Miracle-dz/UniFAR-FTUs huggingface dataset:
Greek-Roman Mythology Redefined: Perceived Usefulness of a Cognitive Tool in an Undergraduate Online Course

ThinkSpace, a cognitive tool with the underlying premise that people learn by creating explanations for any given system, was implemented in redefining an asynchronous online Greek and Roman Mythology undergraduate course. This paper elucidates the re-design of interpretive writing assessments as multi-step cases, the affordances of the tool, and students’ perceived usefulness of using ThinkSpace in learning the course content. Introduction One of the critical aspects of learning is problem solving. According to Gagne “the central point of education is to teach people to think, to use their rational powers, to become better problem solvers” (Gagne,1980). One way of achieving such learning is by the use of cognitive tools that promote active-learning or learning-bydoing (Kozma,1987; Ebner & Holzinger, 2007). Thinkspace is one such cognitive tool that facilitates a scaffolded process of interpreting large amount of information based on comprehension of underlying concepts and rules. It was initially called the Diagnostic Pathfinder and was built in 2007 by researchers from a large land-grant MidWestern University. It was implemented to teach diagnostic problem solving to veterinarian students. It has since evolved with additional tools and is widely used across disciplines such as Communication, Engineering, Geology, Classical Studies and others (Bender & Danielson, 2011; Danielson, Mills, Vermeer, & Bender, 2008; Danielson 1999). This paper describes the use of, Thinkspace, to redesign the interpretive writing assignments in an eight-week asynchronous online course in Greek and Roman Mythology, that was first offered in Summer 2016 and thereafter in Summer 2017. It also shares the student perceptions of usefulness of using such a tool in the course along with the instructor perspectives on Thinkspace and the design and development of the course. The development of the course was funded by a grant from the online learning unit, within the Colleges of Engineering (COE) and Liberal Arts and Sciences (LAS) at a large Midwestern university. The instructor collaborated with an instructional designer (ID) as part of the grant. The main role of the ID was to provide pedagogical, technical, and instructional assistance. The development process followed an adapted ADDIE model in combination with Backward Design (Grant and Wiggins, 2005).
Adaptive onthe-fly compressed execution in Spark

Modern query execution engines rely on hash tables when working with expensive operators like hash aggregate and join. Often, query performance is determined by the hash table representation and its peak memory size. This project investigates the use of compact, compressed data representations in hash tables to make data access faster. Specifically, in the case of Apache Spark, we propose an optimized design for the small hashmap found in the partial aggregation stage. Subsequently, we discuss two orthogonal on-the-fly data-compression approaches to reduce the size of the hash table records. We present an adaptive bit-packing technique that compresses the numeric data tightly, using outlier-free metadata, generated at runtime. Moreover, we explore Strings compression, by building dictionaries that map Strings to codes and, further, work with the codes inside the aggregation. For String compression, we restrict the scope of the project only to aggregations that feed off a Parquet scan, so that we can leverage the Parquet dictionary_encoding and build the dictionaries in sublinear time. Our experiments show that we can speed up query execution performance up to 5x by exploiting the dictionary_encoding from the Parquet files. We demonstrate the benefit of compressed execution on industry standard TPC-H queries 1 and 16, for which we observe a maximum speedup of 1.9x.
Efficient Algorithm for Feature Intruder Detection System

- This paper was carried out to use the Gravitational Search Algorithm for feature selection in IDS to selectively choose significant features which represents categories of network such as DoS, Probe, U2R and R2L and to improve the accuracy and effectiveness of feature selection and to have better detection. This paper aimed to study trends of feature selection in IDS and to implement BGSA for selectively choose features for IDS and to test and validate the performance and feedback of BGSA. In this paper, it validates and evaluates the BGSA algorithm and focuses on the feature selection by implementing of BGSA. The results of BGSA program proves and shows that the selected features which proposed by BGSA in terms of accuracy and efficiency are quite acceptable.
Identifying Ecosystem-Based Alternatives for the Design of a Seaport’s Marine Infrastructure: The Case of Tema Port Expansion in Ghana

Long-term sustainable port development requires accounting for the intrinsic values of ecosystems. However, in practice, ecosystem considerations often only enter the planning and design process of ports when required by an Environmental Impact Assessment. At this late stage, most of the design is already fixed and opportunities to minimize and restore ecosystem impacts are limited. In this paper, we adopt a large-scale, ecosystem perspective on port development with the aim to identify ecosystem-based design alternatives earlier and throughout the planning and design of a port’s marine infrastructure. We present a framework, termed the ‘ecosystem-based port design hierarchy’ (EPDH), to identify ecosystem-based alternatives at four hierarchical design levels: 1) alternatives to port developments, 2) port site selection, 3) port layout design, and 4) design of structures and materials. In applying the EPDH framework retrospectively to a case study of port expansion in Tema, Ghana, we establish that ecosystem considerations played only a limited role in identifying and evaluating alternatives at all four design levels in the case study, whereas more eco-friendly alternatives in terms of port layouts, structures, and materials are identified using the EPDH framework. This reveals that opportunities for ecosystem-friendly port designs may have been missed and demonstrates the need for and the potential added value of our framework. The framework can assist practitioners in earlier and wider identification of ecosystem-based alternatives for a port’s marine infrastructure in future seaport developments and, hence, represents an important step towards more sustainable port designs.
Error Dependent Soft-Decision Power Saving Viterbi Decoder for Mobile Devices

