Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Lexical Classes and Syntax using Word Embeddings ( Report with Annotated Bibliography )

Word embeddings have the power to capture semantics.They have potential to represent syntax and semantics both. We have many sources of unsupervised raw data but not supervised data. Unsupervised techniques could greatly improve existing supervised (Collobert et al.(2013)). By leveraging large amount of data floating around, we can improve existing systems. We want to design a framework that will be unsupervised and can induce lexical classes and syntax from the given data by building vector representations, also popularly known as word embeddings. We may further want to extend this framework for languages such as Hindi which is poor in terms of resources. 1 Problem Statement and Idea LSA and LDA were used to capture word embeddings(not exactly) and hence derive semantic relations. Most of the existing systems treat word as atomic units but words also inherit meanings which can only be defined if we represent it as a vector/combination of latent words. So the goal is to maximize probability of raw text given a context window. So for a given context window of size c: max 1 T ∑T t=1 log p(wt|w t+c t−c)
Comparative Analysis of Wavelet Transform and Wavelet Packet Transform for Image Compression at Decomposition Level 2

Abstract: Methods of digital image compression have been the subject of research over the past decade. Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT) have emerged as a popular technique for image compression. This paper compares Compression Ratio, Energy Ratio, Mean Square Error, and Peak Signal to Noise Ratio at different Threshold values for decomposition level 2 both for DWT and WPT.
Introducing Extreme Programming – An Experience Report

This paper presents a single case study reporting the experiences of introducing extreme programming (XP) in a small development project at Online Telemarketing in Lund, Sweden. The project was a success despite the fact that the customer had a poor idea of the system required at the start of the development. This success is partly due to the introduction of practically all of the XP practices. The practices that worked best were the planning game, collective ownership and customer on site. The practices that were found hard to introduce and not so successful were small releases and testing.
Arabic Audio News Retrieval System Using Dependent Speaker Mode, Mel Frequency Cepstral Coefficient and Dynamic Time Warping Techniques

Recently, audio data has increasingly becomes one of the prevalent source of information, especially after the exponential growth of using Internet, digital libraries systems and digital mobile devices. The currently massive amount of audio data stimulates working on developing custom audio retrieval tools to facilitate the audio retrieval tasks. The most familiar audio retrieval systems are based on searching using keyword, title or authors. This study presents the feasibility of using MEL Frequency Cepstral Coefficients (MFCCs) to extract features and Dynamic Time Warping (DTW) to compare the test patterns for Arabic audio news. The study proposes and implements architecture for content based audio retrieval system that is dedicated for the Arabic Audio News. The proposed architecture (ARANEWS) utilizes automatic speech recognition for isolated Arabic keyword speech mode; template based automatic speech recognition approach, MFCCs and DTW. ARANEWS presents a style of retrieval system that based on modeling signal waves and measuring the similarity between features that are extracted from spoken queries and spoken keywords. One of the major components that compose ARANEWS system is feature Database (ARANEWSDB). ARANEWSDB stores the extracted features (MFCCs) from the spoken keywords that are prepared to retrieve Arabic audio news. ARANEWS supports using Query by Humming (QBH) and Query by Example (QBE) instead of using query by text.
Exploring the Zen Meditation Experiences of Patients With Generalized Anxiety Disorder: A Focus-Group Approach

Background: Most of the many studies on the efficacy of meditation on anxiety disorders over the past half century have been quantitatively studied and lacked consensus. Studies into the experiences of meditation practitioners are lacking. Taiwan has a high prevalence of generalized anxiety disorders (GAD), and Zen meditation is popular. Purpose: This study provides a deeper understanding of Zen meditation practice experience among patients with GAD in Taiwan. Methods: The study site was a psychiatric outpatient department in a general hospital in Taiwan. Two groups of adult patients with GAD (n = 9) and without any kind of meditation experience (n = 12) enrolled in and attended a Zen meditation program. Heidegger’s phenomenology was the core framework used to guide data interpretation. Researchers used six-session focus groups, diaries, and field notes to collect data. To boost trustworthiness, researchers applied investigator triangulation, data triangulation, prolonged engagement, persistent observation, and group debriefing (feedback). Results: A major theme, “The process of Zen meditation,” emerged. It was underpinned by the three categories: “struggling to reach a state of calm,” “signs of improvement,” and “an individual process. ” Signs of improvement included “finding a personal way to enter a state of calm,” “changing the sense of time,” and “adjusting Zen meditation practice goals. ” Conclusions/Implications for Practice: The there-being state (dasein) of participants shaped their understanding of Zen meditation practice. Findings revealed the diverse “phenomena-featuring process” aspect of Zen meditation practice. Findings provide a better understanding of essential elements of Zen meditation complementing quantitative studies and may be used by professionals who intend to apply Zen meditation as an alternative therapy for patients with GAD.
The College Adjustment Questionnaire: A Measure of Students’ Educational, Relational, and Psychological Adjustment to the College Environment

