Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Learning of Important Objects from First-Person Videos

A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important objects from the manually labeled first-person data in a supervised fashion. However, important objects are strongly related to the camera wearer's internal state such as his intentions and attention, and thus, only the person wearing the camera can provide the importance labels. Such a constraint makes the annotation process costly and limited in scalability. In this work, we show that we can detect important objects in first-person images without the supervision by the camera wearer or even third-person labelers. We formulate an important detection problem as an interplay between the 1) segmentation and 2) recognition agents. The segmentation agent first proposes a possible important object segmentation mask for each image, and then feeds it to the recognition agent, which learns to predict an important object mask using visual semantics and spatial features. We implement such an interplay between both agents via an alternating cross-pathway supervision scheme inside our proposed Visual-Spatial Network (VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one of which learns common visual semantics while the other focuses on the spatial location cues. Our unsupervised learning is accomplished via a cross-pathway supervision, where one pathway feeds its predictions to a segmentation agent, which proposes a candidate important object segmentation mask that is then used by the other pathway as a supervisory signal. We show our method's success on two different important object datasets, where our method achieves similar or better results as the supervised methods.
Detection of Children's Voices

This paper reports our recent work on extending our previous gender detector, targeted only at distinguishing between adult male and female voices, to encompass children’s voices as well. The classifiers were based on multilayer perceptrons and Gaussian mixture models and used Perceptual Linear Prediction coefficients, plus deltas, and pitch as features. Despite the small amount of training data for children’s voices, fairly good results were obtained in a test corpus of similar recording conditions (minimum classification error rate of 2.6%). Tests on real life corpora revealed the expected degradation with noisy environments and distant microphones. Tests with transformed female voices intended as cartoon child characters showed that they were mostly classified as children’s voices. Index Terms: gender detection, age effects, children voices.
Poster abstract: TDOA sensor pairing in multi-hop sensor networks

Acoustic source localization based on time difference of arrival (TDOA) measurements from spatially separated sensors is an important problem in wireless sensor networks (WSNs). While extensive research works have been performed on algorithm development, limited attention has been paid in how to form the sensor pairs. In the literature, most of the works adopt a centralized sensor pairing strategy, where only one common sensor node is chosen as the reference. However, due to the multi-hop nature of WSNs, it is well known that this kind of centralized signal processing method is power consuming since raw measurement data is involved in the transmissions. To reduce the requirements for both network bandwidth and power consumptions, we propose an in-network sensor pairing method to collect the TDOA measurements while guaranteeing the quality of source localization. The solution involves finding a minimal sized dominating set (MSDS) for a graph of the muti-hop network. It has been proved that in-network sensor pairing can result in the same Cramer-Rao-Bound (CRB) as the centralized one but at a far more less communication cost. Furthermore, the structure of the proposed in-network sensor pairing coincides with the decentralized source localization, which is an important application of our method.
Les politiques publiques

L’analyse des politiques publiques constitue, plus que jamais, un outil indispensable pour comprendre les enjeux auxquels sont confrontees les societes contemporaines. Multiplication des interventions publiques dans tous les domaines de la vie quotidienne, tentatives de reponses aux grands problemes economiques ou environnementaux, les gouvernements sont ecarteles entre des contraintes toujours plus fortes liees a la mondialisation et des demandes citoyennes de participation politique renouvelee. Dans ce contexte, le modele francais d’action publique pourra-t-il repondre a ces defis ? Cet ouvrage propose, au carrefour des savoirs juridiques, economiques, sociologiques et de science politique, une introduction a la science de l’Etat en action.
Creating Images through English on Yellow Pages: Multilingual Practices in Advertising in the Helsinki Region

In recent years Finland, like the other Nordic countries, has experienced an explosion in the use of English in various domains of public and private life. This trend has caught the attention of linguists and laymen alike, and has become a popular topic of scholarly investigation in several language departments and the Research Institute for the Languages of Finland,1 in addition to being a topic of lively public discussion. With increasing internationalisation, English has become an essential part of the professional life of a steadily growing number of Finns in various fields, be it politics and government, science and education, business, communication and media, transportation, tourism, sports, culture or entertainment. Those Finns who are not actually involved in international affairs are also heavily subjected to English through the mass media, being thus passive consumers of the language.
Description of the NCU Chinese Word Segmentation and Part-of-Speech Tagging for SIGHAN Bakeoff 2007

In Chinese, most of the language processing starts from word segmentation and part-of-speech (POS) tagging. These two steps tokenize the word from a sequence of characters and predict the syntactic labels for each segmented word. In this paper, we present two distinct sequential tagging models for the above two tasks. The first word segmentation model was basically similar to previous work which made use of conditional random fields (CRF) and set of predefined dictionaries to recognize word boundaries. Second, we revise and modify support vector machine-based chunking model to label the POS tag in the tagging task. Our method in the WS task achieves moderately rank among all participants, while in the POS tagging task, it reaches very competitive results.
A Multimodal Approach to the Medical Retrieval Task using IR-n

