Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Interactive Video-Romanian Project Proposal

In the recent years, the globalization and massification of video education offer involved more and more eLearning scenarios within universities. This article refers to interactive video and proposes an overview of it. We analyze the background information, regarding the eLearning campus used in virtual universities around the world, the MOOC movement in the last year, and the related interactive video platforms in the (education) field. At the same time, we pay particular attention to technical aspects of the interactive video: defining concept, types of video metadata, media fragments and types of annotations, as primordial elements that bring interactivity. We tested some free and commercial interactive web application. We gathered all the ideas. We propose a framework for an interactive system web based on the main modules: video resource management (production, transcoding and storage), annotations, Linked Open Data, distribution medium, player interface, data analytics and recommendation system. On the way, we offer our findings, together with our recommendations for an annotation interface and player module. It is our idea for Politehnica University Timisoara, either as a standalone solution or a complement to actual virtual campus (http://cv.upt.ro) depending on future development plans and financial aspects.
An Empirical Study On Sentiment Polarity Classification Of Book Reviews

Sentiment polarity classification deals with automatic classification of text in sentiment polarity categories. While in most of proposed approaches for polarity classification, a dictionary containing polarity-based terms is considered. Such dictionaries are not readily available. We have adopted a machine learning based approach where classifiers are trained over a self-collected corpus of book reviews, annotated with sentimental categories. In this paper, we have presented our investigation of performance evaluation of machine learning classifiers. Five classifiers are evaluated including naive Bayes, k-nearest neighborer, decision tree and support vector machine. Naive Bayes has shown us best results.
Control of endoderm development in Caenorhabditis elegans

The intestine (or gut) of Caenorhabditis elegans consists of 20 cells and is exclusively derived from the E-cell blastomere, by a precise pattern of cell divisions. During post- embryonic development no cell divisions occur in the E-lineage. Instead a nuclear division occurs, such that most cells are binucleate. In addition, endoreduplication occurs once during each larval stage, resulting in a DNA content of 32C in adult intestinal cell nuclei. Although much research has focussed on E-cell specification, not much is known about genes involved in the execution of the E-cell lineage. In this thesis, I have identified and characterised 4 classes of mutants with various E-cell lineage defects. These are mutants with an altered E-cell lineage, a mutant that fails to execute the LI nuclear division, mutants that undergo additional post embryonic nuclear divisions, and a mutant with an abnormal gut morphology. Most of the work in this thesis focussed on the class with an altered E-cell lineage. Two independent genes were identified in this class, Un-60(ij48) and lin-62(ij52), the behaviour of these mutant alleles is genetically very distinct, lin-60 is a maternal gene and ij48 is a gain-of-function allele whereas lin-62 is a zygotic gene and ij52 is a loss-of-function allele. By cell lineage analysis, I showed that extra intestinal cells were generated from the E blastomere in each mutant. Additionally, I have shown that the ij48 lesion creates deregulation of the cell cycle in the E-cell lineage, resulting in a hyperplasia of intestinal cells, while other aspects of intestinal cell function are retained. Using a novel RNA-mediated interference (RNAi) approach, I cloned the wild-type copy of the gene identified by lin-60(ij48) and found that it encoded a previously identified homologue of the cell cycle control gene, Cdc25 (cdc-25.1). While gain of cdc-25.1 created intestinal hyperplasia, loss of the wild-type cdc-25.1 by RNAi caused a failure of proliferation of intestinal and other cell types. I found that the site of the cdc-25.1 mutation in iJ48 mutants identifies a conserved motif However, the distribution of CDC-25.1 protein in ij48 animals does not explain the intestinal hyperplasia.
Hardware Based Stack Smashing Attack Detection Preliminaries on Recovery Procedure

