Miracle-dz/UniFAR-FTUs huggingface dataset:
What does borderline mean

The word borderline, unlike other clinical descriptive terms, has no obvious affective or action referent in the patient. This observation may shed light on the kind of misalliance so endemic to the work that occurs with these patients. The possibility that the diagnosing of «borderline» might reflect a misrecognition of who has what problem within an analytic treatment, that it might seriously affect the frame of the treatment, and that it might be an act of projective identification by the analyst are explored. I argue that (a) borderline pathology is not an entity, but rather the vast developmental territory of severe personality disturbance, and (b) Winnicott's theoretical contributions to an understanding of the psychopathology of the dyad, particularly around boundary development, are especially helpful
Wavefront sensorless adaptive optics for optical coherence tomography guided femtosecond laser surgery in the posterior eye

Surgery with fs-laser in the posterior part of the eye could be useful for separation of tractional epiretinal membrane and vitreous floaters treatment. However, focus degradation occurs near the retina due to induced aberrations by cornea and lens. To overcome this issue, adaptive optics with wavefront sensor and wavefront modulator can be utilized. We demonstrate an alternative concept for image guided femto second laser (fs-laser) surgery in the posterior eye with wavefront sensorless adaptive optics (WFSLAO). Our laboratory setup consists of an 800 nm fs-laser and a superluminescent diode (SLD) with 897.2 nm central wavelength. The SLD is used for optical coherence tomography (OCT) whereby the light for the OCT sample arm and the fs-laser share the same optical path which contains a deformable mirror, scanner and focusing optics. Energy calibrated photodiodes are used to measure the threshold energy for a laser induced optical breakdown inside a water filled chamber that acts as simple eye model. OCT image based metrics were used to determine a set of Zernike polynomials that describe a near optimal deformable mirror state. Such a mirror state improved OCT resolution and at the same time lowered the required fs-laser energy for a laser induced optical breakdown inside the eye model substantially.
A multi-axis space coordinate system calibration method for composite line laser measuring systems using non-feature planes and multi-angle spheres

For Line-Laser sensor products that CCD images are unknown, we present a method for the calibration of Line-Laser sensor measurement system using multi-directional and non-featured planes, and a method for system calibration optimization using multi-angle standard spheres. By building a mathematical model, we convert the line laser sensor measurement data into CMM measurement points. According to the constraint relationships of planes or spheres, the point measured by the Line-Laser sensor and the CMM should conform to the same equation, then we can solve the calibration matrix of the line laser sensor and the coordinate measuring machine by nonlinear optimization. Both simulation analyses and real experiments were conducted. A line laser sensor was used to measure a frosted standard ball with a radius of 12.696 mm. The radius deviation measured by the line laser sensor system and the center deviation of the sphere comparing with the CMM were observed. The experimental results show that the radius deviation of the calibration laser sensor measurement system is less than 0.02mm, and the center distance deviation of the sphere is less than 0.02mm. This method utilizing non-featured planes simplifies the calibration equipment and can reduce the fitting error when using standard ball from multiple angles for calibration. This method is different from the method of calibrating the single direction of the laser sensor. It can simultaneously calibrate the rotation matrix and translation matrix of the two-dimensional line laser sensor to the coordinate measuring machine, and optimize the global optimal calibration parameters.
Research and application of grade prediction model based on decision tree algorithm

With the rapid development of online open courses, it has become a hot issue in educational research to use learning analysis technology to explore online learning characteristics and provide support for the improvement of teaching methods and academic warning. A grade prediction model based on decision tree algorithm according to students' previous learning behavior is designed in this paper. Firstly, ID3, C4.5 and CART classification algorithm are used to construct the initial grade prediction model for the pre-processed learning behavior data set. Secondly, in order to prevent over-fitting, pruning set is used to conduct pruning on the initial grade prediction model. Finally, the performance of three grade prediction models is evaluated by test data set. Experimental results indicate that the grade prediction model constructed by ID3 algorithm has better performance than that constructed by other two algorithms.
Belief propagation: accurate marginals or accurate partition function—where is the difference?

