nils-herrmann/clean_scirepeval huggingface dataset:
Event Cube: Another Perspective on Business Processes

In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples.
Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.
Dose the Use of Big Data Analytics Guarantee a High Firm Performance? An Empirical Study Bases on the Dynamic Capabilities Theory

For the sake of enriching relevant research field, this study focuses on two main questions: (1) What is the effect of DBA usage on firm performance (including market performance and operational performance)? and (2) What factors that drive organizations to use big data analytics (BDA) are key drivers, based on dynamic capabilities theory? And we also identified the moderating effect of environmental dynamism between BDA use and firm performance. Furthermore, we introduce the need pull/technology push framework to identify and theorize paths via which factors influence the use of BDA. Purely from the need pull/technology push theory perspective, we propose that perceived usefulness of BDA should belong to need pull factor. Thus, we select perceived usefulness and satisfaction level with existing IT technology as need pull factors, BDA use of competitors and technology compatibility as technology push factors for big data analytics (BDA) use, and examines if these factors have a positive effect on big data analytics (BDA).
Structure and Levels of Meaning in Life and Its Relationship With Mental Health in Chinese Students Aged 10 to 25

This study examines the usage of the Meaning in Life Questionnaire in Chinese students aged from 10 to 25 within four age groups (N = 5,510): early adolescence (10–13 years old, n = 1,258), middle adolescence (14–17 years old, n = 1,987), late adolescence (18–21 years old, n = 1,950) and early adulthood (22–25 years old, n = 315); and analyses the structure and levels of meaning in life, as well as the relationship between meaning in life and mental health. Results showed that: (1) the Meaning in Life Questionnaire in the four age groups of Chinese students had good construct validity and internal consistency reliability; (2) the average levels of the presence of meaning and search for meaning of Chinese students were moderate or above, and had obvious differences according to gender and family location (i.e., urban vs. rural); (3) the level of presence of meaning showed a trend of rising rapidly in middle adolescence and the level of search for meaning continued to rise in early adolescence and fell rapidly towards the end of adolescence; (4) presence of meaning was positively related to life satisfaction and positive affect and negatively related to depression and negative affect, and the same correlations were found with search for meaning.
DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis

The prevalence of mobile platforms, the large market share of Android, plus the openness of the Android Market makes it a hot target for malware attacks. Once a malware sample has been identified, it is critical to quickly reveal its malicious intent and inner workings. In this paper we present DroidScope, an Android analysis platform that continues the tradition of virtualization-based malware analysis. Unlike current desktop malware analysis platforms, DroidScope reconstructs both the OSlevel and Java-level semantics simultaneously and seamlessly. To facilitate custom analysis, DroidScope exports three tiered APIs that mirror the three levels of an Android device: hardware, OS and Dalvik Virtual Machine. On top of DroidScope, we further developed several analysis tools to collect detailed native and Dalvik instruction traces, profile API-level activity, and track information leakage through both the Java and native components using taint analysis. These tools have proven to be effective in analyzing real world malware samples and incur reasonably low performance overheads.
MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis

There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis.
Learning Robust Dialog Policies in Noisy Environments

Modern virtual personal assistants provide a convenient interface for completing daily tasks via voice commands. An important consideration for these assistants is the ability to recover from automatic speech recognition (ASR) and natural language understanding (NLU) errors. In this paper, we focus on learning robust dialog policies to recover from these errors. To this end, we develop a user simulator which interacts with the assistant through voice commands in realistic scenarios with noisy audio, and use it to learn dialog policies through deep reinforcement learning. We show that dialogs generated by our simulator are indistinguishable from human generated dialogs, as determined by human evaluators. Furthermore, preliminary experimental results show that the learned policies in noisy environments achieve the same execution success rate with fewer dialog turns compared to fixed rule-based policies.
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision – convolutional neural networks (CNNs). We start with “AlexNet” as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.
An optimal technology mapping algorithm for delay optimization in lookup-table based FPGA designs

In this paper we present a polynomial time technology mapping algorithm, called Flow-Map, that optimally solves the LUT-based FPGA technology mapping problem for depth minimization for general Boolean networks. This theoretical breakthrough makes a sharp contrast with the fact that conventional technology mapping problem in library-based designs is NP-hard. A key step in Flow-Map is to compute a minimum height K-feasible cut in a network, solved by network flow computation. Our algorithm also effectively minimizes the number of LUTs by maximizing the volume of each cut and by several postprocessing operations. We tested the Flow-Map algorithm on a set of benchmarks and achieved reductions on both the network depth and the number of LUTs in mapping solutions as compared with previous algorithms.
AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of AI for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The papers are categorised into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorised in terms of the AI techniques used: genetic algorithms, case based reasoning, knowledge based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.
Convolutional Scale Invariance for Semantic Segmentation

