nils-herrmann/clean_scirepeval huggingface dataset:
"Hands On" Visual Recognition for Visually Impaired Users

Blind or visually impaired (BVI) individuals are capable of identifying an object in their hands by combining the available visual cues (if available) with manipulation. It is harder for them to associate the object with a specific brand, a model, or a type. Starting from this observation, we propose a collaborative system designed to deliver visual feedback automatically and to help the user filling this semantic gap. Our visual recognition module is implemented by means of an image retrieval procedure that provides real-time feedback, performs the computation locally on the device, and is scalable to new categories and instances. We carry out a thorough experimental analysis of the visual recognition module, which includes a comparative analysis with the state of the art. We also present two different system implementations that we test with the help of BVI users to evaluate the technical soundness, the usability, and the effectiveness of the proposed concept.
“Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot

Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.
Persistence and periodicity in a dynamic proximity network

The topology of social networks can be understood as being inherently dynamic, with edges having a distinct position in time. Most characterizations of dynamic networks discretize time by converting temporal information into a sequence of network “snapshots” for further analysis. Here we study a highly resolved data set of a dynamic proximity network of 66 individuals. We show that the topology of this network evolves over a very broad distribution of time scales, that its behavior is characterized by strong periodicities driven by external calendar cycles, and that the conversion of inherently continuous-time data into a sequence of snapshots can produce highly biased estimates of network structure. We suggest that dynamic social networks exhibit a natural time scale ∆nat, and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.
Image Segmentation by a Genetic Fuzzy c-Means Algorithm Using Color and Spatial Information

This paper describes a new clustering algorithm for color image segmentation. We combine the classical fuzzy c-means algorithm (FCM) with a genetic algorithm (GA), and we modify the objective function of the FCM for taking into account the spatial information of image data and the intensity inhomogeneities. An application to medical images is presented. Experiments show that the proposed algorithm provides a useful method for image segmentation, without the need of a prefiltering step for background estimation. Moreover, the segmentation of noise images is effectively improved.
Formal analysis of privacy requirements specifications for multi-tier applications

Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.
Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques

Evaluate goodness of fit of the measurement model Examine significance of the χ (p > .10), CFI (> .95), RMSEA (< .06), and SRMR (< .08). Conduct a simultaneous test of the vanishing tetrads implied by the model (Bollen and Ting 2000). Examine significance of the χ (p > .10), CFI (> .95), RMSEA (< .06), and SRMR (< .08). For models including 2+ reflective indicators, conduct a simultaneous test of the vanishing tetrads implied by the model (Bollen and Ting 2000). Assess validity of the set of indicators at the construct level For first-order constructs  Examine whether the average variance extracted (AVE) for the set of indicators is greater than .50 (Fornell and Larcker 1981). For first-order constructs . Values of Ra greater than .50 would mean that, on average, a majority of the variance in the indicators is shared with the construct. Alternatively, if the only antecedents of the composite latent construct are its own formative indicators, the magnitude of the construct level error term could be used to assess validity (Diamantopoulos et al. 2008; Williams et al. 2003). The construct level error variance should be small and constitute no more than half of the total variance of the construct (the smaller the better). For second-order constructs  . Values of Ra greater than .50 would mean that, on average, a majority of the variance in the
A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments

The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow more ecologically-valid measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have
Probabilistic Subgraph Matching Based on Convex Relaxation

We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.
Learning personal + social latent factor model for social recommendation

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.
 In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.
Finding Interesting Rules from Large Sets of Discovered Association Rules

Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.
A multi-DoF anthropomorphic transradial prosthetic arm

An anthropomorphic transradial prosthetic arm is proposed in this paper. In order to generate the wrist flexion/extension and ulna/radial deviation, a novel wrist mechanism is proposed based on the parallel prismatic manipulators. It is expected to realize high speed operation, higher positional accuracy and anthropomorphic features using the proposed mechanism. The prosthetic arm consists of an under-actuated hand as the terminal device. The hand mechanism is capable of providing the grasping adaptation. With the intention of verifying the effectiveness of the mechanisms in motion generation, motion simulation and kinematic analysis are carried out.
P-MOD: Secure Privilege-Based Multilevel Organizational Data-Sharing in Cloud Computing

Cloud computing has changed the way enterprises store, access and share data. Data is constantly being uploaded to the cloud and shared within an organization built on a hierarchy of many different individuals that are given certain data access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. With different access privileges, individuals with more privileges (at higher levels of the hierarchy) are granted access to more sensitive data than those with fewer privileges (at lower levels of the hierarchy). In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme (P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle these concerns. Each level of the privilege-based access structure is affiliated with an access policy that is uniquely defined by specific attributes. Data is then encrypted under each access policy at every level to grant access to specific data users based on their data access privileges. An individual ranked at a certain level can decrypt the ciphertext (at that specific level) if and only if that individual owns a correct set of attributes that can satisfy the access policy of that level. The user may also decrypt the ciphertexts at the lower levels with respect to the user’s level. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds. The comprehensive performance analysis demonstrates that PMOD is more efficient in computational complexity and storage space than the existing schemes in secure data sharing within an organization.
On the evolution of user interaction in Facebook

Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.
A Neuroevolution Approach to General Atari Game Playing

This paper addresses the challenge of learning to play many different video games with little domain-specific knowledge. Specifically, it introduces a neuroevolution approach to general Atari 2600 game playing. Four neuroevolution algorithms were paired with three different state representations and evaluated on a set of 61 Atari games. The neuroevolution agents represent different points along the spectrum of algorithmic sophistication - including weight evolution on topologically fixed neural networks (conventional neuroevolution), covariance matrix adaptation evolution strategy (CMA-ES), neuroevolution of augmenting topologies (NEAT), and indirect network encoding (HyperNEAT). State representations include an object representation of the game screen, the raw pixels of the game screen, and seeded noise (a comparative baseline). Results indicate that direct-encoding methods work best on compact state representations while indirect-encoding methods (i.e., HyperNEAT) allow scaling to higher dimensional representations (i.e., the raw game screen). Previous approaches based on temporal-difference (TD) learning had trouble dealing with the large state spaces and sparse reward gradients often found in Atari games. Neuroevolution ameliorates these problems and evolved policies achieve state-of-the-art results, even surpassing human high scores on three games. These results suggest that neuroevolution is a promising approach to general video game playing (GVGP).
VEWS: A Wikipedia Vandal Early Warning System

We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential vandals so that such users can be presented early to Wikipedia administrators. We leverage multiple classical ML approaches, but develop 3 novel sets of features. Our Wikipedia Vandal Behavior (WVB) approach uses a novel set of user editing patterns as features to classify some users as vandals. Our Wikipedia Transition Probability Matrix (WTPM) approach uses a set of features derived from a transition probability matrix and then reduces it via a neural net auto-encoder to classify some users as vandals. The VEWS approach merges the previous two approaches. Without using any information (e.g. reverts) provided by other users, these algorithms each have over 85% classification accuracy. Moreover, when temporal recency is considered, accuracy goes to almost 90%. We carry out detailed experiments on a new data set we have created consisting of about 33K Wikipedia users (including both a black list and a white list of editors) and containing 770K edits. We describe specific behaviors that distinguish between vandals and non-vandals. We show that VEWS beats ClueBot NG and STiki, the best known algorithms today for vandalism detection. Moreover, VEWS detects far more vandals than ClueBot NG and on average, detects them 2.39 edits before ClueBot NG when both detect the vandal. However, we show that the combination of VEWS and ClueBot NG can give a fully automated vandal early warning system with even higher accuracy.
Social Data Integration and Analytics for Health Intelligence

The aim of this Ph.D. work is to develop a social data analytics framework for healthcare. The user-generated data can provide direct experience and opinions on medical conditions, treatments and insights on population health that can benefit clinical doctors, public health officials as well as patients and researchers. To utilize these social data, we address following research problems: (1) Semantic integration of data sources: Open health data are distributed and can vary from structured to highly unstructured. Different data sources need to be integrated. (2) How to mine sentiments and quantify the Degree of Concern (DOC) about contagious diseases, and how to extend sentiment mining by utilizing topic modeling to detect and quantify topic sentiments (3) Individual disease prediction based on social data. The social health analytics framework can provide healthcare providers with a tool to better utilize social data for evidence-based practice.
The role of debriefing in simulation-based learning.

The aim of this paper is to critically review what is felt to be important about the role of debriefing in the field of simulation-based learning, how it has come about and developed over time, and the different styles or approaches that are used and how effective the process is. A recent systematic review of high fidelity simulation literature identified feedback (including debriefing) as the most important feature of simulation-based medical  Despite this, there are surprisingly few papers in the peer-reviewed literature to illustrate how to debrief, how to teach or learn to debrief, what methods of debriefing exist and how effective they are at achieving learning objectives and goals. This review is by no means a systematic review of all the literature available on debriefing, and contains information from both peer and nonpeer reviewed sources such as meeting abstracts and presentations from within the medical field and other disciplines versed in the practice of debriefing such as military, psychology, and business. It also contains many examples of what expert facilitators have learned over years of practice in the area. We feel this would be of interest to novices in the field as an introduction to debriefing, and to experts to illustrate the gaps that currently exist, which might be addressed in further research within the medical simulation community and in collaborative ventures between other disciplines experienced in the art of debriefing.
B2W2: N-Way Concurrent Communication for IoT Devices

The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.
6-DoF model-based tracking of arbitrarily shaped 3D objects

Image-based 6-DoF pose estimation of arbitrarily shaped 3D objects based on their shape is a rarely studied problem. Most existing image-based methods for pose estimation either exploit textural information in form of local features or, if shape-based, rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but in practice assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on its relative pose to the observing camera. One way to reduce the solution space is to exploit temporal information, i.e. perform tracking. Again, existing model-based tracking approaches rely on relatively simple object geometries. In this paper, we propose a particle filter based tracking approach that can deal with arbitrary shapes and arbitrary or even no texture, i.e. it offers a general solution to the rigid object tracking problem. As our approach can deal with occlusions, it is in particular of interest in the context of goal-directed imitation learning involving the observation of object manipulations. Results of simulation experiments as well as real-world experiments with different object types prove the practical applicability of our approach.
Generating English from Abstract Meaning Representations

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.