Miracle-dz/UniFAR-FTUs huggingface dataset:
Person-independent facial expression recognition based on compound local binary pattern (CLBP)

Automatic recognition of facial expression is an active research topic in computer vision due to its importance in both human-computer and social interaction. One of the critical issues for a successful facial expression recognition system is to design a robust facial feature descriptor. Among the different existing methods, the Local Binary Pattern (LBP) has been proved to be a simple and effective one for facial expression representation. However, the LBP method thresholds P neighbors exactly at the value of the center pixel in a local neighborhood and encodes only the signs of the differences between the gray values. Thus, it loses some important texture information. In this paper, we present a robust facial feature descriptor constructed with the Compound Local Binary Pattern (CLBP) for person-independent facial expression recognition, which overcomes the limitations of LBP. The proposed CLBP operator combines extra P bits with the original LBP code in order to construct a robust feature descriptor that exploits both the sign and the magnitude information of the differences between the center and the neighbor gray values. The recognition performance of the proposed method is evaluated using the CohnKanade (CK) and the Japanese Female Facial Expression (JAFFE) database with a Support Vector Machine (SVM) classifier. Experimental results with prototypic expressions show the superiority of the CLBP feature descriptor against some wellknown appearance-based feature representation methods.
Encoding World Knowledge in the Evaluation of Local Coherence

Previous work on text coherence was primarily based on matching multiple mentions of the same entity in di erent parts of the text; therefore, it misses the contribution from semantically related but not necessarily coreferential entities (e.g., Gates and Microsoft). In this paper, we capture such semantic relatedness by leveraging world knowledge (e.g., Gates is the person who created Microsoft), and use two existing evaluation frameworks. First, in the unsupervised framework, we introduce semantic relatedness as an enrichment to the original graph-based model of Guinaudeau and Strube (2013). In addition, we incorporate semantic relatedness as additional features into the popular entity-based model of Barzilay and Lapata (2008). Across both frameworks, our enriched model with semantic relatedness outperforms the original methods, especially on short documents.
Exact routing for digital microfluidic biochips with temporary blockages

Digital microfluidic biochips enable a higher degree of automation in laboratory procedures in biochemistry and molecular biology and have received significant attention in the recent past. Their design is usually conducted in several stages with routing being a particularly critical challenge. Previously proposed solutions for this design step suffer from two issues: They are mainly of heuristic nature and usually assume that the blockages to be bypassed are present the entire time. In contrast, we present a methodology which exploits the fact that blockages are often only present at certain intervals. At the same time, our approach guarantees exact solutions, i.e. always determines a routing with a minimal number of time steps. Experimental results show that, despite the huge complexity, optimal results can be achieved in reasonable run-time and that the consideration of temporary blockages indeed significantly improves the routing results.
Oxidation of Carbon Monoxide in a Monolithic Reactor

Solution for the complete removal of carbon monoxide from the exhaust gases still poses a challenge to the researchers and this problem is still under development. Modeling for reduction of carbon monoxide is carried out using heterogeneous reaction using low cost non-noble metal based catalysts for the purpose of controlling emissions released to the atmosphere. A simple one-dimensional model was developed for the monolith using hopcalite catalyst. The converter is assumed to be an adiabatic monolith operating under warm-up conditions. The effect of inlet gas temperatures and catalyst loading on carbon monoxide reduction during cold start period in the converter is analysed. Keywords— carbon monoxide, catalytic, modeling, monolith
Computational RAM: Implementing Processors in Memory

Computational RAM is a processor-in-memory architecture that makes highly effective use of internal memory bandwidth by pitch-matching simple processing elements to memory columns. Computational RAM can function either as a conventional memory chip or as a SIMD (single-instruction stream, multiple-data stream) computer. When used as a memory, computational RAM is competitive with conventional DRAM in terms of access time, packaging and cost. Adding logic to memory is not a simple question of bolting together two existing designs. The paper considers how computational RAM integrates processing power with memory by using an architecture that preserves and exploits the features of memory.
RECONSTRUCTION OF 30 M DEM FROM 90 M SRTM DEM WITH BICUBIC POLYNOMIAL INTERPOLATION METHOD

