Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of the Use of Firearms in Wildlife Poaching In Game Parks

DOI: 10.21276/sjhss.2018. 3.8. 12 Abstract: The study sought to find out the impact of the use of firearms in wildlife poaching. Use of firearms in poaching is a threat to the stability and peace in any given country. It threatens the socio-economic environment. There has been a proliferation of use of firearms by poachers and it is in the context of this major concern that this study had to be conducted. The study adopted a qualitative research paradigm and descriptive research design was the preferred research design. The population of this study was the Hwange National Parks staff members. Game rangers and the main camp manager were selected as respondents. An interview was conducted with the main camp manager while game rangers were selected for focus group discussions. The major findings were that that use of firearms in poaching at Hwange National Park is fairly a new phenomenon but it has come with so much intensity such that statistics are rising at an alarming rate. Besides loss of lives among game rangers and poachers there is always the danger of the extinction of animal species like the rhino. The study therefore recommended that there is need for more refresher trainings for staff to enhance anti-poaching skills. More sophisticated and advanced equipment is required for rangers to keep ahead of poachers. A small aircraft for aerial surveillance would almost end use of firearms in wildlife poaching within national parks.
Attent ional modulat ion interacts with or ientat ion anisotropies in contrast sensit iv i ty 1

6 Can attention preferentially enhance representations that we have poorer visual sensitivity for? Although early 7 visual cortex is highly selective for stimulus orientation, not all orientations are represented equally, giving rise 8 to orientation anisotropies. Here, we first assess the interaction between stimulus contrast and the oblique 9 effect, one of the primary anisotropies in orientation sensitivity. Specifically, we examined whether the 10 impairment in behavioral sensitivity for oblique versus cardinal orientations is best explained by a contrastor 11 response gain modulation of the contrast psychometric function. Results revealed a robust oblique effect, 12 whereby asymptotic performance for oblique orientations was substantially lower than cardinal orientations, 13 suggesting a multiplicative attenuation of contrast responses for oblique orientations. Next, we assessed 14 how orientation anisotropies interact with attention by measuring psychometric functions for orientations 15 under a low or high attentional load. Interestingly, attentional load affects the sensitivity for cardinal and 16 oblique orientations differently: while attention multiplicatively boosts the contrast psychometric functions for 17 both cardinal and oblique orientation conditions, the magnitude of this attenuation is greater for the obliques. 18 Thus, attention seems to preferentially strengthen the response for underrepresented, oblique orientations to 19 a larger degree than orientations that we already have high sensitivity for, such as cardinal orientations. 20
Applied Management Support for an Efficient WSN Infrastructure

There are many management applications such as disaster management, air pollution management, weather management, traffic control management etc. which requires continuous attentions and prompt actions. Inclusion of theory of management support and timely decision to any un-managed system will improve the performance, usability and sustainability. Wireless Sensor Network is a self-build infrastructure which deployed on the areas where there is no continuous power supply to the sensor nodes. There are many researches and approach has been proposed and protocols designed to address the network performance. We analyzed and identified the issue areas where management support and timely organized decisions improve the wireless sensor network performance. We categorized the system based on levels of management and presented our proposed protocols in all levels with process flow and performance comparisons through our simulation models.
The biochemical effect of probiotic and /or mesenchymal stem cells on LPS-induced kidney disorder

The current study aimed to evaluate the beneficial effect of kefir probiotic and /or Mesenchymal stem cells (MSCs) on acute kidney injury (AKI) induced by lipopolysaccharide (LPS) challenge, and to investigate could kefir potentiate the therapeutic action of MSCs. Sixty female albino rats were used in this study and divided into 6 groups (10 rats each): control group; LPS-challenged group; LPS + MSCs group; LPS + kefir group; kefir +LPS +kefir (prophylactic) group and kefir + LPS + MSCs + kefir (prophylactic-MSCs) group. Samples were collected at two point's time. Renal function, serum IL-10, TNF-α, renal MDA, GSH contents, SOD and PON-I were assayed. The mRNA expression of NF-κB, iNOS and caspase-3 were monitored in kidney tissue. Our results revealed that LPS significantly increased renal function tests, TNF-α in association with dramatic decrease of creatinine clearance and serum IL-10 levels. Oxidative stress was proved in LPS group by increasing MDA level, reduction in GSH content, SOD and PON-1 activities in kidney. The mRNA expression of NF-κB, iNOS and caspase-3 were significantly up-regulated in AKI. Administration of kefir or MSCs alone significantly attenuated LPS-induced AKI. The pre and co-treatment of kefir with MSCs potentiate their therapeutic action. Conclusion: A combination of kefir probiotic and MSCs may be of interest for clinical use.
Rough-Number-Based Multiple-Criteria Group Decision-Making Method by Combining the BWM and Prospect Theory

