nils-herrmann/clean_scirepeval huggingface dataset:
Measuring the intensity of spontaneous facial action units with dynamic Bayesian network

Automatic facial expression analysis has received great attention in different applications over the last two decades. Facial Action Coding System (FACS), which describes all possible facial expressions based on a set of facial muscle movements called Action Unit (AU), has been used extensively to model and analyze facial expressions. FACS describes methods for coding the intensity of AUs, and AU intensity measurement is important in some studies in behavioral science and developmental psychology. However, in majority of the existing studies in the area of facial expression recognition, the focus has been on basic expression recognition or facial action unit detection. There are very few investigations on measuring the intensity of spontaneous facial actions. In addition, the few studies on AU intensity recognition usually try to measure the intensity of facial actions statically and individually, ignoring the dependencies among multilevel AU intensities as well as the temporal information. However, these spatiotemporal interactions among facial actions are crucial for understanding and analyzing spontaneous facial expressions, since these coherent, coordinated, and synchronized interactions are that produce a meaningful facial display. In this paper, we propose a framework based on Dynamic Bayesian Network (DBN) to systematically model the dynamic and semantic relationships among multilevel AU intensities. Given the extracted image observations, the AU intensity recognition is accomplished through probabilistic inference by systematically integrating the image observations with the proposed DBN model. Experiments on Denver Intensity of Spontaneous Facial Action (DISFA) database demonstrate the superiority of our method over single image-driven methods in AU intensity measurement. & 2015
A Fast Multi-Dimensional Algorithm for Drawing Large Graphs ∗

We present a novel hierarchical force-directed method for drawing large graphs. The algorithm produces a graph embedding in an Euclidean space E of any dimension. A two or three dimensional drawing of the graph is then obtained by projecting a higher-dimensional embedding into a two or three dimensional subspace of E. Projecting high-dimensional drawings onto two or three dimensions often results in drawings that are “smoother” and more symmetric. Among the other notable features of our approach are the utilization of a maximal independent set filtration of the set of vertices of a graph, a fast energy function minimization strategy, efficient memory management, and an intelligent initial placement of vertices. Our implementation of the algorithm can draw graphs with tens of thousands of vertices using a negligible amount of memory in less than one minute on a mid-range PC.
An enterprise application integration methodology for e-government

Achieving goals of better integrated and responsive government services requires moving away from stand alone applications toward more comprehensive, integrated architectures. As a result there is a mounting pressure to integrate disparate systems to support information exchange and cross-agency business processes. There are substantial barriers that governments must overcome to achieve these goals and to profit from enterprise application integration (EAI).  In the research presented here we develop and test a methodology aimed at overcoming the barriers blocking adoption of EAI. This methodology is based on a discrete-event simulation of public sector structure, business processes and applications in combination with an EAI perspective.  The testing suggests that our methodology helps to provide insight into the myriad of existing applications, and the implications of EAI. Moreover, it helps to identify novel options, gain stakeholder commitment, let them agree on the sharing of EAI costs, and finally it supports collaborative decision-making between public agencies.  The approach is found to be useful for making the business case for EAI projects, and gaining stakeholder commitment prior to implementation.  The joint addressing of the barriers of public sector reform including the transformation of the public sector structure, gaining of stakeholders’ commitment, understanding EAI technology and dependencies between cross-agency business processes, and a fair division of costs and benefits over stakeholders.
The Cruelest Cure? Ethical Issues in the Implementation of Exposure-Based Treatments

; Numerous studies have provided supportive evidence for the efficacy of exposure-based treatments for many psychological disorders. However, surprisingly few therapists use exposure therapy in the clinical setting. Although the limited use of exposure-based treatments may be partially attributable to a shortage of suitably trained therapists, exposure therapy also suffers from a “public relations problem” predicated upon concerns that it is cruel and at odds with some ethical considerations (e.g., first do no harm). This article provides an overview of ethical issues and considerations relevant to the use of exposure therapy. It is argued that the degree to which ethical issues become problematic in implementing exposure-based treatments is largely dependent upon the therapist's ability to create an adequately safe and professional context. Specific strategies that may be employed for avoiding potential ethical conflicts in the use of exposure-based treatments are discussed.
Body dysmorphic disorder. A survey of fifty cases.

