Miracle-dz/UniFAR-FTUs huggingface dataset:
“ Like Attracts Like ! ” – A Social Recommendation Framework Through Label Propagation

Recently label propagation recommendation receives much attention from both industrial and academic fields due to its low requirement of labeled training data and effective prediction. Previous methods propagate preferences on a user or item similarity graph for making recommendation. However, they still suffer some major problems, including data sparsity, lack of trustworthiness, cold-start problem. By observation, the currently booming social network has some characteristics to remedy these problems. (1) Most of the user connections in either social network or real life can inflect information about users’ interest similarity by “Like Attracts Like”, which can improve propagation graph construction. (2) Social connections can inflect trustworthiness information for user similarity, where connections are not built randomly but based on their trust. (3)Social network can provide user connection data as the supplementation of sparse ratings, which can also solve the cold-start problem when one new user has no rating history but social network. In order to improve the recommendation accuracy, we propose a social label propagation recommendation framework. In addition, we also construct the traditional user similarity graph for combination with social network to solve the noise and multi-interest problem in social network. Finally, we implement Green’s function semi-supervised learning algorithm for label propagation recommendation on the real world recommendation data. The empirical results demonstrate the effectiveness of our proposed social recommendation framework.
Imprecise RDQL: towards generic retrieval in ontologies using similarity joins

Traditional semantic web query languages support a logic-based access to the semantic web. They offer a retrieval (or reasoning) of data based on facts. On the traditional web and in databases, however, exact querying often provides an incomplete answer as queries are overspecified or the mix of multiple ontologies/modelling differences requires "interpretational flexibility. " Therefore, similarity measures or ranking approaches are frequently used to extend the reach of a query. This paper extends this idea to the semantic web. It introduces iRDQL---a semantic web query language with support for similarity joins. It is an extension of traditional RDQL (RDF Data Query Language) that enables the users to query for similar resources ranking the results using a similarity measure. We show how iRDQL allows to extend the reach of a query by finding additional results. We quantitatively evaluated four similarity measures for their usefulness in iRDQL in the context of an OWL-S semantic web service retrieval test collection and compared the results to a specialized OWL-S matchmaker. Initial results of iRDQL indicate that it is indeed useful for extending the reach of queries and that it is able to improve recall without overly sacrificing precision. We also found that our generic iRDQL approach was only slightly outperformed by the specialized algorithm.
Adaptive home heating under weather and price uncertainty using GPS and mdps

We consider the problem of adaptive home heating in the smart grid, assuming that real-time electricity prices are being exposed to end-users with the goal of realizing demand-side management. To lower the burden on the end-users, our goal is the design of a smart thermostat that automatically heats the home, optimally trading off the user's comfort and cost. This is a challenging problem due to two sources of uncertainty: future weather conditions and future electricity prices. Our main technical contribution is a general technique that uses predictive distributions obtained from Gaussian Process (GP) regressions to compute the state transition probabilities of an MDP, such that the solution to the resulting MDP constitutes a sequentially optimal policy. We apply this general approach to the home-heating problem, where we use the predictive distributions of the GPs for the day-ahead external temperatures and electricity prices. The solution to the home-heating MDP constitutes an optimal heating policy that maximizes the user's utility given the probability information gathered by the Gaussian process model. Via simulations we show that our MDP-based approach outperforms various benchmarks, especially for cost-sensitive users.
Reading Arabic : legibility studies for the Arabic script

What is the cost of visual complexity? This dissertation sets out to determine the effect of the complexity of word formation on the legibility of Arabic and the role that vocalization plays in reading. This is carried out via a holistic approach to legibility research that combines the visual culture with eye movement in reading and legibility studies. To do this it looks at the transition of Arabic manuscript letterforms into typographic ones, the anatomy of the Arabic script, and the predominant typographic styles in use today. It presents the design process of the specially designed Afandem typeface family, a review of eye movement findings, and a new definition of legibility that is rooted in the models of eye movement. The experiment used eye tracking to test 72 subjects in Beirut to determine the effect of the complexity and vocalization on reading measures. The results show that the increased complexity of word formation has a negative effect on the legibility of Arabic typefaces and that the short vowels add a cost to word processing even though they bring with them extra clarity that results in a reduced number of regressions. These results are discussed within the scope of the Arab world today, its cultural and educational setup, and avenues for further research are explored.
Quality configurable reduce-and-rank for energy efficient approximate computing

