nils-herrmann/clean_scirepeval huggingface dataset:
ADAPT-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms

Ridesharing platforms use dynamic pricing as a means to control the network's supply and demand at different locations and times (e.g., Lyft's Prime Time and Uber's Surge Pricing) to increase revenue. These algorithms only consider the network's current supply and demand only at a ride's origin to adjust the price of the ride. In this work, we show how we can increase the platform's revenue while lowering the prices as compared to state-of-the-art algorithms, by considering the network's future demand. Furthermore, we show if rather than setting the price of a ride only based on the supply and demand at its origin, we use predictive supply and demand at both the ride's origin and destination, we can further increase the platform's overall revenue. Using a real-world data set from New York City, we show our pricing method can increase the revenue by up to 15% while reducing the price of the rides by an average of 5%. Furthermore, we show that our methods are resilient to up to 25% error in future demand prediction.
Narcissism and attractiveness

Is narcissism related to observer-rated attractiveness? Two views imply that narcissism is unrelated to attractiveness: positive illusions theory and Feingold’s (1992) attractiveness theory (i.e., attractiveness is unrelated to personality in general). In contrast, two other views imply that narcissism is positively related to attractiveness: an evolutionary perspective on narcissism (i.e., selection pressures in shortterm mating contexts shaped the evolution of narcissism, including greater selection for attractiveness in short-term versus long-term mating contexts) and, secondly, the self-regulatory processing model of narcissism (narcissists groom themselves to bolster grandiose self-images). A meta-analysis (N > 1000) reveals a small but reliable positive narcissism–attractiveness correlation that approaches the largest known personality–attractiveness correlations. The finding supports the evolutionary and self-regulatory views of narcissism.
Issues in implementing ERP: A case study

Information technology/systems play a major role in improving the competitiveness of organizations. There are numerous enterprise information software packages available in the market. One of them is enterprise resource planning (ERP). ERP can provide significant improvements in efficiency across a company, but only when implemented correctly. Otherwise, an ERP system could be a curse and drag the whole enterprise into spiraling inefficiency. Planning for ERP systems and their implementations requires an integrated approach to meet the requirements of various functional areas. With a brief overview of ERP implementations, this paper describes some experiences of an ERP implementation in a water corporation. The case study reveals some of the intricacies during the planning and implementation stages that may occur in any company in any part of the world. Suggestions are offered in resolving the issues of implementing ERP.
Out of Control: Overcoming Control-Flow Integrity

As existing defenses like ASLR, DEP, and stack cookies are not sufficient to stop determined attackers from exploiting our software, interest in Control Flow Integrity (CFI) is growing. In its ideal form, CFI prevents flows of control that were not intended by the original program, effectively putting a stop to exploitation based on return oriented programming (and many other attacks besides). Two main problems have prevented CFI from being deployed in practice. First, many CFI implementations require source code or debug information that is typically not available for commercial software. Second, in its ideal form, the technique is very expensive. It is for this reason that current research efforts focus on making CFI fast and practical. Specifically, much of the work on practical CFI is applicable to binaries, and improves performance by enforcing a looser notion of control flow integrity. In this paper, we examine the security implications of such looser notions of CFI: are they still able to prevent code reuse attacks, and if not, how hard is it to bypass its protection? Specifically, we show that with two new types of gadgets, return oriented programming is still possible. We assess the availability of our gadget sets, and demonstrate the practicality of these results with a practical exploit against Internet Explorer that bypasses modern CFI implementations.
Optimizing Compilers for Modern Architectures: A Dependence-Based Approach [Book Review]