—This paper investigates the Viterbi Algorithm for decoding convolutionally coded messages for forward error correction. A new power saving strategy that may enable mobile receivers to decode convolutionally coded transmissions with lower energy utilization using soft decision decoding techniques is developed. The idea behind the work is the selective use of the Viterbi decoder only when error is present in the transmitted message. When there is no error, a simple decoder is used. The analysis of the proposed method was done using the profiler tool in MATLAB®. Results obtained by MATLAB® simulation demonstrate that, there are no increases in bit-error probability as a result of the proposed method. The MATLAB processing time (and hence energy) shows that, in comparison to that obtained with a standard Viterbi decoder, the proposed method uses less energy at EbNo above 3dB.
Artificial Bee Colony for False Match Filtering in 3D Reconstruction

— The paper discusses the problem for 3D scene reconstruction and presents a general overview of the steps and approaches utilized for image based 3D model generation. A metaheuristics approach based on Artificial Bee Colony (ABC) is suggested to be applied for false match filtering instead of RANSAC or similar methods. The algorithm is based on ABC with Big Valley Landscape Exploration and is applied for finding false feature match by considering them as a possible local extrema in the problem search space. The suggested solution uses a population based heuristics that combines the global exploration of the search space with diversification and evaluation of the local optima. The approach allows fast and efficient feature correspondence refinement. The 3D reconstruction utilizing the suggested metaheuristics based false match filtering is experimentally evaluated using two test datasets with different number of images.
Organized Crime and Firms: Evidence from Anti-Mafia Enforcement Actions

We exploit staggered municipality-level antimafia enforcement actions in Italy over the 1995–2015 period to study how the presence of organized crime affects firms. Following enforcement actions, we find increases in competition (i) among firms and (ii) for public procurement contracts. Firms that do not exit after a weakening of organized crime shrink in size, more so when operating in the nontradable sector. Our findings are consistent with organized crime acting as a barrier to entry and affecting local economic activity. This paper was accepted by David Sraer, finance. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2021. 00859 .
Landslide types in the Slani Potok gully, Croatia

The Slani Potok gully (0.48 km2) is situated in the central part of the Vinodol Valley (64.57 km2) in Croatia, and it was formed in the Eocene flysch deposits. So far, the area of Slani Potok has been mainly referred in the scientific literature as being subjected to unusually intense soil erosion processes (i.e., ‘’excessive erosion’’), associated with landslides. However, the landslides were never investigated in detail, given the available research methods only involved field investigations. Therefore, the landslide types according to the most commonly used landslide classifications have remained undetermined. In this paper, landslide types in the Slani Potok gully are presented, identified and mapped based on the visual interpretation of seven different LiDAR topographic derivatives computed from the 1 x 1 m DTM available from March 2012. The geomorphological historical landslide inventory of the Slani Potok gully was created, consisting of 181 landslide phenomena. Landslides cover 69 % of the area (0.33 km2) of the Slani Potok gully. The size of the smallest landslide is 65 m2, and of the largest is 10,563 m2. Landslides are very small to moderate-small, shallow to moderate-shallow, and mainly successive in distribution. Most of the landslides initiate along the margins of the gully channel walls, and extend to the gully channel bottom. Such a large number of identified landslides, as well as their specific spatial arrangement within the gully, indicates that sliding processes predominantly affect the morphologic development of the Slani Potok gully, and that the soil erosion is the secondary process in the study area.
Key Dimensions and Determinants of women’s Empowerment in Pakistan: Empirical Evidence from Southern Punjab

Women empowerment is indispensable for attaining sustainable development goals (SDGs) 2030. This study empirically identified the key dimensions and determinants of women empowerment in Southern Punjab using primary data taken through multi stage cluster sampling technique. Considering the multidimensional nature and context a comprehensive cumulative women empowerment index is constructed through polychoric principle component analysis with six dimensions of women empowerment. The empirical results revealed that women empowerment is positively influenced by age and job of women, advertency of legal rights, access to medical facilities, participation in social gatherings, safe and clean environment, communication behavior, participation in politics and negatively by area of residence, unpaid domestic care work and fear of violence.
Preliminary Evaluation of CNN Classification by Objective Testers

