Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards better Sentence Classification for Morphologically Rich Languages by

Many methods have been developed for various sentence classification tasks for English, which usually exploit linguistic resources like parsers or rely on the large amount of annotated or unannotated data, making it difficult to adapt them to other languages. In this paper, we present an evaluation of popular deep learning methods for sentence classification on the morphologically rich Indian languages, specifically, Hindi and Telugu. For this purpose, we also created a question classification dataset for Hindi, by translating the TREC-UIUC dataset. We show that character based input can enhance the performance of current classification systems for morphologically rich languages. Finally, we show that our multiInput-CNN variant is able to perform better than our baselines in two out of three tasks in Hindi and Telugu, while giving comparable results for others.
IS RAWLS ’ LIBERAL JUSTICE GENDERED ?

▪ ABSTRACT: John Rawls’s theory of justice is perhaps the contemporary work in political philosophy mostly discussed in current academia. In this article I wish to analyse the challenges that have been put to this Rawls’s theory; namely, that the theory he presents is gendered. I wish to argue that the Theory of Justice has sufficient philosophical resources to respond to the criticisms that Rawls’s theory is gendered.
Supervised Method for Face Photo - Sketch Synthesis and Recognition

Facial Sketches are most widely used in law enforcement agencies for identification and apprehension of suspects, which may involve in several criminal activities. We plan to implement an efficient system to recognize a forensic sketch images to a gallery of mug shot images which will help law enforcement agencies. SIFT algorithm explained for feature extraction. This SIFT feature will possess strong robustness to the accessory, expression, pose, illumination variations. SIFT algorithm have highest percentage of accuracy. Faces are highly deformable objects which may easily change their appearance over time. The feature based sketch matching algorithm (SIFT) we present offers both a simpler and faster design and utilizes localized sketch information in the matching process. SIFT used for extracting local features. Scale invariant feature transform (SIFT) proposed by Lowe has been widely and successfully applied to object detection and recognition. SIFT algorithm’s ability has been leveraged through use of MATLAB code. This helps users to use SIFT algorithm very effectively to match sketches with current database images
Factors Towards Philanthropic Dimension Of CSR in The Nigerian Financial Sector : The Mediating Effects

The purpose of the study is to examine the leading factors towards philanthropic dimension of corporate social responsibility in the Nigerian financial sector. The need to build corporate image, prompt managers towards prioritizing the philanthropic dimension of CSR. Satisfaction of stakeholder needs to reduce the pressure exerted on managers’ leads also to the assumption of more philanthropic activities. Within these set of circumstances, the objective of the study is to explore the possible predictive factors leading towards prioritization of philanthropic dimension of CSR by managers of the Nigerian financial sector. This study examined the relationships between philanthropic dimension of CSR, corporate image, stakeholder pressure and cultural influence. A total of 173 managers from the Nigerian financial sector responded to the survey instruments administered which later on was analyzed using partial least squares-structural equation modeling. The results revealed that corporate image and stakeholder pressure are influencing factors towards prioritization of the the philanthropic dimension of CSR, and are mediated by the role of cultural influence in the Nigerian context. This study highlights the prioritization of philanthropic dimension of CSR by managers of the Nigerian financial sector with respect to cultural influence and predictive factors like building a corporate image and reducing pressures from stakeholders
ICaD 2013 Science or art ? “ Sonification in the age of biocyberneticS reproduction ” : a

With digital art being pervasive, art, technology, and science seem to be no longer separable and have been re-integrated. In fact, art history shows that when combined with them, art could give birth to a ground-breaking masterpiece. Based on that, we pose a simple question, “Can we analyze sonification works from the viewpoint of digital art aesthetics? ” As a case study, we try to place the Accessible Aquarium Project (AAP) at the intersection of scientific research and art. Relying on term, “biocybernetics”, we discuss aesthetic meanings of the AAP in terms of new temporality (dynamicity), transformed relationships (combined gazes), dialectic improvement of the original (interactivity), and enacted collective art-work (embodied cognition). We hope this review will help illuminate the artistic contribution of interactive sonification and explore future directions. Further, this work is expected to contribute to facilitating discussions of aesthetics about the sonification works in the auditory display community.
New algorithm for digital way-finding map