Approximate computing is an emerging design paradigm that exploits the intrinsic ability of applications to produce acceptable outputs even when their computations are executed approximately. In this work, we explore approximate computing for a key computation pattern, Reduce-and-Rank (RnR), which is prevalent in a wide range of workloads including video processing, recognition, search and data mining. An RnR kernel performs a reduction operation (e.g., distance computation, dot product, L1-norm) between an input vector and each of a set of reference vectors, and ranks the reduction outputs to select the top reference vectors for the current input. We propose two complementary approximation strategies for the RnR computation pattern. The first is interleaved reduction-and-ranking, wherein the vector reductions are decomposed into multiple partial reductions and interleaved with the rank computation. Leveraging this transformation, we propose the use of intermediate reduction results and ranks to identify future computations that are likely to have low impact on the output, and can hence be approximated. The second strategy, input similarity based approximation, exploits the spatial or temporal correlation of inputs (e.g., pixels of an image or frames of a video) to identify computations that are amenable to approximation. These strategies address a key challenge in approximate computing - identification of which computations to approximate - and may be used to drive any approximation mechanism such as computation skipping and precision scaling to realize performance or energy improvements. A second key challenge in approximate computing is that the extent to which computations can be approximated varies significantly from application to application, and across inputs for even a single application. Hence, quality configurability, or the ability to automatically modulate the degree of approximation at runtime is essential. To enable quality configurability in RnR kernels, we propose a kernel-level quality metric that correlates well to application-level quality, and identify key parameters that can be used to tune the proposed approximation strategies dynamically. We develop a runtime framework that modulates the identified parameters during execution of RnR kernels to minimize their energy while meeting a given target quality. To evaluate the proposed concepts, we designed quality-configurable hardware implementations of 6 RnR-based applications from the recognition, mining, search and video processing application domains in 45nm technology. Our experiments demonstrate 1.06X-2.18X reduction in energy consumption with virtually no loss in output quality (<;0.5%) at the application-level. The energy benefits further improve up to 2.38X and 2.5X when the quality constraints are relaxed to 2.5% and 5% respectively.
Representation Changes for Efficient Learning in Structural Domains

This paper presents an efficient approach to address the task of learning from large number of learning examples in structural domains. While in attribute-value representations only one mapping is possible between descriptions, in first order logic representations there are potentially many mappings. Classic approaches consider all mappings and then define a restricted hypothesis space to cope with the intractability of exploring all mappings. Our approach is to select one particular type of mapping at a time and use it as a basis to define a new hypothesis space. We show that such a hypothesis space, called a Matching Space, may be represented using attribute-value pairs. In a Matching Space, it is therefore possible to use propositional learners. The concept descriptions found may then be mapped back into the initial first order logic representation. It appears that characterizing a Matching Space is equivalent to shifting the representation of examples: the new learning examples represent only a "part" of the initial examples. Based on a taxonomy of elementary parts provided by the user, we consider a particular set of composite parts —called "morions"— that are used to automatically and iteratively change the representation of examples. Experimental results obtained with an implemented system, REMO, show the benefits of this approach. We have used REMO to learn characteristic descriptions of concepts related to the pronunciation of Chinese characters from a corpus of more than three thousands characters.
Cilengitide: The First Anti-Angiogenic Small Molecule Drug Candidate. Design, Synthesis and Clinical Evaluation

Cilengitide, a cyclic RGD pentapeptide, is currently in clinical phase III for treatment of glioblastomas and in phase II for several other tumors. This drug is the first anti-angiogenic small molecule targeting the integrins αvβ3, αvβ5 and α5β1. It was developed by us in the early 90s by a novel procedure, the spatial screening. This strategy resulted in c(RGDfV), the first superactive αvβ3 inhibitor (100 to 1000 times increased activity over the linear reference peptides), which in addition exhibited high selectivity against the platelet receptor αIIbβ3. This cyclic peptide was later modified by N-methylation of one peptide bond to yield an even greater antagonistic activity in c(RGDf(NMe)V). This peptide was then dubbed Cilengitide and is currently developed as drug by the company Merck-Serono (Germany). This article describes the chemical development of Cilengitide, the biochemical background of its activity and a short review about the present clinical trials. The positive anti-angiogenic effects in cancer treatment can be further increased by combination with “classical” anti-cancer therapies. Several clinical trials in this direction are under investigation.
Toward projectified environmental governance?

