Miracle-dz/UniFAR-FTUs huggingface dataset:
Stacked residual recurrent neural network with word weight for text classification

Neural networks, and in particular recurrent neural networks (RNNs) have recently been shown to give a state-ofthe-art performance on some text classification tasks. However, most existing methods assume that each word in a sentence contributes the same importance, it is different from the real world. For example, if we do sentiment analysis, the word ”awesome” is much more important than any other words in the sentence ”This movie is awesome”. Motivated by this deficiency and in order to achieve a further performance, in this paper, a Stacked Residual RNN with Word Weight method is proposed, we extend the stacked RNN to a deep one with residual network architecture and introduce a word weight based network to consider the weight of each word. Our proposed method is able to learn high the hierarchical meaning of each word in a sentence and consider the weight of each word for text classification task. Experimental result indicates that our method achieves high performance compared with the state-of-the-art approaches.
Studying Software Evolution Using the Time Dependence of Code Changes

Constructing software bears many similarities to constructing buildings. In a building project, each floor builds on the previous structures (walls of the previous floors) with some structures being more foundational (i.e. essential) for other structures and some periods of construction being more foundational (i.e. critical) to the final structure. In a software project, each change builds on the structures created by prior changes with some changes creating foundational structure and with some time periods of changes being more foundational than others. This thesis is inspired by this similarity between constructing buildings and constructing software. The thesis makes use of the similarity to study the evolution of software projects. In particular, we develop the concept of time dependence between code changes to study software evolution through empirical studies on two large open source projects (PostgreSQL and FreeBSD) with more than 25 years of development history. We show that time dependence can be used to study how changes build on prior changes and the impact of this building process on the quality of a project. We show how a development period impacts the development of future periods in a project. We also show how a subsystem (module) of a project builds on other subsystems and we identify the subsystems that have high impact on a project’s development. Using i this knowledge, managers can better monitor the progress of the projects and better plan for future changes.
Biomedical knowledge base construction from text and its applications in knowledge-based systems

While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledge about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: • To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. • To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. • To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationshiporiented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.
Data hiding in digital image: historical and current prospective

Abstract—Steganography is the art that added, imparting mystery information in a proper medium transporter, e.g., picture, sound, and video ﬁles. It goes under the suspicion that if the element is noticeable, the intension of the assault is obvious; accordingly the motive is dependable to cover the very presence of the original information. Be that as it may, similar to some other science it can be utilized for sick expectations. In the steganography, information conceals in terms that cannot change the secret information. It’s mainly purpose is transferred data from one location to another location. It means concealed a secret data inside the other data in a manner that a critic cannot find the existence of real subject matters. Concealing the secret data in the images, various types of methods are used in which some are stronger than others to conceal the data. Steganography's definitive targets, which are imperceptibility, vigor (protection from different picture preparing strategies and pressure) and limit of the concealed information are the primary factors that differentiate it from related procedures, for example, watermarking and cryptography. This paper exhibited an overview of the real calculations of steganography conveyed in advanced imaging. The rising strategies, for example, DCT, DWT and versatile steganography are not very inclined to assaults, particularly when the concealed message is less. Steganography necessity is that the cover picture must be precisely chosen. A natural picture ought not to be utilized, it is better for stenographer to make their own particular pictures. There is some method of image steganography are discussed and these methods are different strong and weak points. In this paper, an overview is given of image steganography and its method.
Improving Pattern Recognition and Neural Network Algorithms With Applications to Solar Panel Energy Optimization

Artificial Intelligence is a big part of automation and with today’s technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures, multilayer percepterons and convolutional neural networks. Our research with neural networks has encountered a great deal of difficulties regarding hyperparameter estimation for good training convergence rate and accuracy. Most hyperparameters, including architecture, learning rate, regularization, trainable parameters (or weights) initialization, and so on, are chosen via a trial and error process with some educated guesses. However, we developed
Improving compiler optimizations using machine learning

The increase in the use of computation in every walk of life and its pivotal role in creating new avenues that were not possible before computers is obvious and does not warrant proof. This dramatic rise in the use of computers would continue for the foreseeable future. The hardware has continued to grow in complexity, and programming languages have evolved to make it easier for a human being to design and construct more and more complex solutions. The only bridge between the ever increasing complexity of the underlying hardware and the increasing simplicity of the language designed to describe the solutions that run on it is the compiler. Compilers have grown more and more complex in the past, and will continue to grow in functionality and complexity in two primary directions. One direction is in the ability of the compiler to increasingly understand languages that are closer and closer to human language, that would drastically reduce the human effort involved in designing a solution. The second direction, that is also the primary focus of this dissertation is in the ability of the compiler to create more and more complex machine code that can not only run on the underlying hardware, but also take advantage of the hardware’s ever increasing complexity in improving application performance. This role of the compiler, to translate the original source code into complex machine code and squeeze all possible performance from the hardware can be quantized into discrete steps. These discrete steps that change and transform the code with the goal of optimizing the final machine code are referred to as optimizations. The number of optimizations that are available in modern day compilers are in their hundreds, and would only grow in number in the future. This increase in the number of optimizations available to the compiler is primarily due to the fact that each
Addressing Inter-set Write-Variation for Improving Lifetime of Non-Volatile Caches