The purpose of this research was to develop a new algorithm to find the shortest pathfinding that can be implemented on a digital way-finding map. The standard algorithm used for pathfinding is Dijkstra and A*. But for a large search space, the A* and Dijkstra need a large amount of CPU and memory resources. Whereas pathfinding algorithm is usually embedded in devices with limited memory. So, we make the algorithm which requires less memory. In order to make a less memory algorithm, we modify the Breadth First Search (BFS) and Bidirectional Search (BS) algorithm and use the cost graph and modify adjacency matrix. We have tried to reduce the runtime of our algorithm using the modify adjacenvy matrix. The proposed algorithm is then tested for path finding at the institution that has several buildings with many floors where staircase position is not uniform on each floor. The test results were then compared with Djikstra, A*, IDA*, and HPA*. The experiment results showed that our algorithm is faster than the A*, IDA* and Djikstra algorithm. This is because the algorithm that we designed only need to make modify adjacenvy matrix once, so it can reduce the computation time. However, for cases involving multiple floors, our algorithm is not faster than HPA *, but approached. From the test results can be concluded that our algorithm can be implemented in the case of searching the route on buildings that have many floors.
Low-Cost Multiple-MAV SLAM Using Open Source Software

Low-Cost Multiple-MAV SLAM Using Open Source Software Andrew Hollenbach, M.S. Rochester Institute of Technology, 2018 Supervisor: Dr. Zack Butler We demonstrate a multiple micro aerial vehicle (MAV) system capable of supporting autonomous exploration and navigation in unknown environments using only a sensor commonly found in low-cost, commercially available MAVs—a front-facing monocular camera. We adapt a popular open source monocular SLAM library, ORB-SLAM, to support multiple inputs and present a system capable of effective cross-map alignment that can be theoretically generalized for use with other monocular SLAM libraries. Using our system, a single central ground control station is capable of supporting up to five MAVs simultaneously without a loss in mapping quality as compared to single-MAV ORB-SLAM. We conduct testing using both benchmark datasets and realworld trials to demonstrate the capability and real-time effectiveness.
Smoking Related Cancers and Loci at Chromosomes 15 q 25 , 5 p 15 , 6 p 22 . 1 and 6 p 21 . 33 in the Polish Population

Genetic factors associated with the risk of smoking related cancers have until recently remained elusive. Since the publication of a genome-wide association study (GWAS) on lung cancer new genetic loci have been identified that appear to be associated with disease risk. In this replication study we genotyped 14 single nucleotide polymorphisms (SNPs) located at the 5p12.3-p15.33, 6p21.3-p22.1, 6q23-q27 and 15q25.1 loci in 874 lung, 450 bladder, 418 laryngeal cancer cases and cancer-free controls, matched by year of birth and sex to the cases. Our results revealed that loci in the chromosome region 15q25.1 (rs16969968[A], rs8034191[G]) and 5p15 (rs402710[T]) are associated with lung cancer risk in the Polish population (smoking status adjusted OR = 1.45, 1.35, 0.77; p#0.0001, 0.0005, 0.002; 95%CI 1.23–1.72, 1.14–1.59, 0.66–0.91 respectively). None of the other regions analyzed herein were implicated in the risk of lung, bladder or laryngeal cancer. This study supports previous findings on lung cancer but fails to show association of SNPs located in 15q25.1 and 5p15 region with other smoking related cancers like bladder and laryngeal cancer. Citation: Jaworowska E, Trubicka J, Lener MR, Masojć B, Złowocka-Perłowska E, et al. (2011) Smoking Related Cancers and Loci at Chromosomes 15q25, 5p15, 6p22.1 and 6p21.33 in the Polish Population. PLoS ONE 6(9): e25057. doi:10.1371/journal.pone. 0025057 Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America Received June 20, 2011; Accepted August 23, 2011; Published September 22, 2011 Copyright: 2011 Jaworowska et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by European Community program ‘‘MarieCurie Host Fellowships for the Transfer of Knowledge’’, nr MTKD-CT-2004-510114. B.M. receives a ‘‘Scholarship for Young Scientists’’ from Polish Ministry of Science and Higher Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: bmasojc@gmail.com . These authors contributed equally to this work.
History Education in Conflict Contexts : Toward Transitional Justice and Emancipation

