Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of Student Performance Prediction using Pre-Course Data

Students at Saudi universities face difficulty registering for the right course since Student performance there is no support offered to students that uniquely consider each situation. Machine learning techniques could be applied to fill this gap by predicting grades of new courses for each student based on their historical data. This paper experiments with nine different prediction algorithms to predict course grades for public university students. The data-set includes grades for 215 students and 180 various courses. The models utilize grades obtained in semesters between the 2015 and 2018 academic years and evaluated on grades obtained in the 2019 academic year. Our result shows that the K-nearest neighbor with ZScore model outperforms the remaining models with respect to the Percentage of Tick Accuracy (PTA), which is the difference between two consecutive letter grades for the predicted letter grade and the observed letter grade. Our work achieved an 84% accuracy score in PTA2, where the difference between the predicted letter grade and the actual letter grade is less than or equal to two consecutive letter grades.
Why Behavioural Economics Will Not Save the World

Behavioural economics is widely considered to be a signifi cant break from standard economic modelling. What is different about the behavioural economics approach? Is it as revolutionary as we are led to believe? What else does research in this fi eld offer? The award of this year’s Nobel Memo rial Prize in Economic Sciences to Richard Thaler has brought renewed attention to the fi eld of behavioural economics.1 Four earlier Nobels have been given to scholars who have integrated insights about human behaviour from psychology into economics: Herbert A Simon (1978), Maurice Allais (1988), Daniel Kahneman (2002) and Robert Shiller, Eugene F Fama and Lars Peter Hansen (2013). This shows that there is a recognition within the discipline of the contribution that the behavioural approach has made to the understanding of economic ph enomena. However, the extent of this contribution is often exaggerated, and behavioural economics is presented as the panacea that will cure economics of all its problems. In this article, I describe how the behavioural approach is different from standard economic models and then argue that, while the shortcomings of the standard models pointed out by this approach are important, the models that are offered as alternatives do not depart from the standard ones in any fundamental way. Many of the problems with these models that came to light in the aftermath of the 2008 fi nancial crisis indicate a need for a much more radical change in the way economic theory is done. I end by highlighting one approach that has the potential to bring about this change.
Introducing Deep Reinforcement Learning to Nlu Ranking Tasks

Natural language understanding (NLU) models in production systems rely heavily on human annotated data, which involves an expensive, time-consuming, and error-prone process. Moreover, the model release process requires offline supervised learning and human in-the-loop. Together, these factors prolong the model update cycle and result in sub-standard model performance in situations where usage behavior is non-stationary. In this paper, we address these issues with a deep reinforcement learning approach that ranks suggestions from multiple experts in an online fashion. Our proposed method removes the reliance on annotated data, and can effectively adapt to recent changes in the data distribution. The efficiency of the new approach is demonstrated through simulation experiments using logged data from voice-based virtual assistants. Our results show that our algorithm, without any reliance on annotation, outperforms offline supervised learning methods.
Supporting features for flow-level packet analysis towards cyber threat detection: a pilot study

Thousands of new threats and threat categories continue to emerge every second in cyberspace, even as known threats keep adapting robustly to existing solutions. This has challenged modern approaches and solutions to threat detection and potentially rendered some of these obsolete even before they are able to find applicability. Much contemporary cyber / network threat detection solutions rely largely on flow-level packet analysis, by monitoring trends and patterns of activity in supporting flow features of interest. However, while this has been the case, little attention has been paid to whether or not such supporting flow features still present an effective means of reaching accurate conclusions regarding imminent or occurrent cyber threat incidents, especially in the face of a rapidly evolving and adapting 21 st century cyber threat landscape. This research is therefore a necessary pilot study to a larger research that aims to develop a state-of-the-art detection solution against a newly uncovered category of cyber threats known as subversive cyber threats. The goal of this pilot study being to reinvestigate four of the more commonly used supporting flow features in modern threat detection solutions, viz. Flow Packet Count, Flow Packet Throughput (bytes/s), Flow Packet Throughput (packets/s), and Average Flow Packet Size (bytes), in trying to ascertain / verify their continued relevance in the development of new cyber threat detection solutions. The study adopts the methodology of data simulation with descriptive infographic analysis using the recent UNSW-NB15 cybersecurity dataset.
Snowfall and snow accumulation processes during the MOSAiC winter and spring season

