Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploratory model analysis for machine learning

Author(s): Jiang, Biye | Advisor(s): Canny, John | Abstract: Machine learning is growing in importance in many different fields. However, it is still very hard for users to tune hyper-parameters when optimizing their models, or perform a comprehensive and interpretable diagnosis for complex models like deep neural nets. Existing developer tool like TensorBoard only provides limited functionality which usually visualizes model statistics based on metrics predefined before the training starts. Almost nothing can be adjusted during the training. But the real model optimization and diagnosis procedures actually involve lots of interaction and continuous experiments. To tackle those challenges, we developed a framework which allows users to perform exploratory model analysis based on the hardware accelerated machine learning toolkit BIDMach. Our system is unique that we allow users to interactively add visualizations and adjust hyper-parameters during training. Also, we use Monte Carlo style algorithms to allow users to explore the entire model space under different user preferences rather than only reaching the local optimums. We demonstrate the usage of our system in several real-world applications. For problems like advertisement optimization or clustering where multiple optimization objectives exist, users can incorporate secondary criteria into the model-generation process and make trade-offs in an interactive way. For deep convolution neural net diagnosis, users can use our LDAM (Langevin Dynamic Activation Maximization) algorithm to systematically explore the images that stimulate a given neuron. We conduct experiments and user studies on several public datasets to show how users from different background can benefit from our tools.
Factors Affecting Implementation of E-Procurement Practices in Public Service in Kenya : A Case of Ministry of Finance

This paper presents a study into the key lessons learnt from e-procurement implementation at public sector organisations in Kenya. The literature relating to e-procurement implementation and operation is reviewed, identifying main variables addressed by the current literature: impact on cost; the impact on governance; e-procurement implementation; and broader IT infrastructure issues. The research carried out was intended to explore the perceptions and reflections of both ‘early’ and ‘late’ adopters of e-procurement. Key lessons are drawn from the study and presented here. I recommend further research, including the need for research into failed eprocurement projects.
Key Pre Distribution for Multicast Groups using Two Server Authentication in WSN

Presented the sensitivity in the potential WSN purposes and on account of resource constraints, key management emerges as being a challenging difficulty for WSNs. One of the many concerns when designing a crucial management scheme would be the network scalability. Certainly, the process should support a large number of nodes permit a significant scale deployment in the network. In this paper, we propose a new scalable crucial management program for WSNs which gives beneficial secure connection coverage. For this purpose, we use the unital layout theory. We show which the basic mapping via unitals to help key pre- distribution we can achieve large network scalability. Even so, this trusting mapping does not guarantee an increased key discussing probability. Therefore, we suggest an increased unital-based crucial pre-distribution program providing large network scalability along with good crucial sharing possibility approximately reduce bounded. We prolong this recommended system by providing new several group crucial management (MGKM) program, named the actual master-key-encryption-based MGKM (MKE-MGKM) program, which can reduce the rekeying cost from taking care of multiple collection keys. The real key idea in the MKEMGKM should be to employ an asymmetric encryption program, called the actual master crucial encryption (MKE), to further improve the rekeying efficiency by relieving the rekeying cost. new several group crucial management (MGKM) program, named the actual master-key-encryption-based MGKM (MKE-MGKM) program, which can reduce the rekeying cost from taking care of multiple collection keys. The real key idea in the MKEMGKM should be to employ an asymmetric encryption program, called the actual master crucial encryption (MKE), to further improve the rekeying efficiency by relieving the rekeying cost. To boost the security we all propose a new symmetric remedy for two- server PAKE. In every existing two-server PAKE standards, two servers are offered random pass word shares pw1 along with pw2 subject to pw1 along with pw2. Within our protocol, you can expect one server S1 through an encryption in the password along with another server S2 through an encryption in the password in which pk1 along with pk2 include the encryption tips of S1 along with S2, respectively. Furthermore, two servers are offered random pass word shares b1 along with b2, where H is a hash purpose.
Generic Method for Prevention of Software Piracy

