Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative study of feature extraction techniques used in sentiment analysis

There is a need to analyze sentiments of people with the advancement of social media like social networks, blogs, and presentations. Sentiment analysis is a very challenging task as there is a huge amount of data present online. It is the analysis of sentiments on a particular entity in terms of positive, negative or neutral polarity. The challenge is the identification of the meaning of the word as it can be different in different situations. Feature extraction and identification is necessary to identify sentiment polarity. This paper shows comparative study of feature extraction techniques used in sentiment analysis.
Interactive venation‐based leaf shape modeling

We describe a representation for tree leaves and an interactive modeling system for creating realistic close‐up images of leaf clusters. The planar outline of the leaf and the larger members of its venation system are strong factors in the recognition of plant species and as such are essential to realistic imaging. The larger veins also play a major biological role in determining the leaf surface shape and it is this role that we mimic in the shape modeling discussed in this paper. The proposed representation uses a model of a leaf consisting of a three‐dimensional skeleton formed by its larger veins and a surface membrane representing the leaf lamina that spans the void between the veins. The veins play two roles. They can be interactively modified to create the 3‐D shape of the leaf model. They also provide for realistic light and shadow effects when rendered as generalized cylinders using measured width parameters. The representation consists of two coupled data structures, a tree data structure of veins for the leaf skeleton and an unstructured triangular mesh for the leaf membrane. The skeleton is modified by the user of the modeling system, and the membrane mesh is a surface mesh that follows the skeleton shape computed using harmonic interpolation. Copyright © 2005 John Wiley & Sons, Ltd.
Symbolic model checking on SystemC designs

SystemC is a de-facto standard for modeling system-level designs in the early design stage. Verifying SystemC designs is critical in the design process since it can avoid error propagation down to the final implementation. Recent works exploit the software model checking techniques to tackle this important issue. But they abstract away relevant semantic aspects or show limited scalability. In this paper, we devise a symbolic model checking technique using bounded model checking and induction to formally verify SystemC designs. We introduce the notions of behavioral states and transitions to guarantee the soundness of our approach. The experiments show the scalability and the efficiency of our method.
Cryptography using artificial intelligence

This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we `teach' a system to approximate the input noise with the aim of generating an output nonlinear function. This output is then treated as an iterator which is subjected to a range of tests to check for potential cryptographic strength in terms of metric such as a (relatively) large positive Lyapunov exponent, high information entropy, a high cycle length and key diffusion characteristics, for example. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalized encryption algorithm rather than just a personal key using a `known algorithm' that may be subject to a `known algorithm attack' and/or is `open' to the very authorities who are promoting its use.
Asymptotic Analysis of the Amount of CSI Feedback in MIMO Broadcast Channels

In this paper, we consider a downlink communication system in which a base station (BS) equipped with M antennas and power constraint P communicates with N users each equipped with K receive antennas. It is assumed that the users have perfect channel state information (CSI) of their own channels, while the BS only knows the partial CSI provided by the receivers via a feedback channel. We study the fundamental limits on the amount of feedback required at the BS to achieve the sum-rate capacity of the system (when BS has perfect CSI for all users) in the asymptotic case of N →∞, considering various signal to noise ratio (SNR) regimes. The main results of this paper can be expressed as follows. 1) In the fixed-SNR regime (where the SNR does not scale with N) and low-SNR regime (where the SNR is much smaller than 1/In(N) ), to achieve the (1 - ε)-portion of the sum-rate capacity, the total amount of feedback should scale at least with (ε-1). In the fixed-SNR regime, to reduce the gap between the achievable sum rate and the sum-rate capacity of the system (which is defined as the sum-rate gap) to zero, the amount of feedback should scale at least logarithmically with the sum-rate capacity, which is achievable by using the random beam-forming (RBF) scheme proposed by Sharif and Hassibi. In the low-SNR regime, we propose an opportunistic beam-forming (OBF) scheme, which is shown to be asymptotically feedback optimal. 2) In the high-SNR regime (where the SNR grows to infinity as N → ∞), the total amount of feedback depends on the number of receive antennas. In particular, to reduce the sum-rate gap to zero in the case of K <; M, the amount of feedback in the SNR regime of In(P)/In(N) >; 1/M - 1, should scale at least logarithmically with the SNR. In the case of K ≥ M , the amount of feedback does not need to scale with the SNR. Moreover, we show that RBF is asymptotically feedback optimal in the high-SNR regime.
Exploring the role of pitch-adaptive cepstral features in context of children's mismatched ASR

