Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation of Handwritten Document Images into Text Lines

There are many governmental, cultural, commercial and educational organizations that manage large number of manuscript textual information. Since the management of information recorded on paper or scanned documents is a hard and time-consuming task, Document Image Analysis (DIA) aims to extract the intended information as a human would (Nagy, 2000). The main subtasks of DIA (Mao et al. 2003) are: i) the document layout analysis, which aims to locate the “physical” components of the document such as columns, paragraphs, text lines, words, tables and figures, ii) the document content analysis, for understanding/labelling these components as titles, legends, footnotes, etc. iii) the optical character recognition (OCR) and iv) the reconstruction of the corresponding electronic document. The proposed algorithms that address the above-mentioned processing stages come mainly from the fields of image processing, computer vision, machine learning and pattern recognition. Actually, some of these algorithms are very effective in processing machineprinted document images and therefore they have been incorporated in the workflows of well-known OCR systems. On the contrary, no such efficient systems have been developed for handling handwritten documents. The main reason is that the format of a handwritten manuscript and the writing style depend solely on the author's choices. For example, one could consider that text lines in a machine-printed document are of the same skew, while handwritten text lines may be curvilinear. Text line segmentation is a critical stage in layout analysis, upon which further tasks such as word segmentation, grouping of text lines into paragraphs, characterization of text lines as titles, headings, footnotes, etc. may be developed. For instance, a task for text-line segmentation is involved in the pipeline of the Handwritten Address Interpretation System (HWAIS), which takes a postal address image and determines a unique delivery point (Cohen et al., 1994). Another application, in which text line extraction is considered as a preprocessing step, is the indexing of George Washington papers at the Library of Congress as detailed by Manmatha & Rothfeder, 2005. A similar document analysis project, called the Bovary Project, includes a text-line segmentation stage towards the transcription of the manuscripts of Gustave Flaubert (Nicolas et al., 2004a). In addition, many recent projects, which focus on digitisation of archives, include activities for document image understanding in terms of automatic or semi-automatic extraction and indexing of metadata such as titles, subtitles, keywords, etc. (Antonacopoulos & Karatzas, 2004, Tomai et al., 2002). Obviously, these activities include text-line extraction.
Speech Recognition System Using a Wavelet Packet and Synergetic Neural Network

A recognition system is proposed, for audio and speech signal processing, which based on a Wavelet Packet (WP) and Synergetic Neural Network (SNN). Firstly, in the step of features extraction, the Mel-frequency cepstral coefficients (MFCC) algorithm is developed by using WP instead of FT. Secondly, we make use of SNN to pattern recognition. Due to the Kohonen rule,the SNN can be combined with improved MFCC very well in the system. Finally, the experiment results show that the speech recognition system has higher recognition rate and potential advantages in automatic speech recognition.
Optimal privacy and authentication on a portable communications system

Beller, Chang and Yacobi [2] have recently proposed a set of secret key and public key protocols to meet authentication and privacy requirements for conversation set-up protocols for the initiating party of portable communication systems. This paper improves upon the protocols to obtain a higher assurance of authentication and key distribution. Conversation set-up protocols for the responding party are discussed, a public key protocol providing end-to-end authentication and privacy is described, and relative as well as collective merits of the protocols are emphasised.
Simulation and analysis of underwater acoustic spread spectrum system based on LabView

Anti-jamming, concealment and low signal noise ratio detection of direct sequence spread spectrum system is much better than regular communication system. Under typical three paths acoustic channels, LabView-based simulations of direct sequence spread spectrum were carried out. Through the combination of LabView and Matlab, the spread spectrum synchronous, decision feedback equalizer and RAKE receiver were used in the spread spectrum systems. The simulation results show that the multi-path interference has been overcomed effectively. And correctness of theoretical analysis was confirmed furthermore, which would give certain reference to analysis and design of the practical system.
Biomedical indexing and retrieval system based on language modeling approach