Software piracy has been major issue for software industries. They have to pay a very large amount to protect their applications. There are several insurance schemes for the protection of software. But they all are very costly and provide services for a limited period of time. This paper will demonstrate a protector tool which can be developed in any platform very easily so that the vendor can protect the software not only for being pirated but also capable of limited period expiration of software. This strategy will provide easy and low cost protection which is more effective than MAC address authentication. This tool is very useful for desktop applications which use internet for their internal working, as it provides server based authentication, but is also feasible for those applications that do not use internet as it has client side authentication also.
Problems in Measuring Capital Structure: Empirical Evidence in Brazil

This empiric-analytic paper, aims at verifying whether the firms’ capital structure is sensitive to leverage measures. It also verifies whether most of the leverage variability is due to financial debts. The sample consists of all non-financial companies with data available, positive equity and stocks listed in Bovespa between 1995 and 2007. Accordingly, we sampled on average 133 companies per year. The data required for the analysis were extracted from the Economatica database. In the methodology section we used cross-section linear regression in order to estimate the coefficients and other statistics of interest, following the methodology of Fama and MacBeth (1973). As main results, more than half of the cross-section heterogeneity of the leverage ratio comes from variations on the Non-financial Debts/Total Assets ratio. With respect to the sensibility of the measures used for the leverage, we conclude that profitability is the only variable that is reasonably consistent, in magnitude, sign and significance, with the different measures of leverage.
Assembly of a Wireless Ultrasonic Backscatter System by Konlin Shen

High fidelity brain recordings have enabled the development of sophisticated closedloop brain-machine interfaces (BMIs) in which electrochemical signals generated by the brain can be used to control machines. This technology has great potential as a therapeutic tool for spinal cord injury, epilepsy, stroke, and other neurological disorders. A key challenge towards implementing BMI technology in medicine is the development of a fully implantable and chronic neural interfaces. This work details the assembly process for a 0.8mm x 3mm x 1mm implantable sensor mote for neural dust, a wireless ultrasonic backscatter system which may provide a path towards truly chronic BMI. Design constraints are delimited and failure modes from previous attempts are explained. Additionally, this work suggests possible improvements, extensions, and variants of the neural dust system for other neural and non-neural applications.
Content-Based Image Retrieval Features:A Survey

Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
The Effects of Oxidative Stress and Some of the Popular Antioxidants onReproductive System: A Mini Review

According to worldwide statistics, at least one out of six couples has fertility problems.Oxidative stress is an important cause of infertility. Excess reactive oxygen radicals could lead to male and female infertility and also some pregnancy complications.Antioxidants are widely used for detoxify the excess reactive oxygen species as called “scavengers”. In this mini-review our aim is to inform about the effects of oxidative stress and some of the popular antioxidants in the reproductive system and also experimental use of this antioxidants under different cases especially in studies which is conducted in the last few years.
Efficient Routing Strategy for Structured Peer to Peer System Using DWT Based Steganography

: In structured P2P systems, message deliverance can be done by identifying the peer IDs of the individual systems. The message passes from one hop to another correctly by identifying the IP address and finally reaches the destination. In this paper we propose an efficient routing strategy to control the routing path and to identify the malicious nodes. We also eliminate the drawbacks of encryption by introducing steganography in message deliverance. A lossless data hiding scheme is presented based on quantized coefficients of discrete wavelet transform (DWT) in the frequency domain to embed secret message. Using the quantized DWT based method; we embed secret data into the successive zero coefficients of the medium-high frequency components in each reconstructed block for 3-level 2-D DWT of cover image. The security of the proposed scheme can be further improved by employing compression and encryption techniques using AES. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover the quality of the messages/images are measured by the PSNR, Figure of Merits, MSSI (Mean Structural Similarity Index) of cover image with stego-image shows better results in comparison with other existing steganography approaches.
Employability of young managers: a question of merit or aristocracy?

