nils-herrmann/clean_scirepeval huggingface dataset:
Emotionally-Relevant Features for Classification and Regression of Music Lyrics

This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams - baseline), adding other features, including novel features, improved the F-measure from 69.9, 82.7 and 85.6 percent to 80.1, 88.3 and 90 percent, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6 percent F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.
No Need to Hide: Protecting Safe Regions on Commodity Hardware

As modern 64-bit x86 processors no longer support the segmentation capabilities of their 32-bit predecessors, most research projects assume that strong in-process memory isolation is no longer an affordable option. Instead of strong, deterministic isolation, new defense systems therefore rely on the probabilistic pseudo-isolation provided by randomization to "hide" sensitive (or safe) regions. However, recent attacks have shown that such protection is insufficient; attackers can leak these safe regions in a variety of ways.
 In this paper, we revisit isolation for x86-64 and argue that hardware features enabling efficient deterministic isolation do exist. We first present a comprehensive study on commodity hardware features that can be repurposed to isolate safe regions in the same address space (e.g., Intel MPX and MPK). We then introduce MemSentry, a framework to harden modern defense systems with commodity hardware features instead of information hiding. Our results show that some hardware features are more effective than others in hardening such defenses in each scenario and that features originally conceived for other purposes (e.g., Intel MPX for bounds checking) are surprisingly efficient at isolating safe regions compared to their software equivalent (i.e., SFI).
6.5 A 64×64-pixel digital silicon photomultiplier direct ToF sensor with 100Mphotons/s/pixel background rejection and imaging/altimeter mode with 0.14% precision up to 6km for spacecraft navigation and landing

Recent technology surveys identified flash light detection and ranging technology as the best choice for the navigation and landing of spacecrafts in extraplanetary missions, working from single-point altimeter to range-imaging camera mode. Among all available technologies for a 2D array of direct time-of-flight (DTOF) pixels, CMOS single-photon avalanche diodes (SPADs) represent the ideal candidate due to their rugged design and electronics integration. However, state-of-the-art SPAD imagers are not designed for operation over a wide variety of scenarios, including variable background light, very long to short range, or fast relative movement.
A Study of Reinforcement Learning for Neural Machine Translation

Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. In this paper, taking several large-scale translation tasks as testbeds, we conduct a systematic study on how to train better NMT models using reinforcement learning. We provide a comprehensive comparison of several important factors (e.g., baseline reward, reward shaping) in RL training. Furthermore, to fill in the gap that it remains unclear whether RL is still beneficial when monolingual data is used, we propose a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data. By integrating all our findings, we obtain competitive results on WMT14 EnglishGerman, WMT17 English-Chinese, and WMT17 Chinese-English translation tasks, especially setting a state-of-the-art performance on WMT17 Chinese-English translation task.
Individual and social benefits of online discussion forums

There has been much debate surrounding the potential benefits and costs of online interaction. The present research argues that engagement with online discussion forums can have underappreciated benefits for users’ well-being and engagement in offline civic action, and that identification with other online forum users plays a key role in this regard. Users of a variety of online discussion forums participated in this study. We hypothesized and found that participants who felt their expectations had been exceeded by the forum reported higher levels of forum identification. Identification, in turn, predicted their satisfaction with life and involvement in offline civic activities. Formal analyses confirmed that identification served as a mediator for both of these outcomes. Importantly, whether the forum concerned a stigmatized topic moderated certain of these relationships. Findings are discussed in the context of theoretical and applied implications.
Tuning of the structure and parameters of a neural network using an improved genetic algorithm

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.
What You See is What You Get? Automatic Image Verification for Online News Content

Consuming news over online media has witnessed rapid growth in recent years, especially with the increasing popularity of social media. However, the ease and speed with which users can access and share information online facilitated the dissemination of false or unverified information. One way of assessing the credibility of online news stories is by examining the attached images. These images could be fake, manipulated or not belonging to the context of the accompanying news story. Previous attempts to news verification provided the user with a set of related images for manual inspection. In this work, we present a semi-automatic approach to assist news-consumers in instantaneously assessing the credibility of information in hypertext news articles by means of meta-data and feature analysis of images in the articles. In the first phase, we use a hybrid approach including image and text clustering techniques for checking the authenticity of an image. In the second phase, we use a hierarchical feature analysis technique for checking the alteration in an image, where different sets of features, such as edges and SURF, are used. In contrast to recently reported manual news verification, our presented work shows a quantitative measurement on a custom dataset. Results revealed an accuracy of 72.7% for checking the authenticity of attached images with a dataset of 55 articles. Finding alterations in images resulted in an accuracy of 88% for a dataset of 50 images.
Bringing the cloud to the edge

