Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Extraction and Classification of EEG Signals Using Wavelet Transform, SVM and Artificial Neural Networks for Brain Computer Interfaces

Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by muscle activity, eye blinks and electrical noise is a common and important problem in electroencephalography research. In this research, we used five different methods for detecting trials containing artifacts. Finally we used two different neural networks, and support vector machine to classify features that are extracted by wavelet transform.
Improved Threshold Voltage Assignment via Combinatorial Implementation Selection

We study the problem of sub-threshold leakage current optimization using dual threshold voltages under timing constraints. We develop an analytical framework that addresses the more general problem of gate implementation selection under timing constraints. While NP-Complete in the general case, we present conditions under which the problem of implementation selection from a library with discrete delay choices can be optimally solved in polynomial time. We also extend our methodology to handle situations that do not satisfy the theoretical conditions. Experimental results show that our algorithm reduces the leakage current by close to an order of magnitude, with no or negligible delay penalty. More importantly, our approach can quickly obtain near-optimal solutions for real life netlists. On average, our results are only 0.2% leakier than the optimal solutions derived using MILP solvers, while our tool runs 73 times faster than GLPK1 solver. Moreover, our algorithm outperforms the result of a recent LP-based competitor by 33%.
A New 3D Segmentation Algorithm Based on 3D PCNN for Lung CT Slices

Three-dimension (3D) based image data analysis has an important role for significantly improving the detection and diagnosis of lung disease with computed tomography (CT). In this paper, we proposed a new volume-based 3D segmentation algorithm based on the extended 3D pulse coupled neural network (PCNN) model. This algorithm was successfully used to segment the lung field in CT slice with the mean distance, root means square distance and Tanimoto coefficient of 0.0029±0.0005, 0.0715±0.0056, 0.9760±0.0093, respectively. Furthermore, the means running time was only 273s, which was much less than those of 2D PCNN segmentation algorithm and Otsu algorithm. The experimental results demonstrated the extended 3D PCNN segmentation algorithm had the advantage of short execution time with good segmentation accuracy. The results suggest that the proposed 3D PCNN algorithm can be potentially used for lung computer-aided diagnosis.
SPREADING RATIONALIZATION: INCREASED SUPPORT FOR LARGE-SCALE AND SMALL-SCALE SOCIAL SYSTEMS FOLLOWING SYSTEM THREAT

System justification theory suggests that individuals defend and rationalize aspects of prevailing social systems, especially in response to system threat. In two experiments we extend this framework by demonstrating that people rationalize small-scale social systems (e.g., local popularity hierarchies and the nuclear family) as well as large-scale social systems (e.g., American society). Furthermore, we find that system threat leads people to bolster not only the legitimacy of the social system that is directly threatened but also the legitimacy of systems at other levels of analysis. These results provide evidence of spreading rationalization, suggesting that people respond defensively to other social systems when one system that they belong to is criticized or attacked.
Visualization of complex data relationships and maps: using the BLOOM platform to provide business insights

Modern information visualization in focussed on new methods for visual mappings and novel strategies for interaction and navigation through various types of view of a visual form. The paper presents the Bloom platform for visualization of complex networks with highly related entities and aggregations, both able to possess complex data attributes. It discusses two case studies assessing the scalability of the tool by creating a visualization of the large, complex networks - one of Ottawa Centre for Research and Innovation, containing data of approximately 1,900 companies aggregated within a cluster hierarchy, and another of Danish companies' cluster. There are presented various visualization layouts of the company network and a representation of some company data properties over Google maps.
Post-Patch Retraining for Host-Based Anomaly Detection

Applying patches, although a disruptive activity, remains a vital part of software maintenance and defense. When host-based anomaly detection (AD) sensors monitor an application, patching the application requires a corresponding update of the sensor’s behavioral model. Otherwise, the sensor may incorrectly classify new behavior as malicious (a false positive) or assert that old, incorrect behavior is normal (a false negative). Although the problem of “model drift” is an almost universally acknowledged hazard for AD sensors, relatively little work has been done to understand the process of re-training a “live” AD model — especially in response to legal behavioral updates like vendor patches or repairs produced by a self-healing system. We investigate the feasibility of automatically deriving and applying a “model patch” that describes the changes necessary to update a “reasonable” host-based AD behavioral model (i.e., a model whose structure follows the core design principles of existing host–based anomaly models). We aim to avoid extensive retraining and regeneration of the entire AD model when only parts may have changed — a task that seems especially undesirable after the exhaustive testing necessary to deploy a patch.
Teaching Music: The First Year

