nils-herrmann/clean_scirepeval huggingface dataset:
Active Pages: A Computation Model for Intelligent Memory

Microprocessors and memory systems suffer from a growing gap in performance. We introduce Active Pages, a computation model which addresses this gap by shifting data-intensive computations to the memory system. An Active Page consists of a page of data and a set of associated functions which can operate upon that data. We describe an implementation of Active Pages on RADram (Reconfigurable Architecture DRAM), a memory system based upon the integration of DRAM and reconfigurable logic. Results from the SimpleScalar simulator [BA97] demonstrate up to 1000X speedups on several applications using the RADram system versus conventional memory systems. We also explore the sensitivity of our results to implementations in other memory technologies.
Android Malware Detection Based on System Calls

With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we propose a simple, and yet highly effective technique for detecting malicious Android applications on a repository level. Our technique performs automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the technique in a tool called MALINE, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation yields an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with up-sampling. This indicates that our technique is viable to be used in practice. Finally, we show that even simplistic feature choices are highly effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated. Android Malware Detection Based on System Calls Marko Dimjašević, Simone Atzeni, Zvonimir Rakamarić University of Utah, USA {marko,simone,zvonimir}@cs.utah.edu Ivo Ugrina University of Zagreb, Croatia
Adapting the Stochastic Block Model to Edge-Weighted Networks

We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduces several technical difficulties for model estimation, which we solve using a Bayesian approach. We introduce a variational algorithm that efficiently approximates the model’s posterior distribution for dense graphs. In specific numerical experiments on edge-weighted networks, this weighted stochastic block model outperforms the common approach of first applying a single threshold to all weights and then applying the classic stochastic block model, which can obscure latent block structure in networks. This model will enable the recovery of latent structure in a broader range of network data than was previously possible.
Semantic Highlight Retrieval and Term Prediction

Due to the unprecedented growth of unedited videos, finding highlights relevant to a text query in a set of unedited videos has become increasingly important. We refer this task as semantic highlight retrieval and propose a query-dependent video representation for retrieving a variety of highlights. Our method consists of two parts: 1) “viralets”, a mid-level representation bridging between semantic ] and visual  spaces and 2) a novel Semantic-MODulation (SMOD) procedure to make viralets query-dependent (referred to as SMOD viralets). Given SMOD viralets, we train a single highlight ranker to predict the highlightness of clips with respect to a variety of queries (two examples in Fig. 1), whereas existing approaches can be applied only in a few predefined domains. Other than semantic highlight retrieval, viralets can also be used to associate relevant terms to each video. We utilize this property and propose a simple term prediction method based on nearest neighbor search. To conduct experiments, we collect a viral video dataset1 including users’ comments, highlights, and/or original videos. Among a testing database with 1189 clips (13% highlights and 87% non-highlights), our highlight ranker achieves 41.2% recall at top-10 retrieved clips. It is significantly higher than the state-of-the-art domain-specific highlight ranker and its extension. Similarly, our method also outperforms all baseline methods on the publicly available video highlight dataset. Finally, our simple term prediction method utilizing viralets outperforms the state-of-the-art matrix factorization method (adapted from Kalayeh et al.). 1 Viral videos refer to popular online videos. We focus on user-generated viral videos, which typically contain short highlight marked by users.
Probabilistic scan matching for motion estimation in unstructured environments

This paper presents a probabilistic scan matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. The general framework follows an iterative process of two steps: (i) computation of correspondences between scans, and (ii) estimation of the relative displacement. The contribution is a probabilistic modelling of this process that takes into account all the uncertainties involved: the uncertainty of the displacement of the sensor and the measurement noises. Furthermore, it also considers all the possible correspondences resulting from these uncertainties. This technique has been implemented and tested on a real vehicle. The experiments illustrate how the performances of this method are better than previous geometric ones in terms of robustness, accuracy and convergence.
Analysis of personal information security behavior and awareness

Hardware and software security mechanisms are widely used to strengthen information systems (IS) against attacks. However, these systems are still highly vulnerable to threats from users’ undesirable behaviors, which are closely related to IS users’ information security awareness. This study has been carried out in an effort to investigate the IS users’ risky behaviors that may threaten information security. The preventive actions employed by users, the threats they may be exposed to, or whether they had an adverse experience or to what extent they perceive risks have also been investigated. Four scales; Risky Behavior Scale (RBS), Conservative Behavior Scale (CBS), Exposure to Offence Scale (EOS) and Risk Perception Scale (RPS) were developed depending on the data collected with the use of surveys. The scales developed from the content of the survey were applied to students, academics and administrative staff of a university, which also embodies hospitals and educational organizations located in different geographical and socio-economical regions of Turkey. On the base of developed scales, the results of the study show that there are significant differences within samples and according to the habits of Internet usage.
Using Both Audio and Visual Clues

