nils-herrmann/clean_scirepeval huggingface dataset:
Attribute Adaptation for Personalized Image Search

Current methods learn monolithic attribute predictors, with the assumption that a single model is sufficient to reflect human understanding of a visual attribute. However, in reality, humans vary in how they perceive the association between a named property and image content. For example, two people may have slightly different internal models for what makes a shoe look "formal", or they may disagree on which of two scenes looks "more cluttered". Rather than discount these differences as noise, we propose to learn user-specific attribute models. We adapt a generic model trained with annotations from multiple users, tailoring it to satisfy user-specific labels. Furthermore, we propose novel techniques to infer user-specific labels based on transitivity and contradictions in the user's search history. We demonstrate that adapted attributes improve accuracy over both existing monolithic models as well as models that learn from scratch with user-specific data alone. In addition, we show how adapted attributes are useful to personalize image search, whether with binary or relative attributes.
Training Deep Spiking Neural Networks Using Backpropagation

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.
Re-ID done right: towards good practices for person re-identification

Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.
MyoVibe: vibration based wearable muscle activation detection in high mobility exercises

Skeletal muscles are activated to generate the force needed for movement in most high motion sports and exercises. However, incorrect skeletal muscle activation during these sports and exercises, can lead to sub-optimal performance and injury. Existing techniques are susceptible to motion artifacts, particularly when used in high motion sports (e.g. jumping, cycling, etc.). They require limited body movement, or experts to manually interpret results, making them unsuitable in sports scenarios.
 This paper presents MyoVibe, a wearable system for determining muscle activation in high motion exercise scenarios. MyoVibe senses muscle vibration signals obtained from a wearable network of accelerometers to determine muscle activation. By modeling the characteristics of muscles and high motion noise using extreme value analysis, MyoVibe can reduce noise due to high mobility exercises. Our system can predict muscle activation with greater than 97% accuracy in isometric low motion exercise cases, up to 90% accuracy in high motion exercises.
Smart distribution protection using current-only directional overcurrent relay

Overcurrent relays are widely used for power systems protection. Transmission side uses more directional type relays, while distribution systems, e.g., radial and ring-main subtransmission systems use non-directional types. The fault direction may be forward (between relay and grid), or reverse (between relay and source), the normal power flow being from source to the grid. Traditional directional overcurrent relays utilize the reference voltage phasor for estimating the direction of the fault. This requires measurement of both current and voltage using respective sensors. This makes the directional overcurrent relays more costly than the non-directional type. In this paper, a novel current-only directional detection possibility is highlighted along with theoretical and test signal analysis. Possible utilization of the current-only directional relay for intelligent directional protection in the distribution systems are described. Directional protection for distribution systems is a key focus area for enabling the smart grid.
Fast nonparametric matrix factorization for large-scale collaborative filtering

With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in computation. In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low-rank matrix factorization methods, the singular value decomposition (SVD) and probabilistic principal component analysis (pPCA), to be data-driven, with the dimensionality increasing with data size. We show that the formulations of the two nonparametric models are very similar, and their optimizations share similar procedures. Compared to traditional parametric low-rank methods, nonparametric models are appealing for their flexibility in modeling complex data dependencies. However, this modeling advantage comes at a computational price--it is highly challenging to scale them to large-scale problems, hampering their application to applications such as collaborative filtering. In this paper we introduce novel optimization algorithms, which are simple to implement, which allow learning both nonparametric matrix factorization models to be highly efficient on large-scale problems. Our experiments on EachMovie and Netflix, the two largest public benchmarks to date, demonstrate that the nonparametric models make more accurate predictions of user ratings, and are computationally comparable or sometimes even faster in training, in comparison with previous state-of-the-art parametric matrix factorization models.
A Distributed 3D Graphics Library

We present Repo-3D, a general-purpose, object-oriented library developing distributed, interactive 3D graphics applications acr a range of heterogeneous workstations. Repo-3D is designe make it easy for programmers to rapidly build prototypes usin familiar multi-threaded, object-oriented programming paradig All data sharing of both graphical and non-graphical data is do via general-purpose remote and replicated objects, presenting illusion of a single distributed shared memory. Graphical obje are directly distributed, circumventing the “duplicate databas problem and allowing programmers to focus on the applicat details. Repo-3D is embedded in Repo, an interpreted, lexically-scop distributed programming language, allowing entire applications be rapidly prototyped. We discuss Repo-3D’s design, and introd the notion of local variations to the graphical objects, which allow local changes to be applied to shared graphical structures. L variations are needed to support transient local changes, suc highlighting, and responsive local editing operations. Finally, w discuss how our approach could be applied using other progr ming languages, such as Java.
Large-Scale Identification of Malicious Singleton Files