The purpose of this study was to contribute to the novice music teacher case literature by studying the prevalent themes in the experiences of five first-year teachers. The teachers submitted periodic journal entries and were also interviewed at the beginning of their second semester of teaching. I analyzed transcripts using HyperResearch and submitted them to the participants for both descriptive and interpretive validity checks. Many themes emerged, but those with the highest frequency counts were: Students (behavior), Students (musical). Administrative, Students (personal), Self-evaluation (discipline), and Self evaluation (personal). The five teachers had varying experiences, and all continued in those situations for the following year. Teacher educators must prepare novice teachers for the varying realities of their early experiences, and school administrators must offer effective support during this critical time.
Frequency analysis of fall detection based on Android system

In order to solve a hot issue about the elderly fall, this paper designs a software algorithm system based on the frequency analysis. This software can judge whether the elderly fall down, so as to take subsequent rescue measures. The paper carries on the analysis from the frequency domain. By using the acceleration sensor to get the information of human body state continuously and with the aid of subsection processing, the software can determine whether the body is in a state of fall. The core of the frequency domain analysis method is to add window to data and use STFT. Because of the particularity of acceleration frequency in the process of the fall, the algorithm can be more accurate to judge whether the elderly fall down.
Characterization of remnant-like particles isolated by immunoaffinity gel from the plasma of type III and type IV hyperlipoproteinemic patients.

Previous studies have investigated the potential atherogenicity and thrombogenicity of triglyceride-rich lipoprotein (TRL) remnants by isolating them from plasma within a remnant-like particle (RLP) fraction, using an immunoaffinity gel containing specific anti-apoB-100 and anti-apoA-I antibodies. In order to characterize lipoproteins in this RLP fraction and to determine to what extent their composition varies from one individual to another, we have used automated gel filtration chromatography to determine the size heterogeneity of RLP isolated from normolipidemic control subjects (n = 8), and from type III (n = 6) and type IV (n = 9) hyperlipoproteinemic patients, who by selection had similarly elevated levels of plasma triglyceride (406 +/- 43 and 397 +/- 35 mg/dl, respectively). Plasma RLP triglyceride, cholesterol, apoB, apoC-III, and apoE concentrations were elevated 2- to 6-fold (P < 0. 05) in hyperlipoproteinemic patients compared to controls. RLP fractions of type III patients were enriched in cholesterol and apoE compared to those of type IV patients, and RLP of type IV patients were enriched in triglyceride and apoC-III relative to those of normolipidemic subjects. In normolipidemic subjects, the majority of RLP had a size similar to LDL or HDL. The RLP of hyperlipoproteinemic patients were, however, larger and were similar in size to TRL, or were intermediate in size (i.e., ISL) between that of TRL and LDL. Compared to controls, ISL in the RLP fraction of type III patients were enriched in apoE relative to apoC-III, whereas in type IV patients they were enriched in apoC-III relative to apoE. These results demonstrate that: 1) RLP are heterogeneous in size and composition in both normolipidemic and hypertriglyceridemic subjects, and 2) the apoE and apoC-III composition of RLP is different in type III compared to type IV hyperlipoproteinemic patients.
PHOTOREALISTIC 3 D VOLUMETRIC MODEL RECONSTRUCTION BY VOXEL COLORING

Building photorealistic 3D models of object or scene from images is still a very difficult task. The built 3D models can have different application in many areas, such as industrial, virtual reality, and movies, among others. This paper shows end to end reconstruction 3d voxels model from images using voxel coloring algorithm. We define a reconstruction area by computing the minimum 3D bounding box of the object, because the size of the bounding box will affects the resolution of the reconstructed model. And background removal on the input images for voxel coloring is a necessary step, we implement the image cutout by graph cut algorithm, and have a good result. We test our method using two group of images, although we just use eight images, our reconstruction results are very good and it can match with the real object and scene in some extent. * Corresponding author.
A Study of Query-Based Dimensionality Reduction

This paper considers two approaches to query-based dimensionality reduction. Given a data set, D, and a query, Q, the first approach performs a random projection on the dimensions of D that are not in Q to obtain the data set DR. A new data set (DRQ) is then formed comprising all the dimensions ofD that are in the query Q together with the dimensions of DR. The resulting data set (Q(DRQ) is obtained by applying the query Q to DRQ. A similar approach is taken in the second approach with the difference that the random projection method is replaced by Principal Component Analysis. Comparisons are made between these two approaches with respect to the inter-point distance preservation and computational complexity.
Information and the Divergence between Willingness to Accept and Willingness to Pay

