nils-herrmann/clean_scirepeval huggingface dataset:
Foundations of Internet-enabled Democracy

The Internet revolution of democracy, which will transform earthly representative democracies by employing the communication and collaboration capabilities of the Internet, has yet to come. Here, we enlist the wisdom of our forefathers to lead the way. By consulting the 1789 Declaration of the Rights of Man and of the Citizen, we distill core values of democracy and derive from them requirements for the foundations of Internet-enabled democracy. Building these foundations can usher the urgently-needed revolution of democracy.
Concolic Execution for Django Applications

Concolic execution systems allow developers to verify that invariants in their applications are not violated no matter what input is given by a user. This was demonstrated in Lab 3, where the Z3 solver was used to find inputs that would trigger inconsistencies in Zoobar balances. Unfortunately, the lab’s framework is written specifically for Zoobar, and would therefore not work for other applications without substantial modifications. Invariant checking is useful for a wide range of applications, and thus we decided to make our 6.858 final project building a generic concolic execution interface for any Django-based web application.
Automation, per se, is not job elimination: How artificial intelligence forwards cooperative human-machine coexistence

Recent advances in artificial intelligence (AI) and machine learning, combined with developments in neuromorphic hardware technologies and ubiquitous computing, promote machines to emulate human perceptual and cognitive abilities in a way that will continue the trend of automation for several upcoming decades. Despite the gloomy scenario of automation as a job eliminator, we argue humans and machines can cross-fertilise in a way that forwards a cooperative coexistence. We build our argument on three pillars: (i) the economic mechanism of automation, (ii) the dichotomy of ‘experience’ that separates the first-person perspective of humans from artificial learning algorithms, and (iii) the interdependent relationship between humans and machines. To realise this vision, policy makers have to implement alternative educational approaches that support lifelong training and flexible job transitions.
A review of smart homes - Present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.
Terminology Extraction: An Analysis of Linguistic and Statistical Approaches

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.
AI for game production

A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms and portals, social and mobile games, and the emergence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game production practices. Specifically, Game AI agents should act as “producers” responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI in game production. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers.
A Dependency Tree Based Approach for Sentence-Level Sentiment Classification

Sentiment classification is a much-researched field that identifies positive or negative emotions in a large number of texts. Most existing studies focus on document-based approaches and documents are represented as bag-of word. Therefore, this feature representation fails to obtain the relation or associative information between words and it can't distinguish different opinions of a sentiment word with different targets. In this paper, we present a dependency tree-based sentence-level sentiment classification approach. In contrast to a document, a sentence just contains little information and a small set of features which can be used for the sentiment classification. So we not only capture flat features (bag-of-word), but also extract structured features from the dependency tree of a sentence. We propose a method to add more information to the dependency tree and provide an algorithm to prune dependency tree to reduce the noisy, and then introduce a convolution tree kernel-based approach to the sentence-level sentiment classification. The experimental results show that our dependency tree-based approach achieved significant improvement, particularly for implicit sentiment classification.
Safety critical systems: challenges and directions

Safety-critical systems are those systems whose failure could result in loss of life, significant property damage, or damage to the environment. There are many well known examples in application areas such as medical devices, aircraft flight control, weapons, and nuclear systems. Many modern information systems are becoming safety-critical in a general sense because financial loss and even loss of life can result from their failure. Future safety-critical systems will be more common and more powerful. From a software perspective, developing safety critical systems in the numbers required and with adequate dependability is going to require significant advances in areas such as specification, architecture, verification, and process. The very visible problems that have arisen in the area of information-system security suggests that security is a major challenge also.
The assessment and analysis of handedness: the Edinburgh inventory.

The need for a simply applied quantitative assessment of handedness is discussed and some previous forms reviewed. An inventory of 20 items with a set of instructions and responseand computational-conventions is proposed and the results obtained from a young adult population numbering some 1100 individuals are reported. The separate items are examined from the point of view of sex, cultural and socio-economic factors which might appertain to them and also of their inter-relationship to each other and to the measure computed from them all. Criteria derived from these considerations are then applied to eliminate 10 of the original 20 items and the results recomputed to provide frequency-distribution and cumulative frequency functions and a revised item-analysis. The difference of incidence of handedness between the sexes is discussed.
Deep Auxiliary Learning for Visual Localization and Odometry

Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work, we propose VLocNet, a new convolutional neural network architecture for 6-DoF global pose regression and odometry estimation from consecutive monocular images. Our multitask model incorporates hard parameter sharing, thus being compact and enabling real-time inference, in addition to being end-to-end trainable. We propose a novel loss function that utilizes auxiliary learning to leverage relative pose information during training, thereby constraining the search space to obtain consistent pose estimates. We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation. Furthermore, we present extensive experimental evaluations utilizing our proposed Geometric Consistency Loss that show the effectiveness of multitask learning and demonstrate that our model is the first deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches.
Self-Compassion and Psychological Well-Being in Older Adults

