nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating CNN inference on FPGAs: A Survey

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classi cation and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to recon gurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the di erent methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on e cient hardware deep learning.
Representing and Using Nonfunctional Requirements: A Process-Oriented Approach

Three most cited publications (on Google Scholar http://scholar.google.com/scholar?q=lawrence+chung&hl=en&lr=): On non-functional requirements in software engineering L Chung... Conceptual Modeling: Foundations and ..., 2009 Springer Cited by 1870 Representing and using nonfunctional requirements: A process-oriented approach J Mylopoulos, L Chung... Software Engineering, IEEE ..., 1992 ieeexplore.ieee.org Cited by 855 From object-oriented to goal-oriented requirements analysis J Mylopoulos, L Chung... Communications of the ACM, 1999 portal.acm.org Cited by 436
CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.
A high performance parallel algorithm for 1-D FFT

In this paper we propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. We use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. We show that the multidimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. We implemented this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.
Fracture detection in x-ray images through stacked random forests feature fusion

Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone fracture detection method that is applicable to multiple bone fracture types and multiple bone structures throughout the body. The method uses features extracted from candidate patches in X-ray images in a novel discriminative learning framework called the Stacked Random Forests Feature Fusion. This is a multilayer learning formulation in which the class probability labels, produced by random forests learners at a lower level, are used to derive the refined class distribution labels at the next level. The candidate patches themselves are selected using an efficient subwindow search algorithm. The outcome of the method is a number of fracture bounding-boxes ranked from the most likely to the least likely to contain a fracture. We evaluate the proposed method on a set of 145 X-rays images. When the top ranking seven fracture bounding-boxes are considered, we are able to capture 81.2% of the fracture findings reported by a radiologist. The proposed method outperforms other fracture detection frameworks that use local features, and single layer random forests and support vector machine classification.
Stable and Flexible Materials to Mimic the Dielectric Properties of Human Soft Tissues

Emerging biomedical applications require realistic phantoms for validation and testing of prototype systems. These phantoms require stable and flexible tissue-mimicking materials with realistic dielectric properties in order to properly model human tissues. To create a tissue-mimicking material to fulfill these needs, carbon powder and urethane rubber mixtures were created, and the dielectric properties were measured using a dielectric probe. Both graphite and carbon black were tested. Mixtures of graphite and urethane (0% to 50% by weight) provided relatively low permittivity and conductivity, suitable for mimicking fatty tissues. Mixtures of carbon black and urethane (0% to 15% by weight) provided a broad range of suitable properties. Samples with 15% carbon black had permittivity and conductivity similar to higher-water-content tissues, however the cured samples were not mechanically suitable for moulding into complex shapes. Finally, mixtures of graphite, carbon black, and urethane were created. These exhibited a range of dielectric properties and can be used to mimic a variety of soft tissues. The mechanical properties of these samples were tested and presented properties that exceed typical phantom requirements. This tissue-mimicking material will be useful when creating thin, flexible, and robust structures such as skin layers.
Super deduper, fast PCR duplicate detection in fastq files

Our goal was to explore the accuracy and utility of identifying and removing PCR duplicates from HTS data using Super Deduper. Super Deduper is a pre-alignment, sequence read based technique developed at the University of Idaho, which examines and uses only a small portion of each read's sequence in order to identify and remove PCR and/or optical duplicates. Through comparisons with well-known pre- and post-alignment techniques, Super Deduper's parameters were optimized and its performance assessed. The results conclude that Super Deduper is a viable pre-alignment alternative to post-alignment techniques. Super Deduper is both independent of a reference genome and choice in alignment application, allowing for its use in a greater variety of HTS applications.
AN EFFICIENT KEYWORD DRIVEN TEST AUTOMATION FRAMEWORK FOR WEB APPLICATIONS

Appointing additional test engineers are not feasible long term solution, but also need to reduce overall cost of the project by adapting test automation frameworks. There are different types of automation frameworks are available for software testing. Keyword driven test automation framework ensures the three main characteristic (Quality, Time and Cost) of software testing. Keyword driven framework reduces the complex script programming knowledge problem for the automation testing. Manual testers are also being able to create test scripts simply by combining the keywords.
Large-Scale Automatic Species Identification

The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of ”family”—when applying automatic specificity control.
An empirical examination of the factor structure of compassion