In our participation in the Medical Retrieval task we wanted to figure out if good results can be achieved with IR-n our IR passage based system for this restricted domain. We have focused on comparing the behaviour of two relevance feedback methods in this task LCA and PRF -. Furthermore, in order to adapt our system to this task we have used two automatic query expansion techniques related with the medical domain. On one hand we have added to our system an automatic query expansion method based on MeSH ontology and on the other hand we have added a negative query expansion based on the acquisition type of the image. Finally we have added a multimodal re-ranking module late fusion -. We have used two operation modes, one merges the two list in a classical re-ranking way, and the other mode bases the calculus of the relevance of an image on the quantity and the quality of the text related to the image in order to take the decision as to which system is more confident for that image the system based on text or the one based on images -. A major finding of the results is that our passage based system fits very well to this task. Within the textual runs submitted by all the participants we have reached the 6th place for our baseline and the 1st place for a run using PRF and query expansion adapted to the medical domain. Our results for multimodal re-ranking have not been successful due to problems with the parameters tuning for the test collection of this year.
CA-SVM : Communication-Avoiding Support Vector Machines on Clusters

We consider the problem of how to design and implement communication-efficient versions of parallel support vector machines, a widely used classifier in statistical machine learning, for distributed memory clusters and supercomputers. The main computational bottleneck is the training phase, in which a statistical model is built from an input data set. Prior to our study, the parallel isoefficiency of a state-of-the-art implementation scaled as W = Ω(P ), where W is the problem size and P the number of processors; this scaling is worse than even an one-dimensional block row dense matrix vector multiplication, which has W = Ω(P ). This study considers a series of algorithmic refinements, leading ultimately to a Communication-Avoiding SVM (CASVM) method that improves the isoefficiency to nearly W = Ω(P ). We evaluate these methods on 96 to 1536 processors, and show average speedups of 3 − 16× (7× on average) over Dis-SMO, and a 95% weak-scaling efficiency on six realworld datasets, with only modest losses in overall classification accuracy. The source code can be downloaded at [1]. Keywords-distributed memory algorithms; communicationavoidance; statistical machine learning
Yoga: A Hybrid Dynamic VLIW/OoO Processor

Out of order (OoO) processors use complex logic to maximize Instruction Level Parallelism (ILP). However, since the resulting dynamic instruction schedule of many applications seldom changes, it is reasonable to store and reuse the schedule instead of reconstructing it each time. To do this, we propose Yoga, a hybrid VLIW/OoO processor that dynamically stores instruction schedules generated while in OoO mode as enlarged basic blocks for future VLIW execution. The enlarged basic blocks are transformed into VLIW words, where the width of the VLIW words corresponds to the ILP found in each block. Yoga switches between OoO mode and VLIW mode, depending on the costs and benefits at each point in time, saving power by turning off OoO structures and unused VLIW lanes when in VLIW mode. Yoga reduces energy consumption by 10% on average, with a maximum of 18%, with negligible performance loss with respect to a conventional OoO processor.
DNA Barcoding the Native Flowering Plants and Conifers of Wales

We present the first national DNA barcode resource that covers the native flowering plants and conifers for the nation of Wales (1143 species). Using the plant DNA barcode markers rbcL and matK, we have assembled 97.7% coverage for rbcL, 90.2% for matK, and a dual-locus barcode for 89.7% of the native Welsh flora. We have sampled multiple individuals for each species, resulting in 3304 rbcL and 2419 matK sequences. The majority of our samples (85%) are from DNA extracted from herbarium specimens. Recoverability of DNA barcodes is lower using herbarium specimens, compared to freshly collected material, mostly due to lower amplification success, but this is balanced by the increased efficiency of sampling species that have already been collected, identified, and verified by taxonomic experts. The effectiveness of the DNA barcodes for identification (level of discrimination) is assessed using four approaches: the presence of a barcode gap (using pairwise and multiple alignments), formation of monophyletic groups using Neighbour-Joining trees, and sequence similarity in BLASTn searches. These approaches yield similar results, providing relative discrimination levels of 69.4 to 74.9% of all species and 98.6 to 99.8% of genera using both markers. Species discrimination can be further improved using spatially explicit sampling. Mean species discrimination using barcode gap analysis (with a multiple alignment) is 81.6% within 10×10 km squares and 93.3% for 2×2 km squares. Our database of DNA barcodes for Welsh native flowering plants and conifers represents the most complete coverage of any national flora, and offers a valuable platform for a wide range of applications that require accurate species identification.
Minimum Latency Link Scheduling for Arbitrary Directed Acyclic Networks under Precedence and SINR Constraints

