Reviewerly/exHarmony huggingface dataset:
Investigating the case of weak baselines in Ad-hoc Retrieval and Question Answering

Weak baselines have been present in Information Retrieval (IR) for decades. They have been associated with IR progress stagnation, baseline selection bias to publish results more readily, and models’ effectiveness reproducibility issues that hinder the validation of results by independent research teams. Weak baselines have been studied by the IR community; however, the focus has been almost exclusive on ad-hoc retrieval, the most popular IR task, leaving outside other IR tasks and datasets recently de- veloped. Current deep neural IR research is particularly vulnerable to the issues with weak baselines due to the hype surrounding deep learning. In this thesis we investigate the cases of weak baselines in ad-hoc retrieval and question answering (QA), two representative IR tasks among 13 cases of weak baselines we found in current deep neural IR research from EMNLP 2018 conference. In particular, we study whether the recently introduced deep neural IR models are actually significantly more effective than the reported IR baselines or than LambdaMART, the Learning to Rank (LTR) model we propose plus hyperparameter optimization (HPO). We also benchmark two HPO methods: RS and BOHB, to determine which method is more efficient to retrieve a good hyperparameter configuration. Throughout our experiments we show that the effectiveness of the novel deep neural IR models can be difficult to replicate, it might be lower than reported, and that it is not necessarily significantly higher than the baseliness. Furthermore, we demonstrate that BOHB is more efficient than RS, but the HPO process not always improves the effectiveness of LambdaMART significantly.
A Study on Enhancing Service Quality in Driving Schools: A Case of Jeddah Driving School

“Currently the economies of the majority of both the developed and developing countries are now heavily relying on services, the quality of service delivered by the vendor has become middle phase for both scientific researchers and business managers of many different businesses including those who only providing service such as driving schools. This research study investigates and evaluates the service quality levels and customer satisfaction at Jeddah driving development school. The preliminary chapter includes an introduction that outlines the research service quality indicating the significance of customer satisfaction at Jeddah Driving School and the development of service quality practices at the driving school. The research aims to measure the service quality at Jeddah driving school. Then, the literature review chapter focuses on the available studies that linked to service quality practices applied by various businesses. Next, is the methodology chapter which discusses the literature eligibility criteria, moreover, the evaluation for the sucrose of information and the research methods that adopted for conducting this work. Basically, the survey of online questionnaires is used in collecting the primary data in this research. Furthermore, the information collected from both primary and secondary data is analyzed in the analysis and discussion chapter by connecting it with the theories that argued in the literature review chapter. Finally, the concluding chapter that discussed the limitations of the study and presented some suitable recommendations from the findings and analysis chapter, which will allow enhancing business development strategies and service quality practices at Jeddah driving school.
Automated Recognitions of White Blood Cells

Healthcare services are important part of   cities. Now a day’s healthcare plays important role in the transformation of cities into smart cities. Leucocytes play a vital role in immune system which helps to protect the body against both infectious disease and foreign invaders. Identification of leukocytes in blood smear provides important information to pathologist as well as doctors to analyze and predicts different types of hematic diseases, such as AIDS and blood cancer (Leukemia). Most of the time, the health professional are interested on leucocytes in white blood cells only. Digital image processing procedures can help them in their examine and detecting. For example, disease like cancer and AIDS is detected based on the total count of the WBC in the human body. In the proposed framework, leucocyte is first segmented into two dominant elements, nucleus and cytoplasm, followed by extraction of features. The identification of leucocytes can be done with the help of adoptive gradient vector flow snake algorithm.How to cite this article:Bajirao MK, Bhaskar SD, Dnyneshwar KK.Automated Recognitions of White Blood Cells.J Adv Res Info Tech Sys Mgmt 2019; 3(2): 4-6
Boosting Projection Neural Features for Semantic Similar Clustered Documents in Cloud

Cloud computing has emerged as real world technology over the Internet. Due to the development of big data with high dimensionality, data storage possibility over cloud has created large scope in recent times. Document clustering is the fundamental topic that turned into an indispensable component in many areas like cloud computing. Document clustering partitions the document into significant classes or groups for retrieving the relevant document. Many researchers used the factorization methods and ontologies for internal and external knowledge based document clustering. However, existing methods failed to provide the semantic feature construction and leads to the information loss while covering all the ideas in documents. In order to address these problems, different document clustering techniques in cloud has been reviewed in this paper. In addition to that Document clustering by Entropy-based Boosting with Projection Neural Feature (EB-PNF) method is presented. The proposed method involves two stages. They are, similar document identification based on semantic similarity score, feature extraction which includes the extraction of both single and multi-label features based on the precision, recall and computational complexity to prove that EB-PNF method produces high-quality clusters comparable to the state-of-the-art methods.
A Comparative Study and Analysis of Evaluation Matrices in Machine Translation