We propose an effective technique to address large scale variation in images taken from a moving car by cross-breeding deep learning with stereo reconstruction. Our main contribution is a novel scale selection layer which extracts convolutional features at the scale which matches the corresponding reconstructed depth. The recovered scaleinvariant representation disentangles appearance from scale and frees the pixel-level classifier from the need to learn the laws of the perspective. This results in improved segmentation results due to more efficient exploitation of representation capacity and training data. We perform experiments on two challenging stereoscopic datasets (KITTI and Cityscapes) and report competitive class-level IoU performance.
A method of 3D CAD model retrieval based on spatial bag of words

In order to improve both the discriminative power for models’ local parts and the searching efficiency in 3D CAD model retrieval, a novel hierarchical feature descriptor for retrieval based on spatial bag of words is proposed in this paper. By extracting the essential information from Boundary Representation (B-Rep), 3D CAD models are transformed to Labelled Attribute Adjacency Graphs (LAAGs). Next, the models in training dataset are segmented into different regions according to their corresponding LAAG with an improved segmentation method. All collections of these local regions are described as local feature vectors with graph spectrum, and the codebook is created by clustering all these vectors. Each library model is then decomposed with the same methods mentioned above and globally represented as a spatial histogram of word pairs along with the adjacent relations of its regions, called Spatial Bags-of-Words (SBoWs),and then, the hierarchical feature descriptor(HFD) of each library model composed of global SBoWs and local graph spectrum is constructed. Finally, according to HFD, a two-level searching framework is presented for CAD model retrieval: the candidates are acquired by comparing the query with each target model based on their SBoWs vectors, and the remaining candidates are verified using optimal matching algorithm according to their local features. Experimental results show that the proposed methods promote both retrieval quality and efficiency significantly, so they can support the effective reuse of CAD models for enterprises.
Adaptive RF Front-Ends Using Electrical-Balance Duplexers and Tuned SAW Resonators

This paper proposes an adaptive RF front-end (RFFE) architecture that uses an electrical-balance duplexer (EBD) and tuned surface acoustic wave (SAW) resonators to enable adaptive dual-frequency isolation suited for LTE’s frequency-division duplex (FDD) mode of operation. Alternatively, in the in-band full-duplex (IBFD) mode, the EBD cancels the in-band TX self-interference directly at RF for increased channel capacity. Furthermore, the EBD’s balance network is optimized to reduce the TX insertion loss (IL) below the nominal 3-dB limit in the FDD mode. A 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SOI CMOS prototype implements the EBD, low-noise amplifier, and SAW-tuning capacitor banks. The chip is mounted to a module substrate together with the SAWs and matching components. The RFFE achieves >50-dB dual-frequency isolation and 2.6–3.4-dB TX IL in FDD mode, has +58/+42-dBm TX/RX-path IIP<sub>3</sub> for an increased IBFD-link budget and handles up to +27.5-dBm TX power in both modes.
Multi-layer dependability: From microarchitecture to application level

We show in this paper that multi-layer dependability is an indispensable way to cope with the increasing amount of technology-induced dependability problems that threaten to proceed further scaling. We introduce the definition of multi-layer dependability and present our design flow within this paradigm that seamlessly integrates techniques starting at circuit layer all the way up to application layer and thereby accounting for ASIC-based architectures as well as for reconfigurable-based architectures. At the end, we give evidence that the paradigm of multi-layer dependability bears a large potential for significantly increasing dependability at reasonable effort.
Free viewpoint video (FVV) survey and future research direction

Free viewpoint video (FVV) is one of the new trends in the development of advanced visual media type that aims to provide a new immersive user experience and interactivity that goes beyond higher image quality (HD/4K TV) and higher realism (3D TV). Potential applications include interactive personal visualization and free viewpoint navigation. The goal of this paper is to provide an overview of the FVV system and some target application scenarios. Associated standardization activities and technological barriers to overcome are also described. This paper is organized as follows: a general description of the FVV system and functionalities is given in Section I. Since an FVV system is composed of a chain of processing modules, an in-depth functional description of each module is provided in Section II. Examples of emerging FVV applications and use cases are given in Section III. A summary of technical challenges to overcome for wider usage and market penetration of FVV is given in Section IV.
Smiles when lying.

