Miracle-dz/UniFAR-FTUs huggingface dataset:
An Information Reinstatement Dealing with Machine Learning

Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. Machine learning programs detect patterns in data and adjust program actions accordingly. In this paper, we are exploring the use of machine learning techniques for information retrieval and we are using machine learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. We are investigating the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large collection and finally approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models.
Robust Destination Jamming Aided Secrecy Precoding for an AF MIMO Untrusted Relay System

We consider a secrecy dual-hop amplify-and-forward (AF) untrusted relay network, where the relay is willing to forward the signal to the destination while acting as a potential eavesdropper. Assuming that all these nodes are equipped with multiple antennas, we propose a joint destination aided cooperative jamming and precoding at both the source and the relay scheme, with the objective to maximize the worst case secrecy rate. The formulated problem is highly non-convex due to the maximization of the difference of several logarithmic determinant (log-det) functions in the CSI uncertainty region. To handle this challenge, we propose to linearize these log-det terms. After linearization, we tackle the CSI uncertainty based on epigraph reformulation and the sign-definiteness lemma. Finally, an alternating optimization (AO) algorithm is proposed to solve the reformulated problem. Numerical results are provided to demonstrate the performance of the proposed scheme.
Large-scale reordering models for statistical machine translation

In state-of-the-art phrase-based statistical machine translation systems (SMT), modelling phrase reorderings is an important need to enhance naturalness of the translate outputs, particularly when the grammatical structures of the language pairs differ significantly. The challenge in developing machine learning methods for machine translation can be summarised in two points. First is the ability to characterise language features such as morphology, syntax and semantics. Second is adapting complex learning algorithms to process large corpora. Posing phrase movements as a classification problem, we exploit recent developments in solving large-scale SVM, Multiclass SVM and Multinomial Logistic Regression. Using dual coordinate descent methods for learning, we provide a mechanism to shrink the amount of training data required for each iteration. Hence, we produce significant saving in time and memory while preserving the accuracy of the models. These efficient classifiers allow us to build large-scale discriminative reordering models. We also explore a generative learning approach namely naive Bayes. Our Bayesian model is shown to be superior to the widely-used lexicalised reordering model. It is fast to train and the storage requirement is many times smaller than the lexicalised model. Although discriminative models might achieve higher accuracy than naive Bayes, the absence of iterative learning is a critical advantage for very large corpora. Our reordering models are fully integrated with the Moses machine translation system, widely used in the community. Evaluated in large-scale translation tasks, our model have proved successful for two very different language pairs: Arabic-English and German-English.
EXAMINING RELATIONSHIP BETWEEN PAKISTAN STOCK EXCHANGE AND BOMBAY STOCK EXCHANGE (A CO-INTEGRATION APPROACH)

International cross listing has enlarged the interest of academics and investors to the subject of co- movement among the stock markets of the world. This study inspects the co integration of Pakistan stock exchange (KSE 100 index) with Bombay stock exchange (BSE 100 index). Daily data of both stock exchanges are collected from 2000 to 2014. Unit root test, Johansen Co-integration test and Engle- Granger tests are used for the data analysis. Results depict that there is no co-integration between the two selected exchanges. The study suggests that investors in Pakistan, may not diversify their risk by selecting in the Bombay equity market and also they may not enjoy benefits of diversification rather than investing in other countries outside Asian region. Also the Indian investors may not maximize their wealth by investing in Pakistani equity market.Keywords: Co integration, Pakistan stock exchange, Bombay stock exchange, Unit root
Industrial Productivity Divergence and Input-Output Network Structures: Evidence from Japan 1973–2012

Since the early 1990s, there have been larger and increasing labor productivity differences across industries in Japan. More specifically, a clear pattern of sigma and beta divergence across industries is observed. To shed light on these stylized facts, we first evaluate the input–output structure of Japan through the lens of a community-detection algorithm from network theory. Results from this analysis suggest the existence of two input–output network structures: a densely-connected group of industries (a stationary community), whose members remain in it throughout the period; and a group of industries (a transitional community) whose members do not belong to this first group. Next, we re-evaluate the industrial divergence pattern of Japan in the context of each network structure. Results suggest that divergence is mostly driven by the transitional community. Interestingly, since 2007, a pattern of sigma convergence started to re-appear only in the stationary community. We conclude suggesting that industrial divergence and instability in community membership are not necessarily indicative of low productivity performance.
The Effects of Fuel-Efficient Cookstoves on Fuel Use, Particulate Matter, and Cooking Practices: Results from a Randomized Trial in Rural Uganda

