nils-herrmann/clean_scirepeval huggingface dataset:
Discussion of functional design options for online rating systems: A state-of-the-art analysis

Ratings are important for building up trust among different parties. Since the arrival of the internet era in the 1990s, countless online rating systems have emerged. For example, Amazon.com established a rating system for books and other products in 1995. Today online rating can be found everywhere, be it e-commerce sites, social networks, and information or recommendation platforms. In most cases, users provide the input to these systems which is then aggregated and directed to appropriate recipients. The increasing relevance of these rating systems forms a new distinct research field with a growing need for research on the design, effects, and validity of rating systems. Hence, we contribute to the body of knowledge by conducting a thorough analysis of the state of the art of online rating systems. We especially focus on the functional design options by analyzing 102 different systems with the help of a criteria catalog of 237 criteria. In this paper, we discuss an excerpt of our findings and present a morphological box that categorizes functional design options for online rating systems.
Derive: a tool that automatically reverse-engineers instruction encodings

Many binary tools, such as disassemblers, dynamic code generation systems, and executable code rewriters, need to understand how machine instructions are encoded. Unfortunately, specifying such encodings is tedious and error-prone. Users must typically specify thousands of details of instruction layout, such as opcode and field locations values, legal operands, and jump offset encodings. We have built a tool called DERIVE that extracts these details from existing software: the system assembler. Users need only provide the assembly syntax for the instructions for which they want encodings. DERIVE automatically reverse-engineers instruction encoding knowledge from the assembler by feeding it permutations of instructions and doing equation solving on the output.
DERIVE is robust and general. It derives instruction encodings for SPARC, MIPS, Alpha, PowerPC, ARM, and x86. In the last case, it handles variable-sized instructions, large instructions, instruction encodings determined by operand size, and other CISC features. DERIVE is also remarkably simple: it is a factor of 6 smaller than equivalent, more traditional systems. Finally, its declarative specifications eliminate the mis-specification errors that plague previous approaches, such as illegal registers used as operands or incorrect field offsets and sizes. This paper discusses our current DERIVE prototype, explains how it computes instruction encodings, and also discusses the more general implications of the ability to extract functionality from installed software.
Effects of loneliness and differential usage of Facebook on college adjustment of first-year students

The popularity of social network sites (SNSs) among college students has stimulated scholarship examining the relationship between SNS use and college adjustment. The present research furthers our understanding of SNS use by studying the relationship between loneliness, varied dimensions of Facebook use, and college adjustment among first-year students. We looked at three facets of college adjustment: social adjustment, academic motivation, and perceived academic performance. Compulsive use of Facebook had a stronger association with academic motivation than habitual use of Facebook, but neither were directly correlated with academic performance. Too much time spent on Facebook was weakly but directly associated with poorer perceived academic performance. Loneliness was a stronger indicator of college adjustment than any dimension of Facebook usage.
Crowdsourcing user studies with Mechanical Turk

User studies are important for many aspects of the design process and involve techniques ranging from informal surveys to rigorous laboratory studies. However, the costs involved in engaging users often requires practitioners to trade off between sample size, time requirements, and monetary costs. Micro-task markets, such as Amazon's Mechanical Turk, offer a potential paradigm for engaging a large number of users for low time and monetary costs. Here we investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks. Although micro-task markets have great potential for rapidly collecting user measurements at low costs, we found that special care is needed in formulating tasks in order to harness the capabilities of the approach.
Applying Electric Field Sensing to Human-Computer Interfaces

A non-contact sensor based on the interaction of a person with electric fields for human-computer interface is investigated. Two sensing modes are explored: an external electric field shunted to ground through a human body, and an external electric field transmitted through a human body to stationary receivers. The sensors are low power (milliwatts), high resolution (millimeter) low cost (a few dollars per channel), have low latency (millisecond), high update rate (1 kHz), high immunity to noise (>72 dB), are not affected by clothing, surface texture or reflectivity, and can operate on length scales from microns to meters. Systems incorporating the sensors include a finger mouse, a room that knows the location of its occupant, and people-sensing furniture. Haptic feedback using passive materials is described. Also discussed are empirical and analytical approaches to transform sensor measurements into position information.
Health literacy and public health: A systematic review and integration of definitions and models

Health literacy concerns the knowledge and competences of persons to meet the complex demands of health in modern society. Although its importance is increasingly recognised, there is no consensus about the definition of health literacy or about its conceptual dimensions, which limits the possibilities for measurement and comparison. The aim of the study is to review definitions and models on health literacy to develop an integrated definition and conceptual model capturing the most comprehensive evidence-based dimensions of health literacy.



A systematic literature review was performed to identify definitions and conceptual frameworks of health literacy. A content analysis of the definitions and conceptual frameworks was carried out to identify the central dimensions of health literacy and develop an integrated model.



The review resulted in 17 definitions of health literacy and 12 conceptual models. Based on the content analysis, an integrative conceptual model was developed containing 12 dimensions referring to the knowledge, motivation and competencies of accessing, understanding, appraising and applying health-related information within the healthcare, disease prevention and health promotion setting, respectively.