The presented work explores the role of pitch-adaptive cepstral features in context of automatic speech recognition (ASR) of children's speech on adults' speech trained acoustic models. On account of large acoustic mismatch between training and test data, highly degraded recognition rates are noted for such cases. Earlier studies have shown that the said acoustic mismatch is aided by the insufficient smoothing of pitch harmonics in the case of mel-frequency cepstral coefficient (MFCC) features for child speakers. Motivated by that, in this work, we explore pitch-adaptive cepstral features for reducing the sensitivity to gross pitch variations. For this purpose, a simple technique based on adaptive-cepstral-truncation is employed for deriving the pitch-adaptive MFCCs. We have also explored the existing STRAIGHT-based MFCCs for contrast. Both the approaches are found to result in significant and similar improvements for children's mismatch ASR case. The effectiveness of the adaptive-truncation-based approach is also demonstrated in context of the deep-neural-network-based acoustic models. Further, it has been shown that the effectiveness of the existing feature normalization techniques remain intact even with the use of the proposed features.
Overcast: reliable multicasting with on overlay network

Overcast is an application-level multicasting system that can be incrementally deployed using today's Internet infrastructure. These properties stem from Overcast's implementation as an overlay network. An overlay network consists of a collection of nodes placed at strategic locations in an existing network fabric. These nodes implement a network abstraction on top of the network provided by the underlying substrate network. Overcast provides scalable and reliable single-source multicast using a simple protocol for building efficient data distribution trees that adapt to changing network conditions. To support fast joins, Overcast implements a new protocol for efficiently tracking the global status of a changing distribution tree. Results based on simulations confirm that Overcast provides its added functionality while performing competitively with IP Multicast. Simulations indicate that Overcast quickly builds bandwidth-efficient distribution trees that, compared to IP Multicast, provide 70%-100% of the total bandwidth possible, at a cost of somewhat less than twice the network load. In addition, Overcast adapts quickly to changes caused by the addition of new nodes or the failure of existing nodes without causing undue load on the multicast source.
Delay-constrained streaming of Gauss-Markov sources over erasure channels

Two setups involving delay-constrained sequential transmission of a vector Gauss-Markov source over a burst-erasure channel are studied. The encoder sequentially compresses the source vectors to be transmitted in a causal fashion. The channel introduces a single erasure burst of length up to B during the transmission. In streaming with controlled-interruption, the decoder reconstructs the source vectors within average distortion D and maximum delay of T, whenever the channel packets are not erased. In streaming with ideal-playback, the decoder reconstructs all the source vectors within average distortion D and maximum delay of T. Upper and lower bounds on the minimum compression rate are derived for each setup. The bounds coincide in the high resolution regime for both cases and in large delay regime for the case of ideal-playback.
Social networking sites as platforms to persuade behaviour change in domestic energy consumption

This MSc project addresses the following research question – “Can online social networks such as Facebook facilitate the motivation and behaviour change to reduce energy consumption in the home? ” An investigation into the role of social interaction in social networks provides evidence to support the research question. The project undertook an extensive literature review and identified a gap in current knowledge regarding energy monitoring systems that are socially enabled. A participatory design workshop was then held to generate initial concept ideas and prototype interfaces. A final system using the Facebook application platform was designed and implemented by the author called Wattsup. An experimental design was then developed to evaluate the system which involved recruiting eight households for an eighteen day trial. This was supplemented with a qualitative study of comments generated by users and also in semi-structured interviews. The results indicate that the users of the Wattsup energy application integrated in Facebook assisted the participants in reducing their energy consumption. Types of energy information displayed in the application were both live and historical measurements as well as Co2 emissions, publicly viewable to participants and others on their personal Facebook friends list. Public viewing was implemented for comparison purposes as well as introducing peer pressure and competition amongst like-minded friends. Further analysis of the participant’s social interaction activity with the energy application revealed higher awareness of their energy usage behaviour was present. Positive effects of the results were lower energy costs and fewer Co2 emissions released upon the environment from the participants by using the socially enabled energy application. To test the theory of a positive effect of social interaction on domestic energy consumption, two Facebook energy applications were developed by the author. One of the applications contained socially enabled features, while the other displayed only the participant’s personal energy usage with no social features or capability to view other participant’s energy information.
Metacognition and Achievement Goals as Correlates of Academic Success