Machine translation is a process of translating one natural language to another without much human interaction. Evaluation of any Machine Translation System (MTS) is the most important factor in a machine learning environment. There are many techniques existing to determine and optimize the quality of output in any MTS. Earlier methods are based on human judgments. Even though human evaluation methods are very much reliable, they suffer due to some disadvantages such as high cost, more time consuming and also poor re-usability. Hence, automatic methods have been proposed to reduce time and cost. In this survey, we have discussed different metrics under the automatic evaluation techniques in order to evaluate the output quality of MTS. It is believed that machine learning system developers at large would get befitted by this survey.
TMRM: Two-Stage Multi-Task Recommendation Model Boosted Feature Selection

For recommendation systems, full learning from features is critical to improving system accuracy. Most recommendation systems collect a large number of features to improve the recommendation accuracy, but they ignore the importance of extracting important feature combinations. Even if some systems manually measure important features based on experience, they cannot automatically select important feature combinations, that is why they can not be used to guide model training. In this paper, a Two-stage Multi-task Recommendation Model(TMRM) is proposed which aims to automatically select important feature combinations from massive features, and it also contributes to achieving better recommendations through a combination of tree-based model and neural network. Extensive experiments on two large public data sets are conducted on TMRM, and the results demonstrate the superiority of our proposed method over state-of-the-art solutions on performance of recommendation systems.
K-Anonymity without the Prior Value of the Threshold k: Revisited

k-Anonymity is a popular concept associated with a data set to designate it some level of anonymity. That is, a dataset is said to be k-anonymous if each of its identity-revealing attribute termed as Quasi-Identifier, appears for a minimum in at least k different tuples of the data set. Since its first publication in the year 2002, that concept has remained a focus of interest in the research arena, where the majority of studies have agreed on the prior delineation of the value of k. This research, however, adlibs one of the recent research that auto assigns to k an amount computed by traversing through each row in an anonymized data set. Our proposed solution suggests having a byte array of a dimension of 100, corresponding to the two-digit suppressed area code whose values can range from 0 to 99. The process requires the initialization of this array at the start with all zero/null values. With each row taken for pre-processing, that is, perform generalization and suppression, the value of the array element whose index corresponds to the value of the two-digit suppressed ZIP/Area Code is incremented by one. Whenever the resultant value exceeds 255, the same gets reinitialized with a value of two. The process terminates with the pre-processing of the dataset after which, a new kanonymized table is generated but only for the tuples whose ZIP/Area Code has a value > 2 in the corresponding index element of the proposed array. Doing so reduces the search iterations, which has a positive effect on process efficiency. Further, the proposed enhancement appreciates human intervention for add-in security during the publication process.
ACA: Attention-Based Context-Aware Answer Selection System

The main goal of question answering system is to develop chatbots capable of answering the questions irrespective of the domain. The system has to provide appropriate answers according to the user queries. The challenge of a question answering system lies in analyzing the question to retrieve the accurate answers from a large amount of data present. The main aim of this paper is to propose a question answering system that analyzes the questions properly and answers according to the type of the question in a precise way. The model uses similarity measures to find the candidate set which are the most relevant answers from the content given. The generated candidate set is then analyzed further using the bidirectional long short-term memory (BiLSTM) to retrieve the appropriate answer. Based on the type of question, we retrieve the exact word/phrase as a response to the user. This is done using an attention mechanism which _nds the exact answer to the query. Results of models without memory and with an attention-based BiLSTM are being compared. Using the attention-based BiLSTM, we found that the retrieved answer had increased accuracy rate by 17% when compared with without-attention models like convolution neural networks (CNN) and recurrent neural networks (RNN).
Complete Rare Event Specification using Stochastic Treatment: CRESST

In the fast moving world today, rare events are becoming increasingly common. Ranging from studying incidents of safety hazards to identifying transaction fraud, they all fall under the radar of rare events. Identifying and studying rare events become of crucial importance, particularly when the underlying event conforms to a sensitive or an adverse issue. The thing to note here is, despite the probability of occurrence being very close to zero, the potential specification of the rare event could be quite extensive. For example, within the parent rare event of Product Safety, there could be multiple types of potential hazard, rendering the sub-classes rarer still. In this paper, we are going to explore a novel algorithm designed to study a rare event and its sub-classes over time with primary focus on forecast and detecting anomalies. The anomalies studied here are relative anomalies i.e., they may not contribute to the long-term trend of the rare time series but represent deviation from the base state as seen in the immediate past.
Innovating Development Strategies in Africa: the role of international, national and regional actors by Landry Signé Cambridge: Cambridge University Press, 2017. Pp. 232. $105 (pbk).