In the medical field, scientific articles represent a very important source of knowledge for researchers of this domain. But due to the large volume of scientific articles published on the web, an efficient detection and use of this knowledge is quite a difficult task. In this paper, we propose our contribution for conceptual indexing of medical articles by using the MeSH (Medical Subject Headings) thesaurus. With this in mind, we propose a tool for indexing medical articles called BIOINSY (BIOmedical Indexing SYstem) which uses a language model for selecting the best representative descriptors for each document. The proposed indexing approach was evaluated by intensive experiments. These experiments were conducted on document test collections of real world clinical extracted from scientific collections, namely PUBMED and CLEF. The results generated by these experiments demonstrate the effectiveness of our indexing approach.
Comparison of Sparing Alternatives for Disk Arrays

This paper explores how choice of sparing methods impacts the performance of RAID level 5 (or parity striped) disk arrays. The three sparing methods examined are dedicated sparing, distributed sparing, and parity sparing. For database type workloads with random single block reads and writes, array performance is compared in four different modes - normal mode (no disks have failed), degraded mode (a disk has failed and its data has not been reconstructed), rebuild mode (a disk has failed and its data is being reconstructed), and copyback mode (which is needed for distributed sparing and parity sparing when failed disks are replaced with new disks). Attention is concentrated on small disk subsystems (fewer than 32 disks) where choice of sparing method has significant impact on array performance, rather than large disk subsystems (64 or more disks). It is concluded that, for disk subsystems with a small number of disks, distributed sparing offers major advantages over dedicated sparing in normal, degraded and rebuild modes of operation, even if one has to pay a copyback penalty. Furthermore, it is better than parity sparing in rebuild mode and similar to it in other operating modes, making it the sparing method of choice.
Does the Listener Matter? The Effects of Capitalization on Storytellers’ Evaluations of Travel Memories

Despite existing studies on memorable tourism experiences that often involve interpersonal sharing, the broader question of how a listener could influence tourists’ memories, including their evaluations of posttravel experiences and destination image, remains relatively unexplored. Interpersonal sharing with a listener could elicit a process called capitalization in which an individual (re)constructs details of an experience to make it more memorable. To address this gap, this research examines the effects of capitalization on travel memories (study 1), and the influence of listener responsiveness on tourists’ destination image (study 2). This research reinforces the notion that separating the act of remembering from the act of sharing is difficult and contributes to research on the malleability of travel memories by highlighting the influence of the listener’s feedback in shaping tourists’ memorable experiences. This research also provides relevant implications for tourism practitioners involved in service experiences and tourist relationship building.
The potential of polyhedral optimization: An empirical study

Present-day automatic optimization relies on powerful static (i.e., compile-time) analysis and transformation methods. One popular platform for automatic optimization is the polyhedron model. Yet, after several decades of development, there remains a lack of empirical evidence of the model's benefits for real-world software systems. We report on an empirical study in which we analyzed a set of popular software systems, distributed across various application domains. We found that polyhedral analysis at compile time often lacks the information necessary to exploit the potential for optimization of a program's execution. However, when conducted also at run time, polyhedral analysis shows greater relevance for real-world applications. On average, the share of the execution time amenable to polyhedral optimization is increased by a factor of nearly 3. Based on our experimental results, we discuss the merits and potential of polyhedral optimization at compile time and run time.
Group Analysis with Both Individual Effect and Within-Subject Variability

Data analysis in FMRI could be performed in one grand multilevel model that incorporates both withinand cross-subjects variabilities. However, it is due to the presence of both variance components that such a nonlinear model makes computation currently impractical, forcing analysts to adopt a two-stage approach: individual analysis with a time series regression model that explores activations in individual brains, and group analysis that integrates the results from all subjects.
Delayed Enrollment and Student Involvement: Linkages to College Degree Attainment

ABSTRACT Students who delay college enrollment after they graduate from high school have a lower chance of completing a college degree compared to students who enroll in college immediately after high school. This article explores delayers’ involvement in high-impact postsecondary campus activities to understand whether participation in high-impact activities is associated with bachelor’s degree attainment for students who delay enrollment. This study found that overall involvement in high-impact activities was associated with greater odds of bachelor’s degree attainment for all students, but students who delay entry into college do not benefit any differently than immediate-enrollment students from involvement in these activities. Participation in high-impact activities is only related to bachelor’s degree attainment in a minor way compared to other variables like students’ socioeconomic background and high school grade point average. This finding suggests that although high-impact practices may play a role in promoting student success in college, they are not as important as other social background and precollege student characteristics.
Customer benefits and value creation in streaming services marketing: a managerial cognitive capability approach