We propose a technique which minimizes inter-set write variation in NVM caches for improving its lifetime. Our technique uses cache coloring scheme to add a software-controlled mapping layer between groups of physical pages (called memory regions) and cache sets. Periodically, the number of writes to different colors of the cache is computed and based on this result, the mapping of a few colors is changed to channel the write traffic to least utilized cache colors. This change helps to achieve wear-leveling.
Gate Level NBTI and Leakage Co-Optimization in Combinational Circuits with Input Vector Cycling

Negative Bias Temperature Instability (NBTI) effect occurs in a PMOS transistor when turned ON leading to threshold voltage degradation. As sub-threshold leakage is significant in nanoscale CMOS circuits, input vector control can be employed wherein a Minimum Leakage Vector (MLV) is applied to the circuit during idle periods. In such a case, the ON PMOS transistors on the critical path are subject to NBTI stress for prolonged periods. Transistors can recover from stress when turned OFF. Based on this observation, we propose a vector cycling based leakage/NBTI co-optimization: a pair of MLVs are identified such that when applied alternately, on the critical path, PMOS transistors activated by one vector are turned OFF by the other vector. We employ Simulated Annealing (SA) for stochastic search of the first vector followed by back tracking to identify the second vector. Experimental results for a subset of ISCAS85 benchmarks implemented in 45 nm technology demonstrate the feasibility of vector cycling approach. When compared to leakage-only optimization, NBTI-only optimization, and co-optimization, on average, vector cycling yields 11, 3, and 6 percent NBTI improvements with 18, −9, and 4 percent leakage overheads respectively. The average area and dynamic power overheads are 13.78 and 0.15 percent respectively.
A Global Network for Non-Collective Communication in Autonomous Systems

Large-scale simulation enables realistic 3D reproductions of micro-structure evolution in many problems of computational material science [1]. With an increasing number of processing units, global communications become a bottleneck and limit the scalability. Therefore, NAStJA decomposes the simulated domain in small blocks and distributes those blocks over the processing units. Interacting processing units build a local neighborhood and act autonomously in this neighborhood. This limits the number of connections for each processing unit and therefore the local communication overhead, and leads to high scalability. Apart from the communication between local neighborhoods, a global information exchange is required. We explain the conditions and requirements for this exchange and present the benefits of a multidimensional Manhattan street network [8, 4, 5]. It is simple but sufficiently fast for a global information exchange, if the information is not time critical, i. e. the exchange has to be global only after several time steps. This global network satisfies the requirements for a global block management that connects the autonomous processes. Because of its super-linear scaling the approach is very useful for massively parallel simulations. The block distribution scales in a linear matter, and the communication overhead of the global block management can be neglected such that small blocks benefit from cache effects and result in a super-linear scaling, i. e. efficiency higher than unity. The global information exchange is based on a multi-hop exchange, where each message is sent to the direct neighbors and then spread to the whole network in a specified number of hops. Between these hops the computation goes on, so that the global exchange overlaps with the computation. The number of hops must be small enough to not influence the simulated physics. NAStJA supports regular grids with a calculating stencil sweeping through the simulated domain. In computational material science many problems can be described using phase-field methods or cellular automata, both based on a regular grid. This is a grateful task for parallel programming. However, many of these problems require calculations only in small regions of the simulated domain. This is why NAStJA allocates and distributes only those blocks that contain such a computing region. As the computing regions move in the simulated domain 1Acronym for Neoteric Autonomous STencil code for Jolly Algorithm, a new developed code for arbitrary algorithms based on stencils or regular grids.
Challenges in network-based classification of gene expression profiles

Classification of gene expression profiles to distinguish one disease state from another is essential for the realization of personalized medicine. Recent approaches towards this problem use prior knowledge about interaction among bio-molecules to improve classification accuracy and the biological relevance of the predictive features. However, many such network-based methods do not significantly outperform their unconstrained counterparts in terms of sensitivity and specificity due to unexplained reasons. This behavior, observed across diverse datasets and methods, is a cause of concern as it implies that something is wrong with the data, the algorithms or both. This work focuses on understanding the reasons behind this problem through extensive simulation of gene expression profile to help the development of better classifiers in the future. We infer that when using networks whose interactions do not agree well with the patterns of gene expression, improvement in classification performance will not be significant. Because this improvement is dependent on the classifier also, future network-based methods need to understand their properties with respect to network noise and know the quality of actual network mapping to make meaningful inferences from the performance results
Determination of necessary geometric parameters of tripod robot workspace, taking into account zones of singularity

