one-to-one correspondence between an ordered list and a set of objects can be used to compute exact cardinality. Nonetheless, this understanding is improved when cues to quantity, such as size, are highlighted. Our results add to a growing body of work suggesting that counting is not a final stage in children’s path to number, but a powerful tool that they can use to build and strengthen their intuitions about cardinalities.
Determinants of Non-Performing Loans in Trinidad and Tobago : A Generalized Method of Moments ( GMM ) Approach Using Micro Level Data

Akeem Rahaman, Timmy Baksh, Reshma Mahabir, Dhanielle Smith 1 The incidence of non-performing loans (NPL), which is usually, expressed a percentage of total loans, can be used as an indicator of credit quality. High NPL implies that poor credit quality can affect the stability of any banking system. Although NPL has been steadily declining at the aggregate level between 2011 and 2014, it has been increasing at the individual bank level for some individual commercial banks thus increasing credit risk for specific institutions, which can have an overall impact on financial stability, given the financial linkages of the system. This paper uses unpublished panel data from the commercial banking sector in Trinidad and Tobago to estimate the bank-specific determinants of NPL using a Generalized Methods of Moments (GMM)/Dynamic Panel model. The model employed the Arellano-Bond (1991) two-step estimation technique which provides unbiased estimators when compared to a pooled regression model. The data satisfied all robust conditions and the results lend support to the ‘moral hazard’ hypothesis where banks may have lower capital levels than that which is optimal (although capital ratios are high), consequently increasing the riskiness of their loan portfolio. Furthermore, the estimation rejected the ‘bad management’ and ‘skimping’ hypothesis. JEL Classification Numbers: C23, C26, G21
Sample Size in Arabic Authorship Verification

Authorship Verification aims at identifying whether a document of questionable authorship is created by a specific author, given a number of documents known to have been written by that author. This type of authorship analysis uses feature engineering of feature sets extracted from large documents. Given the nonlinear morphology and flexible syntax of Arabic, feature extraction in large Arabic texts requires complex preprocessing. The requirement of large training and testing documents is also impractical for domains where large documents are available in print, given the scarcity of reliable Arabic OCR. This problem is approached by investigating the effectiveness of using an author profiling-based approach on a small set of shorter documents. The findings show that it is possible to outperform the state-of-the-art authorship verification method by using a small set of training documents. It is also found that an increase in the size of the training or testing corpus does not correlate with improving the accuracy of the authorship verification method.
Research on Fault Detection System of Power Equipment Based on UV and Infrared Image

UV corona on power system can reflect the location of the fault and the severity of the fault, the traditional UV and infrared detection equipment can only use the band and the visible light band image of the power system fault detection. In this paper, a power system fault detection system based on ultraviolet and infrared dual-band images is designed. The principle of UV imaging detection and image fusion are introduced respectively. The software of the host computer is written by MFC. The software can acquire both ultraviolet and infrared, the two images are fused using the image fusion algorithm based on edge detection and cross correlation and the highest point temperature is plotted. Experiments show that the system can detect the failure of power equipment in time, and has a certain practical value, which puts forward a new idea for fault detection of power equipment.
HALLAM: PROMOTING SOCIAL COHESION DURING BLENDED LEARNING IN A FOUNDATION DEGREE Citation Hallam, I. (2017) ‘Promoting social cohesion during blended learning in a foundation degree’, The STeP

This paper explores curriculum theory and principles, evaluates the social cohesion of curricula, and synthesises theory and policies related to education for sustainable development, whilst evaluating and enhancing a higher education blended learning module. Blended learning is defined as a ‘pedagogical approach that combines the opportunities of face-to-face learning with the opportunities of the online environment’ (Hew and Cheung, 2014, p. 3). The key challenge is social cohesion, specifically learner collaboration and social learning, which Lockhorst, Admiraal and Pilot (2010) break down into participation, interaction and the nature of the communication. To identify strategies to enhance the module, thereby maximising social cohesion and collaboration, this paper explores the module’s curriculum: the policy context, theoretical principles of curriculum design, social cohesion in the context of the parity of esteem between work-based and academic learning, and education for sustainable development. It concludes by proposing enhancements to the module curriculum to meet the demands of twenty-first century graduates (Ashwin et al., 2015, p. 159) and education for sustainable development.
Graph Theoretic Approach for Data Mining