Multimedia content analysis refers to the computerized understanding of the semantic meanings of a multimedia document, such as a video sequence with an accompanying audio track. As we enter the digital multimedia information era, tools that enable such automated analysis are becoming indispensable to be able to efficiently access, digest, and retrieve information. Information retrieval, as a field, has existed for some time. Until recently, however, the focus has been on understanding text information, e.g., how to extract key words from a document, how to categorize a document, and how to summarize a document, all based on written text. With a multimedia document, its semantics are embedded in multiple forms that are usually complimentary of each other. For example, live coverage on TV about an earthquake conveys information that is far beyond what we hear from the reporter. We can see and feel the effects of the earthquake, while hearing the reporter talking about the statistics. Therefore, it is necessary to analyze all types of data: image frames, sound tracks, texts that can be extracted from image frames, and spoken words that can be deciphered from the audio track. This usually involves segmenting the document into semantically meaningful units, classifying each unit into a predefined scene type, and indexing and summarizing the document for efficient retrieval and browsing. In this article, we review recent advances in using audio and visual information jointly for accomplishing the above tasks. We will describe audio and visual features that can effectively characterize scene content, present selected algorithms for segmentation and classification, and review some testbed systems for video archiving and retrieval. We will also briefly describe audio and visual descriptors and description schemes that are being considered by the MPEG-7 standard for multimedia content description.
Rao-Blackwellized Particle Filters multi robot SLAM with unknown initial correspondences and limited communication

Multi robot systems are envisioned to play an important role in many robotic applications. A main prerequisite for a team deployed in a wide unknown area is the capability of autonomously navigate, exploiting the information acquired through the on-line estimation of both robot poses and surrounding environment model, according to Simultaneous Localization And Mapping (SLAM) framework. As team coordination is improved, distributed techniques for filtering are required in order to enhance autonomous exploration and large scale SLAM increasing both efficiency and robustness of operation. Although Rao-Blackwellized Particle Filters (RBPF) have been demonstrated to be an effective solution to the problem of single robot SLAM, few extensions to teams of robots exist, and these approaches are characterized by strict assumptions on both communication bandwidth and prior knowledge on relative poses of the teammates. In the present paper we address the problem of multi robot SLAM in the case of limited communication and unknown relative initial poses. Starting from the well established single robot RBPF-SLAM, we propose a simple technique which jointly estimates SLAM posterior of the robots by fusing the prioceptive and the eteroceptive information acquired by each teammate. The approach intrinsically reduces the amount of data to be exchanged among the robots, while taking into account the uncertainty in relative pose measurements. Moreover it can be naturally extended to different communication technologies (bluetooth, RFId, wifi, etc.) regardless their sensing range. The proposed approach is validated through experimental test.
The Capabilities and Performance Advantages of Market-Driven Firms : An Empirical Investigation

Although progress has been made in understanding the development of businesses competencies from a theoretical perspective, relatively few empirical studies have addressed the capabilities needed to become market-driven and the performance advantages accruing to firms possessing these capabilities. One of the barriers faced has been in defining what is meant by the term ‘market-driven’. This study develops a multi-dimensional measure useful for assessing the degree to which a firm is market-driven. Evidence is presented in this study that strategically focused market-driven business units developed higher levels of capabilities than their less market-driven rivals and significantly outperformed these rival business units on four measures of organisational performance.
An efficient search algorithm for motion data using weighted PCA

Good motion data is costly to create. Such an expense often makes the reuse of motion data through transformation and retargetting a more attractive option than creating new motion from scratch. Reuse requires the ability to search automatically and efficiently a growing corpus of motion data, which remains a difficult open problem. We present a method for quickly searching long, unsegmented motion clips for subregions that most closely match a short query clip. Our search algorithm is based on a weighted PCA-based pose representation that allows for flexible and efficient pose-to-pose distance calculations. We present our pose representation and the details of the search algorithm. We evaluate the performance of a prototype search application using both synthetic and captured motion data. Using these results, we propose ways to improve the application's performance. The results inform a discussion of the algorithm's good scalability characteristics.
: DETECTION & DIAGNOSIS OF PLANT LEAF DISEASE USING INTEGRATED IMAGE PROCESSING APPROACH

Farmers find it difficult to keep an eye on each and every plant in the cultivation area to detect manifestation of any infection. Many an image processing approaches has been developed in recent years to serve the purpose of a watch-dog, i.e. detecting the growth of a disease at an early stage, determining the type of the disease the leaf is infected with and suggesting necessary actions. This paper will go through most of the research work that has been done in the field of Image Processing and Artificial Neural Network for the purpose of detection and diagnosis of disease in a plant leaf from its image.
NILMTK: an open source toolkit for non-intrusive load monitoring

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Special Mapping Crime : Understanding Hot Spots 0 5