Customer value analysis and management is a key theoretical and empirical issue in marketing management and strategic management. However, little is known about the influence of customer benefit on customer value from the microfoundations of the dynamic capabilities perspective. Currently, a boom in online video and music streaming services is changing the entertainment industry structure. Thus, marketing managers in the fast‐growing streaming services industry should have dynamic managerial capabilities to anticipate other service elements that customers consider valuable. Based on managerial cognitive dynamic capabilities, this research explored the influences of customer‐perceived functional benefit, experiential benefit, financial benefit, and psychosocial benefit on perceived instrumental and terminal values. General linear model (GLM) and fuzzy‐set qualitative comparative analysis (fsQCA) were conducted to gain a more nuanced understanding of how different customers’ perceived benefits have different impacts on perceived value. The findings illuminate complex benefit configurations that drive perceived instrumental and terminal values and contribute to the development of value creation and its drivers. The proposed framework can help managers develop managerial cognitive dynamic capabilities by increasing their understanding of the impact of different perceived benefits on value creation for different types of customers.
A Robust Diffusion Adaptive Network Based on the Maximum Correntropy Criterion

Adaptive estimation over distributed networks has received a lot of attention due to its broad range of applications. A useful estimation strategy is diffusion adaptive network, where the parameters of interest can be well estimated from noisy measurements through diffusion cooperation between nodes. The conventional diffusion algorithms exhibit good performance in the presence of Gaussian noise but their performance decreases in presence of impulsive noise. The aim of the present paper is to propose a robust diffusion based algorithm that alleviates the effect of impulsive noise. To this end, we move beyond mean squared error (MSE) criterion and recast the estimation problem in terms of the maximum correntropy criterion (MCC). We use stochastic gradient ascent and useful approximations to derive an adaptive algorithm which is appropriate for distributed implementation. The resultant algorithm has the computational simplicity of the popular LMS algorithm, along with the robustness that is obtained by using higher order moments. We present some simulations results which show that the proposed algorithm outperforms existing alternative that rely MSE criterion.
ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES, Vol. 76 Issue 03

Two studies examined the influence of transient affective states and issue framing on issue interpretation and risk taking within the context of strategic decision making. In Study 1, participants in whom transient positive or negative affective states were induced by reading a short story showed systematic differences in issue interpretation and risk taking in a strategic decision making context. Compared to negative mood participants, those in a positive mood were more likely to interpret the strategic issue as an opportunity and displayed lower levels of risk taking. Study 2 replicated and extended these results by crossing affective states with threat and opportunity frames. Results showed that framing an issue (as a threat or an opportunity) had a stronger impact on issue interpretation among negative affect participants than among positive affect participants. Affective states also moderated the impact of issue framing on risk taking: the effect of framing on risk-taking was stronger under negative rather than positive affect. These results are interpreted via information-processing and motivational effects of affect on a decision maker. q 1998 Academic Press
An Energy Efficient MAC Protocol for Wireless Sensor Surveillance Networks

Data transmitted in large scale wireless sensor surveillance networks is highly spatial correlated resulting in transmission of redundant data, which increase the energy consumption. So it has been proposed to select a subset of sensors nodes to transmit their data so as to prevent transmission of redundant data. In this paper, we investigate the ways of selecting sensor nodes and develop a novel spatial correlation-based energy efficient MAC protocol, referred as SEMAC. SEMAC adopts priority node selection algorithm based on node’s signal strength and collision avoidance mechanism so that transmission of redundant data is prevented while prioritizes the transmission of route-thru packets. Simulation results show that our protocol achieves higher performance than CC-MAC in terms of energy consumption, transmission latency, packet delivery rate and reconstruction distortion.
Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study

Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300–600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our study indicates that the temporal integration underlying multisensory speech perception requires to be understood in the framework of large-scale functional brain network mechanisms in addition to the established cortical loci of multisensory speech perception.
SIAO 1 : A First Order Logic Machine Learning System usingGenetic