The need for mining structured data was apparent to the research community and one such approach focused on the topological view of data structures. Since the graph has a generic topological structure and is one of the most thoroughly researched data structures in Computer Science and Discrete Mathematics, state ofthe-art techniques in graph-based data mining (GDM) have had profound influence. GDM has tremendous utility because graph-structured data occur widely in practical fields like biology, chemistry, material science and communication networking. Graph-based data mining represents a collection of techniques for mining the relational aspects of data represented as a graph. Two major approaches to graph based data mining are frequent sub graph mining and graphbased relational learning. This article will focus on one particular approach embodied in the Subdue system, along with recent advances in graph-based supervised learning, graph-based hierarchical conceptual clustering, and graph-grammar induction. The need for mining structured data has increased rapidly. One of the best studied data structures in computer science and discrete mathematics are graphs. Graph based data mining has become quite popular in the last few years. This study introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.
1 Firm Heterogeneity , Location and Export Performance : Empirical Evidence from Ukrainian Firm-Level Data

This paper studies export performance of Ukrainian firms using the unique micro-level dataset for the years 2005 and 2013. We estimate probit regressions for pooled dataset for all years as well as for specific years of our sample. In addition, we distinguish between manufacturing and service sectors. Our empirical results show the positive link between the export status and total factor productivity, competition in the industry, firm size, capital intensity, ownership status, and foreign sourcing (imports). Moreover, firms located in Western part of the country are found to be more export-oriented compared to firms located in other regions.
Determination of Antioxidant Activity for Seven Types of Macroalgae

The present study was conducted to evaluate the antioxidant activity of seven types of macroalgae extract from Malaysia. The extract was prepared with methanol respectively. 1,1-diphenyl-2picrylhydrazyl (DPPH) assay and reducing power were used to study their antioxidant activity while total phenolic content was measured using Folic-Ciocalteu method. The macroalgae extracts were compared with commercial antioxidant, butylated hydroxyanisole (BHA). Total phenolic content of macroalgae extracts were expressed in Gallic acid equivalent, mg/L. Caulerpa racemosa showed the highest total phenolic content with the value of 19.711 mgGAE/L ± 0.2546. In DPPH free radical scavenging activity assay, Turbinaria conoides showed the highest scavenging activity in 50 mg/ml extract concentration with the value of 73.57 % ± 0.5739 compared to another macroalgae extracts. The IC50 value of Turbinaria conoides extracts is 2.46 mg/ml. Low IC50 value will indicates the strong ability of the macroalgae extract to act as a DPPH scavenger. Caulerpa lentillifera showed the highest absorbance reading in 50 mg/ml extract concentration with the value of 0.603 ± 0.0015 in reducing power assay. Increased absorbance of the reaction mixture indicates greater reducing power.
Improving the manufacturing performance by using the appropriate purchasing strategy

the purchasing strategy as one of vital functional strategies have an effect on the company success, but a few number of practical research have tested such an effect. The purpose of this paper is to examine the effect of different purchasing strategies on manufacturing performance by collecting and analyzing data from the household appliance industry in Egypt. The proposed research model describes the impact of purchasing strategy on manufacturing performance. The proposed research model and hypotheses were tested using Correlation Analysis and structural equation modeling based on data collected from 112 companies in the Egyptian household appliance industry. According to the results of the study, purchasing strategy has a positive significant effect on manufacturing performance.
Rethinking Database High Availability with RDMA Networks

Highly available database systems rely on data replication to tolerate machine failures. Both classes of existing replication algorithms, active-passive and active-active, were designed in a time when network was the dominant performance bottleneck. In essence, these techniques aim to minimize network communication between replicas at the cost of incurring more processing redundancy; a trade-off that suitably fitted the conventional wisdom of distributed database design. However, the emergence of next-generation networks with high throughput and low latency calls for revisiting these assumptions. In this paper, we first make the case that in modern RDMA-enabled networks, the bottleneck has shifted to CPUs, and therefore the existing network-optimized replication techniques are no longer optimal. We present Active-Memory Replication , a new high availability scheme that efficiently leverages RDMA to completely eliminate the processing redundancy in replication. Using Active-Memory, all replicas dedicate their processing power to executing new transactions, as opposed to performing redundant computation. Active-Memory maintains high availability and correctness in the presence of failures through an efficient RDMA-based undo-logging scheme. Our evaluation against active-passive and active-active schemes shows that Active-Memory is up to a factor of 2 faster than the second-best protocol on RDMA-based networks.
A Qualitative Evaluation of User Experiences of a Digitally Enabled Care Pathway in Secondary Care

