nils-herrmann/clean_scirepeval huggingface dataset:
The GDPR and Big Data: Leading the Way for Big Genetic Data?

Genetic data as a category of personal data creates a number of challenges to the traditional understanding of personal data and the rules regarding personal data processing. Although the peculiarities of and heightened risks regarding genetic data processing were recognized long before the data protection reform in the EU, the General Data Protection Regulation (GDPR) seems to pay no regard to this. Furthermore, the GDPR will create more legal grounds for (sensitive) personal data (incl. genetic data) processing whilst restricting data subjects’ means of control over their personal data. One of the reasons for this is that, amongst other aims, the personal data reform served to promote big data business in the EU. The substantive clauses of the GDPR concerning big data, however, do not differentiate between the types of personal data being processed. Hence, like all other categories of personal data, genetic data is subject to the big data clauses of the GDPR as well; thus leading to the question whether the GDPR is creating a pathway for ‘big genetic data’. This paper aims to analyse the implications that the role of the GDPR as a big data enabler bears on genetic data processing and the respective rights of the data
Canonical Genetic Signatures of the Adult Human Brain

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.
Data Recombination for Neural Semantic Parsing

Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a highprecision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties. Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.
Application of data mining techniques in sports training

Data mining techniques have been successfully applied in stock, insurance, medicine, banking and retailing domains. In the sport domain, for transforming sport data into actionable knowledge, coaches can use data mining techniques to plan training sessions more effectively, and to reduce the impact of testing activity on athletes. This paper presents one such model, which uses clustering techniques, such as improved K-Means, Expectation-Maximization (EM), DBSCAN, COBWEB and hierarchical clustering approaches to analyze sport physiological data collected during incremental tests. Through analyzing the progress of a test session, the authors assign the tested athlete to a group of athletes and evaluate these groups to support the planning of training sessions.
Characterizing Driving Styles with Deep Learning

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive industries. A good representation of driving features can be highly valuable for autonomous driving, auto insurance, and many other application scenarios. However, traditional methods mainly rely on handcrafted features, which limit machine learning algorithms to achieve a better performance. In this paper, we propose a novel deep learning solution to this problem, which could be the first attempt of studying deep learning for driving behavior analysis. The proposed approach can effectively extract high level and interpretable features describing complex driving patterns from GPS data. It also requires significantly less human experience and work. The power of the learned driving style representations are validated through the driver identification problem using a large real dataset.
Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

A modeling paradigm is developed to augment predictive models of turbulence by effectively using limited data generated from physical experiments. The key components of the current approach involve inverse modeling to infer the spatial distribution of model discrepancies and machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. The methodology is applied to turbulent flows over airfoils involving flow separation.Model augmentations are developed for the Spalart–Allmarasmodel using adjointbased full-field inference on experimentallymeasured lift coefficient data.When thesemodel forms are reconstructed using neural networks and embeddedwithin a standard solver, it is shown that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train themodel. The neural-network-augmented Spalart–Allmarasmodel also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial, finite element solver. The broader vision is that, by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.
Generalized least squares multiple 3 D surface matching

A method for the simultaneous co-registration and georeferencing of multiple 3D pointclouds and associated intensity information is proposed. It is a generalization of the 3D surface matching problem. The simultaneous co-registration provides for a strict solution to the problem, as opposed to sequential pairwise registration. The problem is formulated as the Least Squares matching of overlapping 3D surfaces. The parameters of 3D transformations of multiple surfaces are simultaneously estimated, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances among the surfaces. An observation equation is written for each surface-to-surface correspondence. Each overlapping surface pair contributes a group of observation equations to the design matrix. The parameters are introduced into the system as stochastic variables, as a second type of (fictitious) observations. This extension allows to control the estimated parameters. Intensity information is introduced into the system in the form of quasisurfaces as the third type of observations. Reference points, defining an external (object) coordinate system, which are imaged in additional intensity images, or can be located in the pointcloud, serve as the fourth type of observations. They transform the whole block of “models” to a unique reference system. Furthermore, the given coordinate values of the control points are treated as observations. This gives the fifth type of observations. The total system is solved by applying the Least Squares technique, provided that sufficiently good initial values for the transformation parameters are given. This method can be applied to data sets generated from aerial as well as terrestrial laser scanning or other pointcloud generating methods.  Corresponding author.
A Survey of Anti-Tamper Technologies