College adjustment has been of interest to researchers for decades, with articles on the topic first appearing in the 1940s. College adjustment research has shifted over time, from a focus on college adjustment as a predictor variable or mediator to increased emphasis on college adjustment as representative of a general level of ability or functioning that has been achieved. In the 1980s, in one of the hallmark studies in college adjustment, Baker and Siryk (1984) investigated the relationship among college adjustment and attrition, connection with mental health services, grade point average, and social activities. In a later study, the impact of expectations to adjustment and actual adjustment were investigated in relation to attrition and academic standing (Gerdes & Mallinckrodt, 1994). In recent years, the construct of college adjustment has been utilized as a primary outcome variable (e.g., Boulter, 2002; Dennis, Phinney, & Chuateco, 2005; Lapsley & Edgerton, 2002; Paul & Brier, 2001), which represents a shift from college adjustment as purely a predictor and increased emphasis on college adjustment as a general level of ability or functioning that can be achieved and is desirable. The authors of previous research have proposed that college adjustment is composed of multiple factors, which can be further grouped into general domains of adjustment (Baker & Siryk, 1984; Credé & Niehorster, 2012; Gerdes & Mallinckrodt, 1994). A large body of literature provides support for this proposition, with research demonstrating that there are several dimensions of adjustment— such as academic, social, and personal/ emotional—that contribute to overall college adjustment (see Gerdes & Mallinckrodt, 1994, for a review of the research). Stress has been identified as a key link in difficulty to adjusting to college (Chemers, Hu, & Garcia, 2001), and considering that stress is high on college campuses and linked to increased susceptibility to mental health concerns (Smedley, Myers, & Harrell, 1993), a multidimensional measure of adjustment may provide relevant information for university personnel interested in student well-being. Several instruments exist to measure college adjustment: the Student Adaptation to College Questionnaire (SACQ; Baker & Siryk, 1989), the College Adjustment Rating Scale (Zitzow, 1984), and the College Adjustment Scales (Anton & Reed, 1991). Of these, the SACQ is the most widely used and
Ontology Mining for Personalized Web Information Gathering

It is well accepted that ontology is useful for personalized Web information gathering. However, it is challenging to use semantic relations of "kind-of", "part-of", and "related-to" and synthesize commonsense and expert knowledge in a single computational model. In this paper, a personalized ontology model is proposed attempting to answer this challenge. A two-dimensional (Exhaustivity and Specificity) method is also presented to quantitatively analyze these semantic relations in a single framework. The proposals are successfully evaluated by applying the model to a Web information gathering system. The model is a significant contribution to personalized ontology engineering and concept-based Web information gathering in Web Intelligence.
Reaching optimally over the workspace: A machine learning approach

Recent theories of Human Motor Control explain our outstanding coordination capabilities by calling upon an Optimal Control (OC) framework. But OC methods are generally too expensive to be applied on-line and in realtime as would be required to perform everyday movements. An alternative method consists in obtaining a pre-computed feedback policy that performs optimally while being executed reactively. One way to get such a pre-computed policy consists in tuning a parametrized reactive controller so that it converges to optimal behavior. In this paper, we demonstrate a method to obtain such a reactive controller that (i) adapts the time of movement based on a compromise between the amount of reward and the effort required to get it, (ii) provides an efficient trajectory from any point to any point in the workspace, (iii) learns from demonstrations of optimal trajectories, (iv) is improving its performance over accumulated experience.
Classification of Concept Drift Data Streams

Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution in concept drifting techniques along different dimensions and it provides lucid vision about the ensemble's behavior when dealing with concept drifts. Key words:data stream,ensemble, class label,concept drift.
Accurate Community Detection in the Stochastic Block Model via Spectral Algorithms