Smoky cookfires contribute to global climate change and kill approximately four million people annually. While many studies have examined the effects of fuel-efficient cookstoves, this study does so while selling stoves at market prices. After introducing a fuel-efficient cookstove, fuelwood use and household air particulates declined by 12% and by smaller percentages after adjusting for observer-induced bias, or the Hawthorne effect. These reductions were less than laboratory predictions and fell well short of World Health Organization pollution targets. Even when introducing a second stove, most households continued to use their traditional stoves for most cooking. Future research should focus on improving the usability of fuel-efficient cookstoves and/or policies that assist consumers to shift to safer fuels like gas or electricity coupled with mechanisms to disable the existing smoky cookfire.
Cruise brand experience: functional and wellness value creation in tourism business

Purpose Experience and value creation have become integral themes for tourism service providers. This study aims to understand the role of different types of experiences in formulating customers’ perceived value. Studies on this area are scarce, especially in the cruise industry. Design/methodology/approach A research model that includes two value variables, namely, functional and wellness values, and four cruise brand experience variables, namely, sensory, affective, behavioral and intellectual factors, was tested through partial least-squares (PLS) structural equation modeling approach. A total of 389 usable cruise customer responses were obtained and analyzed using SmartPLS software. Findings Results revealed the relative effects of sensory, affective, behavioral and intellectual factors on perceived functional and wellness values. The positive impact of perceived value on customer satisfaction and behavioral intention and the moderating role of service expertise in the relationship between cruise experience and perceived value were examined. Research limitations/implications Future research can examine how types of leisure facilities (e.g. casinos, restaurants, bars and shows), cultural differences (e.g. Eastern and Western) and customers’ travel characteristics (e.g. visiting purpose and companions) may influence the relationships between cruise brand experience and perceived value. Practical implications From a practical point, the relationships among multidimensional cruise brand experience, functional and wellness values, customer satisfaction, revisit intention and service expertise provide a clue on how cruise brands can enhance customers’ perceived value to retain current customers. Originality/value This study contributes to literature by providing a theoretical framework and empirical evidence for predicting and explaining cruise customers’ behavior. From a managerial perspective, this study identified critical factors that are essential to value creation and discussed the implications of predictive factors on developing marketing strategies that enhance customers’ positive attitude and behavior toward the cruise brand.
DOES CORRUPTION AFFECTS BUSINESS INNOVATION? INSIGHTS FROM EMERGING COUNTRIES

There are very different outcomes in the literature regarding the influences of corruption on business innovation and also arguments for both “sanding the wheels” and “greasing the wheels” approaches. The main goal pursued in this paper is to broaden the understanding of the corruption influence on business innovation, considering seven representative dimensions of corruption at governmental structures and institutions’ level and also four relevant dimensions of business innovation, less approached so far. The originality and relevance of this paper are based on that these seven different dimensions of corruption are targeting three characteristic features of it, as bribery's prevalence, the bribery's spread and the companies’ propensity to offer gifts for overcoming the bureaucratic pressures. Moreover, the four different new dimensions of business innovation are targeting the company’s propensity for innovating and strengthening its image and the way of connecting with business partners in a changing business environment. Considering an extensive data set for 110 emergent countries from four continents for the period between 2002 and 2014 and using the generalized linear model framework, this research study is emphasizing that corruption at governmental structures and institutional level has a significant negative impact on business innovation, adversely affecting innovation perspectives.
Parallelize DSW Algorithm

In this project, we propose a parallelized DSW algorithm, useful to periodically rebalance an arbitrary unbalanced BST. The proposed algorithm splits the tree into several subtrees and concurrently do ``Tree-to-Vine'' to these subtrees using multiple threads, and finally connects them into a complete vine; It also splits the vine into several semi-equal sized subvines and do ``Vine-to-Tree'' to these subvines using multiple threads, and combines them into a complete balanced tree. Our extensive experiments including BST generation, ``Tree-to-Vine'', and ``Vine-to-Tree'' parallelization comparisons show that this parallelized DSW algorithm performs much better compared to its counterpart sequential DSW algorithm.
Formation of children's human capital in Kenya: The role of teachers, private schools and the family