This study examined the relationship between mastery goals, performance goals, metacognition and academic success. The study adopted an analytical survey involving 179 undergraduates (87 females, 92 males) of the Alvan Ikoku Federal College of Education, Owerri. Two hypotheses guided the study, the study was analyzed using Pearson r. The instruments for data collection were the metacognitive awareness inventory, the mastery goal items and performance goals. Findings of the study showed that mastery goals were related to GPA whereas performance goals were unrelated to GPA performance. Metacognition is also related to academic success and students with good metacognition have good GPAs. Mastery goals influence GPAs through metacognition as students with mastery goals may have superior metacognitive skills and strategies that they use to master information. The researcher recommends that teachers adopt teaching techniques that present information to students in a way that encourages the use of mastery goal and metacognitive strategies. INTRODUCTION This study has focused on variables such as Metacognition, Achievement goals and Academic success. These variables when taught to students is expected to augment their learning and academic outcomes. The study examined how metacognition and achievement goals correlate with academic success.
Analysis of the Sequence and Phenotype of Drosophila Sex combs reduced Alleles Reveals Potential Functions of Conserved Protein Motifs of the Sex combs reduced Protein

The Drosophila Hox gene, Sex combs reduced (Scr), is required for patterning the larval and adult, labial and prothoracic segments. Fifteen Scr alleles were sequenced and the phenotypes analyzed in detail. Six null alleles were nonsense mutations (Scr2, Scr4, Scr11, Scr13, Scr13A, and Scr16) and one was an intragenic deletion (Scr17). Five hypomorphic alleles were missense mutations (Scr1, Scr3, Scr5, Scr6, and Scr8) and one was a small protein deletion (Scr15). Protein sequence changes were found in four of the five highly conserved domains of SCR: the DYTQL motif (Scr15), YPWM motif (Scr3), Homeodomain (Scr1), and C-terminal domain (CTD) (Scr6), indicating importance for SCR function. Analysis of the pleiotropy of viable Scr alleles for the formation of pseudotracheae suggests that the DYTQL motif and the CTD mediate a genetic interaction with proboscipedia. One allele Scr14, a missense allele in the conserved octapeptide, was an antimorphic allele that exhibited three interesting genetic properties. First, Scr14/Df had the same phenotype as Scr+/Df. Second, the ability of the Scr14 allele to interact intragenetically with Scr alleles mapped to the first 82 amino acids of SCR, which contains the octapeptide motif. Third, Scr6, which has two missense changes in the CTD, did not interact genetically with Scr14.
Applications of Hidden Markov Models for Comparative Gene Structure Prediction

Identifying the structure in genome sequences is one of the principal challenges in modern molecular biology, and comparative ge-nomics offers a powerful tool. In this paper we introduce a hidden Markov model that allows a comparative analysis of multiple sequences related by a phylogenetic tree. The model integrates structure prediction methods for one sequence, statistical multiple alignment methods and phylogenetic information and is applied to a variety of homologous sequences.
Approximating Graph Density Problems

The density of a graph G = (V,E) with edge capacities {c(e) : e ∈ E} is c(E)/|V |. Finding dense components in graphs is a major topic in graph theory algorithms. The most basic problem is the Densest Subgraph problem, that seeks a subgraph of maximum density. This problem can be solved in polynomial time, and we will give a systematic description of several polynomial time algorithms for this problem. We then consider maximum density problems of the following type. Given a graph G = (V,E) with edge capacities {c(e) : e ∈ E} and a property Π, find the densest subgraph G ⊆ G that satisfies property Π. Here the most famous example is the Densest k-Subgraph problem, where the property Π is “G has exactly k nodes”. The Densest k-Subgraph problem is strongly NP-hard, and the best approximation ratio known for it is O(n1/4+ε). We consider two other density problems: the Densest Path problem where G is required to be a path, and the Densest Tree problems where G is required to be a tree. These problems are shown to be NP-hard. We describe a general framework to attack some density problems based on decompositions of feasible solutions, and use it to derive a PTAS for the the Densest Path and the Densest Tree problems.
Long-term Potentiation of Inhibitory Synaptic Transmission onto Cerebellar Purkinje Neurons Contributes to Adaptation of Vestibulo-Ocular Reflex