We consider the problem of community detection in the Stochastic Block Model with a finite number $K$ of communities of sizes linearly growing with the network size $n$. This model consists in a random graph such that each pair of vertices is connected independently with probability $p$ within communities and $q$ across communities. One observes a realization of this random graph, and the objective is to reconstruct the communities from this observation. We show that under spectral algorithms, the number of misclassified vertices does not exceed $s$ with high probability as $n$ grows large, whenever $pn=\omega(1)$, $s=o(n)$ and \begin{equation*} \lim\inf_{n\to\infty} {n(\alpha_1 p+\alpha_2 q-(\alpha_1 + \alpha_2)p^{\frac{\alpha_1}{\alpha_1 + \alpha_2}}q^{\frac{\alpha_2}{\alpha_1 + \alpha_2}})\over \log (\frac{n}{s})} >1,\quad\quad(1) \end{equation*} where $\alpha_1$ and $\alpha_2$ denote the (fixed) proportions of vertices in the two smallest communities. In view of recent work by Abbe et al. and Mossel et al., this establishes that the proposed spectral algorithms are able to exactly recover communities whenever this is at all possible in the case of networks with two communities with equal sizes. We conjecture that condition (1) is actually necessary to obtain less than $s$ misclassified vertices asymptotically, which would establish the optimality of spectral method in more general scenarios.
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control

Signal estimation from a sequential encoding in the form of quantized noisy measurements is considered. As an example context, this problem arises in a number of remote sensing applications, where a central site estimates an information-bearing signal from low-bandwidth digitized information received from remote sensors, and may or may not broadcast feedback information to the sensors. We demonstrate that the use of an appropriately designed and often easily implemented additive control input before signal quantization at the sensor can significantly enhance overall system performance. In particular, we develop efficient estimators in conjunction with optimized random, deterministic, and feedback-based control inputs, resulting in a hierarchy of systems that trade performance for complexity.
Experimental Evaluation of 802.11e EDCA for Enhanced Voice over WLAN Performance

The 802.11e MAC protocol extends the 802.11 CSMA/CA contention mechanism by allowing the adjustment of MAC parameters that were previously fixed. While the 802.11e protocol has been extensively studied, this work is almost entirely confined to analytical and simulation studies. In this paper we demonstrate a technique for measuring one-way delay in an 802.11e hardware testbed and thereby study delay in the context of protecting a voice call competing against data traffic. We demonstrate that with the standard 802.11b MAC settings greedy data traffic is able to seize bandwidth from a low-rate voice call. Only 5 competing data stations are needed in order to induce a voice call loss rate exceeding 10%, which in practice would lead to an unacceptable level of voice quality and dropping of the call. We present experimental measurements which demonstrate that the use of 802.11e to provide a practical solution that can successfully deliver quality of service to voice traffic in a mixed voice/data environment. To our knowledge, this is the first experimental demonstration of accurate one way delay measurements being used to show the priorisation of voice in an 802.11e hardware test-bed.
Assembly of Cell Regulatory Systems Through Protein Interaction Domains

The sequencing of complete genomes provides a list that includes the proteins responsible for cellular regulation. However, this does not immediately reveal what these proteins do, nor how they are assembled into the molecular machines and functional networks that control cellular behavior. The regulation of many different cellular processes requires the use of protein interaction domains to direct the association of polypeptides with one another and with phospholipids, small molecules, or nucleic acids. The modular nature of these domains, and the flexibility of their binding properties, have likely facilitated the evolution of cellular pathways. Conversely, aberrant interactions can induce abnormal cellular behavior and disease. The fundamental properties of protein interaction domains are discussed in this review and in detailed reviews on individual domains at Science's STKE at http://www.sciencemag. org/cgi/content/full/300/5618/445/DC1.
Thorsten Steiner and Julian Bösel Options to Restrict Hematoma Expansion After Spontaneous Intracerebral Hemorrhage