Innovating Development Strategies in Africa: the role of international, national and regional actors by Landry Signé Cambridge: Cambridge University Press, 2017. Pp. 232. $105 (pbk). - Volume 58 Issue 1
Navigation and assistance using Augmented Reality

Mixed Reality (MR) is closing the gap between real and digital and is changing how we interact with digital content. Making physical objects a part of the user interface creates a direct mapping between virtual and reality and along with that emerges many design benefits. Current head-worn see through Augmented Reality (AR) headsets are not only capable of delivering a MR experience but are also spatial computers. Equipped with a wide variety of sensors makes the headsets capable of understanding the environment and context. This thesis aims to explore how AR and spatial computing can be used as navigation support in indoor facilities. A prototype implementing real time position visualization and pathfinding was developed and evaluated in a user study conducted on 20 test participants. The results shows that there is great potential in using spatial computing and AR for positioning and navigation. (Less)
Penerapan Metode Asosiasi Datamining Market Basket Analysis Menggunakan Algoritma Apriori Untuk Melakukan Pola Analisis Belanja Konsumen

Application  Of  Data  Mining  Association  Method  Market  Basket  Analysis  Using  Apriori  Algorithm  For  Consumer  Shopping  Analysis  Purpose  (Case  Study:  Sbmart Bukit  Nusa  Indah)  Data  Mining  is  a  discipline  that  studies  techniques  from patterns, statistics, databases, which aims to extract information useful and valuable. Transaction data within  1  year  is  very  unfortunate  if not  analyzed  and  reprocessed  to  obtain  useful  information  data such as to be able to know the product with the most sales and product linkage with each other. For that we need one of the data mining algorithm that is Market Basket Analysis and Apriori algorithm because algorithm apriori is the most famous algorithm to find high frequency pattern. The purpose of this  research  is  to  analyze  sales  sales  data  at  SBmart  Bukit  Nusa  Indah  Store.  The  results  of  the implementation  and  testing  obtained  from  the  processing  of  sales  transaction  data  in  the  form  of itemset data by applying the concept of association analysis of known mining support values on each item, item combination, and confidence value in the formation of frequent itemset using RapidMiner Studio 5.  The  most  purchased  item  simultaneously  by  consumers  of  SBMart  Bukit  Nusa  Indah,  if buying Biscuit to eat will buy Mineral Water with 70,43% confidence, Liquid Milk Tetra-Instant Noodles with 73,16% confidence, Liquid Tea & Cofee-Instant Noodles with confidence 79,33 %, Mineral Water -  Instant  Noodles  with  72.69%  confidence,  Modern  Snack  Pack-Instant  Noodles  with  74.22% confidence,  Instant  Noodles  Bread  with  83.78% confidence,  Egg-Instant  Noodles  with  84.02% confidence, Modern Snack Pack - Mineral Water with confidence 72.02% Egg-Mineral Water with a confidence73.36%.
A CASE STUDY OF CLASSROOM MANAGEMENT: THE IMPLEMENTATION OF LOGICO GAME FOR THE FOURTH GRADERS

Teaching English to young learners is quite challenging since teachers need to create a meaningful learning process and make young learners fun of the activities. One of the ways is using games, especially LOGICO game. This kind of game can motivate young learners to solve the next tasks and check their understanding of English. By using LOGICO game, the researcher intended to analyze and describe the strategies the teacher handled the game, including giving instructions, organizing the classroom, playing the game, and playing the teacher’s role in the classroom. The data was from the video recording in 4B class of SD Kanisius Demangan Baru 1 , Yogyakarta . In this classroom, there were 26 students. The English class observed was on Tuesday, September 15 th , 2015. It started at 9.20 a.m. and finished at 10.50 a.m. The researcher observed and recorded the classroom activities focusing on the game. After getting the data, the researcher listened and watched the video, paid attention more on how the teacher handled the LOGICO game. The results show that the teacher handled the game well, but the teacher should pay attention on the language use, teach the students useful language, be consistent in using the game, and monitor the students in order to prevent them from cheating. The researcher expects that this research can be beneficial for teachers and other researchers to conduct better research in classroom management. DOI : doi.org/24071/lltc.2018.05
The development of a mechano-biological assessment of leukemia cells using optical tweezers

