nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating shallow and deep networks for ransomware detection and classification

Ransomware is one type of malware that covertly installs and executes a cryptovirology attack on a victims computer to demand a ransom payment for restoration of the infected resources. This kind of malware has been growing largely in recent days and causes tens of millions of dollars losses to consumers. In this paper, we evaluate shallow and deep networks for the detection and classification of ransomware. To characterize and distinguish ransomware over benign and various other families of ransomwares, we leverage the dominance of application programming interface (API) invocations. To select a best architecture for the multi-layer perceptron (MLP), we done various experiments related to network parameters and structures. All the experiments are run up to 500 epochs with a learning rate in the range [0.01-0.5]. Result obtained on our data set is more promising to distinguish ransomware not only from benign from its families too. On distinguishing the .EXE as either benign or ransomware, MLP has attained highest accuracy 1.0 and classifying the ransomware to their categories obtained highest accuracy 0.98. Moreover, MLP has performed well in detecting and classifying ransomwares in comparison to the other classical machine learning classifiers.
Most liked, fewest friends: patterns of enterprise social media use

Enterprise social media can provide visibility of users' actions and thus has the potential to reveal insights about users in the organization. We mined large-scale social media use in an enterprise to examine: a) user roles with such broad platforms and b) whether people with large social networks are highly regarded. First, a factor analysis revealed that most variance of social media usage is explained by commenting and 'liking' behaviors while other usage can be characterized as patterns of distinct tool usage. These results informed the development of a model showing that online network size interacts with other media usage to predict who is highly assessed in the organization. We discovered that the smaller one's online social network size in the organization, the more highly assessed they were by colleagues. We explain this inverse relationship as due to friending behavior being highly visible but not yet valued in the organization.
Operating Systems for IoT Devices: A Critical Survey

The future of communication resides in Internet of Things, which is certainly the most sought after technology today. The applications of IoT are diverse, and range from ordinary voice recognition to critical space programmes. Recently, a lot of efforts have been made to design operating systems for IoT devices because neither traditional Windows/Unix, nor the existing Real Time Operating Systems are able to meet the demands of heterogeneous IoT applications. This paper presents a survey of operating systems that have been designed so far for IoT devices and also outlines a generic framework that brings out the essential features desired in an OS tailored for IoT devices.
A Compact EADFB Laser Array Module for a Future 100-Gb/s Ethernet Transceiver

A compact electroabsorption modulators integrated distributed feedback (EADFB) laser array module has been developed for 100-Gb/s Ethernet. Four 25-Gb/s EADFB lasers and an optical multiplexer are monolithically integrated on one chip in an area of only 2 mm × 2.6 mm. As a result, a compact 12 mm × 20 mm EADFB laser array module is achieved. A bridge-type RF circuit board is employed to transmit high-frequency, 4 × 25-Gb/s, electrical signals effectively in a narrow package and improve the electrical/optical response of the module. The module has a 3-dB frequency bandwidth of 20 GHz for the four lanes assigned in 100-Gb/s Ethernet. Furthermore, the possibility of realizing low-driving voltage operation is investigated to reduce the power consumption of the module. An error-free transmission through 10 km of single-mode fiber at 100 Gb/s with a modulation voltage of only 1 V is demonstrated.
FaceTouch: Enabling Touch Interaction in Display Fixed UIs for Mobile Virtual Reality

We present FaceTouch, a novel interaction concept for mobile Virtual Reality (VR) head-mounted displays (HMDs) that leverages the backside as a touch-sensitive surface. With FaceTouch, the user can point at and select virtual content inside their field-of-view by touching the corresponding location at the backside of the HMD utilizing their sense of proprioception. This allows for rich interaction (e.g. gestures) in mobile and nomadic scenarios without having to carry additional accessories (e.g. a gamepad). We built a prototype of FaceTouch and conducted two user studies. In the first study we measured the precision of FaceTouch in a display-fixed target selection task using three different selection techniques showing a low error rate of 2% indicate the viability for everyday usage. To asses the impact of different mounting positions on the user performance we conducted a second study. We compared three mounting positions of the touchpad (face, hand and side) showing that mounting the touchpad at the back of the HMD resulted in a significantly lower error rate, lower selection time and higher usability. Finally, we present interaction techniques and three example applications that explore the FaceTouch design space.
A Control Lyapunov Approach for Feedback Control of Cable-Suspended Robots