Edge services become increasingly important as the Internet transforms into an Internet of Things (IoT). Edge services require bounded latency, bandwidth reduction between the edge and the core, service resiliency with graceful degradation, and access to resources visible only inside the NATed and secured edge networks. While the data center based cloud excels at providing general purpose computation/storage at scale, it is not suitable for edge services. We present a new model for cloud computing, which we call the Edge Cloud, that addresses edge computing specific issues by augmenting the traditional data center cloud model with service nodes placed at the network edges. We describe the architecture of the Edge Cloud and its implementation as an overlay hybrid cloud using the industry standard OpenStack cloud management framework. We demonstrate the advantages garnered by two new classes of applications enabled by the Edge Cloud - a highly accurate indoor localization that saves on latency, and a scalable and resilient video monitoring that saves on bandwidth.
Agile Big Data Analytics for Web-Based Systems: An Architecture-Centric Approach

This article contributes an architecture-centric methodology, called AABA (Architecture-centric Agile Big data Analytics), to address the technical, organizational, and rapid technology change challenges of both big data system development and agile delivery of big data analytics for Web-based Systems (WBS). As the first of its kind, AABA fills a methodological void by adopting an architecture-centric approach, advancing and integrating software architecture analysis and design, big data modeling and agile practices. This article describes how AABA was developed, evolved and validated simultaneously in 10 empirical WBS case studies through three CPR (Collaborative Practice Research) cycles. In addition, this article presents an 11th case study illustrating the processes, methods and techniques/tools in AABA for cost-effectively achieving business goals and architecture agility in a large scale WBS. All 11 case studies showed that architecture-centric design, development, and operation is key to taming technical complexity and achieving agility necessary for successful WBS big data analytics development. Our contribution is novel and important. The use of reference architectures, a design concepts catalog and architectural spikes in AABA are advancements to architecture design methods. In addition, our architecture-centric approach to DevOps was critical for achieving strategic control over continuous big data value delivery for WBS.
Classification and synthesis of quality approaches in e-government services

The purpose of this paper is to present a multi-faceted summary and classification of the existing literature in the field of quality of service for e-government and outline the main components of a quality model for e-government services.  Starting with fundamental quality principles the paper examines and analyzes 36 different quality approaches concerning public sector services, e-services in general and more specifically e-government services. Based on the dimensions measured by each approach the paper classifies the approaches and concludes on the basic factors needed for the development of a complete quality model of e-government services.  Based on the classification of literature approaches, the paper provides information about the main components of a quality model that may be used for the continuous monitoring and measuring of public e-services’ quality. The classification forms the basis for answering questions that must be addressed by the quality model, such as: What to assess?; Who will perform the assessment? and How the assessment will be done?  This model can be used by the management of public organizations in order to measure and monitor the quality of e-services delivered to citizens.  The results of the work presented in this paper form the basis for the development of a quality model for e-government services.
Small-Size Multiresonant Octaband Antenna for LTE/WWAN Smartphone Applications

A compact multiresonant antenna for octaband LTE/WWAN operation in the internal smartphone applications is proposed and discussed in this letter. With a small volume of 15×25×4 mm3, the presented antenna comprises two direct feeding strips and a chip-inductor-loaded two-branch shorted strip. The two direct feeding strips can provide two resonant modes at around 1750 and 2650 MHz, and the two-branch shorted strip can generate a double-resonance mode at about 725 and 812 MHz. Moreover, a three-element bandstop matching circuit is designed to generate an additional resonance for bandwidth enhancement of the lower band. Ultimately, up to five resonances are achieved to cover the desired 704-960- and 1710-2690-MHz bands. Simulated and measured results are presented to demonstrate the validity of the proposed antenna.
Strategic alignment: a practitioner's perspective

Aligning information systems with organizational processes, goals and strategies is becoming increasingly important. Prior research has identified two dimensions of strategic alignment the social and intellectual. The former focuses primarily on the people involved in achieving alignment, whilsrihe latter is more likely to be associated with the investigation oj plans and planning methodologies. Until recently most research has concentrated on the intellectual dimension however the importance of the social dimension is being increasingly recognized. In most instances research is conducted on these two dimensions independently without consideration of the affect on the other. The research presented here, involving the creation of a causal-loop diagram by six senior IS/IT managers, presents a systemic view of the development of alignment within a typical organization and emphasizes the relationship between the social and intellectual dimensions. It indicates that practitioners understand that a high level of connection between IS/IT and business planning processes may be dependent on the level of integration between the IS/IT group and other sections of the organization. However, it appears that the culture oj many organizations is impeding the development of this integration.
User-Based Vehicle Relocation Techniques for Multiple-Station Shared-Use Vehicle Systems