Synaptic plasticity in the cerebellum is thought to contribute to motor learning. In particular, long-term depression (LTD) at parallel fiber (PF) to Purkinje neuron (PN) excitatory synapses has attracted much attention of neuroscientists as a primary cellular mechanism for motor learning. In contrast, roles of plasticity at cerebellar inhibitory synapses in vivo remain unknown. Here, we have investigated the roles of long-lasting enhancement of transmission at GABAergic synapses on a PN that is known as rebound potentiation (RP). Previous studies demonstrated that binding of GABAA receptor with GABAA receptor-associated protein (GABARAP) is required for RP, and that a peptide that blocks this binding suppresses RP induction. To address the functional roles of RP, we generated transgenic mice that express this peptide fused to a fluorescent protein selectively in PNs using the PN-specific L7 promoter. These mice failed to show RP, although they showed no changes in the basal amplitude or frequency of miniature IPSCs. The transgenic mice also showed no abnormality in gross cerebellar morphology, LTD, or other excitatory synaptic properties, or intrinsic excitability of PNs. Next, we attempted to evaluate their motor control and learning ability by examining reflex eye movements. The basal dynamic properties of the vestibulo-ocular reflex and optokinetic response, and adaptation of the latter, were normal in the transgenic mice. In contrast, the transgenic mice showed defects in the adaptation of vestibulo-ocular reflex, a model paradigm of cerebellum-dependent motor learning. These results together suggest that RP contributes to a certain type of motor learning.
Real-time prioritized kinematic control under inequality constraints for redundant manipulators

This paper describes a fast algorithm for the prioritized kinematic control of redundant manipulators. Building on the classical prioritized task framework, the focus is set on efficient computation and handling of inequality constraints throughout priority levels. Classical approaches that tend to account for inequality constraints through potential fields are computationally competitive but have quality issues. Formulating the same control problems with a Quadratic Program (QP) removes these issues but is known to be costly. The following work revisits the formulation of a hierarchy of QPs for the prioritized control of redundant manipulators and proposes an algorithm that can meet real time requirements for current humanoid robots. Because lower control objectives often become infeasible, a particular point of focus is the numerical stability, hereby addressed with Tikhonov regularization. The method was tested in simulation for the control of the humanoid robot HRP-2.
Current Status of Long Non-Coding RNAs in Human Cancer with Specific Focus on Colorectal Cancer

The latest investigations of long non-coding RNAs (lncRNAs) have revealed their important role in human cancers. LncRNAs are larger than 200 nucleotides in length and fulfill their cellular purpose without being translated into proteins. Though the molecular functions of some lncRNAs have been elucidated, there is still a high number of lncRNAs with unknown or controversial functions. In this review, we provide an overview of different lncRNAs and their role in human cancers. In particular, we emphasize their importance in tumorigenesis of colorectal cancer, the third most common cancer worldwide.
An Approach to Discourse Parsing using Sangati and Rhetorical Structure Theory

Sanskrit literature has many nuggets that could be applied to mo dern linguistic applications. One such nugget is the concept of sangati. Sangati expresses continuity and proper positioning of piece of text which is similar to the modern Rhetorical St ructure Theory (RST). We propose two discourse parsers namely sangati based discourse parser and RST-Sangati based discourse parser. The proposed discourse parsers are extensions of t he existing RST based discourse parser. We have used Naive Bayes probabilistic classifier for disco urse relation and sangati labelling. We have tested our discourse parsers using 500 Tamil tourism domain specific documents and 21 RST- Discourse Tree (RST-DT) English documents. We have compared the performance of both the proposed discourse parsers and observed that when RST and sangati are used in union, the performance of the discourse parser is better. Also, we have done a performance comparison with two existing discourse parsers and have shown better performance.
Classification of Parkinson's disease based on Multilayer Perceptrons (MLPs) Neural Network and ANOVA as a feature extraction

Parkinson's disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer's disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD with Analysis of Variance (ANOVA) as a feature selection. The dataset information of this project has been taken from the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of above 90% before and after feature selection whilst SSG attained above 85% subsequent to implementation of ANOVA as feature selection.
Emotional Intensity analysis in Bipolar subjects

The massive availability of digital repositories of human thought opens radical novel way of studying the human mind. Natural language processing tools and computational models have evolved such that many mental conditions are predicted by analysing speech. Transcription of interviews and discourses are analyzed using syntactic, grammatical or sentiment analysis to infer the mental state. Here we set to investigate if classification of Bipolar and control subjects is possible. We develop the Emotion Intensity Index based on the Dictionary of Affect, and find that subjects categories are distinguishable. Using classical classification techniques we get more than 75\% of labeling performance. These results sumed to previous studies show that current automated speech analysis is capable of identifying altered mental states towards a quantitative psychiatry.