Compassion has long been regarded as a core part of our humanity by contemplative traditions, and in recent years, it has received growing research interest. Following a recent review of existing conceptualisations, compassion has been defined as consisting of the following five elements: 1) recognising suffering, 2) understanding the universality of suffering in human experience, 3) feeling moved by the person suffering and emotionally connecting with their distress, 4) tolerating uncomfortable feelings aroused (e.g., fear, distress) so that we remain open to and accepting of the person suffering, and 5) acting or being motivated to act to alleviate suffering. As a prerequisite to developing a high quality compassion measure and furthering research in this field, the current study empirically investigated the factor structure of the five-element definition using a combination of existing and newly generated self-report items. This study consisted of three stages: a systematic consultation with experts to review items from existing self-report measures of compassion and generate additional items (Stage 1), exploratory factor analysis of items gathered from Stage 1 to identify the underlying structure of compassion (Stage 2), and confirmatory factor analysis to validate the identified factor structure (Stage 3). Findings showed preliminary empirical support for a five-factor structure of compassion consistent with the five-element definition. However, findings indicated that the 'tolerating' factor may be problematic and not a core aspect of compassion. This possibility requires further empirical testing. Limitations with items from included measures lead us to recommend against using these items collectively to assess compassion. Instead, we call for the development of a new self-report measure of compassion, using the five-element definition to guide item generation. We recommend including newly generated 'tolerating' items in the initial item pool, to determine whether or not factor-level issues are resolved once item-level issues are addressed.
The neural basis of the central executive system of working memory

WORKING memory refers to a system for temporary storage and manipulation of information in the brain, a function critical for a wide range of cognitive operations. It has been proposed that working memory includes a central executive system (CES) to control attention and information flow to and from verbal and spatial short-term memory buffers1. Although the prefrontal cortex is activated during both verbal and spatial passive working memory tasks2–8, the brain regions involved in the CES component of working memory have not been identified. We have used functional magnetic resonance imaging (fMRI) to examine brain activation during the concurrent performance of two tasks, which is expected to engage the CES. Activation of the prefrontal cortex was observed when both tasks are performed together, but not when they are performed separately. These results support the view that the prefrontal cortex is involved in human working memory.
Crystal Structure of Human Cyclophilin A Bound to the Amino-Terminal Domain of HIV-1 Capsid

The HIV-1 capsid protein forms the conical core structure at the center of the mature virion. Capsid also binds the human peptidyl prolyl isomerase, cyclophilin A, thereby packaging the enzyme into the virion. Cyclophilin A subsequently performs an essential function in HIV-1 replication, possibly helping to disassemble the capsid core upon infection. We report the 2.36 A crystal structure of the N-terminal domain of HIV-1 capsid (residues 1-151) in complex with human cyclophilin A. A single exposed capsid loop (residues 85-93) binds in the enzyme's active site, and Pro-90 adopts an unprecedented trans conformation. The structure suggests how cyclophilin A can act as a sequence-specific binding protein and a nonspecific prolyl isomerase. In the crystal lattice, capsid molecules assemble into continuous planar strips. Side by side association of these strips may allow capsid to form the surface of the viral core. Cyclophilin A could then function by weakening the association between capsid strips, thereby promoting disassembly of the viral core.
Inferring Decision Trees Using the Minimum Description Length Principle

This paper concerns methods for inferring decision trees from examples for classification problems. The reader who is unfamiliar with this problem may wish to consult J. R. Quinlan’s paper (1986), or the excellent monograph by Breiman et al. (1984), although this paper will be self-contained. This work is inspired by Rissanen’s work on the Minimum description length principle (or MDLP for short) and on his related notion of the stochastic complexity of a string Rissanen, 1986b. The reader may also want to refer to related work by Boulton and Wallace (1968, 1973a, 1973b), Georgeff and Wallace (1984), and Hart (1987). Roughly speaking, the minimum description length principle states that the best “theory” to infer from a set of data is the one which minimizes the sum of
Modeling the Predictive Social Mind

The social mind is tailored to the problem of predicting the mental states and actions of other people. However, social cognition researchers have only scratched the surface of the predictive social mind. We discuss here a new framework for explaining how people organize social knowledge and use it for social prediction. Specifically, we propose a multilayered framework of social cognition in which two hidden layers - the mental states and traits of others - support predictions about the observable layer - the actions of others. A parsimonious set of psychological dimensions structures each layer, and proximity within and across layers guides social prediction. This simple framework formalizes longstanding intuitions from social cognition, and in doing so offers a generative model for deriving new hypotheses about predictive social cognition.
Development trends for human monoclonal antibody therapeutics

Fully human monoclonal antibodies (mAbs) are a promising and rapidly growing category of targeted therapeutic agents. The first such agents were developed during the 1980s, but none achieved clinical or commercial success. Advances in technology to generate the molecules for study — in particular, transgenic mice and yeast or phage display — renewed interest in the development of human mAbs during the 1990s. In 2002, adalimumab became the first human mAb to be approved by the US Food and Drug Administration (FDA). Since then, an additional six human mAbs have received FDA approval: panitumumab, golimumab, canakinumab, ustekinumab, ofatumumab and denosumab. In addition, 3 candidates (raxibacumab, belimumab and ipilimumab) are currently under review by the FDA, 7 are in Phase III studies and 81 are in either Phase I or II studies. Here, we analyse data on 147 human mAbs that have entered clinical study to highlight trends in their development and approval, which may help inform future studies of this class of therapeutic agents.
Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition

Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the presence of spatially colored noise leads to a generalized eigenvalue problem. While this approach has extensively been employed in narrowband (antenna) array beamforming, it is typically not used for broadband (microphone) array beamforming due to the uncontrolled amount of speech distortion introduced by a narrowband SNR criterion. In this paper, we show how the distortion of the desired signal can be controlled by a single-channel post-filter, resulting in a performance comparable to the generalized minimum variance distortionless response beamformer, where arbitrary transfer functions relate the source and the microphones. Results are given both for directional and diffuse noise. A novel gradient ascent adaptation algorithm is presented, and its good convergence properties are experimentally revealed by comparison with alternatives from the literature. A key feature of the proposed beamformer is that it operates blindly, i.e., it neither requires knowledge about the array geometry nor an explicit estimation of the transfer functions from source to sensors or the direction-of-arrival.
Security in Internet of Things: Challenges, Solutions and Future Directions

Internet of Things (IoT) is an enabler for the intelligence appended to many central features of the modern world, such as hospitals, cities, grids, organizations, and buildings. The security and privacy are some of the major issues that prevent the wide adoption of Internet of Things. In this paper, with example scenarios, we are presenting review of security attacks from the perspective of layers that comprises IoT. In addition, a review of methods that provide solutions to these issues is presented along with their limitations. To overcome these limitations, we have provided future work recommendations with a framework. Further research and implementation of the framework and our recommendations will further enhance the robustness and reliability of the IoT and their applications against a variety of known attacks.
Innovative technology-based interventions for autism spectrum disorders: a meta-analysis.

This article reports the results of a meta-analysis of technology-based intervention studies for children with autism spectrum disorders. We conducted a systematic review of research that used a pre-post design to assess innovative technology interventions, including computer programs, virtual reality, and robotics. The selected studies provided interventions via a desktop computer, interactive DVD, shared active surface, and virtual reality. None employed robotics. The results provide evidence for the overall effectiveness of technology-based training. The overall mean effect size for posttests of controlled studies of children with autism spectrum disorders who received technology-based interventions was significantly different from zero and approached the medium magnitude, d = 0.47 (confidence interval: 0.08-0.86). The influence of age and IQ was not significant. Differences in training procedures are discussed in the light of the negative correlation that was found between the intervention durations and the studies' effect sizes. The results of this meta-analysis provide support for the continuing development, evaluation, and clinical usage of technology-based intervention for individuals with autism spectrum disorders.
Information Asset Valuation Method for Information Technology Security Risk Assessment

The information security strategic plan is necessarily comprehensive, including business processes, people, and physical infrastructure, as well as the information system. The Security risk evaluation needs the calculating asset value to predict the impact and consequence of security incidents. The return on security investment (ROSI) is defining the value for all invested in terms of security by determining the cost of assets that may disturb in security breaches and the cost of its impact. Knowledge is the source of many competitive advantages for businesses and it should protect against theft, misuse and disasters by adequate security controls. All elements that involved in the knowledge creation process are knowledge assets. An IPO model with a combination of Skandia and Balanced scorecard methods needs to develop a measurement system for knowledge asset value assessment. This model recognizes the role of customers and employees as the natures of knowledge and concentrates on a wide range of factors involved in organization such as processes, structures and development elements that has not been tried before. The model in addition includes structure capital variables that emphasized ICT factors those are investing knowledge into the company's competitive advantage.
Artificial Intelligence , Heuristic Frameworks and Tabu Search

This paper examines some of the characteristics of Al-based heuristic procedures that have emerged as frameworks for solving difficult optimization problems. Consideration of attributes shared to some degree by human prohlem solvers leads to focusing in greater detail on one of the more -ituccessful procedures, tabu search, which employs a flexible memory system (in contrast to 'memoryless^ systems, as in simulated annealing and genetic algorithms, and rigid memory systems as in branch and bound and A* search). Specific attention is given to the short-term memory component of tahu search, which has provided solutions superior to the best obtained by other methods for a variety of problems. Our development emphasizes the principles underlying the interplay between restricting tbe searcb to avoid unproductive retracing of patbs (by tneans of tabu conditions) and freeing the search to explore otherwise forbidden avenues (by aspiration criteria). Finally, we discuss briefly the relevance of a supplementary framework, called target analysis, which is a method for determining good decision rules to enable heuristics to perform more effectively.
Determinants of Information Channel Choice: The Impact of Task Complexity and Dispositional Character Traits

During the last decade, a large set of innovative web-enabled technologies, such as social networks and mobile technologies has considerably changed the way people communicate and express their thoughts and ideas. At the same time, these developments significantly enlarged the number of information channels people have to choose from when looking for information in organizational contexts. This endeavor is further complicated by the fact that a new generation of young high potentials, which is used to naturally applying these technologies in daily life, will soon enter the job market. The goal of this quantitative study among 171 subjects is to (1) get a clear understanding of behavioral patterns with respect to the selection of information channels depending on task complexity and personality traits and to (2) substantiate that communication theories can be applied to IS-related topics in the field of human information behavior.