We study a dataset of billions of program binary files that appeared on 100 million computers over the course of 12 months, discovering that 94% of these files were present on a single machine. Though malware polymorphism is one cause for the large number of singleton files, additional factors also contribute to polymorphism, given that the ratio of benign to malicious singleton files is 80:1. The huge number of benign singletons makes it challenging to reliably identify the minority of malicious singletons. We present a large-scale study of the properties, characteristics, and distribution of benign and malicious singleton files. We leverage the insights from this study to build a classifier based purely on static features to identify 92% of the remaining malicious singletons at a 1.4% percent false positive rate, despite heavy use of obfuscation and packing techniques by most malicious singleton files that we make no attempt to de-obfuscate. Finally, we demonstrate robustness of our classifier to important classes of automated evasion attacks.
An overview of routing optimization for internet traffic engineering

Traffic engineering is an important mechanism for Internet network providers seeking to optimize network performance and traffic delivery. Routing optimization plays a key role in traffic engineering, finding efficient routes so as to achieve the desired network performance. In this survey we review Internet traffic engineering from the perspective of routing optimization. A taxonomy of routing algorithms in the literature is provided, dating from the advent of the TE concept in the late 1990s. We classify the algorithms into multiple dimensions: unicast/multicast, intra-/inter- domain, IP-/MPLS-based and offline/online TE schemes. In addition, we investigate some important traffic engineering issues, including robustness, TE interactions, and interoperability with overlay selfish routing. In addition to a review of existing solutions, we also point out some challenges in TE operation and important issues that are worthy of investigation in future research activities.
A Boolean map theory of visual attention.

A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment?



only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an observer voluntarily select what to access?



in one of two ways: (a) by selecting one feature value in one dimension (e.g., selecting the color red) or (b) by iteratively combining the output of (a) with a preexisting Boolean map via the Boolean operations of intersection and union. Boolean map theory offers a unified interpretation of a wide variety of visual attention phenomena usually treated in separate literatures. In so doing, it also illuminates the neglected phenomena of attention to structure.
The Relationship between Facebook Use and Well-Being depends on Communication Type and Tie Strength

An extensive literature shows that social relationships influence psychological well-being, but the underlying mechanisms remain unclear. We test predictions about online interactions and well-being made by theories of belongingness, relationship maintenance, relational investment, social support, and social comparison. An opt-in panel study of 1,910 Facebook users linked self-reported measures of well-being to counts of respondents’ Facebook activities from server logs. Specific uses of the site were associated with improvements in well-being: Receiving targeted, composed communication from strong ties was associated with improvements in wellbeing while viewing friends’ wide-audience broadcasts and receiving one-click feedback were not. These results suggest that people derive benefits from online communication, as long it comes from people they care about and has been tailored for them.
Detection of unknown computer worms based on behavioral classification of the host

Machine learning techniques are widely used in many fields. One of the applications of machine learning in the field of the information security is classification of a computer behavior into malicious and benign. Anti viruses co nsisting on signature-based methods are helpless against new (unknown) computer worms. This paper focuses on the feasibility of accurately detecting unknown worm activity in indiv i ual computers while minimizing the required set of features collected from the monitor ed computer. A comprehensive experiment for testing the feasibility of detecting unknown computer worms , employing several computer configurations, background applica tions, and user activity, was performed. During the experiments 323 computer feat ur s were monitored by an agent that was developed. Four feature selection me thods were used to reduce the amount of features and four learning algorithms wer e applied on the resulting feature subsets. The evaluation results suggests that using classification algorithms applied on only 20 features the mean detection accuracy exceed ed 90%, and for specific unknown worms accuracy reached above 99%, while mai ntain ng a low level of false positive rate.
Multi-joint gripper with Differential Gear System

This paper proposes a multi-joint-gripper that achieves envelope grasping for unknown shape objects. Proposed mechanism is based on a chain of Differential Gear Systems (DGS) controlled by only one motor. It also has a Variable Stiffness Mechanism (VSM) that controls joint stiffness to relieve interfering effects suffered from grasping environment and achieve a dexterous grasping. The experiments elucidate that the developed gripper achieves envelop grasping; the posture of the gripper automatically fits the shape of the object with no sensory feedback. And they also show that the VSM effectively works to relieve external interfering. This paper shows the mechanism and experimental results of the second test machine that was developed inheriting the idea of DGS used in the first test machine but has a completely altered VSM.
A Case-Based Recommendation Approach for Market Basket Data

