nils-herrmann/clean_scirepeval huggingface dataset:
SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies

Bit coin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bit coin grew to comprise billions of dollars of economic value despite only cursory analysis of the system's design. Since then a growing literature has identified hidden-but-important properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. Meanwhile a large and vibrant open-source community has proposed and deployed numerous modifications and extensions. We provide the first systematic exposition Bit coin and the many related crypto currencies or 'altcoins.' Drawing from a scattered body of knowledge, we identify three key components of Bit coin's design that can be decoupled. This enables a more insightful analysis of Bit coin's properties and future stability. We map the design space for numerous proposed modifications, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We survey anonymity issues in Bit coin and provide an evaluation framework for analyzing a variety of privacy-enhancing proposals. Finally we provide new insights on what we term disinter mediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disinter mediation strategies and provide a detailed comparison.
Simplifying and improving mobile based data collection

Social programs often require collection of demographic and program related information. Pen-paper surveys have been the most favorable way of collecting such information. However, with proliferation of smartphones, low cost mobile connectivity with good coverage and availability of several data collection applications that can even work around the connectivity concerns, pen-paper surveys are now being replaced by mobile based data collection. In this work, we discuss the enhancement of Open Data Kit (ODK), an existing mobile based data collection application, with features such as pre-filling and validation SMS. The additional features were motivated from the real world requirements of reducing the data collection effort and minimizing the discrepancies in records for a project requiring information dissemination to beneficiaries of National Rural Employment Guarantee Scheme (NREGS), a government of India scheme. Data collection exercise, using our extended tool, is currently ongoing in two districts across two different states in India. Preliminary results from one deployment indicate that even with 8% of pre-filled fields in a form, there was a 16.1% decrease in the errors. Additionally, time per survey reduced with the pre-filling option. Preliminary results from another deployment indicate that 35% of the pre-filled forms had mismatched or changed information when compared with the data available in government records.
Comparing the effects of different individualized music interventions for elderly individuals with severe dementia

Individuals with dementia often experience poor quality of life (QOL) due to behavioral and psychological symptoms of dementia (BPSD). Music therapy can reduce BPSD, but most studies have focused on patients with mild to moderate dementia. We hypothesized that music intervention would have beneficial effects compared with a no-music control condition, and that interactive music intervention would have stronger effects than passive music intervention.



Thirty-nine individuals with severe Alzheimer's disease were randomly and blindly assigned to two music intervention groups (passive or interactive) and a no-music Control group. Music intervention involved individualized music. Short-term effects were evaluated via emotional response and stress levels measured with the autonomic nerve index and the Faces Scale. Long-term effects were evaluated by BPSD changes using the Behavioral Pathology in Alzheimer's Disease (BEHAVE-AD) Rating Scale.



Passive and interactive music interventions caused short-term parasympathetic dominance. Interactive intervention caused the greatest improvement in emotional state. Greater long-term reduction in BPSD was observed following interactive intervention, compared with passive music intervention and a no-music control condition.



Music intervention can reduce stress in individuals with severe dementia, with interactive interventions exhibiting the strongest beneficial effects. Since interactive music intervention can restore residual cognitive and emotional function, this approach may be useful for aiding severe dementia patients' relationships with others and improving QOL. The registration number of the trial and the name of the trial registry are UMIN000008801 and "Examination of Effective Nursing Intervention for Music Therapy for Severe Dementia Elderly Person" respectively.
Current Issues and Future Directions in Simulation-Based Training in North America

A number of emerging challenges including globalization, economic pressures, and the changing nature of work have combined to create a business environment that demands innovative, flexible training solutions. Simulations are a promising tool for creating more realistic, experiential learning environments to meet these challenges. Unfortunately, the current literature on simulation-based training paints a mixed picture as to the effectiveness of simulations as training tools, with most of the previous research focusing on the specific technologies used in simulation design and little theory-based research focusing on the instructional capabilities or learning processes underlying these technologies. This article examines the promise and perils of simulation-based training, reviews research that has examined the effectiveness of simulations as training tools, identifies pressing research needs, and presents an agenda for future theory-driven research aimed at addressing those needs.
Trajectory Outlier Detection: A Partition-and-Detect Framework

Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.
Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video

Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.
A Lender-Based Theory of Collateral∗

We consider an imperfectly competitive loan market in which a local relationship lender has an information advantage vis-à-vis distant transaction lenders. Competitive pressure from the transaction lenders prevents the local lender from extracting the full surplus from projects, so that she inefficiently rejects marginally profitable projects. Collateral mitigates the inefficiency by increasing the local lender’s payoff from precisely those marginal projects that she inefficiently rejects. The model predicts that, controlling for observable borrower risk, collateralized loans are more likely to default ex post, which is consistent with the empirical evidence. The model also predicts that borrowers for whom local lenders have a relatively smaller information advantage face higher collateral requirements, and that technological innovations that narrow the information advantage of local lenders, such as small business credit scoring, lead to a greater use of collateral in lending relationships.
The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis

Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for supporting and monitoring business processes. In this paper, we present an event-based business intelligence tool, the Event Tunnel framework. It provides an interactive visualization of event streams to support business analysts in exploring business incidents. The visualization is based on the metaphor of considering the event stream as a cylindrical tunnel, which is presented to the user from multiple perspectives. The information of single events laid out in the Event Tunnel is encoded in event glyphs that allow for a selective mapping of event attributes to colors, size and position. Different policies for the placement of the events in the tunnel as well as a clustering mechanism generate various views on historical event data. The Event Tunnel is able to display the relationships between events. This facilitates users to discover root causes and causal dependencies of event patterns. Our framework couples the event-tunnel visualization with query tools that allow users to search for relevant events within a data repository. Using query, filler and highlighting operations the analyst can navigate through the Event Tunnel until the required information or event patterns become visible. We demonstrate our approach with use cases from the fraud management and logistics domain.
Iris: A Conversational Agent for Complex Tasks

Today, most conversational agents are limited to simple tasks supported by standalone commands, such as getting directions or scheduling an appointment. To support more complex tasks, agents must be able to generalize from and combine the commands they already understand. This paper presents a new approach to designing conversational agents inspired by linguistic theory, where agents can execute complex requests interactively by combining commands through nested conversations. We demonstrate this approach in Iris, an agent that can perform open-ended data science tasks such as lexical analysis and predictive modeling. To power Iris, we have created a domain-specific language that transforms Python functions into combinable automata and regulates their combinations through a type system. Running a user study to examine the strengths and limitations of our approach, we find that data scientists completed a modeling task 2.6 times faster with Iris than with Jupyter Notebook.
StackGuard: Simple Stack Smash Protection for GCC

Since 1998, StackGuard patches to GCC have been used to protect entire distributions from stack smashing buffer overflows. Performance overhead and software compatibility issues have been minimal. In its history, the parts of GCC that StackGuard has operated in have twice changed enough to require complete overhauls of the StackGuard patch. Since StackGuard is a mature technology, even seeing re-implementations in other compilers, we propose that GCC adopt StackGuard as a standard feature. This paper describes our recent work to bring StackGuard fully up to date with current GCC, introduce architecture independence, and extend the protection of stack data structures, while keeping the StackGuard patch as small, simple, and modular as possible.
Adaptive Mixtures of Local Experts

We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.
The Three Pillars of Machine-Based Programming

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machinelearning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.
Autonomous stair climbing algorithm for a small four-tracked robot

In outdoor environments, mobility, adaptability and reliability of a robot are more important than its speed and precise trajectory. From a practical point of view, tracked robots have an advantage over wheeled robots in outdoor applications. The tracked robot is frequently operated by using the remote controller, but the remote operation is not effective for all cases. To overcome some complex obstacles such as rocks or stairs, the information related to the robot posture is required. However, the sensor information is not intuitive to the user to control the robot. In this research, a multi-active crawler robot (MACbot) was developed and the autonomous stair climbing algorithm was implemented to deal with those problems. Various experiments show that the MACbot can climb the stair autonomously.
Image statistics and the perception of surface qualities

The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions. The task of video retrieval using natural language descriptions seeks to address this through rich, unconstrained supervision about complex activities. However, while this formulation offers hope of leveraging underlying compositional structure in activity descriptions, existing approaches typically do not explicitly model compositional reasoning. In this work, we introduce an approach for explicitly and dynamically reasoning about compositional natural language descriptions of activity in videos. We take a modular neural network approach that, given a natural language query, extracts the semantic structure to assemble a compositional neural network layout and corresponding network modules. We show that this approach is able to achieve state-of-the-art results on the DiDeMo video retrieval dataset.
Knowing Yourself: Improving Video Caption via In-depth Recap

Generating natural language descriptions for videos (a.k.a video captioning) has attracted much research attention in recent years, and a lot of models have been proposed to improve the caption performance. However, due to the rapid progress in dataset expansion and feature representation, newly proposed caption models have been evaluated on different settings, which makes it unclear about the contributions from either features or models. Therefore, in this work we aim to gain a deep understanding about "where are we" for the current development of video captioning. First, we carry out extensive experiments to identify the contribution from different components in video captioning task and make fair comparison among several state-of-the-art video caption models. Second, we discover that these state-of-the-art models are complementary so that we could benefit from "wisdom of the crowd" through ensembling and reranking. Finally, we give a preliminary answer to the question "how far are we from the human-level performance in general'' via a series of carefully designed experiments. In summary, our caption models achieve the state-of-the-art performance on the MSR-VTT 2017 challenge, and it is comparable with the average human-level performance on current caption metrics. However, our analysis also shows that we still have a long way to go, such as further improving the generalization ability of current caption models.
Evidence-Based Psychological Assessment.

In recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptualizing and operationalizing evidence-based testing and evidence-based assessment, 6 core skills and 3 meta-skills that underlie proficiency in psychological assessment are described. The integration of patient characteristics, culture, and preferences is discussed in terms of the complex interaction of patient and assessor identities and values throughout the assessment process. A preliminary framework for implementing EBPA is offered, and avenues for continued refinement and growth are described.
Smooth sensitivity and sampling in private data analysis

We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x --- a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy.
 Our framework raises many interesting algorithmic questions. Namely,to apply the framework one must compute or approximate the smoothsensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost ofthe minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on manydatabases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known orwhen f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.