Subtle differences among forms of smiling distinguished when subjects were truthful and when they lied about experiencing pleasant feelings. Expressions that included muscular activity around the eyes in addition to the smiling lips occurred more often when people were actually enjoying themselves as compared with when enjoyment was feigned to conceal negative emotions. Smiles that included traces of muscular actions associated with disgust, fear, contempt, or sadness occurred more often when subjects were trying to mask negative emotions with a happy mask. When these differences among types of smiling were ignored and smiling was treated as a unitary phenomenon, there was no difference between truthful and deceptive behavior.
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences

We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.
Database security - concepts, approaches, and challenges

As organizations increase their reliance on, possibly distributed, information systems for daily business, they become more vulnerable to security breaches even as they gain productivity and efficiency advantages. Though a number of techniques, such as encryption and electronic signatures, are currently available to protect data when transmitted across sites, a truly comprehensive approach for data protection must also include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. Also, techniques for data integrity and availability specifically tailored to database systems must be adopted. In this respect, over the years, the database security community has developed a number of different techniques and approaches to assure data confidentiality, integrity, and availability. However, despite such advances, the database security area faces several new challenges. Factors such as the evolution of security concerns, the "disintermediation" of access to data, new computing paradigms and applications, such as grid-based computing and on-demand business, have introduced both new security requirements and new contexts in which to apply and possibly extend current approaches. In this paper, we first survey the most relevant concepts underlying the notion of database security and summarize the most well-known techniques. We focus on access control systems, on which a large body of research has been devoted, and describe the key access control models, namely, the discretionary and mandatory access control models, and the role-based access control (RBAC) model. We also discuss security for advanced data management systems, and cover topics such as access control for XML. We then discuss current challenges for database security and some preliminary approaches that address some of these challenges.
Content-Based Table Retrieval for Web Queries

Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress towards improving this area requires powerful models of semantic matching and richer training and evaluation resources. To remedy this, we present a ranking based approach, and implement both carefully designed features and neural network architectures to measure the relevance between a query and the content of a table. Furthermore, we release an open-domain dataset that includes 21,113 web queries for 273,816 tables. We conduct comprehensive experiments on both real world and synthetic datasets. Results verify the effectiveness of our approach and present the challenges for this task.
An Ultralow-Power Low-Noise CMOS Biopotential Amplifier for Neural Recording

This brief presents a design strategy for a neural recording amplifier array with ultralow-power low-noise operation that is suitable for large-scale integration. The topology combines a highly efficient but supply-sensitive single-ended first stage with a shared reference channel and a differential second stage to effect feedforward supply noise cancellation, combining the low power of single-ended amplifiers with improved supply rejection. For a two-channel amplifier, the measurements show a midband gain of 58.7 dB and a passband from 490 mHz to 10.5 kHz. The amplifier consumes 2.85 μA per channel from a 1-V supply and exhibits an input-referred noise of 3.04 μVrms from 0.1 Hz to 100 kHz, corresponding to a noise efficiency factor of 1.93. The power supply rejection ratio is better than 50 dB in the passband. The amplifier is fabricated in a 90-nm CMOS process and occupies 0.137 mm2 of chip area.
Tumor Segmentation in Brain MRI using a Fuzzy Approach with Class Center Priors

This paper proposes a new fuzzy approach for automatic segmentation of normal and pathological brain MRI volumetric data sets. MRI is generally useful for brain tumor deduction because it provide more detailed information about its type, position, size. Brain tumor segmentation is the separation of different tumor tissues from normal brain tissue. In automatic brain segmentation MRI is a sophisticated tool for medical imaging. Fuzzy is used to segment all the tissues at the same time, and FCM algorithm is used to speed up tumor performances under and noisy and provide better accuracy and fast response. The result obtain from different segmentation by using fuzzy algorithm and comparing normal and pathological brain by automatic segmentation method.
Availability : A Heuristic for Judging Frequency and Probability 122

This paper explores a judgmental heuristic in which a person evaluates the frequency of classes or the probability of events by availability, i.e., by the ease with which relevant instances come to mind. In general, availability is correlated with ecological frequency, but it is also affected by other factors. Consequently, the reliance on the availability heuristic leads to systematic biases. Such biases are demonstrated in the judged frequency of classes of words, of combinatorial outcomes, and of repeated events. The phenomenon of illusory correlation is explained as an availability bias. The effects of the availability of incidents and scenarios on subjective probability are discussed.