nils-herrmann/clean_scirepeval huggingface dataset:
Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling

In this paper, we apply a context-sensitive technique for multimodal emotion recognition based on feature-level fusion of acoustic and visual cues. We use bidirectional Long ShortTerm Memory (BLSTM) networks which, unlike most other emotion recognition approaches, exploit long-range contextual information for modeling the evolution of emotion within a conversation. We focus on recognizing dimensional emotional labels, which enables us to classify both prototypical and nonprototypical emotional expressions contained in a large audiovisual database. Subject-independent experiments on various classification tasks reveal that the BLSTM network approach generally prevails over standard classification techniques such as Hidden Markov Models or Support Vector Machines, and achieves F1-measures of the order of 72 %, 65 %, and 55 % for the discrimination of three clusters in emotional space and the distinction between three levels of valence and activation, respectively.
Learning deep generative models

Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many artificial intelligence–related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. In this article, we review several popular deep learning models, including deep belief networks and deep Boltzmann machines. We show that (a) these deep generative models, which contain many layers of latent variables and millions of parameters, can be learned efficiently, and (b) the learned high-level feature representations can be successfully applied in many application domains, including visual object recognition, information retrieval, classification, and regression tasks.
Conflict-Resolution Lifecycles for Governed Decentralized Autonomous Organization Collaboration

Recent blockchain-technology related innovations enable the governance of collaborating decentralized autonomous organizations (DAO) to engage in agile business-network collaborations that are based on the novel concept of smart contracting. DAOs utilize service-oriented cloud computing in a loosely coupled collaboration lifecycle with the main steps of setup, enactment, possible rollbacks and finally, an orderly termination. This lifecycle supports the selection of services provided and used by DAOs, smart contract negotiations, and behavior monitoring during enactment with the potential for breach management. Based on a sound understanding of the collaboration lifecycle in a Governance- as-a-Service (GaaS)-platform, a new type of conflict management must safeguard business-semantics induced consistency rules. This conflict management involves breach detection with recovery aspects. To fill the detected gap, we employ a formal design-notation that comprises the definition of structural and behavioral properties for exploring conflict-related exception- and compensation management during a decentralized collaboration. With the formal approach, we generate a highly dependable DAO-GaaS conflict model that does not collapse under left-behind clutter such as orphaned processes and exponentially growing database entries that require an unacceptable periodic GaaS reset.
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

In this paper, we propose a zoom-out-and-in network for generating object proposals. A key observation is that it is difficult to classify anchors of different sizes with the same set of features. Anchors of different sizes should be placed accordingly based on different depth within a network: smaller boxes on high-resolution layers with a smaller stride while larger boxes on low-resolution counterparts with a larger stride. Inspired by the conv/deconv structure, we fully leverage the low-level local details and high-level regional semantics from two feature map streams, which are complimentary to each other, to identify the objectness in an image. A map attention decision (MAD) unit is further proposed to aggressively search for neuron activations among two streams and attend the most contributive ones on the feature learning of the final loss. The unit serves as a decision-maker to adaptively activate maps along certain channels with the solely purpose of optimizing the overall training loss. One advantage of MAD is that the learned weights enforced on each feature channel is predicted on-the-fly based on the input context, which is more suitable than the fixed enforcement of a convolutional kernel. Experimental results on three datasets demonstrate the effectiveness of our proposed algorithm over other state-of-the-arts, in terms of average recall for region proposal and average precision for object detection.
NAVIGATION OF AN AUTONOMOUS CAR USING VECTOR FIELDS AND THE DYNAMIC WINDOW APPROACH

This work presents a safe navigation approach for a carlike robot. The approach relies on a global motion planning based on Velocity Vector Fields along with a Dynamic Window Approach for avoiding unmodeled obstacles. Basically, the vector field is associated with a kinematic, feedback-linearization controller whose outputs are validated, and eventually modified, by the Dynamic Window Approach. Experiments with a full-size autonomous car equipped with a stereo camera show that the vehicle was able to track the vector field and avoid obstacles in its way.
Post-infective and chronic fatigue syndromes precipitated by viral and non-viral pathogens: prospective cohort study.