- In recent years, computer systems belonging to large companies, governments as well as personal computers have been experiencing an increasing wave of attacks that disrupt their normal operation or leak sensitive data. In this context, this paper presents a hardware-based approach (here namely a watchdog connected to the processor bus) which aims at detecting and recovering the system from stack smashing buffer overflow attack. Compared to existing approaches, the proposed technique does not need application code recompilation or use of any kind of supervisor software (e.g., an Operating System - OS) to manage memory usage. To validate the approach, a case-study based on the LEON3 softcore processor and benchmark test codes have been implemented. Experimental results indicate that this approach is able to detect and recover from an intrusion tentative for 100% of the test cases, while yielding low area overhead, negligible attack detection latency and processor performance degradation.
Observations on Typing from 136 Million Keystrokes

We report on typing behaviour and performance of 168,000 volunteers in an online study. The large dataset allows detailed statistical analyses of keystroking patterns, linking them to typing performance. Besides reporting distributions and confirming some earlier findings, we report two new findings. First, letter pairs that are typed by different hands or fingers are more predictive of typing speed than, for example, letter repetitions. Second, rollover-typing, wherein the next key is pressed before the previous one is released, is sur- prisingly prevalent. Notwithstanding considerable variation in typing patterns, unsupervised clustering using normalised inter-key intervals reveals that most users can be divided into eight groups of typists that differ in performance, accuracy, hand and finger usage, and rollover. The code and dataset are released for scientific use.
Improving Students’ Reading Rate and Comprehension by using Timed Repeated Readings

This study was carried out in a classroom action research design. It explored whether timed repeated readings could improve reading rate and comprehension. It examined the reading rate and comprehension at which the students of the Language Center achieved through timed repeated readings and also explained the factors that influence the changes of their reading rate and comprehension. Overall, the results showed that reading rate and comprehension could be improvedby using timed repeated readings and some factors that affected the improvements were identified.
Transitioning the use of cryptographic algorithms and key lengths

At the start of the 21 century, the National Institute of Standards and Technology (NIST) began the task of providing cryptographic key management guidance, which includes defining and implementing appropriate key management procedures, using algorithms that adequately protect sensitive information, and planning ahead for possible changes in the use of cryptography because of algorithm breaks or the availability of more powerful computing techniques. NIST Special Publication (SP) 800-57, Part 1 was the first document produced in this effort, and includes a general approach for transitioning from one algorithm or key length to another. This Recommendation (SP 800-131A) provides more specific guidance for transitions to the use of stronger cryptographic keys and more robust algorithms.
High Performance WLAN Using Smart Antenna

The need for higher data rates in WLANs boosts drastically because tremendous consumer interest in emerging multimedia applications, such as HDTV, has been increased. Currently, the IEEE 802.11a/b/g WLANs provide a limited data rate for the current user application requirements. In order to overcome substantial limitations of the existing WLANs, the next generation of WLANs, IEEE 802.11n, is in the course of development and expected to support higher throughput, larger coverage area and better QoS. The high performance IEEE 802.11n WLAN can improve data rate significantly by using smart antenna systems in the physical layer to take advantage of multi-path fading of wireless channels. In this thesis, an analytical model is developed to study the MAC performance and the underlying smart antenna technologies used in multi-path fading channels. Multiple antennas employed in the AP arise two popular approaches to provide a significant performance improvement, diversity and multiplexing. Considering the diversity gain of multiple antennas at the AP in which the AP with multiple antennas serves one user at a time, the capacity and throughput can be obtained. In addition, the AP is possible to serve multiple users in the downlink, by exploiting the multiplexing gain of the wireless channel. We investigate the maximum network throughput when the traffic intensity of the AP approaches to one. Unlike most of previous research which focus on either the physical or the MAC layer performance, our analytical model jointly considers the MAC protocol and the smart antenna technology.
Locally optimized prediction of nonlinear systems: stochastic and deterministic

It is now generally recognized that very simple dynamical systems can produce apparently random behaviour. Attention has recently turned to focus on the flip-side of this coin: random-looking time series (or random-looking patterns in space) may indeed be the result of very complicated processes or "real noise", but they may equally well be produced by some very simple mechanism (a low-dimensional attractor). In either case, a long-term prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but low-dimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a two-day discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meterology, statistics, epidemiology, earthquake science and many others, each topic covered by a leading expert in the field. The meeting dealt with different basic approaches to the problem of chaos and forecasting, and covered applications to nonlinear forecasting of both artificially-generated time series and real data from context in the above-mentioned diverse fields. This book forms an introduction to the science of chaos, with special reference to real data.
ImageNet Training by CPU: AlexNet in 11 Minutes and ResNet-50 in 48 Minutes