Body dysmorphic disorder (BDD) consists of a preoccupation with an 'imagined' defect in appearance which causes significant distress or impairment in functioning. There has been little previous research into BDD. This study replicates a survey from the USA in a UK population and evaluates specific measures of BDD.



Cross-sectional interview survey of 50 patients who satisfied DSM-IV criteria for BDD as their primary disorder.



The average age at onset was late adolescence and a large proportion of patients were either single or divorced. Three-quarters of the sample were female. There was a high degree of comorbidity with the most common additional Axis l diagnosis being either a mood disorder (26%), social phobia (16%) or obsessive-compulsive disorder (6%). Twenty-four per cent had made a suicide attempt in the past. Personality disorders were present in 72% of patients, the most common being paranoid, avoidant and obsessive-compulsive.



BDD patients had a high associated comorbidity and previous suicide attempts. BDD is a chronic handicapping disorder and patients are not being adequately identified or treated by health professionals.
Accent detection and speech recognition for Shanghai-accented Mandarin

As speech recognition systems are used in ever more applications, it is crucial for the systems to be able to deal with accented speakers. Various techniques, such as acoustic model adaptation and pronunciation adaptation, have been reported to improve the recognition of non-native or accented speech. In this paper, we propose a new approach that combines accent detection, accent discriminative acoustic features, acoustic adaptation and model selection for accented Chinese speech recognition. Experimental results show that this approach can improve the recognition of accented speech.
Logistics supply chain management based on business ecology theory

In the same commercial ecosystem, although the different main bodies of logistics service such as transportation, suppliers and purchasers drive their interests differently, all the different stakeholders in the same business or consumers coexist mutually and share resources with each other. Based on this, this paper constructs a model of bonded logistics supply chain management based on the theory of commercial ecology, focusing on the logistics mode of transportation and multi-attribute behavior decision-making model based on the risk preference of the mode of transport of goods. After the weight is divided, this paper solves the model with ELECTRE-II algorithm and provides a scientific basis for decision-making of bonded logistics supply chain management through the decision model and ELECTRE-II algorithm.
IdeaWall: Improving Creative Collaboration through Combinatorial Visual Stimuli

With the recent advances in computer-supported cooperative work systems and increasing popularization of speech-based interfaces, groupware attempting to emulate a knowledgeable participant in a collaborative environment is bound to become a reality in the near future. In this paper, we present IdeaWall, a real-time system that continuously extracts essential information from a verbal discussion and augments that information with web-search materials. IdeaWall provides combinatorial visual stimuli to the participants to facilitate their creative process. We develop three cognitive strategies, from which a prototype application with three display modes was designed, implemented, and evaluated. The results of the user study with twelve groups show that IdeaWall effectively presents visual cues to facilitate verbal creative collaboration for idea generation and sets the stage for future research on intelligent systems that assist collaborative work.
Forward Thinking: Building and Training Neural Networks One Layer at a Time

We present a general framework for training deep neural networks without backpropagation. This substantially decreases training time and also allows for construction of deep networks with many sorts of learners, including networks whose layers are defined by functions that are not easily differentiated, like decision trees. The main idea is that layers can be trained one at a time, and once they are trained, the input data are mapped forward through the layer to create a new learning problem. The process is repeated, transforming the data through multiple layers, one at a time, rendering a new data set, which is expected to be better behaved, and on which a final output layer can achieve good performance. We call this forward thinking and demonstrate a proof of concept by achieving state-of-the-art accuracy on the MNIST dataset for convolutional neural networks. We also provide a general mathematical formulation of forward thinking that allows for other types of deep learning problems to be considered.
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean

Models that acquire semantic representations from both linguistic and perceptual input are of interest to researchers in NLP because of the obvious parallels with human language learning. Performance advantages of the multi-modal approach over language-only models have been clearly established when models are required to learn concrete noun concepts. However, such concepts are comparatively rare in everyday language. In this work, we present a new means of extending the scope of multi-modal models to more commonly-occurring abstract lexical concepts via an approach that learns multimodal embeddings. Our architecture outperforms previous approaches in combining input from distinct modalities, and propagates perceptual information on concrete concepts to abstract concepts more effectively than alternatives. We discuss the implications of our results both for optimizing the performance of multi-modal models and for theories of abstract conceptual representation.
Sequential Projection Learning for Hashing with Compact Codes

Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing methods either use random projections or extract principal directions from the data to derive hash functions. The resulting embedding suffers from poor discrimination when compact codes are used. In this paper, we propose a novel data-dependent projection learning method such that each hash function is designed to correct the errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-ofthe-art methods on two large datasets containing up to 1 million points.
Analysis of different architectures of counter based Wallace multipliers

Multiplication is one of the most commonly used operations in the signal processing algorithms. Multipliers based on Wallace reduction tree provide an area-efficient strategy for high speed multiplication. A number of modifications are proposed in the literature to optimize the speed and area of the Wallace multiplier. Counter based Wallace multipliers are proved to provide faster operation as compared to the traditional Wallace multipliers. This work proposes a number of architectures for the counter based Wallace multipliers to analyse their performance for various bit lengths. Designs are synthesized using Synopsys Design Compiler in 90 nm process technology and the post synthesis delay and power results are obtained by using Synopsys Prime Time. The proposed counter based Wallace multipliers are also compared with traditional Wallace multiplier to evaluate the energy per operation of both designs. The synthesis results shows that the Power-Delay Product of the counter based Wallace multiplier is up to 17% lower as compared to the traditional Wallace multiplier.
BlueDBM: An appliance for Big Data analytics

Complex data queries, because of their need for random accesses, have proven to be slow unless all the data can be accommodated in DRAM. There are many domains, such as genomics, geological data and daily twitter feeds where the datasets of interest are 5TB to 20 TB. For such a dataset, one would need a cluster with 100 servers, each with 128GB to 256GBs of DRAM, to accommodate all the data in DRAM. On the other hand, such datasets could be stored easily in the flash memory of a rack-sized cluster. Flash storage has much better random access performance than hard disks, which makes it desirable for analytics workloads. In this paper we present BlueDBM, a new system architecture which has flash-based storage with in-store processing capability and a low-latency high-throughput inter-controller network. We show that BlueDBM outperforms a flash-based system without these features by a factor of 10 for some important applications. While the performance of a ram-cloud system falls sharply even if only 5%~10% of the references are to the secondary storage, this sharp performance degradation is not an issue in BlueDBM. BlueDBM presents an attractive point in the cost-performance trade-off for Big Data analytics.
Miniaturized 122 GHz ISM band FMCW radar with micrometer accuracy

In this paper, a miniaturized 122 GHz ISM band FMCW radar is used to achieve micrometer accuracy. The radar consists of a SiGe single chip radar sensor and LCP off-chip antennas. The antennas are integrated in a QFN package. To increase the gain of the radar, an additional lens is used. A combined frequency and phase evaluation algorithm provides micrometer accuracy. The influence of the lens phase center on the beat frequency phase and hence, the overall accuracy is shown. Furthermore, accuracy limitations of the radar system over larger measurement distances are investigated. Accuracies of 200 μm and 2 μm are achieved over a distance of 1.9 m and 5 mm, respectively.
KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts

Many existing knowledge bases (KBs), including Freebase, Yago, and NELL, rely on a fixed ontology, given as an input to the system, which defines the data to be cataloged in the KB, i.e., a hierarchy of categories and relations between them. The system then extracts facts that match the predefined ontology. We propose an unsupervised model that jointly learns a latent ontological structure of an input corpus, and identifies facts from the corpus that match the learned structure. Our approach combines mixed membership stochastic block models and topic models to infer a structure by jointly modeling text, a latent concept hierarchy, and latent semantic relationships among the entities mentioned in the text. As a case study, we apply the model to a corpus of Web documents from the software domain, and evaluate the accuracy of the various components of the learned ontology.
A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction

The inception network has been shown to provide good performance on image classification problems, but there are not much evidences that it is also effective for the image restoration or pixel-wise labeling problems. For image restoration problems, the pooling is generally not used because the decimated features are not helpful for the reconstruction of an image as the output. Moreover, most deep learning architectures for the restoration problems do not use dense prediction that need lots of training parameters. From these observations, for enjoying the performance of inception-like structure on the image based problems we propose a new convolutional network-in-network structure. The proposed network can be considered a modification of inception structure where pool projection and pooling layer are removed for maintaining the entire feature map size, and a larger kernel filter is added instead. Proposed network greatly reduces the number of parameters on account of removed dense prediction and pooling, which is an advantage, but may also reduce the receptive field in each layer. Hence, we add a larger kernel than the original inception structure for not increasing the depth of layers. The proposed structure is applied to typical image-to-image learning problems, i.e., the problems where the size of input and output are same such as skin detection, semantic segmentation, and compression artifacts reduction. Extensive experiments show that the proposed network brings comparable or better results than the state-of-the-art convolutional neural networks for these problems.
Beehive: Simple Distributed Programming in Software-Defined Networks

In this paper, we present the design and implementation of Beehive, a distributed control platform with a simple programming model. In Beehive, control applications are centralized asynchronous message handlers that optionally store their state in dictionaries. Beehive's control platform automatically infers the keys required to process a message, and guarantees that each key is only handled by one light-weight thread of execution (i.e., bee) among all controllers (i.e., hives) in the platform. With that, Beehive transforms a centralized application into a distributed system, while preserving the application's intended behavior. Beehive replicates the dictionaries of control applications consistently through mini-quorums (i.e., colonies), instruments applications at runtime, and dynamically changes the placement of control applications (i.e., live migrates bees) to optimize the control plane. Our implementation of Beehive is open source, high-throughput and capable of fast failovers. We have implemented an SDN controller on top of Beehive that can handle 200K of OpenFlow messages per machine, while persisting and replicating the state of control applications. We also demonstrate that, not only can Beehive tolerate faults, but also it is capable of optimizing control applications after a failure or a change in the workload.
Adaptive Fingerprint Image Enhancement Based on Spatial Contextual Filtering and Pre-processing of Data

Although fingerprint recognition technology has advanced rapidly there are still some challenging research problems. Research has been conducted to develop Automatic Fingerprint Identification Systems (AFIS). The main problem in automatic fingerprint identification is to acquire matching reliable features from poor quality fingerprint images. Another challenging problem is the processing and matching of overlapped fingerprints. So, this paper proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term “adaptive” implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. There are situations where several fingerprints overlap on top of each other. A future enhancement is to separate such overlapped fingerprints into component fingerprints and enhance them so that existing fingerprint matchers can recognize them. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases. KeywordsDirectional filtering, Fourier transform, image processing, spectral feature estimation, successive mean quantization transform.
The "Which Blair project": a quick visual method for evaluating perceptual color maps

We have developed a fast, perceptual method for selecting color scales for data visualization that takes advantage of our sensitivity to luminance variations in human faces. To do so, we conducted experiments in which we mapped various color scales onto the intensitiy values of a digitized photograph of a face and asked observers to rate each image. We found a very strong correlation between the perceived naturalness of the images and the degree to which the underlying color scales increased monotonically in luminance. Color scales that did not include a monotonically-increasing luminance component produced no positive rating scores. Since color scales with monotonic luminance profiles are widely recommended for visualizing continuous scalar data, a purely visual technique for identifying such color scales could be very useful, especially in situations where color calibration is not integrated into the visualization environment, such as over the Internet.
Wafer-level Cu-Cu bonding technology

0026-2714/$ see front matter 2011  E-mail address: knchen@mail.nctu.edu.tw (K.-N. C Semiconductor industry currently utilizes copper wafer bonding as one of key technologies for 3D integration. This review paper describes both science and technology of copper wafer bonding with regard to present applications. The classification of Cu bonding, bonding mechanisms, process developments, its microstructure evolution, as well as other characterizations are reviewed. Researches about patterned Cu bonding, future prospects, and 3D integration using Cu bonding are discussed in this paper.