he unauthorized modification and subsequent misuse of software is often referred to as software cracking. Usually, cracking requires disabling one or more software features that enforce policies (of access, usage, dissemination, etc.) related to the software. Because there is value and/or notoriety to be gained by accessing valuable software capabilities, cracking continues to be common and is a growing problem. To combat cracking, anti-tamper (AT) technologies have been developed to protect valuable software. Both hardware and software AT technologies aim to make software more resistant against attack and protect critical program elements. However, before discussing the various AT technologies, we need to know the adversary's goals. What do software crackers hope to achieve? Their purposes vary, and typically include one or more of the following: • Gaining unauthorized access. The attacker's goal is to disable the software access control mechanisms built into the software. After doing so, the attacker can make and distribute illegal copies whose copy protection or usage control mechanisms have been disabled – this is the familiar software piracy problem. If the cracked software provides access to classified data, then the attacker's real goal is not the software itself, but the data that is accessible through the software. The attacker sometimes aims at modifying or unlocking specific functionality in the program, e.g., a demo or export version of software is often a deliberately degraded version of what is otherwise fully functional software. The attacker then seeks to make it fully functional by re-enabling the missing features. • Reverse engineering. The attacker aims to understand enough about the software to steal key routines, to gain access to proprietary intellectual property , or to carry out code-lifting, which consists of reusing a crucial part of the code (without necessarily understanding the internals of how it works) in some other software. Good programming practices, while they facilitate software engineering, also tend to simultaneously make it easier to carry out reverse engineering attacks. These attacks are potentially very costly to the original software developer as they allow a competitor (or an enemy) to nullify the develop-er's competitive advantage by rapidly closing a technology gap through insights gleaned from examining the software. • Violating code integrity. This familiar attack consists of either injecting malicious code (malware) into a program , injecting code that is not malevolent but illegally enhances a pro-gram's functionality, or otherwise sub-verting a program so it performs new and …
New developments in prosthetic arm systems

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia's flagship solutions and methods.
Personalized Book Recommendation System

Recommender systems are found in many ecommerce applications today. Recommender systems usually provide the user with a list of recommendations that they might prefer, or supply predictions on how much the user might prefer each item. Choosing what book to read next has always been a question for many. Even for students, deciding which textbook or reference book to read on a topic unknown to them is a big question. There are two common approaches for providing recommendations, they are collaborative filtering and content based filtering. In this report we try to present a model for a personalized recommendation system for books that uses hybrid recommendation approach which is combination of content based and collaborative filtering. The proposed recommendation system tries to learn the user’s preferences and recommends the books to the user based on their preferences. The system also recommends the books to the user based on the user’s demographic parameters like age and location. The system also tries to understand the user’s favourite author and recommends accordingly.
An Empirical Analysis of Linkability in the Monero Blockchain

Monero is a privacy-centric cryptocurrency that allows users to obscure their transaction graph by including chaff coins, called “mixins,” along with the actual coins they spend. In this report, we empirically evaluate two weaknesses in Monero’s mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to “chain-reaction” analysis — that is, the real input can be deduced by elimination, e.g. because the mixins they include are spent by 0-mixin transactions. Second, Monero mixins are sampled in such a way that the mixins can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the “newest” input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Our analysis uses only public blockchain data, in contrast to earlier attacks requiring active participation in the network [10, 7]. While the first weakness primarily affects Monero transactions made by older software versions (i.e., prior to RingCT), the second weakness is applicable to the newest versions as well. We propose and evaluate a countermeasure derived from blockchain data that can improve the privacy of future transactions.  This is a draft of work-inprogress. It has not yet been peer-reviewed, and contains preliminary results that may be subject to further revision. ∗University of Illinois at Urbana-Champaign †Initiative for Cryptocurrencies and Contracts, initc3.org ‡Andrew Miller is a consultant to the Zerocoin Electric Coin Company and a board member of the Zcash Foundation. §Princeton University
From Seed AI to Technological Singularity via Recursively Self-Improving Software

Software capable of improving itself has been a dream of computer scientists since the inception of the field. In this work we provide definitions for Recursively Self-Improving software, survey different types of self-improving software, review the relevant literature, analyze limits on computation restricting recursive self-improvement and introduce RSI Convergence Theory which aims to predict general behavior of RSI systems. Finally, we address security implications from self-improving intelligent software.
Multi-task Learning for Universal Sentence Representations: What Syntactic and Semantic Information is Captured?