This paper considers a feedback control technique for cable suspended robots under input constraints, using control Lyapunov functions (CLF). The motivation for this work is to develop an explicit feedback control law for cable robots to asymptotically stabilize it to a goal point with positive input constraints. The main contributions of this paper are as follows: (i) proposal for a CLF candidate for a cable robot, (ii) a CLF based positive controllers for multiple inputs. An example of a three degrees-of-freedom cable suspended robot is presented to illustrate the proposed methods
Real-World Anomaly Detection in Surveillance Videos

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: http://crcv.ucf.edu/projects/real-world/
Timing Analysis for Inferring the Topology of the Bitcoin Peer-to-Peer Network

Flooding Peer-to-Peer (P2P) networks form the basis of services such as the electronic currency system Bitcoin. The decentralized architecture enables robustness against failure. However, knowledge of the network's topology can allow adversaries to attack specific peers in order to, e.g., isolate certain peers or even partition the network. Knowledge of the topology might be gained by observing the flooding process, which is inherently possible in such networks,, performing a timing analysis on the observations. In this paper we present a timing analysis method that targets flooding P2P networks, show its theoretical, practical feasibility. A validation in the real-world Bitcoin network proves the possibility of inferring network links of actively participating peers with substantial precision, recall (both ~ 40%), potentially enabling attacks on the network. Additionally, we analyze the countermeasure of trickling, quantify the tradeoff between the effectiveness of the countermeasure, the expected performance penalty. The analysis shows that inappropriate parametrization can actually facilitate inference attacks.
Factors Influencing a Learner ’ s Decision to Drop-Out or Persist in Higher Education Distance Learning

Previous studies conducted on dropouts within online courses have found inconsistent factors affecting attrition. A literature review was performed, focusing on eight main studies. These studies were performed at both national and international universities. The methodology, participants, research question, and results varied by study. Overall, internal factors of self-efficacy, self-determination, autonomy, and time management along with external factors of family, organizational, and technical support were found to be significant. An additional variable of course factors, which includes course relevance and course design, was found to significantly impact learners’ decisions to persist or drop an online course. These variables were incorporated into a modified version of Bandura’s reciprocal causation theory, which states that each of these variables influences and is influenced by the decision of a student to persist or drop an online course. The model needs statistical testing within the context of an individual study. Further studies are also needed on course factors impacting an online student’s decision to persist or drop an online course.
Aggression and Violent Behavior Bullying and the peer group : A review

a r t i c l e i n f o It is often stated that bullying is a " group process " , and many researchers and policymakers share the belief that interventions against bullying should be targeted at the peer-group level rather than at individual bullies and victims. There is less insight into what in the group level should be changed and how, as the group processes taking place at the level of the peer clusters or school classes have not been much elaborated. This paper reviews the literature on the group involvement in bullying, thus providing insight into the individuals' motives for participation in bullying, the persistence of bullying, and the adjustment of victims across different peer contexts. Interventions targeting the peer group are briefly discussed and future directions for research on peer processes in bullying are suggested. Bullying is a subtype of aggressive behavior, in which an individual or a group of individuals repeatedly attacks, humiliates, and/or excludes a relatively powerless person. The majority of studies on the topic have been conducted in schools, focusing on bullying among the concept of bullying is used to refer to peer-to-peer bullying among school-aged children and youth, when not otherwise mentioned. It is known that a sizable minority of primary and secondary school students is involved in peer-to-peer bullying either as perpetrators or victims — or as both, being both bullied themselves and harassing others.  Children survey (HBSC, see Craig & Harel, 2004), the average prevalence of victims across the 35 countries involved was 11%, whereas bullies represented another 11%. Children who report both bullying others and being bullied by others (so-called bully–victims) were not identified in the HBSC study, but other studies have shown that approximately 4–6% of the children can be classified as bully–victims (Haynie et al., 2001; Nansel et al., 2001). Bullying constitutes a serious risk for the psychosocial and academic adjustment of both victims
Comparing lexical, acoustic/prosodic, structural and discourse features for speech summarization

We present results of an empirical study of the usefulness of different types of features in selecting extractive summaries of news broadcasts for our Broadcast News Summarization System. We evaluate lexical, prosodic, structural and discourse features as predictors of those news segments which should be included in a summary. We show that a summarization system that uses a combination of these feature sets produces the most accurate summaries, and that a combination of acoustic/prosodic and structural features are enough to build a ‘good’ summarizer when speech transcription is not available.
Memory Networks

We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs.
View-dependent refinement of progressive meshes