Since its creation, the ImageNet 1-k benchmark set has played a significant role as a benchmark for ascertaining the accuracy of different deep neural net (DNN) models on the classification problem. Moreover, in recent years it has also served as the principal benchmark for assessing different approaches to DNN training. Finishing a 90-epoch ImageNet1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10 single precision operations in total. On the other hand, the world’s current fastest supercomputer can finish 2× 10 single precision operations per second. If we can make full use of the computing capability of a supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in five seconds. Over the last two years a number of researchers have focused on how to close this significant performance gap through scaling DNN training to larger numbers of processors. Most successful approaches to scaling the training of ImageNet have used the approach of synchronous stochastic gradient descent. However, to scale synchronous stochastic gradient descent one must also increase the batch size used in each iteration. Thus, for many researchers, the focus on scaling DNN training has translated into a focus on developing approaches that enable increasing the batch size in data-parallel synchronous stochastic gradient descent without losing accuracy over a fixed number of epochs. As a result, we have seen the batch size and number of processors successfully utilized increase from 1K batch/128 processors to 8K batch/256 processors over the last two years. The recently published LARS algorithm increased batch size further to 32K for some DNN models. Following up on this work, we wished to confirm that LARS could be used to further scale the number of processors efficiently used in DNN training and, and as a result, further reduce the total training time. In this paper we present the results of this investigation: using LARS we were able to efficiently utilize 1024 CPUs to finish the 100-epoch ImageNet training with AlexNet in 11 minutes, and we finished 90-epoch ImageNet training with ResNet-50 in 48 minutes (batch size = 32K). Furthermore, when we increase the batch size to above 20K, our accuracy is much higher than Facebook’s on corresponding batch sizes (Figure 1). Our source code is available upon request. It will also be released in Intel Caffe. Figure 1: Because we use weaker data augmentation, our baseline’s accuracy is slightly lower than Facebook’s version (76.2% vs 75.4%). However, at very large batch sizes our accuracy is much higher than Facebook’s accuracy. Facebook’s accuracy is from their own report (Goyal et al. 2017). Our accuracy scaling efficiency is much higher than Facebook’s version
Towards inherently adaptive first person shooter agents using reinforcement learning

Reinforcement learning (RL) is a paradigm which involves an agent interacting with an environment. The agent carries out actions in the environment and receives positive reinforcement for actions that are deemed “good” and penalties for “bad” actions based on a reward signal. The goal of the learning agent is to maximise the amount of reward it receives over time. This thesis presents several new behavioural architectures for controlling non-player characters (NPCs) in a modern first-person shooter (FPS) game using reinforcement learning. NPCs are computer-controlled players that are traditionally programmed with scripted, deterministic behaviours. We propose the use of reinforcement learning to enable the NPC to learn its own strategies and adapt them over time. We hypothesise that this will lead to greater variation in gameplay and produce less predictable NPCs. The first contribution of this thesis is the design, development and testing of two general purpose Deathmatch behavioural architectures called Sarsa-Bot and DRE-Bot. These architectures use reinforcement learning to control and adapt their behaviour. We demonstrated that they could learn to play competently and achieve good performance against fix-strategy scripted opponents. Our second contribution is the development of a reinforcement learning architecture, called RL-Shooter, specifically for the task of shooting. The opponent’s movements are read in real-time and the agent chooses shooting actions based on those that caused the most damage to the opponent in the past. We carried out extensive experimentation that showed that the RL-Shooter architecture could produce varied gameplay, however, there was not a clear upward trend in performance over time. This led to our third contribution which involved developing extensions to the SARSA(λ) algorithm called Periodic Cluster-Weighted Rewarding and Persistent Action Selection. We designed these to improve the learning performance of RL-Shooter and we demonstrated that the use of the techniques resulted in a clear upward trend in the percentage hit accuracy achieved over time. Our final contribution is a skill-balancing mechanism that we developed, called Skilled Experience Catalogue, which is based on a by-product of the learning process. The agent systematically stores “snapshots” of what it has learned during the different stages of the learning process. These can then be loaded during the game in an attempt to closely match the abilities of the current opponent. We showed that the technique could successfully match the skill level of five different scripted opponents with varying difficulty settings. I would like to dedicate this thesis to my family. My mother, Ann, my sister, Paula, and my brother, John, have always been exceptionally encouraging and supportive. Thank you for everything.
MutatedSocioAgentSim (MSAS): semisupervised modelling of multiagent simulation to predict and detect the mutation in a camouflaged social network