The international education agenda for post-conflict contexts is often focused on Education For All development goals and is influenced by an economic rationale and efficiency, whereas such contexts demand more focus on education for inclusion and reconciliation. Cole (2007) draws attention to an important path of reform: history education, and its relation with the goals of transitional justice and peace. Research (Foster, 2012) shows that in many countries, textbooks continue to adopt a single nationalistic narrative of the past that promotes some perspectives and ignores and silences others. The study critically approaches history education, its implications for identity formation, its potential relation to emancipation and the social aims of education, and the discursive production of official narratives. The case of history textbooks in conflict-ridden Syria is presented to highlight the need for critical history education in the transitional justice and democratization process. keywords: history education, textbook research, identity, critical education, transitional justice; emancipation.
Vision-Based Parking Entry Control System

Vision-based control systems are very promising in the Intelligent Transportation System (ITS). This paper proposes a system that use the vision system to control vehicle entry at the bridge gate at a certain facility. The system detects the presence of the car and captures the front car image to proceed to plate recognition process. Vehicle plate region is extracted using the size filtering, image thresholding and object counting algorithms. Optical character recognition technique is used in the recognition module. The result from the recognition module is then compared to the record in the database for information like the vehicle owner name, type of car, etc. The overall system is implemented and simulated in LabVIEW and the performance of recognition is tested on the real image. The system can successfully detect and recognize the plate number with minimum error.
EFFECTS OF A HIGH OLEIC ACID BEEF DIET ON CARDIOVASCULAR DISEASE RISK FACTORS OF HUMAN SUBJECTS

Effects of a High Oleic Acid Beef Diet on Cardiovascular Disease Risk Factors of Human Subjects. (August 2012) Thaddeus Hunter Adams, B.S., Texas A&M University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Stephen B. Smith The consumption of high-fat hamburger enriched with saturated fatty acids (SFA) and trans-fatty acids (TFA) may increase risk factors for cardiovascular disease, whereas hamburger enriched with monounsaturated fatty acids (MUFA) may have the opposite effect. Ten mildly hypercholesterolemic men consumed five, 114-g hamburger patties per week for two consecutive phases. Participants consumed low-MUFA (high SFA) hamburger (MUFA:SFA = 0.95; produced from pasture-fed cattle) for 5 wk, consumed their habitual diets for 3 wk, and then consumed high-MUFA hamburger (MUFA:SFA = 1.31; produced from grain-fed cattle) for 5 wk. These MUFA:SFA were typical of ranges observed for retail ground beef. Relative to habitual levels and levels during the high-MUFA phase, the low-MUFA hamburger: increased plasma palmitic acid, palmitoleic acid, and triacylglycerols (P < 0.01); decreased HDL cholesterol (HDL-C) and LDL particle diameter percentile distributions (P < 0.05); and had no effect on LDLC or plasma glucose (P > 0.10). Plasma palmitoleic acid was positively correlated with triacylglycerols (r = 0.90), VLDL-C (r = 0.73), and the LDL:HDL (r = 0.45), and was
Title A Comparison of Maximum-Likelihood and Asymptotically Distribution-Free Methods of Treating Incomplete Non-Normal Data Permalink