This work presents a methodology for the refinement of 90m digital elevation model (DEM) of Shuttle Radar Topographic Mission (SRTM) data. The SRTM DEM is publicly available through the Seamless Data Distribution System (SDDS) at two resolutions: 30m for the United States, and 90m for the areas between 60N and 56S of latitude including the US. The selected test areas stretch eastwest from gently-slope flat land in western part of Kansas (N40W102.5) to rugged mountainous region in eastern Colorado (N40W107). All 90m DEM files, however, are generated from the 30m DEMs by subsampling only the center value of each 3x3 cells of the 30m SRTM DEM. Thus, the 90m DEM can be regarded as, in interpolation sense, regular sampling points that can be used to generate height values over a finer grid, which is set to be 30m in this study. Each height value is computed by choosing 16 nearest sampling points to determine 10 parameters of bivariate cubic polynomial equations in a least-square fashion. The reconstructed 30m DEMs are compared with the 30m SRTM DEMs. The interpolation errors, defined as the height differences at the same location between the 30m SRTM DEMs and the corresponding interpolating DEMs, are computed. Statistics show that the interpolation results are extremely good. For all reconstructed 30m DEM files, the RMS errors of the DEM files N40W102.5 N40W107 are 0.936, 1.024, 1.081, 0.908, 0.817, 1.207, 1.923, 2.307, 2.330 and 1.865m respectively. The files having larger RMS error are those covering mountainous areas in the west (N40W105.5 N40W107) while those in the flat and smooth terrains have RMS error better than 1.2 m. The result clearly demonstrates that for most cases using 90m SRTM DEMs as a source, the generation of 30m resolution DEMs of the same quality as the original 30m SRTM DEMs can be achieved by using the bicubic polynomial interpolation technique. This would be beneficial for a great number of users whose working areas are outside the US and requires higher resolution DEMs than the 90m SRTM DEM currently available.
Assessment of Power Market for Carbon Trading by Modified Particle Swarm Optimization

In recent year, the awareness of environmental protection has made the power dispatch model on longer purely economical-oriented. This paper presents a modified particle swarm optimization (MPSO) approach to solve the Unit Commitment (UC) problem for 24 hours at the maximum profit in the power and carbon market. Solving the UC problem for 24 hours in the interconnected power network that is comprised of three independent areas to approach the best dispatching strategy. The UC problem must satisfy the constraints such as the load demand, generating limits, minimum up/down time and ramp rate limits, and also involves determining the limits of power flow, buses voltage and transmission line capacity. The Independent Power Producer (IPP) can obtain the maximum profit by the power and carbon trading amount and calculating power wheeling expense based on the 24-hour power and carbon forecasting trading data. Furthermore, it can also be assessed the basis of participating in the trading market or not. keywords—Unit Commitment, Multiple Particle Swarm Optimization, Emission, Carbon Trading, Carbon market
A Model for Information Quality in the Banking Industry - The Case of the Public Banks In Brazil

Poor Information Quality (IQ) has a significant impact on the general performance of an organization. Investments in IT have had a great impact on the banking industry in recent years, producing a large increase in the volume of information; these investments, however, have not ensured a corresponding return in terms of the quality of information produced. The aim of the present study is to validate a specific IQ model for the Banking Industry, and specifically for the public banks at a federal and state level in Brazil. A previously proposed IQ measurement instrument consisting of 15 IQ dimensions and 65 items was employed. Statistical techniques such as Exploratory Factor Analysis and Reliability Analysis were first applied in order to get a preliminary assessment of the psychometric properties of the constructs. Subsequently, Confirmatory Factorial Analysis using Structural Equations Modeling (first and second order Measurement Models) was used to refine the instrument and examine validity and reliability in a more stringent sense. The final model has four quality factors: Accessibility, Contextuality, Believability and Comprehensiveness, arranged in 12 items.
Improving the Performance of Collaborative Filtering Recommender Systems through User Profile Clustering

Recommender systems offer personalization to online activities due to their ability to recommend products that are unknown to the user. The most common form of these systems employs collaborative filtering to make recommendations and operate by estimating a preference for an item based on how like minded users have previously rated items. Such methods require large amounts of training data which highlights a scalability problem of collaborative filtering, namely, the trade-off between accurate estimation prediction and the time required to calculate them. This paper demonstrates a novel approach to determine interest thus improving scalability by partitioning training data into user based profile clusters. The partitioned data represents user segments (or profile types) which is used to as a more concise representation of similar users for the target. Experimental results have shown a dramatic increase in prediction speed without a loss in accuracy.
Fast open-loop synchronization for cooperative distributed beamforming

This paper considers the problem of multiple distributed nodes in a wireless network wishing to join forces and cooperatively beamform data to a destination. Since the nodes are not physically connected and have independent oscillators, there are carrier frequency and phase errors that degrade the beamforming performance. This paper proposes a novel open-loop synchronization protocol, which exploits the broadcast nature of the wireless channel to achieve synchronization much faster than prior methods, with the difference increasing as the number of nodes increases.
Usefulness of brain SPECT to evaluate brain tolerance and hemodynamic changes during temporary balloon occlusion test and after permanent carotid occlusion.