Multiple-station shared-use vehicle systems allow users to travel between different activity centers and are well suited for resort communities, recreational areas, as well as university and corporate campuses. In this type of shared-use vehicle system, trips are more likely to be oneway each time, differing from other shared-use vehicle system models such as neighborhood carsharing and station cars where round-trips are more prevalent. Although convenient to users, a multiple-station system can suffer from a vehicle distribution problem. As vehicles are used throughout the day, they may become disproportionally distributed among the stations. As a result, it is necessary on occasion to relocate vehicles from one station to another. Relocations can be performed by system staff, which can be cumbersome and costly. In order to alleviate the distribution problem and reduce the number or relocations, we introduce two user-based relocation mechanisms called trip joining (or ridesharing) or trip splitting. When the system realizes that it is becoming imbalanced, it urges users that have more than one passenger to take separate vehicles when more vehicles are needed at the destination station (trip splitting). Conversely, if two users are at the origin station at the same time traveling to the same destination, the system can urge them to rideshare (trip joining). We have implemented this concept both on a real-world university campus shared vehicle system and in a high-fidelity computer simulation model. The model results show that there can be as much as a 42% reduction in the number of relocations using these techniques.
Differential Effects of Provider Recommendations and Consumer Reviews in E-Commerce Transactions: An Experimental Study

Despite the importance of online product recommendations (OPR) in e-Commerce transactions, there is still very little understanding about how different recommendation sources affect consumers' beliefs and behavior, and whether these effects are additive, complementary or rivals for different types of products. This study investigates the differential effects of provider recommendations (PR) and consumer reviews (CR) on the instrumental, affective and trusting dimensions of consumer beliefs, and show how these beliefs ultimately influence continued OPR usage and product purchase intentions. This study tests a conceptual model linking PR and CR to four consumer beliefs (perceived usefulness, perceived ease of use, perceived affective quality, and trust) in two different product settings (search products vs. experience products). Results of an experimental study (N = 396) show that users of PR express significantly higher perceived usefulness and perceived ease of use than users of CR, while users of CR express higher trusting beliefs and perceived affective quality than users of PR, resulting in different effect mechanisms towards OPR reuse and purchase intentions in e-Commerce transactions. Further, CR were found to elicit higher perceived usefulness, trusting beliefs and perceived affective quality for experience goods, while PR were found to unfold higher effects on all of these variables for search goods.
Gender identity outcome in female-raised 46,XY persons with penile agenesis, cloacal exstrophy of the bladder, or penile ablation.

This review addresses the long-term gender outcome of gender assignment of persons with intersexuality and related conditions. The gender assignment to female of 46,XY newborns with severe genital abnormalities despite a presumably normal-male prenatal sex-hormone milieu is highly controversial because of variations in assumptions about the role of biological factors in gender identity formation. This article presents a literature review of gender outcome in three pertinent conditions (penile agenesis, cloacal exstrophy of the bladder, and penile ablation) in infancy or early childhood. The findings clearly indicate an increased risk of later patient-initiated gender re-assignment to male after female assignment in infancy or early childhood, but are nevertheless incompatible with the notion of a full determination of core gender identity by prenatal androgens.
Two-stage genetic programming (2SGP) for the credit scoring model

Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF–THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models.  Institute of Management of Technology and Institute of Traffic and Transportation College of Management, National Chiao Tung University, 1001 TaHsueh Road, Hsinchu 300, Taiwan. E-mail address: u5460637@ms16.hinet.net (G.-H. Tzeng). 2 J.-J. Huang et al. /  (2005) xxx–xxx ARTICLE IN
Tempo And Beat Estimation Of Musical Signals

Tempo estimation is fundamental in automatic music processing and in many multimedia applications. This paper presents an automatic tempo tracking system that processes audio recordings and determines the beats per minute and temporal beat location. The concept of spectral energy flux is defined and leads to an efficient note onset detector. The algorithm involves three stages: a frontend analysis that efficiently extracts onsets, a periodicity detection block and the temporal estimation of beat locations. The performance of the proposed method is evaluated using a large database of 489 excerpts from several musical genres. The global recognition rate is 89.7 %. Results are discussed and compared to other tempo estimation systems.
Dual-Band Slot Helix Antenna for Global Positioning Satellite Applications

A slot helix antenna working in Global Positioning System (GPS) L1 and L2 bands is proposed in this paper. Adopting sequential rotation technique, the proposed antenna is equipped with six helix radiation arms and fed by a microstrip line with folded shape. The whole antenna can be built on a full printed circuit board; so, it is easy to process the proposed antenna. The circularly polarized radiation mechanism of a circular array is analyzed in a new perspective to guide the design. A double-faced slot radiation structure is also established for dual-band operation. Additionally, a traveling wave feeding network suitable for the proposed antenna is presented. The experimental results show that the proposed antenna has good properties in GPS L1 and L2 bands.
Automatically Classifying Question Types for Consumer Health Questions

We present a method for automatically classifying consumer health questions. Our thirteen question types are designed to aid in the automatic retrieval of medical answers from consumer health resources. To our knowledge, this is the first machine learning-based method specifically for classifying consumer health questions. We demonstrate how previous approaches to medical question classification are insufficient to achieve high accuracy on this task. Additionally, we describe, manually annotate, and automatically classify three important question elements that improve question classification over previous techniques. Our results and analysis illustrate the difficulty of the task and the future directions that are necessary to achieve high-performing consumer health question classification.