In recent years, recommender systems have become an important part of various applications, supporting both customers and providers in their decision-making processes. However, these systems still must overcome limitations that reduce their performance, like recommendations' overspecialization, cold start, and difficulties when items with unequal probability distribution appear or recommendations for sets of items are asked. A novel approach, addressing the above issues through a case-based recommendation methodology, is presented here. The scope of the presented approach is to generate meaningful recommendations based on items' co-occurring patterns and to provide more insight into customers' buying habits. In contrast to current recommendation techniques that recommend items based on users' ratings or history, and to most case-based item recommenders that evaluate items' similarities, the implemented recommender uses a hierarchical model for the items and searches for similar sets of items, in order to recommend those that are most likely to satisfy a user.
Can we beat the prefix filtering?: an adaptive framework for similarity join and search

As two important operations in data cleaning, similarity join and similarity search have attracted much attention recently. Existing methods to support similarity join usually adopt a prefix-filtering-based framework. They select a prefix of each object and prune object pairs whose prefixes have no overlap. We have an observation that prefix lengths have significant effect on the performance. Different prefix lengths lead to significantly different performance, and prefix filtering does not always achieve high performance. To address this problem, in this paper we propose an adaptive framework to support similarity join. We propose a cost model to judiciously select an appropriate prefix for each object. To efficiently select prefixes, we devise effective indexes. We extend our method to support similarity search. Experimental results show that our framework beats the prefix-filtering-based framework and achieves high efficiency.
Characterizing privacy leakage of public WiFi networks for users on travel

Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms.
The QSM Algorithm and its Application to Software Behavior Model Induction

This paper presents a novel application of grammatical inference techniques to the synthesis of behavior models of software systems. This synthesis is used for the elicitation of software requirements. This problem is formulated as a deterministic finite-state automaton induction problem from positive and negative scenarios provided by an end-user of the software-to-be. A query-driven state merging algorithm (QSM) is proposed. It extends the RPNI and Blue-Fringe algorithms by allowing membership queries to be submitted to the end-user. State merging operations can be further constrained by some prior domain knowledge formulated as fluents, goals, domain properties, and models of external software components. The incorporation of domain knowledge both reduces the number of queries and guarantees that the induced model is consistent with such knowledge. The proposed techniques are implemented in the ISIS tool and practical evaluations on standard requirements engineering test cases and synthetic data illustrate the interest of this approach.  Pierre Dupont Department of Computing Science and Engineering (INGI) Université catholique de Louvain Place Sainte Barbe, 2. B-1348 Louvain-la-Neuve Belgium Email:
How a robot should give advice

With advances in robotics, robots can give advice and help using natural language. The field of HRI, however, has not yet developed a communication strategy for giving advice effectively. Drawing on literature in politeness and informal speech, we propose options for a robot's help-giving speech-using hedges or discourse markers, both of which can mitigate the commanding tone implied in direct statements of advice. To test these options, we experimentally compared two help-giving strategies depicted in videos of human and robot helpers. We found that when robot and human helpers used a hedge or discourse markers, they seemed more considerate and likeable, and less controlling. The robot that used discourse markers had even more impact than the human helper. The findings suggest that communication strategies derived from speech used when people help each other in natural settings can be effective for planning the help dialogues of robotic assistants.
Detecting opinion leaders and trends in online social networks

Today, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. A new approach based on text mining and social network analysis is presented which allows detecting opinion leaders and opinion trends. This allows getting a better understanding of the opinion formation. The overall concept is presented and illustrated by an example.
Celebratory technology: new directions for food research in HCI

Food is a central part of our lives. Fundamentally, we need food to survive. Socially, food is something that brings people together-individuals interact through and around it. Culturally, food practices reflect our ethnicities and nationalities. Given the importance of food in our daily lives, it is important to understand what role technology currently plays and the roles it can be imagined to play in the future. In this paper we describe the existing and potential design space for HCI in the area of human-food interaction. We present ideas for future work on designing technologies in the area of human-food interaction that celebrate the positive interactions that people have with food as they eat and prepare foods in their everyday lives.
A Survey on Knowledge Management in Software Engineering

Software development is a knowledge intensive and collaborative activity. The success of the project totally depends on knowledge and experience of the developers. Increasing knowledge creation and sharing among software engineers are uphill tasks in software development environments. The field of knowledge management has emerged into this field to improve the productivity of the software by effective and efficient knowledge creation, sharing and transferring. In other words, knowledge management for software engineering aims at facilitating knowledge flow and utilization across every phases of a software engineering process. Therefore, adaptation of various knowledge management practices by software engineering organizations is essential. This survey identified the knowledge management involvement in software engineering in different perspectives in the recent literature and guide future research in this area.