To delineate the risk factors, symptom patterns, and longitudinal course of prolonged illnesses after a variety of acute infections.



Prospective cohort study following patients from the time of acute infection with Epstein-Barr virus (glandular fever), Coxiella burnetii (Q fever), or Ross River virus (epidemic polyarthritis).



The region surrounding the township of Dubbo in rural Australia, encompassing a 200 km geographical radius and 104,400 residents.



253 patients enrolled and followed at regular intervals over 12 months by self report, structured interview, and clinical assessment.



Detailed medical, psychiatric, and laboratory evaluations at six months to apply diagnostic criteria for chronic fatigue syndrome. Premorbid and intercurrent illness characteristics recorded to define risk factors for chronic fatigue syndrome. Self reported illness phenotypes compared between infective groups.



Prolonged illness characterised by disabling fatigue, musculoskeletal pain, neurocognitive difficulties, and mood disturbance was evident in 29 (12%) of 253 participants at six months, of whom 28 (11%) met the diagnostic criteria for chronic fatigue syndrome. This post-infective fatigue syndrome phenotype was stereotyped and occurred at a similar incidence after each infection. The syndrome was predicted largely by the severity of the acute illness rather than by demographic, psychological, or microbiological factors.



A relatively uniform post-infective fatigue syndrome persists in a significant minority of patients for six months or more after clinical infection with several different viral and non-viral micro-organisms. Post-infective fatigue syndrome is a valid illness model for investigating one pathophysiological pathway to chronic fatigue syndrome.
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension

This paper describes our system for SemEval2018 Task 11: Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b). We use Threeway Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/ intfloat/commonsense-rc.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD are redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270× to 600× without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.
Video-based emotion recognition using CNN-RNN and C3D hybrid networks

In this paper, we present a video-based emotion recognition system submitted to the EmotiW 2016 Challenge. The core module of this system is a hybrid network that combines recurrent neural network (RNN) and 3D convolutional networks (C3D) in a late-fusion fashion. RNN and C3D encode appearance and motion information in different ways. Specifically, RNN takes appearance features extracted by convolutional neural network (CNN) over individual video frames as input and encodes motion later, while C3D models appearance and motion of video simultaneously. Combined with an audio module, our system achieved a recognition accuracy of 59.02% without using any additional emotion-labeled video clips in training set, compared to 53.8% of the winner of EmotiW 2015. Extensive experiments show that combining RNN and C3D together can improve video-based emotion recognition noticeably.
Cryptographic Hash Function using Cellular Automata

Since two distinct messages are extremely unlikely to generate identical message digests, one can use this property of cryptographic hash functions to detect when a message has been altered. If one takes a binary file and computes a digest of the file, one can record this baseline digest. In the future, the digest can be recomputed on the file. If the new digest differs from the original baseline digest, then one can be assured that the file has been altered in some way
Multitask Learning for Fundamental Frequency Estimation in Music

Fundamental frequency (f0) estimation from polyphonic music includes the tasks of multiple-f0, melody, vocal, and bass line estimation. Historically these problems have been approached separately, and only recently, using learning-based approaches. We present a multitask deep learning architecture that jointly estimates outputs for various tasks including multiplef0, melody, vocal and bass line estimation, and is trained using a large, semi-automatically annotated dataset. We show that the multitask model outperforms its single-task counterparts, and explore the effect of various design decisions in our approach, and show that it performs better or at least competitively when compared against strong baseline methods.
Learning a Hierarchical Embedding Model for Personalized Product Search

Product search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not confined to retrieving relevant products. Instead, it focuses on finding items that satisfy the needs of individuals and lead to a user purchase. The unique characteristics of product search make search personalization essential for both customers and e-shopping companies. Purchase behavior is highly personal in online shopping and users often provide rich feedback about their decisions (e.g. product reviews). However, the severe mismatch found in the language of queries, products and users make traditional retrieval models based on bag-of-words assumptions less suitable for personalization in product search. In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different levels with their associated language data. Our contributions are three-fold: (1) our work is one of the initial studies on personalized product search; (2) our hierarchical embedding model is the first latent space model that jointly learns distributed representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data. Experiments show that our hierarchical embedding model significantly outperforms existing product search baselines on multiple benchmark datasets.
AN 1477 Digital Compensator Design for LLC Resonant Converter

