nils-herrmann/clean_scirepeval huggingface dataset:
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign). To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community: a benign/malicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published end-to-end (featureless) deep learning model for malware detection. Results show that even without hyperparameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.
Cyber-Physical Systems-Are Computing Foundations Adequate ?

Cyber-Physical Systems (CPS) are integrations of computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. In the physical world, the passage of time is inexorable and concurrency is intrinsic. Neither of these properties is present in today’s computing and networking abstractions. I argue that the mismatch between these abstractions and properties of physical processes impede technical progress, and I identify promising technologies for research and investment. There are technical approaches that partially bridge the abstraction gap today (such as real-time operating systems, middleware technologies, specialized embedded processor architectures, and specialized networks), and there is certainly considerable room for improvement of these technologies. However, it may be that we need a less incremental approach, where new abstractions are built from the ground up. The foundations of computing are built on the premise that the principal task of computers is transformation of data. Yet we know that the technology is capable of far richer interactions the physical world. I critically examine the foundations that have been built over the last several decades, and determine where the technology and theory bottlenecks and opportunities lie. I argue for a new systems science that is jointly physical and computational.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
Weisfeiler-Lehman Graph Kernels

In this article, we propose a family of efficient kernels for l a ge graphs with discrete node labels. Key to our method is a rapid feature extraction scheme b as d on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequ ence of graphs, whose node attributes capture topological and label information. A fami ly of kernels can be defined based on this Weisfeiler-Lehman sequence of graphs, including a highly e ffici nt kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of e dges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimental evaluation, our kernels outperform state-of-the-art graph kernels on several graph classifica tion benchmark data sets in terms of accuracy and runtime. Our kernels open the door to large-scale ap plic tions of graph kernels in various disciplines such as computational biology and social netwo rk analysis.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-ofthe-art rumor detection models.
Survey of Peer-to-Peer Botnets and Detection Frameworks

Botnet is a network of compromised computers controlled by the attacker(s) from remote locations via Command and Control (C&C) channels. The botnets are one of the largest global threats to the Internet-based commercial and social world. The decentralized Peer-to-Peer (P2P) botnets have appeared in the recent past and are growing at a faster pace. These P2P botnets are continuously evolving from diverse C&C protocols using hybrid structures and are turning to be more complicated and stealthy. In this paper, we present a comprehensive survey of the evolution, functionalities, modelling and the development life cycle of P2P botnets. Further, we investigate the various P2P botnet detection approaches. Finally, discuss the key research challenges useful for the research initiatives. This paper is useful in understanding the P2P botnets and gives an insight into the usefulness and limitations of the various P2P botnet detection techniques proposed by the researchers. The study will enable the researchers toward proposing the more useful detection techniques.
No surgical innovation without evaluation: the IDEAL recommendations

Surgery and other invasive therapies are complex interventions, the assessment of which is challenged by factors that depend on operator, team, and setting, such as learning curves, quality variations, and perception of equipoise. We propose recommendations for the assessment of surgery based on a five-stage description of the surgical development process. We also encourage the widespread use of prospective databases and registries. Reports of new techniques should be registered as a professional duty, anonymously if necessary when outcomes are adverse. Case series studies should be replaced by prospective development studies for early technical modifications and by prospective research databases for later pre-trial evaluation. Protocols for these studies should be registered publicly. Statistical process control techniques can be useful in both early and late assessment. Randomised trials should be used whenever possible to investigate efficacy, but adequate pre-trial data are essential to allow power calculations, clarify the definition and indications of the intervention, and develop quality measures. Difficulties in doing randomised clinical trials should be addressed by measures to evaluate learning curves and alleviate equipoise problems. Alternative prospective designs, such as interrupted time series studies, should be used when randomised trials are not feasible. Established procedures should be monitored with prospective databases to analyse outcome variations and to identify late and rare events. Achievement of improved design, conduct, and reporting of surgical research will need concerted action by editors, funders of health care and research, regulatory bodies, and professional societies.
Addendum to “ The FFX Mode of Operation for Format-Preserving Encryption ” A parameter collection for enciphering strings of arbitrary radix and length

A scheme for format-preserving encryption (FPE) is supposed to do that which a conventional (possibly tweakable) blockcipher does—encipher messages within some message space X—except that message space, instead of being something like X = {0, 1}128, is more gen­ eral [1, 3]. For example, the message space might be the set X = {0, 1, . . . , 9}16, in which case each 16-digit plaintext X ∈ X gets enciphered into a 16-digit ciphertext Y ∈ X . In a stringbased FPE scheme—the only type of FPE that we consider here—the message space is of the form n X = {0, 1, . . . , radix − 1} for some message length n and alphabet size radix.
Planar Waveguide Arrays for Millimeter Wave Systems

Design of high gain and high efficiency antennas is one of the key challenges in antenna engineering and especially in millimeter wave communication systems. Various types of planar waveguide arrays with series-fed traveling wave operation have been developed in Tokyo Tech. Key features of single-layer arrays in terms of mass production and fabrication cost have been studied, developed and demonstrated there. In this talk, in addition to the array design techniques, the novel methods for loss evaluation of materials and also the diffusion bonding for fabricating waveguide fine structure, specially developed for the millimeter wave are discussed. These arrays are now applied to two kinds of systems in the Tokyo Tech millimeter wave project; the indoor short range file-transfer systems and the outdoor communication systems for the medium range backhaul links. The latter has been field-tested in the model network built in Tokyo Tech Okayama campus. The talk covers early stage progress of the project including unique propagation data.
Deep reinforcement learning approaches for process control