The article aims to discuss the relevance of two assumptions about the impact of education on the employability of degree holders: the study of Bourdieu (1988), who questions the relationship between vocational training and integration into the labor market and the human capital theory (SCHULTZ, 1967), which postulates that education increases productivity and employability of individuals. To achieve this goal we carried out a survey to map the employability of trainees in administration at a university in Rio de Janeiro. Students of this institution have different economic backgrounds, which allowed a comparison of their economic position and their access to professional opportunities. Taking in account the graduates’ current conditions of employability, separating them in groups according to their origin, it was not possible to identify differences in the quality of jobs obtained, for both individuals from more privileged families and those less favored, endorsing Schultz's assumption about the importance of education.
Dynamic Data Aggregation Approach for Sensor-Based Big Data

Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.
ANN Based POS Tagging For Nepali Text

The article presents Part of Speech Tagging for Nepali Text using three techniques of Artificial Neural networks. The novel algorithm for POS tagging is introduced . Features are extracted from the marginal probability of Hidden Markov Model. The extracted features are supplied to 3 different ANN architectures viz. Radial Basis Function (RBF) network, General Regression Neural Networks (GRNN) and Feed forward Neural network as an input vector for each word. Two different Annotated Tagged sets are constructed for training and testing purpose. Results are compared using all the 3 techniques and applied on both the sets. GRNN based POS tagging technique is found better as it produces 100% and 98.32% accuracies for both training and testing sets respectively.
Computational problems with the binomial failure rate model and incomplete common cause failure reliability data

In estimating the reliability of a system of components, it is ordinarily assumed that the component lifetimes are independently distributed. This assumption usually alleviates the difficulty of analyzing complex systems, but it is seldom true that the failure of one component in an interactive system has no effect on the lifetimes of the other components. Often, two or more components will fail simultaneously due to a common cause event. Such an incident is called a common cause failure (CCF), and is now recognized as an important contribution to system failure in various applications of reliability. We examine current methods for reliability estimation of system and component lifetimes using estimators derived from the binomial failure rate model. Computational problems require a new approach, like iterative solutions via the EM algorithm.
Identification of Software Rot Using Range Control Limits

In this research work we have developed a threshold algorithm that helps to differentiate the metrics values into four categories for identification of degree for software rot ,that might occur in life cycle of software project been build, development and release based on agile development model . Our algorithm make use of dynamic range values for each metric rather than using simply mean for calculating multiple deviations for degree of software rot . As a result from this algorithm we can infer how much prone is software project leading to software rot or near amendment of software project.
Reviewing Benefits and Barriers Associated with the Participation of Persons with Disabilities in Sport, Exercise, Physical and Leisure Activities

In life barriers to participation in sport and physical activity tend to exist and affect all people i.e. able bodied as well as those living with disabilities. However people with disabilities often experience additional barriers to those experienced by their non disabled counterparts. It is also commonly believed that disabled people can benefit from participating in sport, exercise physical and leisure activities just like their non disabled counterparts. This article reviews, analyses and discusses the critical benefits which persons with disabilities can accrue from participating in sport, exercise, physical and leisure activities. The paper proceeds to review the critical barriers that tend to impede persons with disabilities from participating in these activities and hence prevent them from accruing the envisaged benefits. The paper projects forward by suggesting solutions to the barriers in order to enhance, promote and motivate the involvement and participation of persons with a disability in sport, exercise and leisure activities.
A 60 GHz Development & Test Platform for Wireless Systems Research