Multicriteria group decision-making (MCGDM) problems have been a research hotspot in recent years, and prospect theory is introduced to cope with the risk and imprecision in the process of decision-making. To guarantee the effectiveness of information aggregation and extend the feasibility of prospect theory, this paper proposes a novel decision-making approach based on rough numbers and prospect theory to solve risky and uncertain MCGDM problems. Firstly by combining rough numbers and the best-worst method (BWM), we construct a linear programming model to calculate rough criteria weights, which are defined by lower limitations and upper limitations. Then for the imprecision of value function and weighting function in prospect theory, we propose a novel method with the aid of combining rough numbers and prospect theory to handle the risk in decision-making problems. Finally, a numerical example involving investment is introduced to illustrate the application and validity of the proposed method.
Scalable Analysis on a Cluster of Twitter - Data Using Mining Strategies Project Proposal

Social media produces vast quantities of data on a daily basis [1]. Both researchers and businesses can gain valuable insight into the dynamics of online communities by mining this data. Data mining is the process of generating insightful knowledge by examining very large sets of data [2]. The application of data mining to social media can yield benefits like gauging user sentiment for proactive planning and developing effective recommendation systems. It could also be used to identify influential individuals in communities [1]. However, these data sets are often very complex, because of their unstructured nature [4]. Additionally, the sheer size of the data sets meant that traditional, singlemachine systems could not perform the required mining [4]. New systems that utilize clusters of computers to perform the tasks in parallel were developed [2]. The parallel nature of these systems required traditional data mining and information retrieval (IR) algorithms to evolve [3, 4]. The goal of this project is to build a system that can utilize a cluster to do a scalable analysis of large amounts of Twitter data. More details regarding the data set and the cluster environment is discussed in Section 3. The system will be divided into three logical sections, which will combine to form the whole system. The sections are:
Abstracts of Professor Cox’s Talks

s of Professor Cox’s Talks NSF/CBMS Regional Conference in the Mathematical Sciences Applications of Polynomial Systems June 4-8, 2018 The following description of planned lectures has been provided by Professor Cox. The talks are divided into five topics of two lectures each. Each such pair of lectures will be followed by a complementary lecture given by an expert in that subject area. Topic I: Elimination Theory Elimination theory is an important part of symbolic algebra that can help one understand the structure of a system of polynomial equations. These two lectures will explore elimination theory from a historical point of view. Lecture 1. Elimination Theory in the 18th and 19th Centuries. Although the origins of the subject trace back to the 17th century, our story begins about 100 years later with Bézout’s 1779 book Théorie Générale des Équations Algebriques [3]. These days, Bézout’s Theorem is usually regarded as part of enumerative algebraic geometry, but for Bézout and his contemporaries, elimination was at the heart of the matter. Although Bézout was not the first to state his theorem, his book studied many versions of the theorem, including ones for polynomials whose Newton polytopes are truncated simplices. This lecture will also explore Minding’s 1841 paper Ueber die Bestimmung des Grades einer durch Elimination hervorgehenden Gleichung [42], where he gave a formula for the number of solutions of f(x, y) = g(x, y) = 0 that can be seen as computing the mixed volume of two polygons by means of a mixed subdivision. Elimination also plays an important role in Kronecker’s great 1882 paper Grundzüge einer arithmetischen Theorie der algebraischen Grössen [40]. Another important development is the emergence of the theory of resultants. Lecture 2. Elimination Theory in the 20th Century. The century began with the maturation of resultants (Netto [43]; Macaulay [41]) and the Principle of Conservation of Number (Severi [47]), which involves generic points and multiplicities. The first rigorous definitions of generic point and multiplicity used elimination theory and resultants (van der Waerden [51] and [52]). As the century progressed, the development of algebra “eliminates from algebraic geometry the last traces of elimination theory” (Weil [55]), a trend amplified by the theory of schemes (Grothendieck and Dieudonné [28]). But the 1960s also witnessed the birth of Gröbner bases (Buchberger [7]) and computer algebra in general. A decade later, resultants made a comeback (Jouanolou [35]; Sederberg [44]) and exploded with the introduction of sparse resultants (Gel’fand, Kapranov and Zelevinsky [26]; Sturmfels [50]). Two extended examples will give the flavor of these developments:
Hidden CP-ABE to Enhance Patient Data Privacy in Smart Healthcare Systems

Internet of Things (IOT) envisions the interconnectivity among miscellaneous devices and human beings. This future internet will not only bring into existence a new era of living but also a new age of cyber threats and attacks. The main cause of these cyber-attacks would be the explosion of data that is caused due to 24 * 7 connectivity of devices and humans to the internet. Successful implementation of IOT is hindered by the concern of user data security and privacy, as it exposes all the sensitive and non-sensitive information on the web and user has a very restricted control over the data flow in IOT. Cloud computing would be an inevitable part of this IOT setup. Cloud computing also poses privacy issues such as data manipulation, repudiation and third party illegal access.
Development and evaluation of aonla-papaya toffee