The article considers the problem of providing the necessary geometric parameters of the workspace of a flat robot with 3 degrees of freedom. The influence of the geometric parameters of the robot mechanism configuration (drive mechanisms rod lengths and working platform parameters) as well as the working platform angle of rotation on the dimensions of the workspace was investigated. Special provisions (singularity zones) of the mechanism should be excluded from the robot workspace. The mechanism under investigation has singularities of the second type. Increasing angle φ that decreases the size of the workspace, but increases radius θ of the circle, on which the points of singularity are located, is shown. Thus, it is necessary to limit the swing angle range and expand the variation range of the lengths of the rods’ change to ensure the necessary size of the workspace mechanism. For the given dimensions of the workspace (based on the requirements of the technological process), the task of determining the geometric parameters of the robot mechanism configuration is solved. Mathematical modeling is performed. The results of computational experiments are presented. Keywords— tripod robot,. robot mechanism configuration, mathematical modeling, computational experiments
The application of two-dimensional wavelets and filterbanks for convergence enhancement in adaptive arrays for space communications

A linearly constrained wideband adaptive sensor array is described whose adaptive processor is realized with a two-dimensional tree-structured filterbank. The goal is to obtain a fast converging adaptive processor subject to a constraint on the number of adaptive weights and a requirement that the weight updating algorithm exhibit a low computational complexity. It is demonstrated via simulation that the filter bank processor utilizing the computationally simple normalized LMS algorithm, coupled with low-order multiplier-free QMF banks to provide a spatio-temporal subband decomposition, is capable of faster convergence speeds than the standard tapped-delay-line filter structure with an equivalent number of adaptive weights. <<ETX>>
Human action recognition using saliency-based global and local features

Recognising human actions from video sequences is one of the most important topics in computer vision and has been extensively researched during the last decades; however, it is still regarded as a challenging task especially in real scenarios due to difficulties mainly resulting from background clutter, partial occlusion, as well as changes in scale, viewpoint, lighting, and appearance. Human action recognition is involved in many applications, including video surveillance systems, human-computer interaction, and robotics for human behaviour characterisation. In this thesis, we aim to introduce new features and methods to enhance and develop human action recognition systems. Specifically, we have introduced three methods for human action recognition. In the first approach, we present a novel framework for human action recognition based on salient object detection and a combination of local and global descriptors. Saliency Guided Feature Extraction (SGFE) is proposed to detect salient objects and extract features on the detected objects. We then propose a simple strategy to identify and process only those video frames that contain salient objects. Processing salient objects instead of all the frames not only makes the algorithm more efficient, but more importantly also suppresses the interference of background pixels. We combine this approach with a new combination of local and global descriptors, namely 3D SIFT and Histograms of Oriented Optical Flow (HOOF). The resulting Saliency Guided 3D SIFT and HOOF (SGSH) feature is used along with a multi-class support vector machine (SVM) classifier for human action recognition. The second proposed method is a novel 3D extension of Gradient Location and Orientation Histograms (3D GLOH) which provides discriminative local features representing both the gradient orientation and their relative locations. We further propose a human action recognition system based on the Bag of Visual Words model, by combining the new 3D GLOH local features with Histograms of Oriented Optical Flow (HOOF) global features. Along with the idea from our first work to extract features only in salient regions, our overall system outperforms existing feature descriptors for human action recognition for challenging video datasets. Finally, we propose to extract minimal representative information, namely deforming skeleton graphs corresponding to foreground shapes, to effectively represent actions and remove the influence of changes of illumination, subject appearance and backgrounds. We propose a novel approach to action recognition based on matching of skeleton graphs, combining static pairwise graph similarity measure using Optimal Subsequence Bijection with Dynamic TimeWarping to robustly handle topological and temporal variations. We have evaluated the proposed methods by conducting extensive experiments on widely-used human action datasets including the KTH, the UCF Sports, TV Human Interaction (TVHI), Olympic Sports and UCF11 datasets. Experimental results show the effectiveness of our methods for action recognition.
Student Perceptions on the Cost-Benefit of Screencasts

This paper describes the work completed in regards to determining how students perceive different course activities. Specifically, students’ views about “flipping” the classroom through the use of screencasts are of particular interest as they relate to the more overt activities enabled by such a course structure. A survey has been created to examine this. Since the survey results are not available presently, this research is being submitted as a work-in-progress. However, some general student feedback is presented as are lessons learned by the authors’ in terms of best creating and utilizing screencasts.
Institutional vs Generic Web Search: A Results-centric Comparison

We conduct a principled comparison of search results found using an institutional web search engine, and those found using a generic web search engine. We use the top 100 searches recorded against the institutional web search engine during 2005, and programatically execute the same queries against the institutional web search engine, and a selection of broad coverage web search engines, collecting the top 1000 ranked results. These results are compared across various dimensions as a method of evaluating the overall performance of the institutional web search engine across a number of metrics. The motivation for this work is twofold: both to establish a methodology for benchmarking institutional web search engine performance, and to acquire baseline measurements in the light of a pending infrastructure upgrade.
Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain

In this paper, Zernike moment features extracted from rice grains are used in classifying normal and damaged rice. Genetic algorithm (GA) is used to reduce the number of features while maximizing the classification performance. The GA chromosome fitness is evaluated using a multilayer perceptron (MLP) trained by backpropagation learning algorithm.