Wireless networking has become one of the predominant last-hop networking technologies of choice for consumer and enterprise applications. While this popularity has lead to increased investment in wireless research and wireless deployment, it has also led to increased contention for shared spectral resources as well as increasing expectations from end users. Increased use of millimeter wave (mmWave) spectrum for communications is one compelling technique to address both the increased demands of end users as well as congestion in lower frequency bands. However, mmWave provides its own set of benefits and challenges when constructing networks, leaving many open research questions. To evaluate these questions, a testing and development platform that is both flexible and capable of real time operation with acceptable data rates is required. The creation of such a development platform is the focus of this project. The resulting hardware platform is based around a Field Programmable Gate Array (FPGA) and an accompanying baseband design that resides completely on the FPGA. The baseband design exhibited good performance in simulated tests and performance evaluations with the entire platform demonstrating a reliable 60 GHz link in a series of physical tests.
Functional Neurorehabilitation using the Hybrid Assistive Limb (HAL): A First Experience in the United States

Introduction: The Hybrid Assistive Limb (HAL, Cyberdyne, Japan) facilitates voluntary, user-driven ambulation through a neurologically-controlled system based on bioelectrical signals derived from the user. This allows for the repeated execution of physiologically faithful gait patterns, crucial to recovery in cases of neurologic motor deficit. In this series, we present the first three patients in the United States to undergo HAL neurorehabilitation training. Patient and methods: A case series of three patients participating in a single-center prospective, interventional pilot study, suffering neurologic motor deficits secondary to spinal cord infarct following a pulmonary embolism (patient 1), multiple sclerosis (patient 2) and the surgical resection of a petroclival meningioma (patient 3). The patients underwent 60 sessions of body weight-supported treadmill training in the HAL over the course of 12 weeks. Measures of functional ambulation (10 Minute Walk Test, 10MWT) were performed out of the HAL before and after each session and at the 12 week and 6 month follow-up. Timed Up & Go (TUG) test was performed each week. Treadmill data (time, distance) while in HAL was recorded at each session. Measures of endurance (6 Minute Walk Test, 6MWT), risk of fall (TUG), balance impairment (Berg Balance Scale) and improvements in walking performance (Walking Index for Spinal Cord Injury II, WISCI II) were measured at baseline, after 12 weeks and at 6 months follow-up. Results: Patients 2 and 3 completed 60 visits, patient 1 completed 56 visits. All patients achieved markedly increased treadmill paces, improved functional scores, increased distance in the 6MWT and decreased TUG times at 6-month follow-up. In the 10MWT, all patients achieved a clinically significant decrease in time and steps and showed improvements in the required assistance level to perform the test. Patients 1 and 3 showed improvement on the Berg Balance Scale. Patient 2 had no change between baseline and 6-month follow-up. Only minor adverse effects were reported, including skin abrasions and irritation secondary to chaffing of the HAL unit and EMG electrodes. Conclusion: These data show that HAL training is both feasible and effective in the neurorehabilitation of patients suffering neurologic motor deficits secondary to trauma and/or pathological/neurodegenerative processes after they have undergone normal rehab. A greater number of patients are required to meaningfully assess the differences in improvement from baseline, based upon underlying pathologies.
Handling Uncertainty under Spatial Feature Extraction through Probabilistic Shape Model (PSM)

: The Extraction of Spatial Features from remotely sensed data and the use of this information as input into further decision making systems such as geographical information systems (GIS) has received considerable attention over the few decades. The successful use of GIS as a decision support tool can only be achieved, if it becomes possible to attach a quality label to the output of each spatial analysis operation. Thus the accuracy of Spatial Feature Extraction gained more attention as geographic features can hardly formulated in a certain pattern due to intra-class variation and inter-class similarity. Besides these Spatial Feature Extraction further include positional uncertainty, attribute uncertainty, topological uncertainty, inaccuracy, imprecision/inexactitude, inconsistency, incompleteness, repetition, vagueness, noisy, omittance, misinterpretation, misclassification, abnormalities and knowledge uncertainty. To control and reduce uncertainty in an acceptable degree, a Probabilistic shape model is described for Extracting Spatial Features from multi-spectral image. The advantages of this, as opposed to the conventional approaches, are greater accuracy and efficiency, and the results are in a more desirable form for most purposes.