Projects are often praised for their efficiency, responsiveness to local context, and capacity to spur innovation, especially in comparison to more permanent organizations. Projects – cross-cutting organizational forms chartered to advance well-defined objectives during a specified period of time – have been a staple organizational form in the private sector, but only recently have scholars started to evaluate their relevance to governance within developed economies. In this paper, we explore projectification – i.e. expanded reliance on temporally bounded organizations – as a conceptual frame to advance understanding of environmental governance and as an empirical vehicle to incorporate temporal scales into a literature that has largely been focused on questions of spatial scale and levels of social organization. Through a case study of the United States Department of Agriculture’s recently created Regional Conservation Partnership Program, we critically assess the concept of projectification. Based on interviews with key policy analysts and administrators and a review of policy documents, we critically evaluate prospects for project forms to empower local actors, produce new knowledge, and disrupt the policy field.
Delay efficient cooperation in public safety vehicular networks using LTE and IEEE 802.11p

Cooperative schemes for critical content distribution over vehicular networks are presented and analyzed. The first scheme is based on unicasting from the base station, whereas the second is based on threshold based multicasting. Long Term Evolution (LTE) is used for long range communications with the base station (BS) and 802.11p is considered for inter-vehicle collaboration on the short range. A high mobility environment with correlated shadowing is adopted. Both schemes are shown to outperform non-cooperative unicasting and multicasting, respectively, when the appropriate 802.11p power class is used. The first scheme achieves the best performance among the compared methods, and a practical approximation of that scheme is shown to be close to optimal performance.
Neural Network approach for image retrieval based on preference elicitation

: Multimedia technologies have been developing rapidly over the last few years and have yielded a large number of databases containing graphical documents. Tools for content-based search of graphical objects have been the subject of intensive research, but their performance is still unsatisfactory for many applications, opening up afield for further research and technology development. Up till now, all popular Internet search engines have been only text-based, including those that search for images. In this paper We propose an image retrieval system based on neural networks. The advantage of using the neural network is that the amount of semantic gap can be reduced when compared to other techniques which may be based on clustering. The methodology proposed below is designed for a specific class of objects, which can be broken down into sub-objects in such a way that the main object can be classified by shape, color distribution and texture of the sub objects and the spatial spatial relations between the sub-objects in a 2-dimensional image. We also assume that translation, scaling and 2D rotation do not change the class of the object, but we do not consider 3Dtransformation.Therefore, photos of the same 3D object from different positions for example are considered to be objects belonging to different class
The very flat radio-millimetre spectrum of Cygnus X-1

We present almost-simultaneous detections of Cygnus X-1 in the radio and mm regimes, obtained during the low/hard X-ray state. The source displays a flat spectrum between 2 and 220 GHz, with a spectral index |α|0.15 (3σ). There is no evidence for either a low- or high-frequency cut-off, but in the mid-infrared (∼30 μm) thermal emission from the OB-type companion star becomes dominant. The integrated luminosity of this flat-spectrum emission in quiescence is 2×1031 erg s−1 (2×1024 W). Assuming the emission originates in a jet for which non-radiative (e.g. adiabatic expansion) losses dominate, this is a very conservative lower limit on the power required to maintain the jet. A comparison with Cyg X-3 and GRS 1915+105, the other X-ray binaries for which a flat spectrum at shorter than cm wavelengths has been observed, shows that the jet in Cyg X-1 is significantly less luminous and less variable, and is probably our best example to date of a continuous, steady, outflow from an X-ray binary. The emissive mechanism responsible for such a flat spectral component remains uncertain. Specifically, we note that the radio–mm spectra observed from these X-ray binaries are much flatter than those of the ‘flat-spectrum’ AGN, and that existing models of synchrotron emission from partially self-absorbed radio cores, which predict a high-frequency cut-off in the mm regime, are not directly applicable.
Evidence of Specialized Tissue in Human Interatrial Septum: Histological, Immunohistochemical and Ultrastructural Findings

Background There is a paucity of information on structural organization of muscular bundles in the interatrial septum (IAS). The aim was to investigate histologic and ultrastructural organization of muscular bundles in human IAS, including fossa ovalis (FO) and flap valve. Methods Macroscopic and light microscopy evaluations of IAS were performed from postmortem studies of 40 patients. Twenty three IAS specimens underwent serial transverse sectioning, and 17 - longitudinal sectioning. The transverse sections from 10 patients were immunolabeled for HCN4, Caveolin3 and Connexin43. IAS specimens from 6 other patients underwent electron microscopy. Results In all IAS specimens sections the FO, its rims and the flap valve had muscle fibers consisting of working cardiac myocytes. Besides the typical cardiomyocytes there were unusual cells: tortuous and horseshoe-shaped intertangled myocytes, small and large rounded myocytes with pale cytoplasm. The cells were aggregated in a definite structure in 38 (95%) cases, which was surrounded by fibro-fatty tissue. The height of the structure on transverse sections positively correlated with age (P = 0.03) and AF history (P = 0.045). Immunohistochemistry showed positive staining of the cells for HCN4 and Caveolin3. Electron microscopy identified cells with characteristics similar to electrical conduction cells. Conclusions Specialized conduction cells in human IAS have been identified, specifically in the FO and its flap valve. The cells are aggregated in a structure, which is surrounded by fibrous and fatty tissue. Further investigations are warranted to explore electrophysiological characteristics of this structure.
Frontiers of music information research based on signal processing