O ptimizing Compilers for Modern  A Dependence-Based Approach, Randy Allen and Ken Kennedy. Although modern computer architectures designed with high-speed microprocessors offer potential performance gains, their complexity makes it increasingly difficult to produce efficient code. The authors assert that compilers can play a vital role in addressing this issue. They base their methods on data dependence, a fundamental compiler analysis tool for optimizing programs on high-performance microprocessors and parallel architectures. Allen and Kennedy provide a broad introduction to data dependence, the many transformation strategies it supports , and its application to optimization problems. They apply their techniques to several languages, including Fortran 77, C, hardware description languages, Fortran 90, and High-Performance Fortran. Worked examples demonstrate each compiled transformation , while two case-study compilers implement the theories described in each chapter. S ecure Communicating Systems: Design, Analysis, and Implementation , Michael R.A. Huth. Increasingly , computer professionals must deal with the use, maintenance, or cus-tomization of cryptographic components and certification mechanisms for local or mobile code. Huth explores the essentials of cryptographic systems , security protocols, and secure information flow in programs. Moving beyond this standard material , the author describes NIST's advanced encryption standard, an optimal public-key encryption using RSA that supports semantic security through a theoretical analysis conducted in the random oracle model, a formal method for specifying and debugging security protocols, and other timely topics. Intended for advanced undergraduate and beginning graduate students, this text also contains many implementation projects and a proof of primitive roots for prime powers. Huth also examines the moral, legal, and political ramifications of cryptology. and Safwat Zaky. In this updated, first-level electrical, computer engineering, and computer science text, the authors present key computer architectural concepts drawn from commercially successful computers. The first eight chapters cover computer organization, operation, and performance. The remaining four deal with embedded systems , peripheral devices, processor-family evolution patterns, and large computer systems. Hamacher and colleagues use the Motorola 68000, Intel Pentium, and ARM RISC processors to provide detailed examples early in the book. These examples demonstrate how to implement basic concepts in reduced-instruction-set and complex-instruction set computing design styles, making it easier for students to write simple programs.  Testing for the New Century, W. Eric Wong, ed. Mutation testing— based upon applying a mutation operator that seeds the test implementation with a fault, then determining whether testing identifies this fault—has drawn the attention of developers …
Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.
Detecting Patterns of Crime with Series Finder

Many crimes can happen every day in a major city, and figuring out which ones are committed by the same individual or group is an important and difficult data mining challenge. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a “seed” of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern. We compared Series Finder with classic clustering and classification models applied to crime analysis. It has promising results on a decade’s worth of crime pattern data from the Cambridge Police Department.
Kademlia: A Peer-to-Peer Information System Based on the XOR Metric

We describe a peer-to-peer distributed hash table with provable consistency and performance in a fault-prone environment. Our system routes queries and locates nodes using a novel XOR-based metric topology that simplifies the algorithm and facilitates our proof. The topology has the property that every message exchanged conveys or reinforces useful contact information. The system exploits this information to send parallel, asynchronous query messages that tolerate node failures without imposing timeout delays on users.
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a method of general applicability, that does not rely on domain knowledge such as document or argument structure. We evaluate our method on a challenging dataset consisting of usergenerated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.
Diagnostic criteria for complicated grief disorder.

Some prolonged and turbulent grief reactions include symptoms that differ from the DSM-IV criteria for major depressive disorder. The authors investigated a new diagnosis that would include these symptoms.



They developed observer-based definitions of 30 symptoms noted clinically in previous longitudinal interviews of bereaved persons and then designed a plan to investigate whether any combination of these would serve as criteria for a possible new diagnosis of complicated grief disorder. Using a structured diagnostic interview, they assessed 70 subjects whose spouses had died. Latent class model analyses and signal detection procedures were used to calibrate the data against global clinical ratings and self-report measures of grief-specific distress.



Complicated grief disorder was found to be characterized by a smaller set of the assessed symptoms. Subjects elected by an algorithm for these symptoms patterns did not significantly overlap with subjects who received a diagnosis of major depressive disorder.