Background: One reason for the introduction of digital technologies into health care has been to try to improve safety and patient outcomes by providing real-time access to patient data and enhancing communication among health care professionals. However, the adoption of such technologies into clinical pathways has been less examined, and the impacts on users and the broader health system are poorly understood. We sought to address this by studying the impacts of introducing a digitally enabled care pathway for patients with acute kidney injury (AKI) at a tertiary referral hospital in the United Kingdom. A dedicated clinical response team—comprising existing nephrology and patient-at-risk and resuscitation teams—received AKI alerts in real time via Streams, a mobile app. Here, we present a qualitative evaluation of the experiences of users and other health care professionals whose work was affected by the implementation of the care pathway. Objective: The aim of this study was to qualitatively evaluate the experience of mobile results viewing and automated alerting as part of a digitally enabled care pathway on the working practices of users and their interprofessional relationships. Methods: A total of 19 semistructured interviews were conducted with members of the AKI response team and clinicians with whom they interacted across the hospital. Interviews were analyzed using inductive and deductive thematic analysis. Results: The digitally enabled care pathway improved access to patient information and expedited early specialist care. Opportunities were identified for more constructive planning of end-of-life care due to the earlier detection and alerting of deterioration. However, the shift toward early detection also highlighted resource constraints and some clinical uncertainty about the value of intervening at this stage. The real-time availability of information altered communication flows within and between clinical teams and across professional groups. Conclusions: Digital technologies allow early detection of adverse events and of patients at risk of deterioration, with the potential to improve outcomes. They may also increase the efficiency of health care professionals’ working practices. However, when planning and implementing digital information innovations in health care, the following factors should also be considered: the provision of clinical training to effectively manage early detection, resources to cope with additional workload, support to manage perceived information overload, and the optimization of algorithms to minimize unnecessary alerts. (J Med Internet Res 2019;21(7):e13143) doi:10.2196/13143 J Med Internet Res 2019 | vol. 21 | iss. 7 | e13143 | p.1 http://www.jmir. org/2019/7/e13143/ (page number not for citation purposes) Connell et al JOURNAL OF MEDICAL INTERNET RESEARCH
Proximal envelopes: Smooth optimization algorithms for nonsmooth problems

Nonsmooth optimization problems arise in an ever-growing number of applications in science and engineering. Proximal (or splitting) algorithms are a general approach to a variety of nonsmooth problems, but as with all first order methods their convergence properties are severely affected by ill conditioning of the problem. In this thesis, an interpretation to proximal algorithms as unconstrained gradient methods over an associated function function is provided. Such functions are called proximal envelopes, in analogy with the well-known Moreau envelope. Proximal envelopes provide a link between nonsmooth and smooth optimization, and allow for the application of more efficient and robust smooth optimization algorithms to the solution of nonsmooth, possibly constrained problems. We consider the case of the forward- backward and Douglas-Rachford splitting methods. In the first case, based on generalized differentiability properties on the original problem terms, we devise superlinearly convergent line-search algorithms based on quasi-Newton directions, that use the same oracle as the forward-backward splitting; furthermore, the analysis is extended to the case where the dual problem is concerned. In the second case a global convergence rate for the Douglas-Rachford splitting is obtained, while an optimal stepsize selection strategy and an accelerated variant of the method is proposed.
Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.
Embedded Functional Dependencies and Data-completeness Tailored Database Design

We establish a principled schema design framework for data with missing values. The framework is based on the new notion of an embedded functional dependency, which is independent of the interpretation of missing values, able to express completeness and integrity requirements on application data, and capable of capturing redundant data value occurrences that may cause problems with processing data that meets the requirements. We establish axiomatic, algorithmic, and logical foundations for reasoning about embedded functional dependencies. These foundations enable us to introduce generalizations of Boyce-Codd and Third normal forms that avoid processing difficulties of any application data, or minimize these difficulties across dependency-preserving decompositions, respectively. We show how to transform any given schema into application schemata that meet given completeness and integrity requirements, and the conditions of the generalized normal forms. Data over those application schemata are therefore fit for purpose by design. Extensive experiments with benchmark schemata and data illustrate the effectiveness of our framework for the acquisition of the constraints, the schema design process, and the performance of the schema designs in terms of updates and join queries.