This document is not intended to create, does not create, and may not be relied upon to create any rights, substantive or procedural, enforceable by law by any party in any matter civil or criminal. Findings and conclusions of the research reported here are those of the authors and do not necessarily reflect the official position or policies of the U.S. Department of Justice. The products, manufacturers, and organizations discussed in this document are presented for informational purposes only and do not constitute product approval or endorsement by the Much of crime mapping is devoted to detecting high-crime-density areas known as hot spots. Hot spot analysis helps police identify high-crime areas, types of crime being committed, and the best way to respond. This report discusses hot spot analysis techniques and software and identifies when to use each one. The visual display of a crime pattern on a map should be consistent with the type of hot spot and possible police action. For example, when hot spots are at specific addresses, a dot map is more appropriate than an area map, which would be too imprecise. In this report, chapters progress in sophis­ tication. Chapter 1 is for novices to crime mapping. Chapter 2 is more advanced, and chapter 3 is for highly experienced analysts. The report can be used as a com­ panion to another crime mapping report ■ Identifying hot spots requires multiple techniques; no single method is suffi­ cient to analyze all types of crime. ■ Current mapping technologies have sig­ nificantly improved the ability of crime analysts and researchers to understand crime patterns and victimization. ■ Crime hot spot maps can most effective­ ly guide police action when production of the maps is guided by crime theories (place, victim, street, or neighborhood).
All I Really Need to Know About Pair Programming I Learned in Kindergarten

All I Really Need to Know I Learned in Kindergarten By Robert Fulghum (Fulghum 1988) Share everything. Play fair. Don’t hit people. Put things back where you found them. Clean up your own mess. Don’t take things that aren’t yours. Say you’re sorry when you hurt somebody. Wash your hands before you eat. Flush. Warm cookies and cold milk are good for you. Live a balanced life – learn some and think some and draw and paint and sing and dance and play and work every day some. Take a nap every afternoon. When you go out into the world, watch out for traffic, hold hands and stick together. Be aware of wonder.  Pair programming is a style of programming in which two programmers work side-by-side at one computer, continuously collaborating on the same design, algorithm, code or test. As discussed below, use of this practice has been demonstrated to improve productivity and quality of software products. Additionally, based on a survey(Williams 1999) of pair programmers (hereafter referred to as “the pair programming survey"), 100% agreed that they had more confidence in their solution when pair programming than when they program alone. Likewise, 96% agreed that they enjoy their job more than when programming alone.
Latent Bayesian melding for integrating individual and population models

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the idea of generalized moment matching, in that they allow matching expectations between two models, but sometimes both models are most conveniently expressed as latent variable models. We propose latent Bayesian melding, which is motivated by averaging the distributions over populations statistics of both the individual-level and the population-level models under a logarithmic opinion pool framework. In a case study on electricity disaggregation, which is a type of singlechannel blind source separation problem, we show that latent Bayesian melding leads to significantly more accurate predictions than an approach based solely on generalized moment matching.
Using graded implicit feedback for bayesian personalized ranking

In many application domains of recommender systems, explicit rating information is sparse or non-existent. The preferences of the current user have therefore to be approximated by interpreting his or her behavior, i.e., the implicit user feedback. In the literature, a number of algorithm proposals have been made that rely solely on such implicit feedback, among them Bayesian Personalized Ranking (BPR).
 In the BPR approach, pairwise comparisons between the items are made in the training phase and an item i is considered to be preferred over item j if the user interacted in some form with i but not with j. In real-world applications, however, implicit feedback is not necessarily limited to such binary decisions as there are, e.g., different types of user actions like item views, cart or purchase actions and there can exist several actions for an item over time.
 In this paper we show how BPR can be extended to deal with such more fine-granular, graded preference relations. An empirical analysis shows that this extension can help to measurably increase the predictive accuracy of BPR on realistic e-commerce datasets.
Attested append-only memory: making adversaries stick to their word

Researchers have made great strides in improving the fault tolerance of both centralized and replicated systems against arbitrary (Byzantine) faults. However, there are hard limits to how much can be done with entirely untrusted components; for example, replicated state machines cannot tolerate more than a third of their replica population being Byzantine. In this paper, we investigate how minimal trusted abstractions can push through these hard limits in practical ways. We propose Attested Append-Only Memory (A2M), a trusted system facility that is small, easy to implement and easy to verify formally. A2M provides the programming abstraction of a trusted log, which leads to protocol designs immune to equivocation -- the ability of a faulty host to lie in different ways to different clients or servers -- which is a common source of Byzantine headaches. Using A2M, we improve upon the state of the art in Byzantine-fault tolerant replicated state machines, producing A2M-enabled protocols (variants of Castro and Liskov's PBFT) that remain correct (linearizable) and keep making progress (live) even when half the replicas are faulty, in contrast to the previous upper bound. We also present an A2M-enabled single-server shared storage protocol that guarantees linearizability despite server faults. We implement A2M and our protocols, evaluate them experimentally through micro- and macro-benchmarks, and argue that the improved fault tolerance is cost-effective for a broad range of uses, opening up new avenues for practical, more reliable services.
A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN's robust, accurate decoding abilities.