Scheduling wireless links under the SINR model has attracted increasing attention in the past few years [1–6, 8–15, 18–20, 23–25, 27, 28, 33–36, 39, 41, 42, 45, 46]. However, most of previous work did not account for the precedence constraint that might exist among the wireless links. Precedence constraints are common in data aggregation problems where a sensor can not send data to its parent node before it has received data from all of its children. Existing solutions to the so-called minimum latency aggregation scheduling problem [7, 16, 21, 26, 29, 30, 32, 40, 43, 44] mainly focus on specific tree topologies rooted at the sink node. In this paper, we study the minimum latency link scheduling problem for arbitrary directed acyclic networks under both precedence and SINR constraints. Our formulation allows multiple sinks, and each sensor may transmit data to more than one parent node. We first show that the problem is NP-hard, and then propose a linear power assignment based polynomial time approximation algorithm and a dynamic labeling based heuristic algorithm. We have carried out extensive simulations for both dense and sparse arbitrary directed acyclic networks. The simulation results show that: (1) compared with both uniform and linear power assignments based algorithms, we can achieve much shorter scheduling lengths using our proposed labeling algorithm, and (2) the dynamic labeling based heuristic algorithm can lead to significantly shorter scheduling lengths than the heuristic algorithm which does not use labeling.
Learning real-world stimuli by single-spike coding and tempotron rule

In this paper, a system model is built for pattern recognition by using spiking neurons. The system contains encoding, learning and readout. The schemes used in this network are efficient and biologically plausible. Through the encoding of our network, the external stimuli (images) are converted into spatiotemporal spiking patterns. These spiking patterns are then efficiently learned through a supervised temporal learning rule. Through simulation, the properties of the system model are shown. It turns out that this network can successfully recognize different patterns very fast.
Low Space Order Analysis of Radial Pressure in SPMSM With Analytical and Convolution Approaches

This paper presents an analysis of low space order of the air-gap radial Maxwell pressures in surface permanent magnet synchronous machines with fractional slot concentrated windings. The air-gap Maxwell pressures result from the multiplication of the flux density harmonics due to magnetomotive forces and permeance linked to the magnet, the armature, and the stator slots and their interactions. One low space order is selected, and different approaches are compared to determine the origin of this pressure. First, an analytical prediction tool analytical calculation of harmonic force orders (ACHFO) is used to calculate the space and time orders of these magnetic pressure harmonics while identifying their origin in terms of interactions between magnet, armature, and teeth effects. In addition, the analytical prediction of ACHFO is compared with the flux density convolution and finite-element approaches. The main advantage of our tool is the speed of computation. Finally, an experimental operational deflection shape measurement is performed to show the deflection shape of the low space order selected.
Anytime A* Algorithm – An Extension to A* Algorithm

— A* algorithm is a powerful tool that can be used to solve a large number of different problems. However, its computation cost may be unaffordable for some applications. To ease this problem and to adopt different application contexts, various extensions of A* have been anticipated. The main extension to the A* algorithm is Anytime Algorithm. This approach find improved solution as well as improve a bound on the sub optimality of the current solution. Anytime A* Algorithm is algorithms those can optimize the memory and time resources and are considered best for A* algorithm. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible substitution between search time and solution quality. They can generate a fast, non-optimal solution and then refine it to the optimal if more time is provided. Some applications like Real Time Strategy (RTS) games have applied these algorithms to find a fast solution that will help the algorithm to prune some paths during the subsequent computation that will find the optimal solution.
HIERARCHICAL BLOCK-BASED DISPARITY ESTIMATION USING MEAN ABSOLUTE DIFFERENCE AND DYNAMIC PROGRAMMING

A disparity field is required in many video processing and computer vision applications. Many current approaches to disparity estimation tend to fail in low textured areas unless an additional interpolation step is added. To overcome this problem, an approach based on the hierarchical block-matching technique is proposed. To avoid ambiguous matching problems in low textured areas, a cost function is developed, which considers similarity measure of current corresponding points and estimates of neighboring disparities. For similarity measure, the mean absolute difference of rectangular blocks surrounding the points to be evaluated is exploited. Cost minimization is carried out by means of dynamic programming along each scan line. Preliminary results show that the percent of matching points is increased by 4% and computation time is reduced by 70% compared to the current approach.
Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks

In this paper, a modification of the Sugeno integral with interval type-2 fuzzy logic is proposed. The modification includes changing the original equations of the Sugeno Measures and the Sugeno integral that were initially proposed for type-1 fuzzy logic. The proposed modification enables calculation of the interval type-2 Sugeno integral for combining multiple source of information with a higher degree of uncertainty than with the traditional type-1 Sugeno integral. The advantages of the interval type-2 Sugeno integral are illustrated by reporting improved recognition rates in benchmark face databases. This new concept could also be a useful tool in other areas of applications. Also, the improvement provided by the type-2 integral is verified to be statistically significant in the recognition results for complex face databases (like the FERET database) when compared with the type-1 Sugeno integral. The proposed Sugeno integral is used to combine the modules' outputs of a modular neural network for face recognition. Simulation results show that the interval type-2 Sugeno integral is able to improve the recognition rate for the benchmark face databases. Recognition results are better or comparable to results produced by alternative approaches present in the literature reported for the same benchmark problems.
Construction of Probabilistic Boolean Network for Credit Default Data

In this article, we consider the problem of construction of Probabilistic Boolean Networks (PBNs). Previous works have shown that Boolean Networks (BNs) and PBNs have many potential applications in modeling genetic regulatory networks and credit default data. A PBN can be considered as a Markov chain process and the construction of a PBN is an inverse problem. Given the transition probability matrix of the PBN, we try to find a set of BNs with probabilities constituting the given PBN. We propose a revised estimation method based on entropy approach to estimate the model parameters. Practical real credit default data are employed to demonstrate our proposed method.
EFFECTIVENESS OF VIDEO-BASED COOPERATIVE LEARNING STRATEGY ON HIGH, MEDIUM AND LOW ACADEMIC ACHIEVERS

This study investigated the effect of cooperative, competitive and individualistic instructional strategies on the performance of high, medium and low academic achievers using video instructional package. A total of 120 senior secondary school mathematics students were randomly assigned into cooperative, competitive, individualized, and conventional teaching methods. Students from each group were stratified into high, medium and low achievers. Video Instructional Package (VIP) on mathematics and Geometry Achievement Test (GAT) were used as treatment and test instruments, respectively. Analysis of Variance and Scheffe test were used for data analysis. Findings indicated that there was significant difference in the performance of the groups in favour of cooperative learning strategy. Students’ achievement levels had significant influence on their performance in competitive and individualized instructional settings. It was recommended that mathematics teachers should employ cooperative learning strategies to improve students’ performance to bridge the gap among high, medium and low achievers.
Individualising dual antiplatelet therapy after percutaneous coronary intervention: the IDEAL-PCI registry

Objective To evaluate the clinical utility of individualising dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) in an all-comers population, including ST-elevation myocardial infarction (STEMI) patients. Setting Tertiary care single centre registry. Participants 1008 consecutive PCI patients with stent implantation, without exclusion criteria. Intervention Peri-interventional individualisation of DAPT, guided by multiple electrode aggregometry (MEA), to overcome high on-treatment platelet reactivity (HPR) to ADP-induced (≥50 U) and arachidonic acid (AA)-induced aggregation (>35 U). Outcome measures The primary efficacy end point was definite stent thrombosis (ST) at 30 days. The primary safety end point was thrombolysis in myocardial infarction (TIMI) major and minor bleeding. Secondary end points were probable ST, myocardial infarction, cardiovascular death and the combined end point: major cardiac adverse event (MACE). Results 53% of patients presented with acute coronary syndrome (9% STEMI, 44% non-ST-elevation). HPR to ADP after 600 mg clopidogrel loading occurred in 30% of patients (73±19 U vs 28±11 U; p<0.001) and was treated by prasugrel or ticagrelor (73%), or clopidogrel (27%) reloading (22±12 U; p<0.001). HPR to ADP after prasugrel loading occurred in 2% of patients (82±26 U vs 19±10 U; p<0.001) and was treated with ticagrelor (34±15 U; p=0.02). HPR to AA occurred in 9% of patients with a significant higher proportion in patients with HPR to ADP (22% vs 4%, p<0.001) and was treated with aspirin reloading. Definite ST occurred in 0.09% of patients (n=1); probable ST, myocardial infarction, cardiovascular death and MACE occurred in 0.19% (n=2), 0.09% (n=1) and 1.8% (n=18) of patients. TIMI major and minor bleeding did not differ between patients without HPR and individualised patients (2.6% for both). Conclusions Individualisation of DAPT with MEA minimises early thrombotic events in an all-comers PCI population to an unreported degree without increasing bleeding. A randomised multicentre trial utilising MEA seems warranted. Trial registration number http://www.clinicaltrials. gov; NCT01515345.
High-performance BWT-based Encoders

In 1994, Burrows and Wheeler developed a data compression algorithm which performs significantly better than Lempel-Ziv based algorithms. Since then, a lot of work has been done in order to improve their algorithm, which is based on a reversible transformation of the input string, called BWT (the Burrows-Wheeler transformation). In this paper, we propose a compression scheme based on BWT, MTF (move-to-front coding), and a version of the algorithms presented in [Dragos Trinca, ITCC-2004].