Patients with B-ALL (Ph +ve) acute lymphoblastic leukemia are at high risk of relapse and mortality. We seek to establish a mechano-biological testing technique to assess biophysical properties of cell motility, potentiating the ability to distinguish between high and low risk leukemia populations and behaviors. Two experiments were performed to test the mechano-biological behavior of B-ALL using the ThorLabs modular optical tweezers and a microfluid chemotaxis chamber. The first experiment measured the relative mechanical energy carried by a cell in the optical trap, which was performed on B-ALL in control conditions and under SDF1 chemotaxis. The relative mechanical energy was found through an extension of the back-focal-plane calibration method for optical tweezers, and assumed that increased cellular activity manifests as random movements. There was no discernable difference in the relative mechanical energies between the control B-ALL, B-ALL under SDF1 chemotaxis, and B-ALL in the presence of mesenchymal stem cells. The second experiment quantified the real-time migrational force of B-ALL under SDF1 chemotaxis. This was found through prior calibration of the optical tweezers through determination of the terms &kappa; and &beta;. Of the viable measurements, 3 of 8 cells exhibited a significant force towards the SDF1 gradient. Further experimentation is necessary to normalize the experimental set-up conditions, and increase the number of viable measurements during cell migration.
Dynamic Range Enhancement Using New MSF Image and CLAHE

Low dynamic range (LDR) image may contain low-light and highlight areas due to the limitations of the dynamic range of LDR image sensor. To overcome this, high dynamic range (HDR) images have been developed with rich colors such as those seen by the human eye. Most HDR images are produced by using a good image sensor or by composing photographed pictures under different exposure conditions. In this paper, we propose a method to enhance the dynamic range of a single LDR image by removing the specular component from highlight pixels and strengthening the brightness of low-light area with preserving actual color. Next, we apply a contrast limited adaptive histogram equalization (CLAHE) to expand the dynamic range by varying the R, G, and B values according to the brightness. We verify real-time use based on the execution speed of the algorithm. We evaluate our proposed method by using natural image quality evaluator (NIQE) and histogram balance (HB) quality metrics. We also show the improvement in the highlight and low-light areas and confirm that our results can be applied in the field of image recognition.
Programming Abstractions for Configurable Hardware

Programming abstractions decrease the cognitive gap between program idealization and expression. In the software domain, this high-level expressive power is achieved through layered abstractions - virtual machines, compilers, operating systems - which translate, at design and runtime, programmer visible code into hardware-compatible code. While this paradigm is ideal for static, i.e., unmodifiable, hardware, several of these abstractions break down when programming configurable hardware. State of the art hardware/software co-design techniques (e.g., High Level Synthesis (HLS), Intermediate Fabrics) are, for the most part, ad hoc patches to the traditional abstraction stack, applicable only to specific toolchains or software components. In this paper, we survey current hardware design and hardware/software co-design abstractions, from the perspective of the design language/toolchain. We perform a systematic analysis of different design paradigms, including HLS, Domain Specific Languages (DSL), and new-generation Hardware Description Languages (HDL). We analyze how these paradigms differ in expressiveness, support for hardware/software interaction, hierarchy and modularity, HDL interoperability, and interface with the outside world.
The Construction of a Corpus from the Brazilian Historical-Biographical Dictionary

We present our ongoing efforts towards the creation of a new Portuguese corpus based on the “Dicionário Histórico-Bibliográfico Brasileiro”. The aim to add as many linguistic annotations as possible using widely accepted annotation schemas and distributing all data in standard formats. This first exploratory work revisits what is already done and tests different tools to detect errors and look for the best methods to tackle the problem. Data is available at https://github.com/cpdoc/dhbb-nlp , and it will be continuously improved.
Service Composition Mechanism of Internet of Things

As an important part of the next generation of information technology, the Internet of Things has rapidly changed people’s lifestyles and promoted the development of productivity. In practical applications, the Internet of Things realizes user dynamic information processing through service combination to meet actual needs. Due to the particularity of Web services in service description objects, service resources and service environments in the IoT environment, existing Web service composition technologies It is difficult to apply directly to the Internet of Things. To this end, it is necessary to explore the service combination technology in the Internet of Things environment. This paper expounds the service architecture and related technologies of the service composition under the Internet of Things environment, and derives the content of the web service composition mechanism method. At the same time, the importance of QoS-based IoT service process is proposed.
Malicious website detection

Malicious websites are among the major security threats on the Internet. This threat has been existing for years yet the best solution to overcome it has not been implemented by many people. Most of the existing methods for detecting malicious websites are focusing towards specific attacks. However, attacks are getting more complex and hackers have become more sophisticated with their blended techniques to evade existing countermeasures. In this thesis, a method will be introduced. With previous existing methods in consideration, the method to use for this project is by using heuristic-based detection with machine learning technique and the feature that will be used together with the technique is URL based feature. The purpose of this method is to classify benign and malicious website using machine learning and then will automatically detect malicious websites. By using this method is also to ensure the detection accuracy is high and all malicious websites can be detected even the latest one prompted by the hackers. In conclusion, the proposed method is the most effective way to detect malicious websites and easy to be implemented.