Based upon this review, a model is proposed integrating medical and public health views of health literacy. The model can serve as a basis for developing health literacy enhancing interventions and provide a conceptual basis for the development and validation of measurement tools, capturing the different dimensions of health literacy within the healthcare, disease prevention and health promotion settings.
Spotify -- Large Scale, Low Latency, P2P Music-on-Demand Streaming

Spotify is a music streaming service offering low-latency access to a library of over 8 million music tracks. Streaming is performed by a combination of client-server access and a peer-to-peer protocol. In this paper, we give an overview of the protocol and peer-to-peer architecture used and provide measurements of service performance and user behavior. The service currently has a user base of over 7 million and has been available in six European countries since October 2008. Data collected indicates that the combination of the client-server and peer-to-peer paradigms can be applied to music streaming with good results. In particular, 8.8% of music data played comes from Spotify's servers while the median playback latency is only 265 ms (including cached tracks). We also discuss the user access patterns observed and how the peer-to-peer network affects the access patterns as they reach the server.
Cubrick : A Scalable Distributed MOLAP Database for Fast Analytics

This paper describes the architecture and design of Cubrick, a distributed multidimensional in-memory database that enables real-time data analysis of large dynamic datasets. Cubrick has a strictly multidimensional data model composed of dimensions, dimensional hierarchies and metrics, supporting sub-second MOLAP operations such as slice and dice, roll-up and drill-down over terabytes of data. All data stored in Cubrick is chunked in every dimension and stored within containers called bricks in an unordered and sparse fashion, providing high data ingestion ratios and indexed access through every dimension. In this paper, we describe details about Cubrick’s internal data structures, distributed model, query execution engine and a few details about the current implementation. Finally, we present some experimental results found in a first Cubrick deployment inside Facebook.
EasyLocal++: an object-oriented framework for the flexible design of local-search algorithms

We propose EASYLOCAL++, an object-oriented framework for the design and the analysis of local search algorithms. The abstract classes that compose the framework specify and implement the invariant part of the algorithm, and are meant to be specialized by concrete classes that supply the problem-dependent part. The framework provides the full control structures of the algorithms, and the user has only to write the problem-specific code. Furthermore, the framework comes out with some tools that simplify the analysis of the algorithms.
A 2d + 3d rich data approach to scene understanding

On your one-minute walk from the coffee machine to your desk each morning, you pass by dozens of scenes – a kitchen, an elevator, your office – and you effortlessly recognize them and perceive their 3D structure. But this one-minute scene-understanding problem has been an open challenge in computer vision since the field was first established 50 years ago. In this dissertation, we aim to rethink the path researchers took over these years, challenge the standard practices and implicit assumptions in the current research, and redefine several basic principles in computational scene understanding. The key idea of this dissertation is that learning from rich data under natural setting is crucial for finding the right representation for scene understanding. First of all, to overcome the limitations of object-centric datasets, we built the Scene Understanding (SUN) Database, a large collection of real-world images that exhaustively spans all scene categories. This scene-centric dataset provides a more natural sample of human visual world, and establishes a realistic benchmark for standard 2D recognition tasks. However, while an image is a 2D array, the world is 3D and our eyes see it from a viewpoint, but this is not traditionally modeled. To obtain a 3D understanding at high-level, we reintroduce geometric figures using modern machinery. To model scene viewpoint, we propose a panoramic place representation to go beyond aperture computer vision and use data that is close to natural input for human visual system. This paradigm shift toward rich representation also opens up new challenges that require a new kind of big data – data with extra descriptions, namely rich data. Specifically, we focus on a highly valuable kind of rich data – multiple viewpoints in 3D – and we build the SUN3D database to obtain an integrated place-centric representation of scenes. We argue for the great importance of modeling the computer’s role as an agent in a 3D scene, and demonstrate the power of place-centric scene representation.  Associate Professor
A framework for evaluating quality-driven self-adaptive software systems

Over the past decade the dynamic capabilities of self-adaptive software-intensive systems have proliferated and improved significantly. To advance the field of self-adaptive and self-managing systems further and to leverage the benefits of self-adaptation, we need to develop methods and tools to assess and possibly certify adaptation properties of self-adaptive systems, not only at design time but also, and especially, at run-time. In this paper we propose a framework for evaluating quality-driven self-adaptive software systems. Our framework is based on a survey of self-adaptive system papers and a set of adaptation properties derived from control theory properties. We also establish a mapping between these properties and software quality attributes. Thus, corresponding software quality metrics can then be used to assess adaptation properties.
Examining the Testing Effect with Open-and Closed-Book Tests

Two experiments examined the testing effect with open-book tests, in which students view notes and textbooks while taking the test, and closed-book tests, in which students take the test without viewing notes or textbooks. Subjects studied prose passages and then restudied or took an openor closed-book test. Taking either kind of test, with feedback, enhanced long-term retention relative to conditions in which subjects restudied material or took a test without feedback. Open-book testing led to better initial performance than closed-book testing, but this benefit did not persist and both types of testing produced equivalent retention on a delayed test. Subjects predicted they would recall more after repeated studying, even though testing enhanced long-term retention more than restudying. These experiments demonstrate that the testing effect occurs with both openand closedbook tests, and that subjects fail to predict the effectiveness of testing relative to studying in enhancing later recall.
Safe & Efficient Gradual Typing for TypeScript