Aonla-Papaya toffee was developed and evaluated for changes in chemical and sensory parameters at monthly intervals for three months storage period. Total soluble solids (TSS) increased significantly, while acidity, ascorbic acid, total carotenoids and total phenols decreased significantly in aonla-papaya toffee with the advancement in three months storage duration. Colour and appearance, flavour, taste, mouthfeel and overall acceptability of aonla-papaya toffee decreased significantly during three months storage, however, the products acceptable even after three months storage. Toffee prepared with 80 aonla:20 papaya pulp ratio was found most acceptable.
A Survey on Ontology Enrichment from Text

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.
OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion

State-of-the-art models for Knowledge Base Completion (KBC) for large KBs (such as FB15k and YAGO) are based on tensor factorization ( TF ), e.g, DistMult, ComplEx. While they produce good results, they cannot expose any rationale behind their predictions, potentially reducing the trust of a user in the outcome of the model. Previous works have explored creating an inherently explainable model, e.g. Neural Theorem Proving (NTP), DeepPath, MINERVA, but explainability in them comes at the cost of performance. Others have tried to create an auxiliary explainable model having high ﬁdelity with the underlying TF model, but unfortunately, they do not scale well to large KBs. In this work, we propose O X KBC – an O utcome e X planation engine for KBC , which provides a post-hoc explanation for every triple inferred by a (uninterpretable) factorization based model. It ﬁrst augments the underlying Knowledge Graph by introducing weighted edges between entities based on their similarity given by the underlying model. It then deﬁnes a notion of human-understandable explanation paths along with a language to generate them. Depending on the edges, the paths are aggregated into second–order templates for further selection. The best template with its grounding is then selected by a neural selection module that is trained with minimal supervision by a novel loss function. Experiments over Mechanical Turk demonstrate that users overwhelmingly ﬁnd our explanations more trustworthy compared to rule mining.
A Study of Apache Index and Its Relationship with Oxidative Stress in Cardiac Patients in Hospital Located in Mangalore, Karnataka

Objective: This preliminary study aims (1) at validating the use of Acute Physiology and Chronic Health Evaluation II (APACHE II) scoring system in cardiac patients admitted to the intensive cardiac care unit (ICCU) for prediction of the risk for mortality, (2) to evaluate the use of a modified APACHE II (mAPACHE II) with addition of the scores related to urinary levels of oxidative stress parameters in prediction of the risk for mortality. Design: Hospital based prospective study. Setting: Tertiary care hospital in South India. Methods: Cardiac patients (n=100) admitted to the intensive cardiac care unit between the age of 18-77 years were enrolled. APACHE II score was calculated. The predicted mortality was calculated on the basis of this score. Urinary malondialdehyde (MDA) and ceruloplasmin (Cp) levels were analyzed and mAPACHE II was calculated. Results: The mean APACHE score was 11 ± 4.14. The ICCU mortality rate within 24 hours after admission was 8%. The area under the receiver operating curve (ROC) for APACHE II score was found to be 0.8 indicating good discrimination and for mAPACHE II score was 0.7 indicating moderate discrimination. Conclusion: APACHE II scoring system has a good discrimination when applied to cardiac patients. However, mAPACHE II also shows moderate discrimination when applied to these patients.
Supplemental Appendix to Semiparametric quantile regression with random censoring

This supplemental appendix additional results and all the proofs of the results presented in Semiparametric quantile regression with random censoring ∗Address correspondence to: Department of Economics, University of York, York YO10 5DD, UK. E-mail: francesco.bravo@york.ac. uk. Web Page: https://sites.google. com/a/york.ac. uk/francescobravo/
MapRecorder: analysing real-world usage of mobile map applications

ABSTRACT Millions of people use mobile map applications like Google Maps on a regular basis. However, despite these applications' ubiquity, the literature contains very little information about how these applications are used in the real world. As such, many researchers and practitioners seeking to improve mobile map applications may not be able to identify important challenges and may miss major opportunities for innovation. To address this paucity of usage information, we collected and analysed data during unsupervised usage of Google Maps by replacing the standard application with a wrapped version called MapRecorder. In two studies we recorded data from locals and tourists using our application and collected over 580 minutes of actual application usage from 34 users, spanning 555 unique sessions. We identify typical usage scenarios, observe a large amount of map exploration and elucidate generalisable interaction patterns.
Scalable Analysis of Social Media Logs Literature Review