Level-of-detail (LOD) representations are an important tool for realtime rendering of complex geometric environments. The previously introduced progressive mesh representation defines for an arbitrary triangle mesh a sequence of approximating meshes optimized for view-independent LOD. In this paper, we introduce a framework for selectively refining an arbitrary progressive mesh according to changing view parameters. We define efficient refinement criteria based on the view frustum, surface orientation, and screen-space geometric error, and develop a real-time algorithm for incrementally refining and coarsening the mesh according to these criteria. The algorithm exploits view coherence, supports frame rate regulation, and is found to require less than 15% of total frame time on a graphics workstation. Moreover, for continuous motions this work can be amortized over consecutive frames. In addition, smooth visual transitions (geomorphs) can be constructed between any two selectively refined meshes. A number of previous schemes create view-dependent LOD meshes for height fields (e.g. terrains) and parametric surfaces (e.g. NURBS). Our framework also performs well for these special cases. Notably, the absence of a rigid subdivision structure allows more accurate approximations than with existing schemes. We include results for these cases as well as for general meshes. CR Categories: I.3.3 [Computer Graphics]: Picture/Image Generation Display algorithms; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling surfaces and object representations.
Modeling spectral variability for the classification of depressed speech

Quantifying how the spectral content of speech relates to changes in mental state may be crucial in building an objective speech-based depression classification system with clinical utility. This paper investigates the hypothesis that important depression based information can be captured within the covariance structure of a Gaussian Mixture Model (GMM) of recorded speech. Significant negative correlations found between a speaker’s average weighted variance a GMMbased indicator of speaker variability and their level of depression support this hypothesis. Further evidence is provided by the comparison of classification accuracies from seven different GMM-UBM systems, each formed by varying different parameter combinations during MAP adaption. This analysis shows that variance-only adaptation either outperforms or matches the de facto standard mean-only adaptation when classifying both the presence and severity of depression. This result is perhaps the first of its kind seen in GMM-UBM speech classification.
Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices

The rapid development of deep learning are enabling a plenty of novel applications such as image and speech recognition for embedded systems, robotics or smart wearable devices. However, typical deep learning models like deep convolutional neural networks (CNNs) consume so much on-chip storage and high-throughput compute resources that they cannot be easily handled by mobile or embedded devices with thrifty silicon and power budget. In order to enable large CNN models in mobile or more cutting-edge devices for IoT or cyberphysics applications, we proposed an efficient on-chip memory architecture for CNN inference acceleration, and showed its application to our in-house general-purpose deep learning accelerator. The redesigned on-chip memory subsystem, Memsqueezer, includes an active weight buffer set and data buffer set that embrace specialized compression methods to reduce the footprint of CNN weight and data set respectively. The Memsqueezer buffer can compress the data and weight set according to their distinct features, and it also includes a built-in redundancy detection mechanism that actively scans through the work-set of CNNs to boost their inference performance by eliminating the data redundancy. In our experiment, it is shown that the CNN accelerators with Memsqueezer buffers achieves more than 2x performance improvement and reduces 80% energy consumption on average over the conventional buffer design with the same area budget.
Question Asking as Program Generation

A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing humanlike questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.
Semantics and Analysis of Instruction List Programs

Instruction List (IL) is a simple typed assembly language commonly used in embedded control. There is little tool support for IL and, although defined in the IEC 61131-3 standard, there is no formal semantics. In this work we develop a formal operational semantics. Moreover, we present an abstract semantics, which allows approximative program simulation for a (possibly infinte) set of inputs in one simulation run. We also extended this framework to an abstract interpretation based analysis, which is implemented in our tool Homer. All these analyses can be carried out without knowledge of formal methods, which is typically not present in the IL community.
Learning to Compose Words into Sentences with Reinforcement Learning

We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task. Experiments demonstrate the benefit of learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations. We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g., noun phrases, simple verb phrases), they are different than conventional English syntactic structures.
Measuring KMS success: A respecification of the DeLone and McLean's model

We proposed and empirically assessed a KMS success model. This was derived through an analysis of current practice of knowledge management and review of IS success literature. Five variables (system quality, knowledge or information quality, perceived KMS benefits, user satisfaction, and system use) were used as dependent variables in evaluating KMS success, and their interrelationships were suggested and empirically tested. The results provide an expanded understanding of the factors that measure KMS success and implications of this work are discussed.
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge

Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.
Neural Network Translation Models for Grammatical Error Correction

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.