We analyze belief propagation on patch potential models—attractive models with varying local potentials—obtain all of the potentially many fixed points, and gather novel insights into belief propagation properties. In particular, we observe and theoretically explain several regions in the parameter space that behave fundamentally differently. We specify and elaborate on one specific region that, despite the existence of multiple fixed points, is relatively well behaved and provides insights into the relationship between the accuracy of the marginals and the partition function. We demonstrate the inexistence of a principal relationship between both quantities and provide sufficient conditions for a fixed point to be optimal with respect to approximating both the marginals and the partition function.
On Densification for Minwise Hashing

One Permutation Hashing (OPH) is a significantly more efﬁcient alternative to the popular minwise hashing. To produce a sketch of size k , OPH requires just one hash function whereas the classical minwise hashing requires k hash functions. However, OPH does not have the desirable locality sensitive hashing (LSH) property that is important for indexing. [Sri-vastava and Li 2014] proposed the novel idea of densiﬁcation to produce LSH sketches from OPH sketches, and gave the ﬁrst densiﬁcation routine. In this paper, we give a necessary and sufﬁcient condition for a densiﬁcation routine to result in LSH sketches when applied to OPH sketches. Furthermore, we give a novel densiﬁcation routine that for every input, takes O ( k log k ) time in expectation and achieves better variance than the previous best bound obtained by [Srivastava 2017]. The running time of the densiﬁcation routine given in [Sri-vastava 2017] for worst case inputs is O ( k 2 ) in expectation.
Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank

Online learning , position bias , and diversiﬁed retrieval are three crucial aspects in designing ranking systems based on user clicks. One simple click model which explains the position bias is the cascade model . Many online learning variants of the cascade model have been proposed, but none so far captures diversity. Similarly, online learning to rank methods which capture diversity do not handle position bias. Motivated by these limitations, we pro-pose a novel click model, and the associated online learning variant to address both position bias and diversiﬁed retrieval in a uniﬁed framework. Despite the objective function not being a standard submodular set function, we prove that a greedy-approach is a reasonable approximation. We then propose, CascadeLSB , an al-gorithm whose goal is to recommend K most attractive items from a large set of L items with the attractiveness of items being modeled as a submodular utility function. We analyze the al-gorithm and prove a gap-free upper bound on its n -step regret. We comprehensively evaluate CascadeLSB on synthetic and real datasets, where it outperforms all the baselines.
Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.
An emotion recognition system based on edge computing and cloud computing applied in the kindergarten

As much attention is paid to the safety and education of children in the kindergarten, the importance of analyzing emotions of children is realized. With the adventure of the Internet of Things, more and more visual sensors are connected to the network, which makes the emotion analysis available through the recognition of images. It is necessary to response quickly when negative emotions of children are recognized, which means the processing of images containing negative emotions is delay-sensitive. The processing of an enormous amount of images brings challenge for emotion recognition based on traditional cloud computing. Cloud computing is of high scalability and sufficient capability, but cannot meet the need of real-time detection due to the time delay of transmission and computing of large amounts of images. Emerging edge computing can help to solve the problem by offloading computing from cloud center to edge nodes. However, because the resource of edge nodes is limited, only part of the computing can be placed at the edge nodes. In our work, an emotion recognition system combined with edge computing and cloud computing is proposed to apply in the kindergarten. Due to delay-sensitive negative emotions in this case, a priority scheduling algorithm is also proposed to improve the computing effectiveness of the edge. In this way, the low latency of the edge and the high scalability of the cloud are utilized to reduce the time delay and improve the capacity of the emotion recognition system. The experiments in the real scene of the kindergarten verify that the system performs well.
Real-Time Robotic Search using Structural Spatial Point Processes

Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial ﬁelds for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e.g. geographic or cell phone trafﬁc data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, ﬁnding many more injured in the crucial ﬁrst hours. ∗ Equal contribution.
The Different Brain Mechanisms of Object and Spatial Working Memory: Voxel-Based Morphometry and Resting-State Functional Connectivity

