Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Personality-Aware Recommendation The ADS Dataset

In the last decade new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. The two main reasons are: firstly, a person’s buying choices are influenced by psychological factors like impulsiveness, and secondly, some consumers may be more susceptible to making impulse purchases than others. To the best of our knowledge, the impact of personality factors on advertisements has been largely neglected at the level of recommender systems. This work proposes a highly innovative research which uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. As a matter of fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of state-of-the-art algorithms. We present the ADS Dataset, a publicly available benchmark for computational advertising enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures. The proposed benchmark allows two main tasks: rating prediction over 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) and click-through rate prediction. Moreover, this work carries out experiments, reviews various evaluation criteria used in the literature, and provides a library for each one of them within one integrated toolbox.
A threshold size for microsatellite expansion.

When is a DNA repeat sequence a microsatellite? Microsatellites are tandem arrays of short (1–5 bp) repeats, characterized by rapid expansion and contraction through a process of ‘‘dynamic mutation’’ (Sutherland and Richards 1995). Despite their importance in modern genetic analyses (Bruford and Wayne 1993; Dib et al. 1996) and association with several human genetic diseases (Mandel 1994), little is known about the initial conditions necessary for dynamic mutation to occur— the evolutionary origin of a microsatellite. Here, we use data on the frequency of large arrays in the genome of Saccharomyces cerevisiae to demonstrate the existence of a minimum threshold size necessary for a repeat sequence to undergo dynamic mutation. The existence of this threshold provides an important insight into the evolutionary properties of repeat arrays in the genome. The major component of the dynamic mutation process is thought to be polymerase slippage during replication (Strand et al. 1993), resulting in an increase or decrease in array size of one repeat unit. Estimates of slippage mutation rates range from 1025 to 1022, values which are orders of magnitude greater than point nucleotide substitution rates (Edwards et al. 1992; Mahtani and Willard 1993). Large in vivo studies of pedigrees show a 2:1 bias in favor of gain of repeats (Weber and Wong 1993; Banchs et al. 1994), and, coupled with high mutation rates, this suggests that microsatellites show a tendency to rapidly increase in size over time. Phylogenetic analysis of two putative primate microsatellite loci further suggests that dynamic mutation may only affect arrays with a certain minimum number of repeat units (Messier, Li, and Stewart 1996). Thus, an understanding of the rapid evolutionary dynamics of microsatellites is developing, but both in vivo and phylogenetic studies are limited by the large amount of data which must be accumulated in order to observe a few mutation events. Large-scale genome sequencing projects allow a novel approach to the analysis of slippage mutation and the evolutionary origins of microsatellites. We present a study of the size distribution of short tandem repeats in the genome of yeast (S. cerevisiae) to illustrate the mutational processes acting on these arrays. We assume a null hypothesis in which there is no dynamic mutation, and repeat units (i.e., CA, AT, AGAT, etc.) are randomly distributed within the genome. This null hypothsesis is consistent with a simple model of genome evolution in
When Gaps Become Huuuuge: Donald Trump and Beliefs About Immigration

Immigration became a hot issue during the 2016 presidential election, in part due to Donald Trump’s offensive campaign against immigrants and minorities in general. Drawing upon the belief gap hypothesis, we tested if support for Donald Trump increased false beliefs about immigrants. The belief gap hypothesis explains differences in beliefs about empirically verifiable and politically contested issues, relying on ideology and partisanship—instead of education— to predict people’s beliefs. By using nationally representative panel data, this study explored how political ideology and education work together to predict belief gaps about immigrants. Findings suggest that conservative ideology and education interact to predict attitudes, showing that highly educated conservatives hold more negative beliefs about immigrants as compared to highly educated liberals or less educated conservatives. We also found that Trump’s supporters exhibit negative attitudes and beliefs about immigration—yet results indicate that Donald Trump is not the cause of such attitudes but the catalyst that reveals them. Implications for theory and future research are discussed.
Stepwise Signal Extraction via Marginal Likelihood

This article studies the estimation of a stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out an extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. A detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array comparative genomic hybridization (CGH) data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice. Supplementary materials for this article are available online.
An empirical comparison of joint optimization techniques for speech translation