UNLABELLED The purpose of this study was to evaluate the usefulness of SPECT during temporary carotid balloon occlusion testing and to evaluate the changes in regional cerebral blood flow (CBF) and regional cerebral perfusion reserve (CPR) after permanent carotid occlusion. METHODS Temporary balloon occlusion testing was performed on 40 patients (24 head and neck tumors, 16 aneurysms). During the balloon occlusion (total time, 30 min), (99m)Tc-ethylcysteinate dimer (ECD) was injected intravenously about 5 min before balloon deflation, followed by SPECT data acquisition. SPECT images were visually evaluated, and the severity of hypoperfusion on the occluded side was classified using 4 grades (normal, mild, moderate, and severe). The count ratio of the occluded side to the contralateral side (L/N ratio) was also analyzed. In 7 patients who subsequently underwent permanent carotid occlusion, CBF and CPR were quantitatively assessed using (133)Xe inhalation dynamic SPECT at rest and after acetazolamide (ACZ) enhancement (CPR was defined as the percentage increase in CBF after ACZ), and the patients were followed up periodically. RESULTS SPECT after temporary occlusion showed moderate or severe hypoperfusion in 12 patients, whereas neurologic deterioration was observed in only 4 patients. The L/N ratios were 0.96 +/- 0.03 in normal perfusion (13 patients), 0.93 +/- 0.03 in mild hypoperfusion (15 patients), 0.83 +/- 0.03 in moderate hypoperfusion (10 patients) and 0.66 +/- 0.09 in severe hypoperfusion (2 patients) (P < 0.0001). In the 7 patients who underwent permanent carotid occlusion, CPR decreased after surgery (35% +/- 7% vs. 7% +/- 14%, P < 0.05), even though the resting CBF did not change (54 +/- 8 mL/100 g/min vs. 52 +/- 6 mL/100 g/min, not statistically significant). The steal phenomenon (rCPR < 0%) was observed in 3 patients, 1 of whom experienced transient hemiparesis when blood pressure dropped soon after surgery. On follow-up, the decreased CPR gradually improved and no infarction developed. CONCLUSION SPECT is useful to detect cerebral hypoperfusion during carotid occlusion. Assessment of CPR is recommended to predict the potential risk of postsurgical complications and to follow up patients after permanent carotid occlusion.
Outcomes of Collective Decisions With Externalities Predicted

In collective decision making bilateral deals can increase or decrease the likelihood of finding compromises, depending on whether such deals have externalities. Positive externalities mean third actors profit from bilateral deals, whereas negative externalities mean bilateral deals hurt third actors. We develop the first model of collective decision making that takes externalities into account. The model computes the expected outcomes of the issues to be decided and construes four coalitions of actors on each pair of issues. Then it searches for a set of alternative expected outcomes, such that no coalition can further increase the payoffs of one of its members, either (i) without decreasing the payoffs of one of its members, or (ii) without decreasing the payoffs of any actor. The Generalized Nash Bargaining Solution is used to pick a single outcome. The model is tested on data from decisions in the European Union.
Probabilistic foreground detector for sterile zone monitoring at night time

This paper proposes probabilistic foreground detector based on Gaussian Mixture Models (GMM) for sterile zone monitoring at night time. The foreground object may exhibit camouflage effect due to the thermal camera. GMM is prone to camouflage effect. The proposed system consists of three modules namely pre-processing, foreground detection, and post processing. Firstly, every frame is enhanced using histogram equalization in pre-processing before feeding into foreground detection. After foreground mask is obtained from GMM, it is fed into post processing which uses morphological operations to improve output. The proposed method detected camouflage object in all sequences of i-LIDs dataset for sterile zone monitoring and outperforms conventional GMM.
Bionic eyeglass: An audio guide for visually impaired

A bionic eyeglass is proposed in this paper, a device that helps blind and visually impaired people in basic tasks of their everyday life by converting visual information into speech. The indoor and outdoor situations and tasks have been selected by a technical committee consisting of blind and visually impaired persons, considering their most important needs and potential practical benefits that an audio guide can provide. Two types of cellular wave computing algorithms are used: general spatial-temporal event detection by analogic subroutines, and recently developed multi-channel mammalian retinal model followed by a classifier. The basic idea is to mimic the way the nervous system discriminates relevant information from the irrelevant - namely realize an attention model. Typical indoor and outdoor event detection processes are considered and explained through examples. We present advances in adaptive color processing and number recognition.
Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction

applications require a careful selection of attributes or features from a much larger set of data. This attributes selection problem need to optimized. In order to tackle this problem this paper proposes a binary-real code multi-gen multi-parameter genetic algorithm for attributes selection from large seismic data and prediction of effective porosity. Genetic Algorithm (GA) uses three selection methods for this purpose, mean square error and correlation coefficient are two witness criteria to choose the best subset of attributes that minimize the error and give high prediction of porosity. Keywordsenetic Algorithm, Multi-Gene, Multi-parameter, Attributes selection, Attributes prediction.
Inflation and Globalisation: A Modelling Perspective