This article describes a Monte Carlo study of 2 methods for treating incomplete non-normal data. Skewed, kurtotic data sets conforming to a single structured model, but varying in sample size, percentage of data missing, and missing-data mechanism, were produced. An asymptotically distribution-free available-case (ADFAC) method and structured-model expectation-maximization (EM) with non-normality corrections were applied to these data sets, and the 2 methods were then compared in terms of bias in parameter estimates, bias in standard-error estimates, efficiency of parameter estimates, and model chi-squares. The results favored the non-normality corrected EM over the ADFAC method in almost all respects, the only important exception involving bias in standard-error estimates with large samples.
THE PRESERVATION OF INFORMATION RIGHT-WAY REGULATE FOR MULTI EXPERT CLOUD CACHE CONNECTIONS

This focuses on encrypted data search, crucial encryption files, prior to exit, for example protection protection on cloud computing, or typically in any network system system where the server does not make it completely reliable. We officially submit that our proposed system is secure against attacked keywords. Create a consistent keyword search in the encrypting system that supports multiple number users and multiple information suppliers. We distinguish features and keywords in our configurations. Keywords are the content made by the files, and the attributes refer to the user's attribute. Additionally, using proxy encryption and system error query strategies, the proposed system is better than the cloud removal model and has an effective user effect. Unlike the keyword search plan that has been accepted by the public key, our system can be able to achieve distribution and impartiality at the same time. Unlike the filename search search plan, our system allows search for relevant keywords that can be converted into scheduled data. Continuous difficulties are the precise way of work within the system compared with the number of authorized users. Therefore, one way for exporting is best for any larger system, for example, the cloud. The proposed ABKS-UR program and the verification system verification confirmation using the actual country data collection and the comparisons of rating calculation in relation to the clicking process.
Using automated keyword extraction to facilitate team discovery in a digital forensic investigation of electronic communications

A major problem that often occurs in Digital Forensics (DF) is the huge volumes of data that has to be searched, filtered, and indexed to discover patterns that could lead to forensic evidence. The nature of, and the process by which the data gets collected, implies that the data also contain information about persons that are not implicated, or only incidentally involved in the crime under investigation. Privacy is therefore an important issue that needs to be managed in a DF investigation. This paper shows that techniques used in the Team Formation (TF) task can be successfully applied to address both the problems of data volume and privacy. The TF task can be re-formulated to fit the DF arena: to commit a crime, the culprit(s) may require the assistance of several other individuals, which implies that a team of some sort gets established. During a post-mortem DF analysis, an investigator may only have one, or a few names to start with. One of the key challenges is finding possible co-conspirators. From a TF point of view, the culprit is trying to find the best team to commit the crime, given some constraints. The TF task in DF requires the recording of skill-sets, and the generation and/or discovery of a graph depicting interaction between candidates. If the data consist of an email corpus and peoples’ roles in an organisation (such as in the Enron data), both of these are readily available. In this paper we consider the TF problem in general and extend it to the DF arena by considering the information that an investigator may have access to during the investigation. We also show that simple information retrieval and keyword extraction techniques (such as RAKE) can be used to automatically discover potential teams from the data, while preserving privacy; results from a series of experiments (using the new definitions of TF and the proposed information retrieval techniques) on the Enron data is then presented.
Wireless device identification using descriptive statistics

Physical layer identification systems classify the wireless devices by exploiting some distinctive device characteristics which can be measured from transmissions. In this paper, characteristic features based on descriptive statistics are extracted from the transient part of the received WiFi signals for classification purpose. Classification performance of the features is evaluated through experimental data. Feature sets consisting of combinations of descriptive statistics are tested in order to evaluate the distinctiveness of statistics which measures the central tendency and dispersion of data. Classification results for independent trials are presented in terms of mean and standard deviation of classification accuracy as well as confusion matrix.
The Importance of Distributed Theory on Artificial Intelligence