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.
First year report of EDGE project: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

Drilling optimization may have several definitions, but what they all have in common is the concept of drilling time and well failure reduction, which is of fundamental importance in reducing the overall costs of a geothermal project. To this purpose, an international research coordination network aimed at developing machine learning strategies to improve geothermal drilling efficiency has been established under the support of the EDGE Program of the US Department of Energy (DOE) Geothermal Technologies Office. The EDGE research collaboration involves two US Universities, a DOE National Laboratory and four Geothermal and Oil and Gas companies from several countries (Iceland, Norway, USA). The first year of the project consisted of four major tasks: 1) Data gathering from more than 100 wells from different companies and geothermal fields; 2) Exploratory Data Analysis (EDA) to assess both the quality and the structure of the data, i.e. the presence of gaps, outliers, typos, the correlation between variables and their distribution, and also to define which variables might be more useful for drilling efficiency prediction in such a way that a data format/structure can be defined as a standard for the machine learning procedures; 3) Development of a well data repository for the data products (data, code, analysis workflows and models) developed during the project; and 4) Initial development and testing of machine learning (ML) techniques. The main findings of the exploratory data analysis and initial machine learning testing can be summarized as follows: i ) information related to drill bit life cycle and bottom hole assembly are necessary to improve the data clustering as well as to improve the accuracy of machine learning algorithms; ii ) lithological classifications usually used to describe well cuttings are too specific and idiosyncratic to be useful for machine learning purposes in their raw form; iii) both Random Forest and Deep Learning models were tested. At present, their accuracy in predicting drilling parameters is similar overall, with Deep Learning models slightly outperforming the Random Forest ones as the number of input parameters increases. With regard to idiosyncratic lithological information as appearing in the raw mudlog data, we have tried both dummy encoding and text embedding to encode the lithological information but none of them has resulted in an improvement in the accuracy of the machine learning algorithms in predicting drilling parameters. A new “rock-strength” description needs to be defined for this purpose.
FORAGE AND PASTURE

https://doi.org/10.17523/bia.2020. v77.e1488 Bol. Ind. Anim. , Nova Odessa, v. 77, 2020 DEGRADAÇÃO RUMINAL DA MATÉRIA SECA E FRAÇÃO FIBROSA DO GRÃO DE SORGO ALTO TANINO Resumo Objetivou-se com o estudo avaliar o efeito da substituição do milho por sorgo grão com alto teor de tanino na dieta de novilhos sobre a degradação ruminal da matéria seca e fração fibrosa. Foram utilizados quatro bovinos fistulados no rúmen, com peso corporal médio de 519 ± 31 kg, distribuídos em delineamento experimental quadrado latino 4 ́4. Os animais foram submetidos a quatro dietas, constituídas em função dos níveis de substituição de fubá de milho pelo grão de sorgo alto tanino, nas proporções de 0, 33, 67 e 100%, no concentrado. A quantificação da degradabilidade da matéria seca (MS) e fibra em detergente neutro (FDN) ocorreu por incubação in situ com amostras do grão de sorgo. Os tempos de permanência das amostras no rúmen para estimativa do desaparecimento da MS e FDN foram: 0, 12, 24, 48, 72, 96, 120 e 144 horas. Houve efeito quadrático na fração “c” da MS e na fração solúvel “a” da FDN do grão de sorgo em função dos níveis de substituição do milho pelo sorgo tanino de alto grão. A degradabilidade da MS e FDN dos grãos de sorgo com alto teor de tanino até 144 horas de permanência no rúmen mostrou um comportamento crescente para todos os níveis de inclusão de sorgo na dieta. No entanto, o nível de substituição 67 mostrou o máximo desaparecimento da MS e da fração fibrosa do grão de sorgo até 144 horas de incubação. O grão de sorgo apresentou a máxima degradação ruminal da matéria seca e da fração fibrosa no nível 67% de substituição de milho por sorgo grão na dieta de novilhos.
Agent-based approach to the design of a multimodal interface for cyber-security event visualisation control