In general the most recent methods such as ResNet and the residual based methods are considered state-of-the-art and are frequently used for benchmarking and initial features. One of the dangers of using standardized datasets is that it is possible to accidentally mix the test set with the training set, which will usually give higher accuracy. This is particularly difficult to check for in neural networks because the model is simply a set of millions of weights. The real goal here is to do an acid test between VGG and ResNet which is getting results from real users with imagery that has not been seen by the classifier before. When accidental mistakes are not possible in the training procedure, which one achieves the highest accuracy?
Qualitative research for breakthrough innovation

Introduction Organisational innovation is desirable because it can create extra competitiveness in firms as well as revenue growth and additional jobs in society (Roper, 1997; Design Council, 2011). At a product level, innovation objectives can include a desire to match competitors’ product offerings, replace or enhance existing products, extend product ranges, produce products aimed at new markets, reduce production costs, improve product quality and produce environmentally friendly products (Roper, 1997). Innovation is sought after by firms because it is a good source of incremental financial turnover in manufacturing businesses. For example, in the UK, it was found that turnover from new products, from new innovations and from improved products equalled about 12.5 per cent of manufacturers’ annual turnover in 2001 (Laursen and Salter, 2006). Innovation can also ensure that firms remain relevant to customers rather than obsolete and thereby stay competitive in a changing market (Tushman and Nadler, 1986). Despite its mystique, innovation can easily be fostered and encouraged within organisations as the calculated outcome of forward thinking leadership (Tushman and Nadler, 1986). This current paper outlines how qualitative research can be used to create breakthrough innovation. The aim of the current paper is therefore to take ideas from the literature on innovation, creativity, idea generation and qualitative research and synthesise these into a roster of research aimed at the generation of breakthrough innovation. In an organisational context, breakthrough innovation can be defined as the concrete implementation of novel creative ideas which advance group objectives in a groundbreaking manner. At the level of strategic entrepreneurship the aim of idea generating research would thus be to facilitate the emergence of innovation within one or more of the following (Kuratko andMorris, 2018):
Jalapeno or jalapeño: Do diacritics in consonant letters modulate visual similarity effects during word recognition?

Abstract Prior research has shown that word identification times to DENTIST are faster when briefly preceded by a visually similar prime (dentjst; i↔j) than when preceded by a visually dissimilar prime (dentgst). However, these effects of visual similarity do not occur in the Arabic alphabet when the critical letter differs in the diacritical signs: for the target the visually similar one-letter replaced prime (compare and is no more effective than the visually dissimilar one-letter replaced prime Here we examined whether this dissociative pattern is due to the special role of diacritics during word processing. We conducted a masked priming lexical decision experiment in Spanish using target words containing one of two consonants that only differed in the presence/absence of a diacritical sign: n and ñ. The prime-target conditions were identity, visually similar, and visually dissimilar. Results showed an advantage of the visually similar over the visually dissimilar condition for muñeca-type words (muneca-MUÑECA < museca-MUÑECA), but not for moneda-type words (moñeda-MONEDA = moseda-MONEDA). Thus, diacritical signs are salient elements that play a special role during the first moments of processing, thus constraining the interplay between the “feature” and “letter” levels in models of visual word recognition.
Second Language Tutoring using Social Robots

In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the inter-relations between the learner’s skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner’s knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words.
Graphene oxide on the electrical properties D.C and A.C and dielectric constant of PVP/PVA Nanocomposites

Graphene oxide (GO) nanofiller were incorporated in PVP/PVA blend films for the preparation of nanocomposite polymer films by the solution cast method. The films were characterized using SEM, D.C and A.C and dielectric properties studies at room temperature. SEM structure shows that the GO exfoliated and uniformly dispersed in PVP/PVA matrix. DC conductivity studies were under taken at (303-336) K, and the conductivity was found to be 3.3×10 Ω. cm for the polymer film prepared at 336K. The A.C electrical conductivity increase with the increase of frequency. The dielectric properties were measured the frequency range 100Hz-5MHz at room temperature show that the dielectric constant (real and imaginary) decrease with the increase of Graphene oxide nanoparticles concentrations.
Impacts of Organic Matter Migrating from Pipe Materials on Microbial Regrowth in Drinking Water