This paper describes SIAO1 , a genetic-based machine learning (GBML) system that learns rst order logic (FOL) rules. Recent work is detailed and experimental results are provided. Two topics relative to the application of evolutionary techniques to machine learning are also discussed: First, the representation problem and several solutions are investigated. In particular we show how the non-standard approach used in SIAO1 (from a genetic point of view) concerning the representation of FOL formulae in the chromosomes can be useful from a machine learning point of view to take into account domain knowledge and to deal with numerical values. Second, we show recent improvements to SIAO1 , especially how to deal with missing, indiierent or undeened values, and how to optimize the search strategy using biases. Finally the tness function complexity is studied and reduced.
On Improving DREAM Framework with Estimations and ProgME

Software Defined Networking (SDN) is an emerging architecture that is dynamic, manageable, cost-effective and adaptable, making it ideal for the high-bandwidth, dynamic nature of today’s applications. Using SDN, networks can enable a variety of concurrent, dynamically instantiated measurement tasks, that provide fine-grain visibility into network traffic by configuring Ternary Content Address Memory (TCAM) counters in hardware switches. However, TCAM memory is limited, thus the accuracy of measurement tasks depends on the number of resources devoted to them on each switch. In this thesis, we propose a solution that improves Dynamic Resource Allocation for Software-defined Measurements (DREAM), a framework with an adaptive step size search that achieves a desired level of accuracy for measurement tasks. We have enabled prediction capabilities in the framework to generate better counters configurations using previous network traffic information. We implement four estimation techniques (EWMA-based Prediction, Polynomial Curve Fitting, KMeans++ Cluster and PseudoLinear Extrapolation) that have been tested with simulations running three types of measurement tasks (heavy hitters, hierarchical heavy hitters and traffic change detection) that show the proposed techniques improve task accuracy and tasks concurrency in DREAM. Existing traffic measurements tools usually rely on some predetermined concept of flows to collect traffic statistics. Thus, they usually have issues in adapting to changes in traffic condition and present scalability issues with respect to the number of flows and the heterogeneity of the monitoring applications. We propose an integration of the Programmable MEasurements (ProgME) paradigm, which defines a novel approach to defined measurement tasks in a programmable way using the concept of flowsets, on top of the DREAM framework. This enables better scalability for measurement tasks that deal with large amounts of traffic flows on DREAM while reducing the required number of counters allocations for the tasks.
Poliovirus 2A Protease Induces Apoptotic Cell Death

ABSTRACT A cell line was generated that expresses the poliovirus 2A protease in an inducible manner. Tightly controlled expression was achieved by utilizing the muristerone A-regulated expression system. Upon induction, cleavage of the eukaryotic translation initiation factor 4GI (eIF4GI) and eIF4GII is observed, with the latter being cleaved in a somewhat slower kinetics. eIF4G cleavage was accompanied by a severe inhibition of protein synthesis activity. Upon induction of the poliovirus 2A protease, the cells displayed fragmented nuclei, chromatin condensation, oligonucleosome-size DNA ladder, and positive TUNEL (terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling) staining; hence, their death can be characterized as apoptosis. These results indicate that the expression of the 2A protease in mammalian cells is sufficient to induce apoptosis. We suggest that the poliovirus 2A protease induces apoptosis either by arresting cap-dependent translation of some cellular mRNAs that encode proteins required for cell viability, by preferential cap-independent translation of cellular mRNAs encoding apoptosis inducing proteins, or by cleaving other, yet unidentified cellular target proteins.
Video skimming and characterization through the combination of image and language understanding

Digital video is rapidly becoming important for education, entertainment and a host of multimedia applications. With the size of the video collections growing to thousands of hours, technology is needed to effectively browse segments in a short time without losing the content of the video. We propose a method to extract the significant audio and video information and create a skim video which represents a very short synopsis of the original. The goal of this work is to show the utility of integrating language and image understanding techniques for video skimming by extraction of significant information, such as specific objects, audio keywords and relevant video structure. The resulting skim video is much shorter; where compaction is as high as 20:1, and yet retains the essential content of the original segment. We have conducted a user-study to test the content summarization and effectiveness of the skim as a browsing tool.