Convenient human-computer interaction is essential to carry out many exhausting and concentration-demanding activities. One of them is cyber-situational awareness as well as dynamic and static risk analysis. A specific design method for a multimodal human-computer interface (HCI) for cyber-security events visualisation control is presented. The main role of the interface is to support security analysts and network operators in their monitoring activities. The proposed method of designing HCIs is adapted from the methodology of robot control system design. Both kinds of systems act by acquiring information from the environment, and utilise it to drive the devices influencing the environment. In the case of robots the environment is purely physical, while in the case of HCIs it encompasses both the physical ambience and part of the cyber-space. The goal of the designed system is to efficiently support a human operator in the presentation of cyberspace events such as incidents or cyber-attacks. Especially manipulation of graphical information is necessary. As monitoring is a continuous and tiring activity, control of how the data is presented should be exerted in as natural and convenient way as possible. Hence two main visualisation control modalities have been assumed for testing: static and dynamic gesture commands and voice commands, treated as supplementary to the standard interaction. The presented multimodal interface is a component of the Operational Centre, which is a part of the National Cybersecurity Platform. Creation of the interface out of embodied agents proved to be very useful in the specification phase and facilitated the interface implementation.
Civil engineering students’ beliefs about the technical and social implications of global warming and when global warming will impact them personally and others

The United Nations recognizes reducing the effects of global warming as a Sustainable Development Goal (SDG) (#13). This goal is interconnected and critical to improving health and education, reducing inequality, and spurring economic growth globally. Civil engineers will play a vital role in meeting this goal. To understand how civil engineering students perceive global warming, we surveyed a national sample of civil engineering students in their final semester of college ( n = 524). We asked them (a) if they recognize both the technical and social issues associated with global warming and (b) when they believe global warming will start to have a severe effect on themselves, others, and the planet. Civil engineering students are significantly more likely to recognize the technical issues associated with global warming than social issues. In particular, the majority of students understand global warming is an immediate issue for the environment, engineering, health, and science, but less than half recognize global warming presents social justice, poverty, and national security issues. Moreover, civil engineering students hold an inverse relationship between spatial distance and the timing of the effects of global warming. The majority of students believe global warming is currently having a severe impact on plant and animal species, the environment, people in developing countries, and the world's poor but do not recognize themselves in this group. Instead, civil engineering students predominantly believe the effects of global warming will start to have a serious impact on themselves, their family, and people in their community in 25 to 50 years. These results are troubling because if those beliefs translate into students waiting to address climate change for another two to five decades locks in more emissions and increases the chance of future and more severe global humanitarian crises. Educational interventions are needed to change these perspectives about time and impact.
Isolation of a starfish myorelaxant peptide (SMP) isotype from the
 pyloric caeca of Patiria pectinifera

Peptides are naturally occurring biological molecules that are found in all living organisms. These biologically active peptides play a key role in various biological processes. The aim of this study is the extraction and the purification of bioactive materials that induce relaxation of an apical muscle from the pyloric caeca of Patiria pectinifera. The acidified pyloric caeca extract was partially separated by the solid phase extraction using a stepwise gradient on Sep-Pak C18 cartridge. Among the fractions, materials eluted with 60% methanol/0.1% trifluoroacetic acid was put a thorough of a series of high performance liquid chromatography (HPLC) steps to isolate a neuropeptide with relaxation activity. The purified compound was eluted at 28% acetonitrile in 0.1% trifluoroacetic acid with retention time of 25.8 min on the CAPCELL-PAK C18 reversed-phase column. To determine the molecular weight and the amino acid sequence of the purified peptide, LC-MS and Edman degradation method were used, respectively. The primary structure of the peptide was determined to be FGMGGAYDPLSAGFTD which corresponded to the amino acid sequence of a starfish myorelaxant peptide (SMP) isotype (SMPb) found in the cDNA sequence encoding SMPa and its isotypes. In this study, a muscle relaxant neuropeptide (SMPb) has been isolated from pyloric caeca of starfish P. pectinifera. This is the first report of SMPb isolation on the protein level from P. pectinifera.
ASSESSMENT OF QUALITY OF LIFE PATIENTS SUFFERING FROM BREAST CANCER AND LUNG CANCER