In this thesis, we investigate the role of teachers, private schools and the family in the formation of children’s human capital in Kenya. We focus on Kenya due to the declining learning outcomes the country is experiencing, in the wake of increasing public spending in the education sector. The first essay examines the effect of teacher subject knowledge, pedagogical skill, teacher effective instruction time and teacher classroom practices on grade 4 language and maths test scores. Our results show that a one standard deviation increase in the teacher’s knowledge in language (maths) increases student test scores by 0.075 (0.126) of a standard deviation in language (maths). An additional hour of teacher effective instruction time increases student achievement by 0.051 and 0.059 score standard deviations in language and maths, respectively. The second essay estimates the size of the effect of private school attendance on literacy (language) and numeracy (maths) skill acquisition among children drawn from lower primary grades (grades 2-4) in Kenya. Using a household survey data, we apply different estimation techniques (OLS, fixed effects and propensity score matching) to deal with the potential endogeneity of school choice. We find positive and significant effects of private school attendance on both language and maths achievements across all the estimation techniques. For instance, the household fixed effects yield a private school premium of 0.13 to 0.21 score standard deviation in maths and language, respectively. The third essay examines the effect of the gender and order of birth of a child on intrahousehold investments in, and educational outcomes of, children in Kenya. We measure the intra-household education investment in children by the household’s decision to enrol a child in a private school. We define educational outcomes by two variables: completed years of education and relative grade progression. We control for the potential endogeneity of child’s gender, birth order, family size and household level unobservables using household fixed effects model. We find no female advantage in terms of private school enrolment. However, there is a consistent female advantage in terms of completed years of education and relative grade progression. We find significant negative birth order effects on private school enrolment, completed years of education and relative grade progression.
Self-Modeling Neural Systems

Self-Modeling Neural Systems Gregory D. Wayne Goal-directedness is a fundamental property of all living things, but it is perhaps most easily identified in the movement patterns of animals. Ethologists have divided the basic forms of animal behavior into three categories: reproductive, defensive, and ingestive, all of which depend on the complex orchestration of motor control. In this dissertation, we use the framework of optimal control theory to model goal-directed behavior and repurpose it in new ways. We demonstrate a method for creating a hierarchical control network in which higher levels of the control hierarchy deal with tasks of increased abstractness. In a two-level system, the lower-level deals with short time-scale, low-dimensional motor control, and the higher-level is charged with longer time-scale, higher-dimensional planning. Central to our approach to joining the levels is the construction of a forward model of the behavior of the lower-level by the higher-level. Thus, we extend ideas of optimal control theory from controlling a “plant” to controlling a controller. We apply our method to the example problem of guiding a semi-truck in reverse around a field of obstacles. The lower-level controller drives the truck, and the higher-level detects obstacles and plans routes around them. In other work, we consider whether it is possible for a neural system that obeys certain biological constraints to solve optimal control problems. We exhibit a simple method to train a different kind of internal model, a neural network model of the Jacobian of the plant, and we integrate the internal model in a forward-in-time computation that produces an optimal feedback controller. We apply our method to two well-known model problems in optimal control, the torque-limited pendulum and cart-pole swing-up problems.
THE INDEPENDENCE OF THE JUDICIARY

The purpose of this article is to examine Canada’s concept of judicial independence as inherited from England, in order to stress the importance of autonomous courts. First, the historical elements of judicial independence as developed in England are examined. The author then explains the delay in extending these principles to British North America until after the American Revolution and describes the position under Imperial constitutional law on the eve of Confederation. Next, he analyses relevant federal statutes and provisions of the B.N. A. Act in an attempt to uncover the present constitutional position of Canada on the subject of judicial independence, addressing a series of questions dependent on that position. Finally, he conducts some theoretical analyses, and assesses the nature of the judicial function and the modern importance of judicial autonomy.
Frame Complexity-Based Adaptive Bit Rate Normalization

Due to the advances in hardware technologies for low-power CMOS cameras, there have been various researches on wireless video sensor network(WVSN) applications including agricultural monitoring and environmental tracking. In such a system, its core technologies include video compression and wireless transmission. Since data of video sensors are bigger than those of other sensors, it is particularly necessary to estimate precisely the traffic after video encoding. In this paper, we present an estimation method for the encoded video traffic in WVSN networks. To estimate traffic characteristics accurately, the proposed method first measures complexities of frames and then applies them to the bit rate estimation adaptively. It is shown by experimental results that the proposed method improves the estimation of bit rate characteristics by more than 12% as compared to the existing method.
The Application of Conditional Reprogramming to Drug Discovery and Regenerative Medicine