Self-compassion refers to a kind and nurturing attitude toward oneself during situations that threaten one’s adequacy, while recognizing that being imperfect is part of being human. Although growing evidence indicates that selfcompassion is related to a wide range of desirable psychological outcomes, little research has explored self-compassion in older adults. The present study investigated the relationships between self-compassion and theoretically based indicators of psychological adjustment, as well as the moderating effect of self-compassion on self-rated health. A sample of 121 older adults recruited from a community library and a senior day center completed self-report measures of self-compassion, self-esteem, psychological well-being, anxiety, and depression. Results indicated that self-compassion is positively correlated with age, self-compassion is positively and uniquely related to psychological well-being, and self-compassion moderates the association between self-rated health and depression. These results suggest that interventions designed to increase self-compassion in older adults may be a fruitful direction for future applied research.
VALIDATION OF NECK INJURY CRITERIA USING RECONSTRUCTED REAL-LIFE REAR-END CRASHES WITH RECORDED CRASH PULSES

To date no AIS1 neck injury mechanism has been established, thus no neck injury criterion has been validated against such mechanism. Validation methods not related to an injury mechanism may be used. The aim of this paper was to validate different proposed neck injury criteria with reconstructed reallife crashes with recorded crash pulses and with known injury outcomes. A car fleet of more than 40,000 cars fitted with crash pulse recorders have been monitored in Sweden since 1996. All crashes with these cars, irrespective of repair cost and injury outcome have been reported. With the inclusion criteria of the three most represented car models, single rear-end crashes with a recorded crash pulse, and front seat occupants with no previous long-term AIS1 neck injury, 79 crashes with 110 front seat occupants remained to be analysed in this study. Madymo models of a BioRID II dummy in the three different car seats were exposed to the recorded crash pulses. The dummy readings were correlated to the real-life injury outcome, divided into duration of AIS1 neck injury symptoms. Effectiveness to predict neck injury was assessed for the criteria NIC, Nkm, NDC and lower neck moment, aimed at predicting AIS1 neck injury. Also risk curves were assessed for the effective criteria as well as for impact severity. It was found that NICmax and Nkm are applicable to predict risk of AIS1 neck injury when using a BioRID dummy. It is suggested that both BioRID NICmax and Nkm should be considered in rear-impact test evaluation. Furthermore, lower neck moment was found to be less applicable. Using the BioRID dummy NDC was also found less applicable.
List-wise learning to rank with matrix factorization for collaborative filtering

A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is obtained by minimizing a loss function that represents the uncertainty between training lists and output lists produced by a MF ranking model. ListRank-MF enjoys the advantage of low complexity and is analytically shown to be linear with the number of observed ratings for a given user-item matrix. We also experimentally demonstrate the effectiveness of ListRank-MF by comparing its performance with that of item-based collaborative recommendation and a related state-of-the-art collaborative ranking approach (CoFiRank).
An Analysis of Neural Language Modeling at Multiple Scales

Many of the leading approaches in language modeling introduce novel, complex and specialized architectures. We take existing state-of-the-art word level language models based on LSTMs and QRNNs and extend them to both larger vocabularies as well as character-level granularity. When properly tuned, LSTMs and QRNNs achieve stateof-the-art results on character-level (Penn Treebank, enwik8) and word-level (WikiText-103) datasets, respectively. Results are obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single modern GPU.
Strategic leadership of ethical behavior in business

Executive Overview The strategic leadership of ethical behavior in business can no longer be ignored. Executives must accept the fact that the moral impact of their leadership presence and behaviors will rarely, if ever, be neutral. In the leadership capacity, executives have great power to shift the ethics mindfulness of organizational members in positive as well as negative directions. Rather than being left to chance, this power to serve as ethics leaders must be used to establish a social context within which positive self-regulation of ethical behavior becomes a clear and compelling organizational norm and in which people act ethically as a matter of routine. This article frames the responsibility for strategic leadership of ethical behavior on three premises: (1) It must be done—a stakeholder analysis of the total costs of ethical failures confirms the urgency for ethics change; (2) It can be done—exemplars show that a compelling majority of an organization’s membership can be influenced to make ethical choices; (3) It is sustainable—integrity programs help build and confirm corporate cultures in which principled actions and ethics norms predominate.
Taming Information-Stealing Smartphone Applications (on Android)