This paper examines some standard open-economy New Keynesian models to address the question of how globalisation affects the inflation process. Specifically, it investigates how the Phillips curve for consumer price inflation in a country is affected by openness, and how the optimal choice of monetary policy is influenced by openness. The paper compares models that assume producer currency pricing with ones that assume local currency pricing. It also considers the role of financial market completeness. Full publication: Globalisation and Inflation Dynamics in Asia and the Pacific
High-throughput image rotation using sign-prediction based redundant CORDIC algorithm

Real-time image rotation forms a core operation in many applications such as medical image processing and computer vision. High-throughput computations for image rotation are a common requirement in real-time image processing. A VLSI design for image rotation that employs CORDIC was discussed in Ghosh et al. (1994). In this paper, we propose novel techniques to increase the throughput of the CORDIC computations, thereby notably improving the overall performance of the rotation unit with acceptable increase in VLSI area. An efficient sign-prediction method called BTSP (Binary-Tree based Sign Prediction) is proposed in our redundant CORDIC system to eliminate the sign detection process. Our investigations show that the BTSP based CORDIC algorithm for realtime rotation of a 512/spl times/512-pixel image, has a significant speed-up over existing redundant CORDIC methods with reduced hardware area.
Image-Based Recognition using Learning for Generalizing Parameters

We describe a self-organizing framework for content-based retrieval of images from large image databases at the object recognition level. The system uses the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class. The described system uses learning for generalizing parameters such as object position and size in the image by enhancing the training set of images.
Controlling impact while aggressively scavenging idle resources

As personal computers become ubiquitous, more and more idle resources are available at workplaces. Scavenging (or resource borrowing) is a common approach to harness unused resources to perform useful computation. Because resource owners donate these contributions, it is vital to minimize the impact of scavenging activities on these owners’ native workloads. To this end, existing impact control methods are either overly conservative in stopping scavenging altogether (such as executing scavenging applications only in the screen-saver mode), or lack the (cid:13)exibility and user autonomy in regulating resource usage (such as priority-based scheduling). In this paper, we propose a systematic impact control framework for resource scavenging, by quantifying the performance impact a scavenging application imposes on a set of tasks stressing di(cid:11)erent system resources. The framework monitors the native workload and adaptively throttles the scavenging application to keep the impact below a user-de(cid:12)ned impact level. This novel approach has unique bene(cid:12)ts of (1) making impact control explicit to resource owners with an intuitive tuning \knob" and (2) automatically adapting to di(cid:11)erent scavenging applications and native workloads. Our experiments with multiple scavenging applications, which use resources in very di(cid:11)erent ways, demonstrate that this framework allows both more aggressive resource scavenging and less impact on native workloads at the same time, compared to a priority-based method. Finally, the framework itself is a lightweight user-level process whose monitoring induces less than 1% overhead on native workloads.
Generating Test Inputs for Fault-Tree Analyzers using Imperative Predicates

This paper presents a case study on how Korat can be used in system testing, specifically in testing a large fault-tree analyzer developed for NASA. A fault-tree analyzer takes as input a fault tree that models how combinations of failures in the components of a system produce overall failures of the system. Testing a fault-tree analyzer requires generating fault trees. Korat is a previously developed testing tool that automates generation of structurally complex test inputs. Fault trees are structural in that they can be represented as graphs, and the nodes in the graphs need to satisfy certain complex constraints. Korat allows the user to express these constraints in widely used imperative programming languages such as Java. Previous research has shown how to test a fault-tree analyzer using another testing tool that requires the user to express constraints in a declarative language. This paper compares these two approaches. The results show that Korat generates a larger number of inputs but does not prune out non-equivalent inputs and thus can generate inputs that reveal errors in the system under test.
Precise Model-Driven Transformations Based on Graphs and Metamodels

Presenting precisely models and supporting automatic manipulation of models are at the heart of model-centric software development. A formal foundation for these tasks is a necessity as well as a challenge. We present a model-driven approach based on the integration of two light-weight formal methods, the Object Constraint Language (OCL) and Triple Graph Grammars (TGGs). OCL together with metamodeling allows us to present precisely models. With TGGs we can carry out model manipulations, especially model transformations. We focus on explaining the tool which realizes our approach. This tool is developed as an extension of the UML-based Specification Environment (USE), which offers full OCL support. A case study showing the transformation between statecharts and extended hierarchical automata explains our approach.