Abstract There is considerable empirical and experimental evidence that there is a divergence between willingness to accept compensation to give up a good and willingness to pay to obtain a good. This divergence persists even when the good in question is small relative to income, a result in apparent conflict with standard economic theory. This paper develops a theoretical bidding model with costly information acquisition to explain this divergence. The model generates a gap between offers to sell and bids to buy consistent with the experimental results. We argue that the model does a better job of explaining empirical and experimental data than either of the two commonly invoked theoretical explanations: the endowment effect and the substitution effect.
Liquidity Risk and Stock Returns Around the World

The recent global financial crisis demonstrates that market liquidity is a prominent systematic risk globally. We find that local liquidity risk, in addition to the local market, value and size factors, demands a systematic premium across stocks in 11 developed markets. This local pricing premium is smaller in countries where the country-level corporate boards are more effective and where there are less insider trading activities. We also discover that global liquidity risk is a significant pricing factor across all developed country market portfolios after controlling for global market, value, and size factors. The contribution of this risk to the return on a country market portfolio is economically and statistically significant within and across regions.
Acute ENaC Stimulation by cAMP in a Kidney Cell Line is Mediated by Exocytic Insertion from a Recycling Channel Pool

Acute hormonal regulation of the epithelial sodium channel (ENaC) in tight epithelia increases transcellular Na+ transport via trafficking of intracellular channels to the apical surface. The fate of the channels removed from the apical surface following agonist washout is less clear. By repetitively stimulating polarized mouse cortical collecting duct (mCCD, MPKCCD14) epithelia, we evaluated the hypothesis that ENaC recycles through an intracellular pool to be available for reinsertion into the apical membrane. Short circuit current (ISC), membrane capacitance (CT), and conductance (GT) were recorded from mCCD epithelia mounted in modified Ussing chambers. Surface biotinylation of ENaC demonstrated an increase in channel number in the apical membrane following cAMP stimulation. This increase was accompanied by a 83 ± 6% (n = 31) increase in ISC and a 15.3 ± 1.5% (n = 15) increase in CT. Selective membrane permeabilization demonstrated that the CT increase was due to an increase in apical membrane capacitance. ISC and CT declined to basal levels on stimulus washout. Repetitive cAMP stimulation and washout (∼1 h each cycle) resulted in response fatigue; ΔISC decreased ∼10% per stimulation–recovery cycle. When channel production was blocked by cycloheximide, ΔISC decreased ∼15% per stimulation cycle, indicating that newly synthesized ENaC contributed a relatively small fraction of the channels mobilized to the apical membrane. Selective block of surface ENaC by benzamil demonstrated that channels inserted from a subapical pool made up >90% of the stimulated ISC, and that on restimulation a large proportion of channels retrieved from the apical surface were reinserted into the apical membrane. Channel recycling was disrupted by brefeldin A, which inhibited ENaC exocytosis, by chloroquine, which inhibited ENaC endocytosis and recycling, and by latrunculin A, which blocked ENaC exocytosis. A compartment model featuring channel populations in the apical membrane and intracellular recycling pool provided an adequate kinetic description of the ISC responses to repetitive stimulation. The model supports the concept of ENaC recycling in response to repetitive cAMP stimulation.
Solving Multiclass Classification Problems by Genetic Programming

A mirror assembly is provided adapted for use with a vehicle and includes a mirror housing, a mirror, a support bracket for mounting the mirror housing on an associated vehicle and an expandable ball and socket arrangement associated with the mirror housing and support bracket for selectively retaining the mirror in a desired rotational position relative to the vehicle. The ball and socket arrangement comprises a partially spherical socket formed in the mirror housing, a thin-walled socket liner retained therein and an expandable ball securely connected to the mounting bracket. The expandable ball comprises a plurality of partially spherical segments each attached to a shank and resiliently deformable outwardly from an axial bore therethrough. A draw bolt is received in the axial bore and when drawn therethrough, engages the spherical segments biasing same outwardly to resistably engage the socket liner.
Mind Wandering While Driving

Distracted driving has been identified as a major concern in highway safety. Research to-date, however, has largely focused on distraction related to external events or intentional engagement in non-driving activities. Internal distraction (“mind wandering”) is now being recognized as a significant source of driver distraction that requires rigorous study. There are substantial challenges in detecting, measuring, and addressing mind wandering while driving. Panelists with expertise in driver distraction in general, and mind wandering in particular, will discuss both scientific and pragmatic issues in addressing this problem. Each panelist will present a brief perspective on the problem from the point of view of their experience and expertise. This will be followed by an open discussion period.
Intra-Symbol Windowing for Egress Reduction in DMT Transmitters