A half-bridge LLC resonant converter with Zero Voltage Switching (ZVS) and Pulse Frequency Modulation (PFM) is a lucrative topology for DC/DC conversion. A Digital Signal Controller (DSC) provides component cost reduction, flexible design, and the ability to monitor and process the system conditions to achieve greater stability. The dynamics of the LLC resonant converter are investigated using the small signal modeling technique based on Extended Describing Functions (EDF) methodology. Also, a comprehensive description of the design for the compensator for control of the LLC converter is presented.  The LLC resonant converter topology, illustrated in Figure 1, allows ZVS for half-bridge MOSFETs, thereby considerably lowering the switching losses and improving the converter efficiency. The control system design of resonant converters is different from the conventional fixed frequency Pulse-Width Modulation (PWM) converters. In order to design a suitable digital compensator, the large signal and small signal models of the LLC resonant converter are derived using the EDF technique.  Meeravali Shaik and Ramesh Kankanala Microchip Technology Inc.
Facial feature detection using Haar classifiers

Viola and Jones  introduced a method to accurately and rapidly detect faces within an image. This technique can be adapted to accurately detect facial features. However, the area of the image being analyzed for a facial feature needs to be regionalized to the location with the highest probability of containing the feature. By regionalizing the detection area, false positives are eliminated and the speed of detection is increased due to the reduction of the area examined.  The human face poses even more problems than other objects since the human face is a dynamic object that comes in many forms and colors [7]. However, facial detection and tracking provides many benefits. Facial recognition is not possible if the face is not isolated from the background. Human Computer Interaction (HCI) could greatly be improved by using emotion, pose, and gesture recognition, all of which require face and facial feature detection and tracking . Although many different algorithms exist to perform face detection, each has its own weaknesses and strengths. Some use flesh tones, some use contours, and other are even more complex involving templates, neural networks, or filters. These algorithms suffer from the same problem; they are computationally expensive . An image is only a collection of color and/or light intensity values. Analyzing these pixels for face detection is time consuming and difficult to accomplish because of the wide variations of shape and JCSC 21, 4 (April 2006) 128 Figure 1 Common Haar Features pigmentation within a human face. Pixels often require reanalysis for scaling and precision. Viola and Jones devised an algorithm, called Haar Classifiers, to rapidly detect any object, including human faces, using AdaBoost classifier cascades that are based on Haar-like features and not pixels . HAAR CASCADE CLASSIFIERS The core basis for Haar classifier object detection is the Haar-like features. These features, rather than using the intensity values of a pixel, use the change in contrast values between adjacent rectangular groups of pixels. The contrast variances between the pixel groups are used to determine relative light and dark areas. Two or three adjacent groups with a relative contrast variance form a Haar-like feature. Haar-like features, as shown in Figure 1 are used to detect an image . Haar features can easily be scaled by increasing or decreasing the size of the pixel group being examined. This allows features to be used to detect objects of various sizes. Integral Image The simple rectangular features of an image are c a l c u l a t e d u s i n g a n intermediate representation of an image, called the integral image . The integral image is an array containing the sums of the pixels’ intensity values located directly to the left of a pixel and directly above the pixel at location (x, y) inclusive. So if A[x,y] is the original image and AI[x,y] is the integral image then the integral image is computed as shown in equation 1 and illustrated in Figure 2. (1) [ ] AI x y A x y x x y y , ( ' , ' ) ' , ' =
Improvement of bidirectional recurrent neural network for learning long-term dependencies