The Conditional Reprogramming (CR) method has been shown to induce an in vitro stem-like state allowing for the continuous growth of patient-derived epithelial cells. Due to this new culture system, it has been possible to identify potential therapies for multiple cancer types and to generate differentiated tissue from cultures of normal epithelial cells. Hence, CR has broad applications in the field of drug discovery and regenerative medicine. Based upon the above findings, we evaluate the ability of CR to identify potential drugs to treat a rare but morbid disease called recurrent respiratory papillomatosis (RRP). A robotic high-throughput screening was performed in collaboration with the National Center for Advanced Technology (NCATS). The conditionally reprogrammed cells, GUMC-403 cells, were screened against two libraries of >4700 drugs. From the two libraries, we identified a total of 13 drugs that induced substantial cytotoxicity in GUMC-403. Furthermore, we validated the efficacy of the drugs in vitro using 2D and 3D models and further polished our list of drugs to panobinostat, dinaciclib and forskolin as potential treatments for RRP patients. On another front, we investigated the ability of conditional reprogramming to propagate equine keratinocyte cultures for long-term expansion. 3D air-liquid interface culture was used to investigate the ability of CR equine keratinocytes to differentiate and form stratified squamous epithelium that was positive for terminal differentiation markers. These unlimited supplies
Investigating & Improving The Reliability And Repeatability Of Keystorke Dynamics Timers

One of the most challenging tasks facing the security expert remains the correct authentication of human being which has been crucial to the fabric of our society. The emphasis is now on reliable person identification for computerized devices as the latter forms an integral part of our daily activities. Moreover with increasing geographical mobility of individuals, the identification problem has become more acute. One alternative, to curb down the increasing number of computer related crimes, is through the use of keystroke biometric technology which represents an enhancement to password mechanisms by incorporating typing rhythms in it. Time captured being critical to the performance of the identifier, it is primordial that it satisfies certain requirements at a suitable degree of acceptability This paper presents an evaluation of timing options for keystroke dynamics paying attention to their repeatability and reliability as well as their portability on different systems. In actual passwords schemes users enroll using one computer and access resources using other configurations at different locations without bothering about the different underlying operating systems.
Cloud Analyst: An Insight of Service Broker Policy

Cloud computing is one of the most promising computing field, which has given the new vision to the computing field. Cloud computing has opened a door as a new model for hosting and delivering services over the Internet. The main aim of cloud computing is to provide the resources as a services to the client. The new concept of Federated Cloud Computing in which multiple datacenters are distributed over different regions. Since the evolution of Cloud Computing: load balancing, energy management, VM migration, brokerage policies, cost modelling and security issues are popular research topics in the field. Deployment of real cloud environment for testing or for commercial use is very costly. Cloud simulators help to model various cloud applications and it is very easy to analyse. In this survey, two cloud simulators: CloudSim and CloudAnalyst, with their overview are presented so it can be easily decided which one is suitable for particular research topic. And also the survey on the service broker policy, its issues and available solutions are presented. Because there is always been the requirement to select appropriate datacenter so that further tasks for processing the request should be carried out with efficiency in least response time. So the issue of selecting appropriate datacenter which is known as service broker policy is kind of important.
MANAGEMENT OF PSYCHOSOCIAL RISKS IN THE EDUCATIONAL SECTOR OF LATVIA

Risk management is gaining acknowledgement in organisational management due to the benefits it brings with it, such as ability of managers to forecast possible threats and plan the most appropriate prevention measures. Psychosocial risk group is one of the largest risk group types and is related to the aspects of staff overburning, lack of employee motivation, health problems due to stress, etc. Study carried in Latvia about risk management showed that psychosocial risks are significant for educational institutions. Empirical research carried out in Latvia has shown that the top risks perceived by the education sector professionals in Latvia are overloading of employees and lack of employee motivation. Hypothesis of this study is that psychosocial risks, such as overloading and lack of motivation, are not dealt well with due to low awareness of effective psychosocial risk management techniques. There was no profound research performed on this topic in Latvia in the last ten years. The aim of this study is to explore different methods researched by other authors how the risk can be managed, analyse causes of these risks and provide the recommendation on how to deal with the psychosocial risks in the educational sector.