Twitter generates large volumes of user generated data on a daily basis. An opportunity to extract valuable information arises to try and find patterns within this data and find interesting or unexpected related topics within communities or individuals in Twitter, but the problem of processing large volumes of data has been a problem that cannot be trivially done. This review will discuss techniques and methods that can help solve this scalability issue such as: retrieving a smaller subset of tweets to process with and compare efficient pattern recognition algorithms to process these small subsets of the data. These will be used to find frequencies and relations in the behaviours of communities or individuals on Twitter. The patterns found can be used in many ways such as finding related interests, to study behavioral changes or even used for targeted advertising to different communities. What is found in the discussed techniques is that efficiency can be achieved through many aspects when dealing with large scale data, such as the combining of different methods in the same field, as with the case of Information Retrieval, or by implementing standard algorithms different frameworks to reduce overall computation, as with the case of association rule mining techniques and implementations.
Effect of Some Natural Plant Extracts & Plant Essential Oils on Suppressive of Citrus Green Mold Caused by Penecillium digitatium (Pers

Green mold of citrus, caused by Penicillium digitutum (Pers.:Fr.) Sacc. is one of the most economically important postharvest diseases of citrus worldwide. In Egypt, losses of about 10% and 15% of orange and lemons in storage, respectively, occur each year. Thyme oil at 1.0 and 2.0 % and sweet almond oil at 2.0% concentration caused a complete reduction of P. digitatum linear growth in vitro, while bitter almond oil resulting moderate effect to reducing the fungus growth if compared with other treatments and control treatment. Aloe Vera gel at all concentrations were used gave a highly significantly reduction of fungus linear growth, at high concentration was used 1.0 % and 2.0% Aloe Vera gel caused reduction of fungus growth by 63.3% and , 77.2% respectively. Means that, Olive water extract followed by aloe Vera extract result a moderate effect to reducing the fungus growth. Olive water extract (4%) gave a complete reduction of disease incidence (DI) and disease severity ( DS) after the first storage period but after 45 days and 60 days of storage gave highly significant reduction of both DI and DS by 86.8% &80.0% and 73.3% & 89.5%, respectively . Orange fruits coated with Gum Arabic (5%) resulting a moderate effect to reducing DI and DS at all storage periods if compared with control treatment and other tested plant extracts. On the other hand, at highly concentration was tested (4%) , Sweet almond, bitter almond and Thyme oil gave a complete reduction of DI and DS at the first storage period , at the second storage period , previous treatments gave a complete reduction of disease incidence (DI) but gave a highly reduction of DS, while after 60 days of storage, fruits coated with gum Arabic (5%) and Thyme oil at high concentration (4%) resulting a significantly reduction of DI by 88% and by 81.1% and on DS by 81.5 and by 89.0%. Thyme oil at 1.0 and 2.0 concentrations gave a non-activity of pectinase enzyme and result low activity of enzyme activity (10.5 U/ml) at low tested concentration was used if compared with control treatment, while, olive water waste at 0.5 and 1.0 concentrations caused low activity of pectinase enzyme (9.7, 10.9U/ ml). At high plant extract concentration (2.0) for all tested extracts caused non-effect of enzyme activity.
Retransmission Strategy Performance Evaluation of Relay network based on IEEE 802.11 WLAN

Combining adaptive transmission rate and ARQ, the performance of the end-to-end packet loss rate, delay and spectral efficiency of the systems with multiple sources is evaluated. The evaluation is performed for two different scenarios: 1) Server retransmission only and 2) Source retransmission only. It is shown that the first scenario has the better performance regarding to the end-to-end delay, packet loss and TCP throughput in low SNRs, however the second scenario demonstrates better performance in high SNRs. Two scenarios fairly show similar spectral efficiency over wireless link
Do Sibilants Fly? Evidence from a Sound Symbolic Pattern in Pokémon Names

Abstract Ancient writers, including Socrates and the Upanishads, argued that sibilants are associated with the notions of wind, air and sky. From modern perspectives, these statements can be understood as an assertion about sound symbolism, i.e., systematic connections between sounds and meanings. Inspired by these writers, this article reports on an experiment that tests a sound symbolic value of sibilants. The experiment is a case study situated within the Pokémonastics research paradigm, in which the researchers explore the sound symbolic patterns in natural languages using Pokémon names. The current experiment shows that when presented with pairs of a flying-type Pokémon character and a normal-type Pokémon character, Japanese speakers are more likely to associate the flying-type Pokémons with names that contain sibilants than those names that do not contain sibilants. As was pointed out by Socrates, the sound symbolic connection identified in the experiment is likely to be grounded in the articulatory properties of sibilants – the large amount of oral airflow that accompanies the production of sibilants. Various implications of the current experiment for the sound symbolism research are discussed throughout the article.