nils-herrmann/clean_scirepeval huggingface dataset:
Combining Models from Multiple Sources for RGB-D Scene Recognition

Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at highlevels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities.
High Efficiency Video Coding(HEVC)

High Efficiency Video Coding (HEVC) is a proposed video compression standard, a successor to H.264/MPEG-4 AVC (Advanced Video Coding), currently under joint development by the ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG). MPEG and VCEG have established a Joint Collaborative Team on Video Coding (JCT-VC) to develop the proposed HEVC standard. HEVC aims to substantially improve coding efficiency compared to AVC High Profile, i.e. reduce bitrate requirements by half with comparable image quality, probably at the expense of increased computational complexity. Depending on the application requirements, HEVC should be able to trade off computational complexity, compression rate, robustness to errors and processing delay time. HEVC is targeted at next-generation HDTV displays and content capture systems which feature progressive scanned frame rates and display resolutions from QVGA (320x240) up to 1080p and Super Hi-Vision, as well as improved picture quality in terms of noise level, color gamut and dynamic range. Features The JCT-VC is currently evaluating modifications to current coding tools, such as:  adaptive loop filter (ALF),  extended macroblock size (EMS),  larger transform size (LTS),  internal bit depth increasing (IBDI), and  adaptive quantization matrix selection (AQMS), as well as new coding tools, such as:  modified intra prediction,  modified de-block filter, and  decoder-side motion vector deviation (DMVD). Many new features are proposed to meet the requirements:  2-D non-separable adaptive interpolation filter (AIF)  Separable AIF  Directional AIF  "Supermacroblock" structure up to 64x64 with additional transforms  Adaptive prediction error coding (APEC) in spatial and frequency domain  Competition-based scheme for motion vector selection and coding  Mode-dependent KLT for intra coding It is speculated that these techniques are most beneficial with multi-pass encoding. History The ITU-T Video Coding Experts Group (VCEG) began significant study of technology advances that could enable creation of a new video compression standard (or substantial compression-oriented enhancements of the H.264/MPEG-4 AVC standard) in about 2004.
Improved Neural Machine Translation with Source Syntax

Neural Machine Translation (NMT) based on the encoder-decoder architecture has recently achieved the state-of-the-art performance. Researchers have proven that extending word level attention to phrase level attention by incorporating source-side phrase structure can enhance the attention model and achieve promising improvement. However, word dependencies that can be crucial to correctly understand a source sentence are not always in a consecutive fashion (i.e. phrase structure), sometimes they can be in long distance. Phrase structures are not the best way to explicitly model long distance dependencies. In this paper we propose a simple but effective method to incorporate source-side long distance dependencies into NMT. Our method based on dependency trees enriches each source state with global dependency structures, which can better capture the inherent syntactic structure of source sentences. Experiments on Chinese-English and English-Japanese translation tasks show that our proposed method outperforms state-of-the-art SMT and NMT baselines.
Regulating the new information intermediaries as gatekeepers of information diversity

Natali Helberger is Professor at the Institute for Information Law, University of Amsterdam, Amsterdam, The Netherlands.  Kleinen-von Königslöw is Assistant Professor for Political Communication at the University of Zurich, Zurich, Switzerland. Rob van der Noll is Senior Researcher at SEO Economic Research, Amsterdam, The Netherlands.  The purposes of this paper are to deal with the questions: because search engines, social networks and app-stores are often referred to as gatekeepers to diverse information access, what is the evidence to substantiate these gatekeeper concerns, and to what extent are existing regulatory solutions to control gatekeeper control suitable at all to address new diversity concerns? It will also map the different gatekeeper concerns about media diversity as evidenced in existing research before the background of network gatekeeping theory critically analyses some of the currently discussed regulatory approaches and develops the contours of a more user-centric approach towards approaching gatekeeper control and media diversity.
MEMS Reconfigurable Optimized E-Shaped Patch Antenna Design for Cognitive Radio

Reconfigurable antennas offer attractive potential solutions to solve the challenging antenna problems related to cognitive radio systems using the ability to switch patterns, frequency, and polarization. In this paper, a novel frequency reconfigurable E-shaped patch design is proposed for possible applications in cognitive radio systems. This paper provides a methodology to design reconfigurable antennas with radio frequency microelectromechanical system (RF-MEMS) switches using particle swarm optimization, a nature-inspired optimization technique. By adding RF-MEMS switches to dynamically change the slot dimensions, one can achieve wide bandwidth which is nearly double the original E-shaped patch bandwidth. Utilizing an appropriate fitness function, an optimized design which works in the frequency range from 2 GHz to 3.2 GHz (50% impedance bandwidth at 2.4 GHz ) is obtained. RF-MEMS switch circuit models are incorporated into the optimization as they more effectively represent the actual switch effects. A prototype of the final optimized design is developed and measurements demonstrate good agreement with simulations.
Algorithms for Hyper-Parameter Optimization

Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P (y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
Hybrid attribute and personality based recommender system for book recommendation

In recent years, with the rapid increases of books, finding relevant books has been a problem. For that, people might need their peers' opinion to complete this task. The problem is that relevant books can be gained only if there are other users or peers have same interests with them. Otherwise, they will never get relevant books. Recommender systems can be a solution for that problem. They work on finding relevant items based on other users' experience. Although research on recommender system increases, there is still not much research that considers user personality in recommender systems, even though personal preferences are really important these days. This paper discusses our research on a hybrid-based method that combines attribute-based and user personality-based methods for book recommender system. The attribute-based method has been implemented previously. In our research, we have implemented the MSV-MSL (Most Similar Visited Material to the Most Similar Learner) method, since it is the best method among hybrid attribute-based methods. The personality factor is used to find the similarity between users when creating neighborhood relationships. The method is tested using Book-crossing and Amazon Review on book category datasets. Our experiment shows that the combined method that considers user personality gives a better result than those without user personality on Book-crossing dataset. In contrary, it resulted in a lower performance on Amazon Review dataset. It can be concluded that user personality consideration can give a better result in a certain condition depending on the dataset itself and the usage proportion.
Orbitofrontal cortex, decision-making and drug addiction

The orbitofrontal cortex, as a part of prefrontal cortex, is implicated in executive function. However, within this broad region, the orbitofrontal cortex is distinguished by its unique pattern of connections with crucial subcortical associative learning nodes, such as basolateral amygdala and nucleus accumbens. By virtue of these connections, the orbitofrontal cortex is uniquely positioned to use associative information to project into the future, and to use the value of perceived or expected outcomes to guide decisions. This review will discuss recent evidence that supports this proposal and will examine evidence that loss of this signal, as the result of drug-induced changes in these brain circuits, might account for the maladaptive decision-making that characterizes drug addiction.
Attitudes toward homosexuality among young adults: connections to gender role identity, gender-typed activities, and religiosity.

Individual differences in attitudes toward homosexuality have been linked to numerous personality and demographic variables. This study investigated the influence that gender role identity, involvement in gender-typed activities, and religiosity plays in this relationship. The sample included 194 undergraduate students from a Northeastern university. Analyses revealed that both males and females who held a more masculine gender role identity and individual commitment to religion scored higher on measures of homophobia and heteronormativity, whereas there was no association between spiritual meaning in life and attitudes toward homosexuality. Among males, but not females, more masculine gender identity and less spiritual meaning in life was associated with greater homophobia. The importance of the findings for research on the origins of attitudes toward individuals with a homosexual orientation are discussed, as well as the potential directions for future research on connections between gender role identity, religious affiliation, and attitudes toward gays and lesbians.
Automatic personalization based on Web usage mining

The ease and speed with which business transactions can be carried out over the Web have been a key driving force in the rapid growth of electronic commerce. Business-to-business e-commerce is the focus of much attention today, mainly due to its huge volume. While there are certainly gains to be made in this arena, most of it is the implementation of much more efficient supply management, payments, etc. On the other hand, e-commerce activity that involves the end user is undergoing a significant revolution. The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.
A rational analysis of the effects of memory biases on serial reproduction

Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the cognitive capacities of the agents involved. Bartlett (1932) explored the influence of memory biases on the "serial reproduction" of information, in which one person's reconstruction of a stimulus from memory becomes the stimulus seen by the next person. These experiments were done using relatively uncontrolled stimuli, but suggested that serial reproduction could transform information in a way that reflected the biases inherent in memory. We formally analyze serial reproduction using a Bayesian model of reconstruction from memory, giving a general result characterizing the effect of memory biases on information transmission. We then test the predictions of this account in four experiments using simple one-dimensional stimuli. Our results provide theoretical and empirical justification for the idea that serial reproduction reflects memory biases.
Using one-time passwords to prevent password phishing attacks

Phishing is now a serious threat to the security of Internet users’ confidential information. Basically, an attacker (phisher) tricks people into divulging sensitive information by sending fake messages to a large number of users at random. Unsuspecting users who follow the instruction in the messages are directed to well-built spoofed web pages and asked to provide sensitive information, which the phisher then steals. Based on our observations, more than 70% of phishing activities are designed to steal users’ account names and passwords. With such information, an attacker can retrieve more valuable information from the compromised accounts. Statistics published by the anti-phishing working group (APWG) show that, at the end of Q2 in 2008, the number of malicious web pages designed to steal users’ passwords had increased by 258% over the same period in 2007. Therefore, protecting users from phishing attacks is extremely important. A naı̈ve way to prevent the theft of passwords is to avoid using passwords. This raises the following question: Is it possible to authenticate a user without a preset
A customer classification prediction model based on machine learning techniques

Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data . In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naïve Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set.
The Sensory Gating Inventory as a potential diagnostic tool for attention-deficit hyperactivity disorder.