Introduction: Assessment of quality of life is an important subject of research in various disciplines and today it is an integral part of the evaluation of rehabilitation and therapeutic procedures. The aim of the study was to assess the quality of life and to assess the impact of depression on the quality of life in patients with breast cancer and lung cancer. Material and methods : The study included 60 people, of whom 30 were diagnosed with lung cancer and 30 with breast cancer. The questionnaire was constructed by researchers for this research. Quality of Life Assessment Questionnaire SF-36 and assessment of depression, Beck Depression Scale. Results: The presence of complications is a statistically significant source of differences in the domain of the total physical score, and is not a statistically significant source of differences in other domains from the SF36 questionnaire. Based on the data from the previous table, we can see that the values on the Beck Depression Inventory range from 23 to 35, with AS = 30.00 and SD = 2.584. Conclusion: Respondents who have a higher score on the Beck Depression Inventory also have a higher score on the dimension of physical functioning.
A Linguistic Analysis of Translation Errors on Selected Public Notices in Kenya

---------------------------------------------------------------------***--------------------------------------------------------------------How to cite this article in APA (6th Edition) Macharia, D. M. (2019). A linguistic analysis of translation errors on selected public notices in Kenya. Editon Cons. J. Lit. Linguist. Stud. , 1(1), 1-12. ---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACT: This paper purposes to analyse translation errors in selected Kenyan public notices. Specifically, it examines how translation faults possess unique linguistic resources. There is an extensive existence of this genre countrywide albeit without proper academic scrutiny to further interrogate fundamental linguistic concepts therein. It involves the application of the Computer-Mediated Discourse Analysis (CMDA) which is unique to the analysis of this genre. Specifically, this research involves collecting data from the social media of the relevant public notices in Kenya. Generally, twelve signage were analysed. The sampling procedure was done purposively to include the diversity of Kenya. To qualify, the data collected had to reflect translation blunders. Then, the data was analysed for linguistic resources. The data was presented using a table showing the relationship between the components sought. Consequently, the study enriched translational linguistics, evaluated textual analysis and critiqued the linguistic concepts of performance and competence. Additionally, it provided useful insights into the cognitive mechanisms used during humour production and understanding.
Application Trend of Augmented Reality in Intelligent Packaging

The work aims to study the application status and future trend of augmented reality technology in intelligent packaging. The interactive experience of augmented reality technology in intelligent packaging was expounded, and the application trend of augmented reality in intelligent packaging was emphatically introduced. These aspects were studied, including the application of augmented reality technology in different packaging materials, enhancing the information transmission surface of packaging products, enhancing the socializing of packaging products, breaking through the marketing application effect of traditional packaging, and the replacement of traditional paper instructions. With the continuous development of technology, more new technologies and processes were added to the intelligent packaging through innovative thoughts, so that it not only had the traditional functions, but also more new application directions, such as RFID inventory detection, detection of product temperature and freshness, and exhibition industry publishing platform of augmented reality, etc. Through the study on the application of current augmented reality technology in intelligent packaging, the applications in the reality and virtual world can be connected.
EXTRACTION AND CHARACTERIZATION OF FISH OIL FROM CHANNA STRAITA WASTE COLLECTED FROM ANANTAPURAM FISH MARKET

Mopuru Rishitha, Ph.D Research Scholar Department of Biotechnology, Sri Krishnadevaraya University, Anantapur, Andhra Pradesh, India Janardhan Gude Senior scientist, Micro SynbiotiX, Ireland Dowlathabad Muralidhara Rao* Associate Professor, Department of Biotechnology, Sri Krishnadevaraya University, Anantapuramu, Andhra Pradesh, India ABSTRACT Channa straita waste was collected from fish landings of Anantapuram, for a period between October 2013-September 2014. Since these species are landed only during certain corners of the year, specimens of uniform size from all three species were alone taken into consideration for the production of fish oil. The fish body oil was extracted from the tissues of Channa straita employing four different extraction methods, namely Bligh & Dyer, Modified Bligh & Dyer, Mcgill & Moffat and Direct Steaming. From the results it is proved that direct steaming method as the finest extraction process due to its winsome qualities such as higher yield, economic viability, less laborious and less time consumption. Analytical properties of the crude fish oil were evaluated separately for freshly prepared samples and for 30 days old samples which were stored in refrigerator at 0C. These analytical values of the crude oil are well within the acceptable standard values for both fresh and stocked samples. It is important to note that the values for both fresh and 30 days old refrigerated samples did not having much variation.