In working memory (WM), the ability to concurrently integrate different types of information and to maintain or manipulate them promotes the flow of ongoing tasks. WM is a key component of normal human cognition. In this study, we applied a combined voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analysis to investigate the relationship between the ability of object and spatial working memory (WM), and regional gray matter density (GMD), as well as intrinsic functional connectivity. The VBM analysis showed a positive correlation between the individual difference of object WM and GMD in the right middle occipital gyrus (MOG) and the left superior temporal gyrus (STG), which are responsible for coding object information and processing the shape of an object. The individual difference of the spatial WM was positively related to GMD in the right middle frontal gyrus (MFG) located in the dorsolateral prefrontal cortex (dlPFC), which confirmed that it is an important region for memory stores and maintains WM spatial representations. Further functional connectivity analysis revealed that the individual difference of object WM was significantly correlated with the rsFC of right intraparietal sulcus (IPS) – left postcentral gyrus (PostCG)/right precentral gyrus (PreCG)/left Supplementary Motor Area (SMA). While the capacity of spatial WM was significantly associated with the FC strength of the left dlPFC – left precuneus, right dlPFC – right MFG, and the left superior frontal sulcus (SFS) – left SMA/ right inferior parietal lobe (IPL). Our findings suggest that object WM is associated with the structure and functional organization of the brain regions involved in the ventral pathway (occipital – temporal regions) and the capacity of spatial WM is related to the dorsal pathway (frontal – parietal regions).
Altered Prefrontal Theta and Gamma Activity during an Emotional Face Processing Task in Parkinson Disease

Patients with Parkinson disease (PD) often experience nonmotor symptoms including cognitive deficits, depression, and anxiety. Cognitive and affective processes are thought to be mediated by prefrontal cortico-basal ganglia circuitry. However, the topography and neurophysiology of prefrontal cortical activity during complex tasks are not well characterized. We used high-resolution electrocorticography in pFC of patients with PD and essential tremor, during implantation of deep brain stimulator leads in the awake state, to understand disease-specific changes in prefrontal activity during an emotional face processing task. We found that patients with PD had less task-related theta–alpha power and greater task-related gamma power in the dorsolateral pFC, inferior frontal cortex, and lateral OFC. These findings support a model of prefrontal neurophysiological changes in the dopamine-depleted state, in which focal areas of hyperactivity in prefrontal cortical regions may compensate for impaired long-range interactions mediated by low-frequency rhythms. These distinct neurophysiological changes suggest that nonmotor circuits undergo characteristic changes in PD.
Individual Differences in the Diurnal Cortisol Response to Stress

The objectives of this study were to explore individual differences associated with diverse reactions in cortisol secretion under different stress levels. This study was part of a larger project concerning working hours and health. Thirty-four white-collar workers participated under two different conditions; one work week with a high stress level (H) and one with a lower stress level (L) as measured through self-rated stress during workdays. Based on the morning cortisol concentration during a workday subjects were divided into two groups. One group consisted of subjects whose morning level of cortisol increased in response to the high-stress week, compared to their morning levels in the low-stress condition (Group 1). The other group consisted of subjects whose morning cortisol response was the opposite, with a lower level under the high stress condition (Group 2). Subjects wore actiwatches, completed a sleep diary, and rated their sleepiness and stress for one work week in each condition, i.e., high and low stress. Saliva samples for measures of cortisol were collected on a Wednesday. Group 2 reported higher workload, fatigue, and exhaustion during both weeks. Since there were no differences in perceived stress, neither within nor between groups, the data indicate that there are other factors influencing morning cortisol. The results suggest that one component modulating the cortisol response might be the level of exhaustion, probably related to work overload. Higher levels of stress in exhausted individuals might suppress morning cortisol levels.
MSNdroid: the Android malware detector based on multi-class features and deep belief network

Android operating system has become a very popular mobile operating platform. However, the popularity and openness of the Android has also made it a major target for malicious application developers. In recent years, many researchers have conducted research on Android malware detection, but almost all static analysis techniques focus on the analysis of Manifest.xml and java-layer code. This results many malware producers hide malicious code in the native-layer to evade existing detection techniques. Therefore, in this paper we innovatively add the feature of native-layer to the features data. We extract the corresponding native_apis feature, combined with permissions feature and system_apis feature to form complete features. Using the Deep Belief Networks (DBN) algorithm, we achieve classification accuracy of 98.71% and false negative rate of 0.7%. To the best of our knowledge, this is the first study and MSNdroid is the first tool to apply deep learning to native code features for Android malware detection.
Domain adaptation for retinal vessel segmentation using asymmetrical maximum classifier discrepancy