Diagnoses of attention-deficit hyperactivity disorder (ADHD) are often made rapidly in physicians' offices without thorough assessment. We examined whether adults diagnosed with ADHD would score differently from controls on a modified Sensory Gating Inventory (SGI: Hetrick et al.  2012; Kisley et al. in Psychophysiol 41:604-612, 2004), which would facilitate rapid and easy preliminary assessment of ADHD status. The modified SGI was administered to 22 controls and 22 adults with physician diagnoses of ADHD. Analysis was performed on the 17 SGI items and the three categories to which they belong (Perceptual Modulation, Distractibility, and Over-Inclusion). The Distractibility category, and its individual items, showed large group differences. In spite of a relatively small sample size, we found large effect sizes between those with and without ADHD diagnoses. The SGI is a simple, quick, paper/pencil method that may be used to facilitate accurate diagnosis of individuals experiencing ADHD symptoms, which may be especially useful when evaluations are made in settings such as physicians' offices.
Brain Activity during Episodic Retrieval of Autobiographical and Laboratory Events: An fMRI Study using a Novel Photo Paradigm

Functional neuroimaging studies of episodic memory retrieval generally measure brain activity while participants remember items encountered in the laboratory (controlled laboratory condition) or events from their own life (open autobiographical condition). Differences in activation between these conditions may reflect differences in retrieval processes, memory remoteness, emotional content, retrieval success, self-referential processing, visual/spatial memory, and recollection. To clarify the nature of these differences, a functional MRI study was conducted using a novel photo paradigm, which allows greater control over the autobiographical condition, including a measure of retrieval accuracy. Undergraduate students took photos in specified campus locations (controlled autobiographical condition), viewed in the laboratory similar photos taken by other participants (controlled laboratory condition), and were then scanned while recognizing the two kinds of photos. Both conditions activated a common episodic memory network that included medial temporal and prefrontal regions. Compared with the controlled laboratory condition, the controlled autobiographical condition elicited greater activity in regions associated with self-referential processing (medial prefrontal cortex), visual/ spatial memory (visual and parahippocampal regions), and recollection (hippocampus). The photo paradigm provides a way of investigating the functional neuroanatomy of real-life episodic memory under rigorous experimental control.
MBMF: Model-Based Priors for Model-Free Reinforcement Learning

Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time dataefficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. As a proof-of-concept, we demonstrate on simulated tasks that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.
Enterprise Architecture Characteristics in Context Enterprise Governance Base On COBIT 5 Framework

The existence of the enterprise architecture is an attempt of managing and planning over the evolution of information systems in the sphere of an enterprise with model-based. In developing the enterprise architecture, there are several tools definition of components in the system. This tool is known as enterprises architecture (EA) framework. In this paper, we present a method to build a model of enterprise architecture in accordance with the needs of the Organization by Understanding the characteristics of the EA framework such as Zachman, TOGAF, and FEAF. They are selected as the EA framework will be used to determine the characteristics of an EA because the framework is most widely used in corporate or Government. In COBIT 5 framework, there is a process associated with enterprise architecture it is APO03 Manage Enterprise Architecture. At this stage of the research, we describe the link between the characteristics of the EA with one process in COBIT 5 framework. The results contribute to give a recommendation how to design EA for organization based on the characteristic of EA in Context Enterprise Governance using COBIT 5 Framework.
ShiDianNao: Shifting vision processing closer to the sensor

In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications.
 Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are Convolutional Neural Networks (CNN), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs.
 In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is 60&times more energy efficient than the previous state-of-the-art neural network accelerator. We present a full design down to the layout at 65 nm, with a modest footprint of 4.86mm2 and consuming only 320mW, but still about 30× faster than high-end GPUs.
Discovering Diverse and Salient Threads in Document Collections

We propose a novel probabilistic technique for modeling and extracting salient structure from large document collections. As in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of information. We are particularly interested in revealing and exploiting relationships between documents. To this end, we focus on extracting diverse sets of threads—singlylinked, coherent chains of important documents. To illustrate, we extract research threads from citation graphs and construct timelines from news articles. Our method is highly scalable, running on a corpus of over 30 million words in about four minutes, more than 75 times faster than a dynamic topic model. Finally, the results from our model more closely resemble human news summaries according to several metrics and are also preferred by human judges.