How will music information research open up the future to new ways of enjoying music? Today it is the fate of all music in the world to be digitized, and the era in which we can listen to a large selection of our favorite music anytime and anywhere has arrived. In the past we generally listened to music passively, but in the future we will be able to enjoy music in a more active manner. In this keynote paper, I will discuss how music technologies will open up new possibilities in both music appreciation and music creation.
Prokaryotic respiration and production in the meso‐ and bathypelagic realm of the eastern and western North Atlantic basin

We measured prokaryotic production and respiration in the major water masses of the North Atlantic down to a depth of ~4,000 m by following the progression of the two branches of North Atlantic Deep Water (NADW) in the oceanic conveyor belt. Prokaryotic abundance decreased exponentially with depth from 3 to 0.4 x 105 cells mL‐1 in the eastern basin and from 3.6 to 0.3 x 105 cells mL‐1 in the western basin. Prokaryotic production measured via 3H‐leucine incorporation showed a similar pattern to that of prokaryotic abundance and decreased with depth from 9.2 to 1.1 µmol C m‐3 d‐1 in the eastern and from 20.6 to 1.2 µmol C m‐3 d‐1 in the western basin. Prokaryotic respiration, measured via oxygen consumption, ranged from about 300 to 60 µmol C m‐3 d‐1 from ~100 m depth to the NADW. Prokaryotic growth efficiencies of ~2% in the deep waters (depth range 1,200‐4,000 m) indicate that the prokaryotic carbon demand exceeds dissolved organic matter input and surface primary production by 2 orders of magnitude. Cell‐specific prokaryotic production was rather constant throughout the water column, ranging from 15 to 32 x 10‐3 fmol C cell‐1 d‐1 in the eastern and from 35 to 58 x 10‐3 fmol C cell‐1 d‐1 in the western basin. Along with increasing cell‐specific respiration towards the deep water masses and the relatively short turnover time of the prokaryotic community in the dark ocean (34‐54 d), prokaryotic activity in the meso‐ and bathypelagic North Atlantic might be higher than previously assumed.
AND/OR Tree Search for Constraint Optimization

The paper presents and evaluates the power of a new framework for constraint optimization, based on the concept of AND/OR search trees. The virtue of the AND/OR search tree representation is that its size may be smaller than that of a traditional OR search tree. We introduce a new generation of depth first Branch-and-Bound algorithms that traverse an AND/OR search space and use the Mini-Bucket approximation scheme to generate heuristics to guide the search. Our preliminary experimental work shows that the new approach is competitive and in many cases superior to state of the art systematic search algorithms that explore the regular OR space.
Attention acts to suppress goal-based conflict under high competition

It is known that when multiple stimuli are present, top-down attention selectively enhances the neural signal in the visual cortex for task-relevant stimuli, but this has been tested only under conditions of minimal competition of visual attention. Here we show during high competition, that is, two stimuli in a shared receptive field possessing opposing modulatory goals, top-down attention suppresses both task-relevant and irrelevant neural signals within 100 ms of stimuli onset. This non-selective engagement of top-down attentional resources serves to reduce the feedforward signal representing irrelevant stimuli.
Arching Over the Mobile Chasm : Platforms and Runtimes

Mobile computing is one of the breakthrough technologies of today with over three billion mobile phones in use. As the computing power and capabilities of the devices are rapidly improving, software has become a crucial issue in the mobile marketplace. Indeed, the current trend is towards converged communication where Web resources integrate seamlessly with mobile systems. Platforms, runtimes, and middleware play a vital role in current distributed systems. This wide technology domain consists of infrastructure and support services for applications and application developers. Middleware typically provides support for various interoperable service deployment and execution related functions. In this article, we give an update on the stateof-the-art in the area and discuss current trends and research challenges.
A novel approach to orientation estimation using inertial cues and visual feature locality constraint

This paper presents an orientation estimation methods using inertial cues (IMU) and visual feature constraint. Our proposed approach combines both of these two modalities in an original way. Two feature-point correspondences between consecutive frames are firstly selected that not merely meet the requirement of descriptor similarity constraint but the locality constraint. Secondly, these two selected correspondences together with inertial quaternions are jointly employed to derive the initial body pose. Thirdly, a coarse-to-fine procedure proceeds in removing visual false matches and in estimating body poses iteratively using the Posteriori Bayes Rule and Expectation Maximization. Eventually, the optimal orientation is estimated via the iteratively selected visual inliers. Experimental results validate that our proposed strategy is effective and accurate in orientation estimate.