Polymeric materials are widely used in premise plumbing. To assess the potential of pipe materials to promote microbial regrowth in drinking water, we incubated drinking water in a new cross-linked polyethylene pipe (N-PEX), a new steel pipe lined with powdered polyethylene (N-SPE), and an old steel pipe lined with powdered polyethylene (O-SPE). As a reference, we also incubated drinking water in a carbon-free glass bottle (REF). Free chlorine rapidly depleted to below the quantification limit in the pipe incubations. While the original drinking water contained 0.4 mg C/L of dissolved organic carbon (DOC), the DOC concentration in N-PEX, N-SPE and O-SPE increased to 5.2, 3.1 and 1.7 mg C/L, respectively, after 8 days of incubation. The total cell count (TCC) in N-PEX, N-SPE, and O-SPE increased from <103 cells/mL to 6.0 × 105, 2.8 × 105, and 3.6 × 106 cells/mL after 8 days of incubation, respectively. On the other hand, the TCC in REF increased to 1.4 × 105 cells/mL after 14 days of incubation. Dominant bacterial groups were different under the different incubation conditions. These results indicated that organic matter migrating from pipe materials promote microbial regrowth and affect the microbial community composition in drinking water.
DECOLOURIZATION OF METHYL ORANGE IN UV ASSISTED FENTON-LIKE REACTION USING FE-

Accepted 29 June 2017 In this work, two types montmorillonite clay (MKSF and MK10) were applied as catalyst support in heterogeneous UV assisted Fe nton-like reaction. MKSF was chosen for the iron immobilization based o n catalytic activity performance. Fe-supported montmorillonite KSF (Fe-M KSF) exhibited both increment in surface area, 175.208 m /g and pore volume, 0.3536 cm /g compared to the pristine MKSF as a result of incorp ration of iron species onto MKSF matrix. The reactivity of Fe-MKSF was fur ther tested on the effect of UV irradiation. Fe-MKSF exhibited 99% of methyl orange (MO) removal within 45 min of reaction under UVC illumin ation but it takes more than 120 min under UVA illumination. These findings proved Fe-MKSF as a promising alternative catalyst in decolourization o f recalcitrant contaminant in wastewater.
Big SQL 3.0 Functionality: A comprehensive approach to SQL-on-Hadoop

- Undoubtedly, Apache Hadoop TM (Hadoop) has received huge recognition in the Big Data market space because of the great flexibility in processing heterogeneous data structures. However, for adding greater value, it is critical for Hadoop to integrate with various analytical tools that are widely used by different enterprises. Most of these tools utilize standard Structure Query Language (SQL) and SQL procedural functionality. It is also important to allow the enterprises to utilize their existing SQL expertise, for which they have been building skills for years.
Low-Latency Communication for Fast DBMS Using RDMA and Shared Memory

While hardware and software improvements greatly accelerated modern database systems’ internal operations, the decades-old stream-based Socket API for external communication is still unchanged. We show experimentally, that for modern high-performance systems networking has become a performance bottleneck. Therefore, we argue that the communication stack needs to be redesigned to fully exploit modern hardware—as has already happened to most other database system components.We propose L5, a high-performance communication layer for database systems. L5 rethinks the flow of data in and out of the database system and is based on direct memory access techniques for intra-datacenter (RDMA) and intra-machine communication (Shared Memory). With L5, we provide a building block to accelerate ODBC-like interfaces with a unified and message-based communication framework. Our results show that using interconnects like RDMA (InfiniBand), RoCE (Ethernet), and Shared Memory (IPC), L5 can largely eliminate the network bottleneck for database systems.
nalysis of railroad tank car releases using a generalized binomial odel

The United States is experiencing an unprecedented boom in shale oil production, leading to a dramatic growth in petroleum crude oil traffic by rail. In 2014, U.S. railroads carried over 500,000 tank carloads of petroleum crude oil, up from 9500 in 2008 (a 5300% increase). In light of continual growth in crude oil by rail, there is an urgent national need to manage this emerging risk. This need has been underscored in the wake of several recent crude oil release incidents. In contrast to highway transport, which usually involves a tank trailer, a crude oil train can carry a large number of tank cars, having the potential for a large, multiple-tank-car release incident. Previous studies exclusively assumed that railroad tank car releases in the same train accident are mutually independent, thereby estimating the number of tank cars releasing given the total number of tank cars derailed based on a binomial model. This paper specifically robability accounts for dependent tank car releases within a train accident. We estimate the number of tank cars releasing given the number of tank cars derailed based on a generalized binomial model. The generalized binomial model provides a significantly better description for the empirical tank car accident data through our numerical case study. This research aims to provide a new methodology and new insights regarding the further development of risk management strategies for improving railroad crude oil transportation safety.
New Disease Reports

Figure 1 Figure 2 Foxtail millet (Setaria italica) is one of the oldest grain crops. It is still widely cultivated in arid and semiarid regions worldwide, particularly in China and India (Bettinger et al., 2010). In 2016, ears of foxtail millet with severe symptoms of Fusarium ear rot were collected from Yulin, Shanxi Province, with an incidence of up to 10%. Typical disease symptoms included ears with pink or salmon-coloured mould at the ear tip of affected plants (Fig. 1).