Speech translation (ST) systems consist of three major components: automatic speech recognition (ASR), machine translation (MT), and speech synthesis (SS). In general the ASR system is tuned independently to minimize word error rate (WER), but previous research has shown that ASR and MT can be jointly optimized to improve translation quality [1]. Independently, many techniques have recently been proposed for the optimization of MT, such as empirical comparison of joint optimization using minimum error rate training (MERT) [2], pairwise ranking optimization (PRO) [3] and the batch margin infused relaxed algorithm (MIRA) [4]. The first contribution of this paper is an empirical comparison of these techniques in the context of joint optimization. As the last two methods are able to use sparse features, we also introduce lexicalized features using the frequencies of recognized words. In addition, motivated by initial results, we propose a hybrid optimization method that changes the translation evaluation measure depending on the features to be optimized. Experimental results for the best combination of algorithm and features show a gain of 1.3 BLEU points at 27% of the computational cost of previous joint optimization methods. Index Terms: speech translation, machine translation, automatic speech recognition, joint optimization
Using Acoustic Diarization for Duplicate Detection

The following article describes the use of an acoustic diarization engine for duplicate detection on broadcast news. Diarization is typically used to partition audio into speaker homogeneous regions, or in other words, to determine “who spoke when. ” In this setting, however, we use diarization to segment the recordings and group the segments into homogeneous clusters. Diarization is performed both on the full length broadcast news recordings as well as the short clips (which we are classifying as either a duplicate or not). We then compare the similarity of models trained on the clusters to determine whether the time allocated to the cluster from the short clip is from the original broadcast news recording, or a duplicate. We tested our system under a variety of audio conditions: unmodified, with reverberation, resampled, and lowpass filtered. On our test set, the areas under the receiver operating characteristic curve for the audio conditions were 0.91, 0.89, 0.61, and 0.64 respectively.
Massively parallel XML twig filtering using dynamic programming on FPGAs

In recent years, XML-based Publish-Subscribe Systems have become popular due to the increased demand of timely event-notification. Users (or subscribers) pose complex profiles on the structure and content of the published messages. If a profile matches the message, the message is forwarded to the interested subscriber. As the amount of published content continues to grow, current software-based systems will not scale. We thus propose a novel architecture to exploit parallelism of twig matching on FPGAs. This approach yields up to three orders of magnitude higher throughput when compared to conventional approaches bound by the sequential aspect of software computing. This paper, presents a novel method for performing unordered holistic twig matching on FPGAs without any false positives, and whose throughput is independent of the complexity of the user queries or the characteristics of the input XML stream. Furthermore, we present experimental comparison of different granularities of twig matching, namely path-based (root-to-leaf) and pair-based (parent-child or ancestor-descendant). We provide comprehensive experiments that compare the throughput, area utilization and the accuracy of matching (percent of false positives) of our holistic, path-based and pair-based FPGA approaches.
Differential nuclear localization of complexes may underlie in vivo intrabody efficacy in Huntington's disease

Intrabodies offer attractive options for manipulating the protein misfolding that triggers neurodegenerative diseases. In Huntington's disease, where the expanded polyglutamine tract in the extreme N-terminal region of huntingtin exon1 misfolds, two lead intrabodies have been selected against an adjacent peptide, using slightly different approaches. Both are effective at preventing aggregation of a reporter fragment in transient co-transfection assays. However, after intracranial delivery to mutant mouse brains, VL12.3, which is mainly localized to the nucleus, appears to accelerate the mutant phenotype, while C4 scFv, which is largely cytoplasmic, shows partial phenotypic correction. This comparison highlights parameters that could inform intrabody therapeutics for multiple proteostatic diseases.
Estimation on Economic Cost of China ' s New De-sulfur Policy During Her Gradual Accession to WTO : The Case of Industrial SO 2 Emission

To understand the potential impacts of China’s accession to WTO in her new de-sulphur policy (reduction of 10% of SO2 emission in 2005 with respect to that of 2000), we construct a CGE model in which the SO2 emission is linked directly to energy intermediary consumption in production. The positive externality of trade on China’s economy is also included. This model is then calibrated into a 55-sector Chinese SAM of year 1997. Four policy simulations (BaU, Open, Desulfur, Open+Desulfur) are made for 1997 till 2005 and the Divisia index decomposition method is used to analysis the simulation results. The principal results show the environmental impact of trade, though proven to be “negative”, stays rather modest. This is due to the effect of industrial composition transformation that deviates towards labor-intensive sector specialization under the new trade liberalization process. We also find supposed some modest trade externality effect to contribute to pollution reduction and we do not find proof for “pollution haven” hypothesis. Although seemingly to be quite ambitious, the new de-sulphur policy will only bring very slight economic growth lose. The most part of pollution reduction will be realized by the substitution between polluting and less or non-polluting energies. The combination of the trade liberalization and pollution control policies seems to give China more flexibility in adapting her economy to the new de-sulphurs objective. Considering different aspect together, the total economy loss due to new de-sulphur policy will be limited to only –0.18% under the presence of trade liberalization.
Ostentatious Adoption of Hand Gestures for Intelligent HCI

Gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. Since there are limitations of these devices the useable command set based diligences is also limited. This research project deals with using appropriate image processing algorithms to recognize hand gestures and then performs actions depending on the gesture made. The gesture does not require any skills or prior training. This research deals with the user being able to communicate with the computer by moving and planting out fingers of his hand - all without help of any input device control and use Power Point, as an application.We use running average over a sequence of images to get the average image which will be thebackground too. We make use of the equation∇BG[i][j] =∝ ∇BG[i][f] + (1- ∝)∇Frame[i][j] with an assumption that the background is more or less static all throughout.This equation works because of the assumption that the background is mostly static. Hence for those stationary item, those pixels aren't affected by this weighted averagingand∝ x = (1-∝)x = x. Hence those pixels that are constantly changing isn't a part of the background, hence those pixels will get weighed down.The main aim of Human Computer Interaction (HCI) is to research and develop new and simpler ways to interact with computers and many other devices as well. Hand Gesture Recognition is one such area of active research for computer scientists.
SB202190, a selective inhibitor of p38 mitogen‐activated protein kinase, is a powerful regulator of LPS‐induced mRNAs in monocytes

Inhibitors of p38 mitogen‐activated protein kinase (p38) have been reported to block tumor necrosis factor α (TNF‐α) and interleukin‐1β (IL‐1β) production in monocytes at the level of mRNA translation. Yet, several studies document that p38 can phosphorylate and activate specific transcription factors. Thus, to understand better the role of p38 during monocyte activation, we sought to determine the extent to which p38 is required for lipopolysaccharide (LPS)‐induced gene expression. For this, differential mRNA display was used to identify LPS‐induced genes whose expression was blocked by SB202190, a specific inhibitor of p38. A partial screen identified 10 genes in monoyctes induced 4‐ to 74‐fold by LPS. Of these, genes encoding interferon‐induced gene 15, neuroleukin, radiation‐inducible immediate‐early gene‐1, A20, IL‐1β, and superoxide dismutase were suppressed >50% by SB202190. LPS‐induced gene activation was not blocked by cycloheximide, indicating that synthesis of intermediate proteins was not required. SB202190 blocked gene induction by 50% when present between 41 and 123 nM, consistent with the potency of this compound as a p38 inhibitor. Furthermore, the ability of SB202190 to block gene activation was stimulus‐dependent. LPS and interferon‐α (IFN‐α) both up‐regulated neuroleukin mRNA, but only LPS‐induced neuroleukin mRNA was suppressed by SB202190. In contrast, TNF‐α and LPS both induced IL‐8 mRNA, and induction by either TNF‐α or LPS was blocked by SB202190. These data were consistent with the ability of LPS and TNF‐α, but not IFN‐α, to activate p38 in monocytes. The results provide pharmacological evidence that p38 may be a key mediator of inducible gene expression in monocytes, but its role is stimulus and gene specific. J. Leukoc. Biol. 64: 409–417; 1998.
Black Holes and the Super Yang-Mills diagram, 2

The complete phase diagram of objects in M-theory compactified on tori $T^p$, $p=1,2,3$, is elaborated. Phase transitions occur when the object localizes on cycle(s) (the Gregory-Laflamme transition), or when the area of the localized part of the horizon becomes one in string units (the Horowitz-Polchinski correspondence point). The low-energy, near-horizon geometry that governs a given phase can match onto a variety of asymptotic regimes. The analysis makes it clear that the matrix conjecture is a special case of the Maldacena conjecture.
Discovery of Critical Nodes in Road Networks Through Mining From Vehicle Trajectories

Road networks are extremely vulnerable to cascading failure caused by traffic accidents or anomalous events. Therefore, accurate identification of critical nodes, whose failure may cause a dramatic reduction in the road network transmission efficiency, is of great significance to traffic management and control schemes. However, none of the existing approaches can locate city-wide critical nodes in real road networks. In this paper, we propose a novel data-driven framework to rank node importance through mining from comprehensive vehicle trajectory data, instead of analysis solely on the topology of the road network. In this framework, we introduce a trip network modeled by a tripartite graph to characterize the dynamics of the road network. Furthermore, we present two algorithms, integrating the origin-destination entropy with flow (ODEF) algorithm and the crossroad-rank (CRRank) algorithm, to better exploit the information included in the tripartite graph and to effectively assess the node importance. ODEF absorbs the idea of the information entropy to evaluate the centrality of a node and to calculate its importance rating by integrating its centrality with the traffic flow. CRRank is a ranking algorithm based on eigenvector centrality that captures the mutual reinforcing relationships among the OD-pair, path, and intersection. In addition to the factors considered in ODEF, CRRank considers the irreplaceability of a node and the spatial relationships between neighboring nodes. We conduct a synthetic experiment and a real case study based on a real-world dataset of taxi trajectories. Experiments verify the utility of the proposed algorithms.
Non-English speaking background patients in a predominantly English-speaking region may be more likely to present with a dementia other than Alzheimer’s disease