Smartphones have been becoming ubiquitous and mobile users are increasingly relying on them to store and handle personal information. However, recent studies also reveal the disturbing fact that users’ personal information is put at risk by (rogue) smartphone applications. Existing solutions exhibit limitations in their capabilities in taming these privacy-violating smartphone applications. In this paper, we argue for the need of a new privacy mode in smartphones. The privacy mode can empower users to flexibly control in a fine-grained manner what kinds of personal information will be accessible to an application. Also, the granted access can be dynamically adjusted at runtime in a fine-grained manner to better suit a user’s needs in various scenarios (e.g., in a different time or location). We have developed a system called TISSA that implements such a privacy mode on Android. The evaluation with more than a dozen of information-leaking Android applications demonstrates its effectiveness and practicality. Furthermore, our evaluation shows that TISSA introduces negligible performance overhead.
Field-aware Factorization Machines in a Real-world Online Advertising System

Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only effective to win challenges but is also valuable in a real-world prediction system. We also discuss some specific challenges and solutions to reduce the training time, namely the use of an innovative seeding algorithm and a distributed learning mechanism.
Ethical Analyses of Smart City Applications

When it comes to smart cities, one of the most important components is data. To enable smart city applications, data needs to be collected, stored, and processed to accomplish intelligent tasks. In this paper we discuss smart cities and the use of new and existing technologies to improve multiple aspects of these cities. There are also social and environmental aspects that have become important in smart cities that create concerns regarding ethics and ethical conduct. Thus we discuss various issues relating to the appropriate and ethical use of smart city applications and their data. Many smart city projects are being implemented and here we showcase several examples to provide context for our ethical analysis. Law enforcement, structure efficiency, utility efficiency, and traffic flow control applications are some areas that could have the most gains in smart cities; yet, they are the most pervasive as the applications performing these activities must collect and process the most private data about the citizens. The secure and ethical use of this data must be a top priority within every project. The paper also provides a list of challenges for smart city applications pertaining in some ways to ethics. These challenges are drawn from the studied examples of smart city projects to bring attention to ethical issues and raise awareness of the need to address and regulate such use of data.
On Scaling Decentralized Blockchains - (A Position Paper)

The increasing popularity of blockchain-based cryptocurrencies has made scalability a primary and urgent concern. We analyze how fundamental and circumstantial bottlenecks in Bitcoin limit the ability of its current peer-to-peer overlay network to support substantially higher throughputs and lower latencies. Our results suggest that reparameterization of block size and intervals should be viewed only as a first increment toward achieving next-generation, high-load blockchain protocols, and major advances will additionally require a basic rethinking of technical approaches. We offer a structured perspective on the design space for such approaches. Within this perspective, we enumerate and briefly discuss a number of recently proposed protocol ideas and offer several new ideas and open challenges.
How to Train a Compact Binary Neural Network with High Accuracy?

How to train a binary neural network (BinaryNet) with both high compression rate and high accuracy on large scale datasets? We answer this question through a careful analysis of previous work on BinaryNets, in terms of training strategies, regularization, and activation approximation. Our findings first reveal that a low learning rate is highly preferred to avoid frequent sign changes of the weights, which often makes the learning of BinaryNets unstable. Secondly, we propose to use PReLU instead of ReLU in a BinaryNet to conveniently absorb the scale factor for weights to the activation function, which enjoys high computation efficiency for binarized layers while maintains high approximation accuracy. Thirdly, we reveal that instead of imposing L2 regularization, driving all weights to zero which contradicts with the setting of BinaryNets, we introduce a regularization term that encourages the weights to be bipolar. Fourthly, we discover that the failure of binarizing the last layer, which is essential for high compression rate, is due to the improper output range. We propose to use a scale layer to bring it to normal. Last but not least, we propose multiple binarizations to improve the approximation of the activations. The composition of all these enables us to train BinaryNets with both high compression rate and high accuracy, which is strongly supported by our extensive empirical study.
Learning and transferring movie styles

There is currently a lot of interest in analyzing large movie databases to automatically extract film style and aesthetics . This analysis has, however, been largely limited to identifying the duration, size, color and motion of movie shots. While these low-level details are pertinent, they do not provide a high-level understanding of cinematography, such as identifying the inherent style in a movie. Ultimately, they are unable to answer challenging questions such as, can we identify the author of a movie clip? Can we generative movie shots in the style of a given director or cinematographer ? can we generate shot sequences on the style of a given director or film editor ?
Cascaded Pyramid Network for Multi-person Pose Estimation

The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: GlobalNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the GlobalNet together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve state-of-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge. Code1 and the detection results for person used will be publicly available for further research.