Bidirectional recurrent neural network (BRNN) is a non-causal generalization of recurrent neural networks (RNNs). Due to the problem of vanishing gradients, BRNN cannot learn long-term dependencies efficiently with gradient descent. To tackle the long-term dependency problem, we propose segmented-memory recurrent neural network (SM-RNN) and develop a bidirectional segmented-memory recurrent neural network(BSMRNN). We test the performance of BSMRNN on the problem of information latching. Our experimental results show that BSMRNN outperforms BRNN on long-term dependency problems.
What We Don't Know About Spreadsheet Errors Today: The Facts, Why We Don't Believe Them, and What We Need to Do

Research on spreadsheet errors is substantial, compelling, and unanimous. It has three simple conclusions. The first is that spreadsheet errors are rare on a per-cell basis, but in large programs, at least one incorrect bottom-line value is very likely to be present. The second is that errors are extremely difficult to detect and correct. The third is that spreadsheet developers and corporations are highly overconfident in the accuracy of their spreadsheets. The disconnect between the first two conclusions and the third appears to be due to the way human cognition works. Most importantly, we are aware of very few of the errors we make. In addition, while we are proudly aware of errors that we fix, we have no idea of how many remain, but like Little Jack Horner we are impressed with our ability to ferret out errors. This paper reviews human cognition processes and shows first that humans cannot be error free no matter how hard they try, and second that our intuition about errors and how we can reduce them is based on appallingly bad knowledge. This paper argues that we should reject any prescription for reducing errors that has not been rigorously proven safe and effective. This paper also argues that our biggest need, based on empirical data, is to do massively more testing than we do now. It suggests that the code inspection methodology developed in software development is likely to apply very well to spreadsheet inspection.
An Encoder-decoder Approach to Predicting Causal Relations in Stories

We address the task of predicting causally related events in stories according to a standard evaluation framework, the Choice of Plausible Alternatives (COPA). We present a neural encoder-decoder model that learns to predict relations between adjacent sequences in stories as a means of modeling causality. We explore this approach using different methods for extracting and representing sequence pairs as well as different model architectures. We also compare the impact of different training datasets on our model. In particular, we demonstrate the usefulness of a corpus not previously applied to COPA, the ROCStories corpus. While not state-of-the-art, our results establish a new reference point for systems evaluated on COPA, and one that is particularly informative for future neural-based approaches.
App platforms as two-sided markets: analysis and modeling of application distribution platforms for mobile devices

On two-sided markets, a platform intermediates between two distinct groups of customers linked by network effects. Over the last decade, a large body of literature on theoretical as well as applied aspects of two-sided markets has been published, for example, Rochet and Tirole (2003, 2006), Caillaud and Jullien (2003), Evans (2003) and Armstrong (2006), to name just a few. At the same time, newly launched Internet platforms such as Apple’s App Store call for new extensions and provide motivation for the further development of two-sided market models. This is the area of research to which this paper contributes. The objectives of this study are: i) the integration of prevalent parameters for monopoly two-sided markets models (like usage fees, membership fees, payment between customer groups, quality review of participants) into one model; ii) the introduction of new parameters such as commission fee, the segmentation of participants and adjustments for the number of interactions, and iii) development of a solution for the new model. Subsequently, we demonstrate how the integrated model can be applied to model app distribution platforms like Apple’s App Store.
Comparison of dilute acid and ionic liquid pretreatment of switchgrass: Biomass recalcitrance, delignification and enzymatic saccharification.

The efficiency of two biomass pretreatment technologies, dilute acid hydrolysis and dissolution in an ionic liquid, are compared in terms of delignification, saccharification efficiency and saccharide yields with switchgrass serving as a model bioenergy crop. When subject to ionic liquid pretreatment (dissolution and precipitation of cellulose by anti-solvent) switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. Pretreated material was characterized by powder X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and chemistry methods. Ionic liquid pretreatment enabled a significant enhancement in the rate of enzyme hydrolysis of the cellulose component of switchgrass, with a rate increase of 16.7-fold, and a glucan yield of 96.0% obtained in 24h. These results indicate that ionic liquid pretreatment may offer unique advantages when compared to the dilute acid pretreatment process for switchgrass. However, the cost of the ionic liquid process must also be taken into consideration.