A new diagnosis of complicated grief disorder may be indicated. Its criteria would include the current experience (more than a year after a loss) of intense intrusive thoughts, pangs of severe emotion, distressing yearnings, feeling excessively alone and empty, excessively avoiding tasks reminiscent of the deceased, unusual sleep disturbances, and maladaptive levels of loss of interest in personal activities.
Box 1 . Common neural network models Neuron

Deep learning is the trendiest tool in a computational biologist’s toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.
On First-Order Definability and Computability of Progression for Local-Effect Actions and Beyond

This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points, and by maintaining only one value hyperplane per point, it is able to successfully solve large problems, including the roboticTagdomain, a POMDP version of the popular game of lasertag.
Industrie 4.0: Enabling technologies

With the development of industries, we have realized the third industrial revolution. Following the development of Cyber-Physical Systems (CPS), industrial wireless network and some other enabling technologies, the fourth industrial revolution is being gradually rolled out. This paper presents an overview of the background, concept, basic methods, major technologies and application scenarios for industrie 4.0. In our view, industrie 4.0 as an abstract concept can closely integrate the physical world with virtual world. This strategy of industrie 4.0 will lead to more and more people coming to participate in the manufacturing process and further popularize our products through CPS technology. The typical approach for industrie 4.0 is the social manufacturing. In fact, the social manufacturing can directly link our customers' need and our industries, but it must be based on the enabling technologies, such as embedded systems, wireless sensor network, industrial robots, 3D printing, cloud computing, and big data. Therefore, this paper in detail explains these concepts, advantages and the relations to industries. We can foresee that our life will be changed to be more efficient, fast, safe and convenient due to the development of industrie 4.0 in the near future.
An Example Usage of the JCSE

This is an example tex file which demonstrates the functionalities of JCSE LTEXtemplate. Lengthening the abstract for testing purpose: Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Blah blah blah blah blah blah blah blah blah blah blah blah. Gigabit switches must work. After years of key research into checksums, we confirm the simulation of cache coherence, which embodies the confusing principles of cryptoanalysis. GulyFlea, our new methodology for read-write information, is the solution to all of these grand challenges.
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
Antecedents of Brand Recall and Brand Attitude towards Purchase Intention in advergames

Research on purchase intention had showed remarkable work in contributing to the theory of buyer behaviour. However, the impact of brand recall and brand attitude on purchase intention in advergames has not yet been well explored especially in a developing economic country such as Malaysia. Furthermore, the influences of selfbrand congruity, entertainment and brand placement acceptance as antecedents to brand recall and brand attitude are expecting to put more “value added” to the brand recall, brand attitude and purchase intention relationships. This paper aims to investigate the impact of brand recall and brand attitude on purchase intention in advergames among Malaysian gamers. Also, the antecedents towards brand recall and brand attitude were examined to measure their influences on purchase intention. A total of 350 Malaysian gamers have been interviewed through an online survey for this study. The results showed that self-brand congruity, entertainment and brand placement acceptance were found to have significant relationship with brand attitude and purchase intention. Also, both entertainment and brand placement acceptance were found to have significant relationship with brand recall. As expected, brand recall and brand attitude have positive relationship with consumer purchase intention in advergames. Applications of the study are discussed.
Intrinsic network connectivity and own body perception in gender dysphoria

Gender dysphoria (GD) is characterized by incongruence between one’s identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception –default mode network (DMN) and salience network – and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher “self” ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.
Paradigm Whose Time Has Come by

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CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks

With the widespread applications of deep convolutional neural networks (DCNNs), it becomes increasingly important for DCNNs not only to make accurate predictions but also to explain how they make their decisions. In this work, we propose a CHannel-wise disentangled InterPretation (CHIP) model to give the visual interpretation to the predictions of DCNNs. The proposed model distills the class-discriminative importance of channels in networks by utilizing the sparse regularization. Here, we first introduce the network perturbation technique to learn the model. The proposed model is capable to not only distill the global perspective knowledge from networks but also present the class-discriminative visual interpretation for specific predictions of networks. It is noteworthy that the proposed model is able to interpret different layers of networks without re-training. By combining the distilled interpretation knowledge in different layers, we further propose the Refined CHIP visual interpretation that is both high-resolution and class-discriminative. Experimental results on the standard dataset demonstrate that the proposed model provides promising visual interpretation for the predictions of networks in image classification task compared with existing visual interpretation methods. Besides, the proposed method outperforms related approaches in the application of ILSVRC 2015 weakly-supervised localization task.
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture is stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-arts in both photo sketch generation and face sketch verification.