In recent years, much research has been de- voted to the synthesis of e-commerce; contrarily, few have synthesized the exploration of Boolean logic. After years of compelling research into expert systems, we disconfirm the study of the transistor, which embodies the extensive princi- ples of robotics. We explore an analysis of vacuum tubes (GLYN), which we use to argue that the famous multimodal algorithm for the development of the World Wide Web [5] runs in Θ(n! ) time.
LODsyndesis: Global Scale Knowledge Services

In this paper, we present LODsyndesis, a suite of services over the datasets of the entire Linked Open Data Cloud, which offers fast, content-based dataset discovery and object co-reference. Emphasis is given on supporting scalable cross-dataset reasoning for finding all information about any entity and its provenance. Other tasks that can be benefited from these services are those related to the quality and veracity of data since the collection of all information about an entity, and the cross-dataset inference that is feasible, allows spotting the contradictions that exist, and also provides information for data cleaning or for estimating and suggesting which data are probably correct or more accurate. In addition, we will show how these services can assist the enrichment of existing datasets with more features for obtaining better predictions in machine learning tasks. Finally, we report measurements that reveal the sparsity of the current datasets, as regards their connectivity, which in turn justifies the need for advancing the current methods for data integration. Measurements focusing on the cultural domain are also included, specifically measurements over datasets using CIDOC CRM (Conceptual Reference Model), and connectivity measurements of British Museum data. The services of LODsyndesis are based on special indexes and algorithms and allow the indexing of 2 billion triples in around 80 min using a cluster of 96 computers.
Force and Motion Based Methods for Planar Human-Robot Co-manipulation of Extended Objects

Force and Motion Based Methods for Planar Human-Robot Co-Manipulation of Extended Objects Erich Allen Mielke Department of Mechanical Engineering, BYU Master of Science As robots become more common operating in close proximity to people, new opportunities arise for physical human-robot interaction, such as co-manipulation of extended objects. Co-manipulation involves physical interaction between two partners where an object held by both is manipulated in tandem. There is a dearth of viable high degree-of-freedom co-manipulation controllers, especially for extended objects, as well as a lack of information about how humanhuman teams perform in high degree-of-freedom tasks. One method for creating co-manipulation controllers is to pattern them off of human data. This thesis uses this technique by exploring a previously completed experimental study. The study involved human-human dyads in leader-follower format performing co-manipulation tasks with an extended object in 6 degrees of freedom. Two important tasks performed in this experiment were lateral translation and planar rotation tasks. This thesis focuses on these two tasks because they represent planar motion. Most previous control methods are for 1 or 2 degrees-of-freedom. The study provided information about how human-human dyads perform planar tasks. Most notably, planar tasks generally adhere to minimum-jerk trajectories, and do not minimize interaction forces between users. The study also helped solve the translation versus rotation problem. From the experimental data, torque patterns were discovered at the beginning of the trial that defined intent to translate or rotate. From these patterns, a new method of planar co-manipulation control was developed, called Extended Variable Impedance Control. This is a novel 3 degree-offreedom method that is applicable to a variety of planar co-manipulation scenarios. Additionally, the data was fed through a Recursive Neural Network. The network takes in a series of motion data and predicts the next step in the series. The predicted data was used as an intent estimate in another novel 3 degree of freedom method called Neural Network Prediction Control. This method is capable of generalizing to 6 degrees of freedom, but is limited in this thesis for comparison with the other method. An experiment, involving 16 participants, was developed to test the capabilities of both controllers for planar tasks. A dual manipulator robot with an omnidirectional base was used in the experiment. The results from the study show that both the Neural Network Prediction Control and Extended Variable Impedance Control controllers performed comparably to blindfolded humanhuman dyads. A survey given to participants informed us they preferred to use the Extended Variable Impedance Control. These two unique controllers are the major results of this work.