Background and Purpose—Secondary expansion of hematoma after spontaneous intracerebral hemorrhage occurs frequently and early with the potential sequelae of functional deterioration or death. The aim of this topical review is to give a summary of current evidenceand experience-based options to avoid or attenuate hematoma expansion. Method—We reviewed the literature of the past 10 years on efforts to restrict spontaneous intracerebral hemorrhage expansion by searching Medline and adding related articles known to us. Based on evidence, current guidelines, and our own clinical practice, we have collected consistent and inconsistent pieces of data. These were differentiated according to surgical versus medical approaches, weighed and discussed with regard to expectable benefit, potential risk, and practicability. Finally, we have outlined promising future approaches. Results—Although consistent evidence on the topic is generally limited, some important studies have provided data on risk factors predicting spontaneous intracerebral hemorrhage expansion implying ways of directing therapy toward these risk factors. Large trials have shed light on 4 major efforts to avoid hematoma expansion: surgical hematoma treatment, reduction of hypertension, reversal of coagulopathies or anticoagulants, and hemostatic therapy. The results were largely disappointing but provide insights for new trials. Future strategies include the combination of surgical and medical treatment and the use of neuroprotectants. Conclusions—Early restriction of intracerebral hemorrhage is of paramount importance because secondary volume expansion leads to outcome deterioration and death. Although there appear to be few indications for neurosurgical measures, nonsurgical measures such as reduction of hypertension and normalization of altered coagulation seem to be beneficial. However, the routine use of coagulation factors outside of warfarin-associated spontaneous intracerebral hemorrhage cannot generally be recommended at present. The same applies for future approaches such as combined medical–surgical approaches and neuroprotective therapies at this point. (Stroke. 2010;41:402-409.)
Problem of Denoising in Digital Image Processing and Its Solving Techniques

In this new era of communication the image and video is important as Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This paper reviews the existing denoising algorithms, performs their study. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. In this paper we introduced all the possible techniques of digital image denoising.
Adaptive operator selection for path planning in static environments

Genetic algorithms have been fairly successful in solving difficult and ill-behaved optimization problems, such as path planning. In this paper we use the operator selection approach and introduce new one operator to solve the path planning problem. The Adaptive Operator Selection is utilized in the genetic algorithm to select the operators optimally in a given situation. In this paper, two different probability-based methods, namely the Probability Matching and the Adaptive Pursuit are used for this purpose. The methods are implemented and simulated in several static environments.
Modeling of cloud computing security using Kolmogorov complexity

As Clouds are complex, large-scale, and heterogeneous distributed systems management of their resources is a challenging task. It requires co-optimization at multiple layers (infrastructure, platform and application) exhibiting autonomic properties. Autonomic systems exhibit the ability of self-monitoring, self-repairing, and self-optimizing by constantly sensing themselves and tuning their performance. The notions of autonomic components (ACs) and Autonomic-Component Ensembles (ACEs) are considered in the paper. A language for coordinating ensemble components (SCEL) is used to represent specificity of security issues in autonomic computing environment.
Implementation of the Standard I-vector System for the Kaldi Speech Recognition Toolkit

This report describes implementation of the standard i-vector-PLDA framework for the Kaldi speech recognition toolkit. The current existing speaker recognition system implementation is based on the Subspace Gaussian Mixture Model (SGMM) technique although it shares many similarities with the standard implementation. In our implementation, we modified the code so that it mimics the standard algorithms in the ivector based speaker recognition system. The implementation is compared with the existing Kaldi recipe for speaker recognition on the NIST SRE 2008 evaluation set. The entire implementation is made available at https://github.com/idiap/kaldi-ivector under the Apache 2.0 license.
Advanced helmet mounted display (AHMD)

Due to significantly increased U.S. military involvement in deterrent, observer, security, peacekeeping and combat roles around the world, the military expects significant future growth in the demand for deployable virtual reality trainers with networked simulation capability of the battle space visualization process. The use of HMD technology in simulated virtual environments has been initiated by the demand for more effective training tools. The AHMD overlays computer-generated data (symbology, synthetic imagery, enhanced imagery) augmented with actual and simulated visible environment. The AHMD can be used to support deployable reconfigurable training solutions as well as traditional simulation requirements, UAV augmented reality, air traffic control and Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) applications. This paper will describe the design improvements implemented for production of the AHMD System.
FastSlim: prefetch-sfe trace reduction for I/O cache simulation

Trace-driven simulation is a valuable tool for evaluating I/O systems. This article presents a new algorithm, called FASTSLIM, that reduces the size of I/O traces and improves simulation performance without compromising simulation accuracy. FASTSLIM is more general than existing trace reduction algorithms in two ways. First, it is prefetch-safe: traces reduced by FASTSLIM yield provably exact simulations of I/O systems that use prefetching, a key technique for improving I/O performance. Second, FASTSLIM is compatible with a wide range of replacement policies, including common practical approximations to LRU. FASTSLIM-reduced traces are safe for simulations of storage hierarchies and systems with parallel disks. This article gives a formal treatment of prefetching and replacement issues for trace reduction, introduces the FASTSLIM algorithm, proves that FASTSLIM and variants are safe for a broad range of I/O caching and prefetching systems, and presents empirical results comparing FASTSLIM to competing trace reduction algorithms.