A social network is a networked structure formed by a set of agents/actors. It describes their interrelationships that facilitate the exchange and flow of resources and information. A camouflaged social network is one such community that influences the underlying structure and the profile of the agents, to cause mutation. The proposed MSAM is a novel system that simulates a multiagent network whose community structure is analyzed to identify the critical agents by studying the mutations caused due to attachment and detachment of agents. The isolation of the tagged agents will demonstrate disruption of information flow, which leads to the dismantling of the camouflaged community and giving scope for a predictive study about near future reconciliation. The proposed system simulates the 9/11 covert network based on the belief matrix and uses the novel density-based link prediction and suite of fragmentation algorithms for predictive community analysis. MSAM is claimed to be an intelligent system as agents perceive the knowledge from the dynamic environment through the belief matrix and further co-evolve as a community upon which semisupervised methodologies are used to predict the critical agents causing serious mutation.
Networked model predictive control using a wavelet neural network

This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The MPC uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay.
SoftwareSimple Tiling ARRay analysis of Affymetrix ChIP-chip data

Background: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression. Results: We present a free, open-source R/Bioconductor package Starr that facilitates comparative analysis of ChIPchip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. Starr includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, Starr automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome. Conclusion: Starr is an R package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e.g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. Starr enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.
Implementation of Searchable Encryption using Key Aggregation for Group Data Sharing in Cloud

Cloud computing is gaining popularity because of the services it provides but security is still an issue to be addressed. The data must be stored securely on cloud to avoid the security breaches such as data leaks. In order to store data securely, different encryption keys are used for sharing documents with different users. The users must generate large number of trapdoors for performing search over the files and decrypt them. This project aims to implement searchable encryption using key aggregation scheme, in which the data owner who uploads the file needs to distribute a single key to user for sharing number of files and the user needs to produce and send only one trapdoor to the cloud for querying over all the documents.
Distributing the computation in combinatorial optimization experiments over the cloud

Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer general conclusions. Number of scenarios can be overwhelming, especially when modeling uncertainty in some of the problem’s parameters. Since the process is also iterative and many ideas and hypotheses may be tested, execution time of each experiment has an important role in the efficiency and successfulness. Structure of such experiments allows for significant execution time improvement by distributing the computation. We focus on the cloud computing as a cost- efficient solution in these circumstances. In this paper we present a system for validating and comparing stochastic combinatorial optimization algorithms. The system also deals with selection of the optimal settings for computational nodes and number of nodes in terms of performance-cost tradeoff. We present applications of the system on a new class of project scheduling problem. We show that we can optimize the selection over cloud service providers as one of the settings and, according to the model, it resulted in a substantial cost-savings while meeting the deadline.
Channel and Clipping Level Estimation For OFDM in IoT –Based Networks: A Review

Internet of Things (IoT) is the idea to connect all devices to the internet. To implement such systems, we need to design low cost and less complex transmitters and make the receiver side complex. Nowadays OFDM is mainly used for communication due to its great advantages. But it faces the main problem such as PAPR due to the non-linear performance of High power amplifiers. There are so many methods are available to reduce the effect of PAPR in OFDM transmission, among this clipping is the simplest one. In this paper, we propose two algorithms to find the clipping level as well as the channel estimation. The efficiency of these algorithms is evaluated by using CLRB calculation.