Discrete multitone (DMT) uses an inverse discrete Fourier transform (IDFT) to modulate data on the carriers. The high sidelobes of the IDFT filter bank used can lead to spurious emissions (egress) in unauthorized frequency bands. Applying a window function within the DMT symbol can alleviate this. However, window functions either require additional redundancy or will introduce distortions that are generally not easy to compensate for. In this paper, a special class of window functions is constructed that corresponds to a precoding at the transmitter. These do not require any additional redundancy and need only a modest amount of additional processing at the receiver. The results can be used to increase the spectral containment of DMT-based wired communications such as ADSL and VDSL (i.e., asymmetric, resp., very-high-bitrate digital subscriber loop).
Almost Sure Stabilizability of Controlled Degenerate Diffusions

We develop a direct Lyapunov method for the almost sure open-loop stabilizability and asymptotic stabilizability of controlled degenerate diffusion processes. The infinitesimal decrease condition for a Lyapunov function is a new form of Hamilton--Jacobi--Bellman partial differential inequality of second order. We give local and global versions of the first and second Lyapunov theorems, assuming the existence of a lower semicontinuous Lyapunov function satisfying such an inequality in the viscosity sense. An explicit formula for a stabilizing feedback is provided for affine systems with smooth Lyapunov function. Several examples illustrate the theory.
Deep Active Learning for Civil Infrastructure Defect Detection and Classification

Automatic detection and classiﬁcation of defects in infrastructure surface images can largely boost its maintenance eﬃciency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i
Unraveling the Mechanisms of Endocrine Resistance in Breast Cancer: New Therapeutic Opportunities

Two thirds of breast cancers express the estrogen receptor (ER), which contributes to tumor development and progression. ER-targeted therapy is therefore widely used in breast cancer to inhibit signaling through ER and disrupt breast cancer growth. This therapeutic strategy, particularly using the antiestrogen tamoxifen, is proven to increase the cure rates in early breast cancer, improve patient outcomes in advanced disease, and reduce breast cancer incidence in the prevention setting. Despite the recent integration of more powerful endocrine agents into breast cancer care, resistance to all forms of endocrine therapy remains a major problem. New insight into ER biology and progress in understanding resistance mechanisms, mediated by molecular crosstalk between ER and various growth factor signaling pathways, are generating tremendous promise for new therapeutic opportunities to target resistance and improve breast cancer disease outcomes.
Who gives? A literature review of predictors of charitable giving. Part two: gender, marital status, income, and wealth

This is the second of two articles in which we present a comprehensive review of the multidisciplinary academic literature on philanthropy. In this article, we identify the following predictors of charitable giving by individuals and/or households: gender, family composition and income. For each predictor, we discuss the evidence for the mechanisms that may explain why the predictor is correlated with giving. We conclude with a brief agenda for future research.
Secure and robust error correction for physical unclonable functions

Physical unclonable functions (PUFs) offer a promising mechanism that can be used in many security, protection, and digital rights management applications. One key issue is the stability of PUF responses that is often addressed by error correction codes. The authors propose a new syndrome coding scheme that limits the amount of leaked information by the PUF error-correcting codes.
Interorganizational Cooperation to Develop Information Systems

Interorganizational cooperative relationships offer the potential to develop high quality, low cost information systems. Yet, such relationships are conspicuous by their absence, and important theoretical issues remain to be examined. In this paper, we analyze three cases of cooperative development of information systems, using the frameworks of cooperation theory and configuration theory. Cooperation theory highlights the role of trust in cooperation whereas configuration theory highlights the role of configurational compatibility. The within-case and cross-case analysis finds the pattern of cooperation to be paradoxical within the framework of cooperation theory, but, consistent with configuration theory, cooperation fell with increasing configurational distance. We find that the organizational changes required to overcome small configurational distances are complex and difficult to implement, raising barriers to cooperation. We examine the implications of configurational distance for the adoption and implementation of information systems innovations and suggest that configurational distance is a useful contingency variable in implementation research.
Music Mood Classification of Television Theme Tunes

This paper introduces methods used for Music Mood Classification to assist in the automated tagging of television programme theme tunes for the first time. The methods employed use a knowledge driven approach with tailored parameters extractable from the Matlab MIR Toolbox [1]. Four new features were developed, three based on tonality and one on tempo, to enable a degree of quantified tagging, using support vector machines, employing various kernels, optimised along six mood axes. Using a “nearest neighbour” method of optimisation, a success rate in the range of 80-94% was achieved in being able to classify musical audio on a five point mood scale.