Learning distributed sentence representations is one of the key challenges in natural language processing. Previous work demonstrated that a recurrent neural network (RNNs) based sentence encoder trained on a large collection of annotated natural language inference data, is efficient in the transfer learning to facilitate other related tasks. In this paper, we show that joint learning of multiple tasks results in better generalizable sentence representations by conducting extensive experiments and analysis comparing the multi-task and single-task learned sentence encoders. The quantitative analysis of the syntactic and semantic information captured by the sentence embeddings show that multi-task learning captures better syntactic information while the single task learning summarizes the semantic information coherently.
Deep Bayesian Active Learning with Image Data

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, we have to handle small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many acquisition functions rely on model uncertainty. In deep learning on the other hand we rarely represent such model uncertainty. Relying on Bayesian approaches to deep learning, in this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining significant improvement on existing active learning approaches.
Recommendation as Classification: Using Social and Content-Based Information in Recommendation

Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount of other information that is often available about the nature of each artifact -such as cast lists or movie reviews, for example. This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users.  Recommendations are a part of everyday life. We usually rely on some external knowledge to make informed decisions about a particular artifact or action, for instance when we are going to see a movie or going to see a doctor. This knowledge can be derived from social processes. At other times, our judgments may be based on available information about an artifact and our known preferences. There are many factors which may influence a person in making choices, and ideally one would like to model as many of these factors as possible in a recommendation system. There are some general approaches to this problem. In one approach, the user of the system provides ratings of some artifacts or items. The system makes informed guesses about other items the user may like based on ratings other users have provided. This is the framework for social-filtering methods (Hill, Stead, Rosenstein Furnas 1995; Shardanand & Maes 1995). In a second approach, the system accepts information describing the nature of an item, and based on a sample of the user’s preferences, learns to predict which items the user will like (Lang 1995; Pazzani, Muramatsu, & Billsus 1996). We will call this approach content-based filtering, as it does not rely on social information (in the form of other users’ ratings). Both social and content-based filtering can be cast as learning problems: the objective is to *Department of Computer Science, Rutgers University, Piscataway, NJ 08855 We would like to thank Susan Dumais for useful discussions during the early stages of this work.
The Darker Side of Lean : An Insider ’ s Perspective on the Realities of the Toyota Production System

Executive Overview The Toyota Production System (TPS) has been lauded as the pinnacle of flexible, just-in-time manufacturing and design and the founder of “lean work” systems, which claim to improve product quality and employee productivity. American automobile manufacturers readily adopted the “Toyota Way” and many of the management practices in service industries, such as Total Quality Management (TQM) are biased on its fundamental principles. The author of this paper, Darius Mehri, is an American-born computer simulation engineer who worked in a Toyota group company for three years, observing this system firsthand and conducting his own qualitative research on what he considers the true impact of lean work: the human cost. As a participant observer who was inculcated in Japanese social and workplace culture, Mehri takes an examination of TPS well beyond what many studies American and European scholars have been able to go. His assessment is guided by a distinction which is fundamental to understanding Japanese culture and business: tatemae (what you are supposed to feel or do) and honne (what you actually feel or do). Mehri believes that international enthusiasm for the Toyota Production System results from western observers’ failure to discern the honne within the tatemae. He lifts the curtain of formality and messages from management at Toyota—the tatemae—that obscures the realities— the honne—of the Toyota Way: limited potential for creativity and innovation, narrow professional skills, worker isolation and harassment, dangerous conditions on the production line, accident cover-ups, excessive overtime, and poor quality of life for workers.