Current proposals for adding gradual typing to JavaScript, such as Closure, TypeScript and Dart, forgo soundness to deal with issues of scale, code reuse, and popular programming patterns. We show how to address these issues in practice while retaining soundness. We design and implement a new gradual type system, prototyped for expediency as a 'Safe' compilation mode for TypeScript. Our compiler achieves soundness by enforcing stricter static checks and embedding residual runtime checks in compiled code. It emits plain JavaScript that runs on stock virtual machines. Our main theorem is a simulation that ensures that the checks introduced by Safe TypeScript (1) catch any dynamic type error, and (2) do not alter the semantics of type-safe TypeScript code.
 Safe TypeScript is carefully designed to minimize the performance overhead of runtime checks. At its core, we rely on two new ideas: differential subtyping, a new form of coercive subtyping that computes the minimum amount of runtime type information that must be added to each object; and an erasure modality, which we use to safely and selectively erase type information. This allows us to scale our design to full-fledged TypeScript, including arrays, maps, classes, inheritance, overloading, and generic types.
 We validate the usability and performance of Safe TypeScript empirically by type-checking and compiling around 120,000 lines of existing TypeScript source code. Although runtime checks can be expensive, the end-to-end overhead is small for code bases that already have type annotations. For instance, we bootstrap the Safe TypeScript compiler (90,000 lines including the base TypeScript compiler): we measure a 15% runtime overhead for type safety, and also uncover programming errors as type safety violations. We conclude that, at least during development and testing, subjecting JavaScript/TypeScript programs to safe gradual typing adds significant value to source type annotations at a modest cost.
A 88-dB DR, 84-dB SNDR Very Low-Power Single Op-Amp Third-Order $\Sigma \Delta$ Modulator

A low-power switched-capacitor third-order sigma- delta (ΣΔ) modulator suitable for portable sensor systems is described. The architecture uses only one operational amplifier working in a time-interleaved fashion. The architecture employs swing reduction techniques to limit the swing and the slew-rate requirements of all internal nodes. Realized in a 0.18-μm 2P6M CMOS technology, the modulator provides 84-dB SNDR and 88-dB dynamic range in a signal bandwidth of 100 kHz and clock at 3.2 MHz. The 1.5-V supplied prototype dissipates 140 μW with a FoM of 54 fJ/conversion-level.
The emergence of lncRNAs in cancer biology.

The discovery of numerous noncoding RNA (ncRNA) transcripts in species from yeast to mammals has dramatically altered our understanding of cell biology, especially the biology of diseases such as cancer. In humans, the identification of abundant long ncRNA (lncRNA) >200 bp has catalyzed their characterization as critical components of cancer biology. Recently, roles for lncRNAs as drivers of tumor suppressive and oncogenic functions have appeared in prevalent cancer types, such as breast and prostate cancer. In this review, we highlight the emerging impact of ncRNAs in cancer research, with a particular focus on the mechanisms and functions of lncRNAs.
Next-Term Student Performance Prediction: A Recommender Systems Approach

An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students’ grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Linear Multiple Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.
Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.
Emotion detection from EEG recordings

Human brain behavior is very complex and it is difficult to interpret. Human emotion might come from brain activities. However, the relationship between human emotion and brain activities is far from clear. In recent years, more and more researchers are trying to discover this relationship by recording brain signals such as electroencephalogram (EEG) signals with the associated emotion information extracted from other modalities such as facial expression. In this paper, machine learning based methods are used to model this relationship in the publicly available dataset DEAP (Database for Emotional Analysis using Physiological Signals). Different features are extracted from raw EEG recordings. Then Maximum Relevance Minimum Redundancy (mRMR) was used for feature selection. These features are fed into machine learning methods to build the prediction models to extract the emotion information from EEG signals. The models are evaluated on this dataset and satisfactory results are achieved.
NICBPM: Non-invasive cuff-less blood pressure monitor

An ultra-low power, ultra portable, wearable integrated bio-medical device, the Non-Invasive Cuff-less Blood Pressure Monitor (NICBPM) is proposed to measure the Pulse Transit Time (PTT). The relationship between PTT and Blood Pressure (BP) is investigated using linear and non linear regression models. Simultaneous reading obtained from an FDA approved, commercially available Blood Pressure monitor and the (Electrocardiogram) ECG and (photoplethsymogram) PPG signal from the NICBPM are used to calibrate the device for 30 subjects during cardiopulmonary exercise test. Bland-Altman plots for the linear regression model revealed limits of agreement of −10.6 to 10.6 mmHg for systolic BP (sBP) and −10.5 to 10.5 mmHg for distolic BP (dBP). Bland-Altman plots for the non linear regression model revealed marginally better limits of agreement between −8.5 to 8.5 mmHg for sBP and −6.6 to 6.6 mmHg for dBP. sBP can be estimated from the NICBPM with 99% accuracy and 99% sensitivity.