Information on 54 patients was retrospectively collected to compare the presentation trends of cognitive disorders in those of non-English speaking background (NESB)to English speaking background(ESB)attending an Australian memory clinic that extensively uses fluorodeoxyglucose positron emission tomography(FDG PET) in the diagnosis of cognitive concerns. NESB patients were less likely to be diagnosed with Alzheimer’s disease(AD)as the sole neurodegenerative diagnosis (Fisher exact test, p = 0.08), and NESB patients with dementia were more likely to have non-AD dementia (Fisher exact test, p = 0.06). They experienced symptoms 18 months longer before receiving a formal diagnosis (t(46) = 2.2, p = 0.03). Older elderly NESB females were under represented in those presenting to the clinic (Fisher exact test, p = 0.04). The clinical work-up of NESB patients as opposed to those of ESB relied more heavily on FDG PET (Fisher exact test, p = 0.04). ESB and NESB patients may have different attitudes towards dementia, affecting how they present, and biomarkers may be more heavily relied on when language affects history taking and neuropsychological testing.
Multiscale Visualization of Trajectory Data

This paper proposes a novel three-dimensional (3D) visualization tool for the trajectory analysis, helping users understand the trajectory data from different perspectives. The details of a single and a set trajectories are well covered in multiscale views by the four main linked windows, namely Traj View, Color Bar, Multi Property and Track Map. We take advantage of and further improve the color bar and the parallel coordinates to effectively present the important attributes of trajectories and also the relationship between different attributes. In addition, the interactive actions, such as keyboard and mouse operations, provide a rich and wonderful user experience.
REGIMRobvid: Objects and Scenes Detection for Robot Vision 2013

This paper describes the participation of the REGIM team in the ImageCLEF 2013 Robot Vision Challenge. The competition was focused on the problem of objects and scenes classification in indoor environments. Objects and scenes are considered as concepts. During the competition, we aim to classify images according to the room in which they were acquired, using the information provided by the visual images only. Our system is based on PHOW features extraction and PEGASOS SVM algorithm to learn a multi-class classifier that is enable to detect the objects and the adequate scene. For this end, we focus on how to interpret the scores provided by the SVM classifier. Our system was ranked 4th among 6 teams.
An analytical approach to architecture-based software reliability prediction

Prevalent approaches to software reliability modeling are black-box based, i.e., the the software system is treated as a monolithic entity and only its interactions with the outside world are modeled. However with the advancement and widespread use of object oriented systems design and web-based development, the use of component-based software development is on the rise. Software systems are being developed in a heterogeneous fashion using components developed in-house, contractually, or picked off-the-shelf and hence it may be inappropriate to model the overall failure process of such systems using the existing software reliability growth models. Predicting the reliability of a heterogeneous software system based on its architecture, and the failure behavior of its components is thus absolutely essential. In this paper we present an analytical approach to architecture-based software reliability prediction. The novelty of this approach lies in the idea of parameterizing the analytic model of the software using measurements obtained from testing. To facilitate this we use a coverage analysis tool called ATAC (Automatic Test Analyzer in C), which is a part of a Software Understanding and Diagnosis System (/spl chi/Suds) developed at Bellcore. We demonstrate the methodology by predicting the reliability of an application called as SHARPE (Symbolic Hierarchical Automated Reliability Predictor), which has been used to solve stochastic models of reliability, performance and performability.
State-of-the-art in distributed privacy preserving data mining

Privacy preserving data mining has become an important research problem. The chief research is how to mine the potential knowledge and not to reveal the sensitive data. In reality, large amounts of data are stored in distributed sites, so the DPPDM (Distributed Privacy Preserving Data Mining) is very important. This paper gave a survey on the DPPDM. Based on different underlying technologies, there are three kinds of techniques: perturbation, secure multi-party computation and restricted query. It provides a detailed description of the research in this area, compares the advantages and disadvantages of each method, foucs on the hot topic in this field, points out the future research directions.