Retinal vessel segmentation is indispensable for ophthalmic computer-aided diagnosis (CAD) and large-scale automatic detection system of retinal diseases. Recently, various convolutional neural networks (CNN) have been applied in retinal vessel segmentation and obtain impressive performance. However, due to the domain shift caused by the variation between source and target datasets, these methods poorly generalize across different datasets. In this paper, we develop asymmetrical maximum classifier discrepancy (AMCD) approach from maximum classifier discrepancy for unsupervised domain adaptation. We utilize labeled source data and unlabeled target data to train a model and test it on the target domain. In our work, we use three classifiers asymmetrically which means that two assist classifiers are used to maximize the discrepancy on target samples and one main classifier is trained only by the source samples. We have validated our approach on DRIVE, STARE, CHASE-DB1 and IOSTAR eye vessel segmentation datasets and the experimental results showed that our method can achieve significantly accuracy in the setting of unsupervised domain adaptation.
Stochastic functional central limit theorems with applications to control of uncertain systems

A persistent concern of control engineering is the performance of systems in the presence of uncertainty. In this paper, we consider uncertainties affecting systems as stochastic processes of independent stationary increments. We show that in many situations the performance of an uncertain system can be measured by a function of a parametric stopping time and associated values of stochastic processes. Under some mild regularity conditions, we demonstrate that the performance measure is governed by stochastic functional central limit theorems as the parameters of the stopping time tend to certain values. Such results can be applied to the analysis and design of control systems affected by uncertainties.
CORDIC Based SSIM Computation in FPGA

Images may be distorted by imaging system or by transfer it from one system to another or to process for different application. So, it is important to calculate the quality of the image before presenting to the viewer. This distortion measured by some traditional parameters like PSNR, SSIM and RMSE. But, SSIM is well established method, provides nearly same result as human visual system. The distortion in the image has been calculated using SSIM in this work. The compressed SSIM has been measured in this work in frequency domain in Discrete Cosine Transform (DCT) dictionary. Images are 2D data matrix, so this work aims to calculate 2D DCT using 1D DCT. 1D DCT is calculated by well-established CORDIC algorithm. The CORDIC algorithm is sequential in nature. But can also be implemented parallel using FPGA, as different processes in FPGA produces output simultaneously. The output of VHDL core is compared with MATLAB inbuilt SSIM function’s output.
A User-Centric Data Secure Creation Scheme in Cloud Computing

Due to the use of the cloud computing technology, the ownership is separated from the administration of the data in cloud and the shared data might be migrated between different clouds, which would bring new challenges to data secure creation, especially for the data privacy protection. We propose a User-centric data secure creation scheme (UCDSC) for the security requirements of resource owners in cloud. In this scheme, a data owner first divides the users into different domains. The data owner encrypts data and defines different secure managing policies for the data according to domains. To encrypt the data in UCDSC, we present an algorithm based on Access control conditions proxy re-encryption (ACC-PRE), which is proved to be master secret secure and Chosen-ciphertext attack (CCA) secure in random oracle model. We give the application protocols and make the comparisons between some existing approaches and UCDSC.
STARLIT: 3D indoor positioning with smartphones and reflection light

This paper presents STARLIT, a system that enables a single LED to localize smartphone to within sub-meter. The LED transmits its coordinates encoded in human-imperceptible optical pulses. Different from the existing camera-based approaches, which need to capture images of the luminaires within a short light-to-camera distance, we utilize the reflection light from the floor for positioning. As the camera sensor contains millions of pixels, it can be considered as a sensor array. By exploiting the rolling shutter mechanism in the smartphone cameras, we propose a solution to extract the received signal strengths (RSSs) of sampled pixel rows from the captured images. Given the measured RSSs, we establish an equation set with Lambertian model and the camera projection model to solve the location of the smartphone. We have implemented STARLIT and evaluated its performance in a lab room. Our experiments demonstrate that STARLIT can achieve average positioning error around 40 cm and 80-percentile error around 55 cm.