In this work, we have extended the current success of deep learning and reinforcement learning to process control problems. We have shown that if reward hypothesis functions are formulated properly, they can be used for industrial process control. The controller setup follows the typical reinforcement learning setup, whereby an agent (controller) interacts with an environment (process) through control actions and receives a reward in discrete time steps. Deep neural networks serve as function approximators and are used to learn the control policies. Once trained, the learned network acquires a policy that maps system output to control actions. Though the policies are not explicitly specified, the deep neural networks were able to learn policies that are different from the traditional controllers. We evaluated our approach on Single Input Single Output Systems (SISO), Multi-Input Multi-Output Systems (MIMO) and tested it under various scenarios.
Crowdsourcing Critical Success Factor Model Strategies to harness the collective intelligence of the crowd Ankit

Crowdsourcing, simply referring to the act of outsourcing a task to the crowd, is one of the most important trends revolutionizing the internet and the mobile market at present. This paper is an attempt to understand the dynamic and innovative discipline of crowdsourcing by developing a critical success factor model for it. The critical success factor model is based on the case study analysis of the mobile phone based crowdsourcing initiatives in Africa and the available literature on outsourcing, crowdsourcing and technology adoption. The model is used to analyze and hint at some of the critical attributes of a successful crowdsourcing initiative focused on socio-economic development of societies. The broader aim of the paper is to provide academicians, social entrepreneurs, policy makers and other practitioners with a set of recommended actions and an overview of the important considerations to be kept in mind while implementing a crowdsourcing initiative.
ParsCit: an Open-source CRF Reference String Parsing Package

We describe ParsCit, a freely available, open-source imple entation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label th token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference string s from a plain text file, and to retrieve the citation contexts . The package comes with utilities to run it as a web service or as a standalone uti lity. We compare ParsCit on three distinct reference string datasets and show that it compares well with other previously published work.
Poster : Android Collusive Data Leaks with Flow-sensitive DIALDroid Dataset

We present DIALDroid, a scalable and accurate tool for analyzing inter-app Inter-Component Communication (ICC) among Android apps, which outperforms current stateof-the-art ICC analysis tools. Using DIALDroid, we performed the first large-scale detection of collusive and vulnerable apps based on inter-app ICC data flows among 110,150 real-world apps and identified key security insights.
Patch-Based Image Inpainting with Generative Adversarial Networks

Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks. However, current network solutions still introduce undesired artifacts and noise to the repaired regions. We present an image inpainting method that is based on the celebrated generative adversarial network (GAN) framework. The proposed PGGAN method includes a discriminator network that combines a global GAN (G-GAN) architecture with a patchGAN approach. PGGAN first shares network layers between G-GAN and patchGAN, then splits paths to produce two adversarial losses that feed the generator network in order to capture both local continuity of image texture and pervasive global features in images. The proposed framework is evaluated extensively, and the results including comparison to recent state-of-the-art demonstrate that it achieves considerable improvements on both visual and quantitative evaluations.
Review in terahertz spectral analysis

Nowadays terahertz spectroscopy is a well-established technique and recent progresses in technology demonstrated that this new technique is useful for both fundamental research and industrial applications. Varieties of applications such as imaging, non destructive testing, quality control are about to be transferred to industry supported by permanent improvements from basic research. Since chemometrics is today routinely applied to IR spectroscopy, we discuss in this paper the advantages of using chemometrics in the framework of terahertz spectroscopy. Different analytical procedures are illustrates. We conclude that advanced data processing is the key point to validate routine terahertz spectroscopy as a new reliable analytical technique.
Collaborative Knowledge Base Embedding for Recommender Systems

Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid collection of information on the web, the knowledge base provides heterogeneous information including both structured and unstructured data with different semantics, which can be consumed by various applications. In this paper, we investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems. First, by exploiting the knowledge base, we design three components to extract items' semantic representations from structural content, textual content and visual content, respectively. To be specific, we adopt a heterogeneous network embedding method, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items' textual representations and visual representations, respectively. Finally, we propose our final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering as well as items' semantic representations from the knowledge base. To evaluate the performance of each embedding component as well as the whole system, we conduct extensive experiments with two real-world datasets from different scenarios. The results reveal that our approaches outperform several widely adopted state-of-the-art recommendation methods.
The Need for Open Source Software in Machine Learning

Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.
Fixed Dose Combination of Magaldrate Plus Domperidone Is More Effective than Domperidone Alone in the Treatment of Patients with Gastroesophageal Reflux Symptoms: A Randomized Double-Blind Study

Gastroeophageal reflux is a condition in which the acidified liquid content of the stomach backs up into the esophagus. The antiacid magaldrate and prokinetic domperidone are two drugs clinically used for the treatment of gastroesophageal reflux symptoms. However, the evidence of a superior effectiveness of this combination in comparison with individual drugs is lacking. A double-blind, randomized and comparative clinical trial study was designed to characterize the efficacy and safety of a fixed dose combination of magaldrate (800 mg)/domperidone (10 mg) against domperidone alone (10 mg), in patients with gastroesophageal reflux symptoms. One hundred patients with gastroesophageal reflux diagnosed by Carlsson scale were randomized to receive a chewable tablet of a fixed dose of magaldrate/domperidone combination or domperidone alone four times each day during a month. Magaldrate/domperidone combination showed a superior efficacy to decrease global esophageal (pyrosis, regurgitation, dysphagia, hiccup, gastroparesis, sialorrhea, globus pharyngeus and nausea) and extraesophageal (chronic cough, hoarseness, asthmatiform syndrome, laryngitis, pharyngitis, halitosis and chest pain) reflux symptoms than domperidone alone. In addition, magaldrate/domperidone combination improved in a statistically manner the quality of life of patients with gastroesophageal reflux respect to monotherapy, and more patients perceived the combination as a better treatment. Both treatments were well tolerated. Data suggest that oral magaldrate/domperidone mixture could be a better option in the treatment of gastroesophageal reflux symptoms than only domperidone.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn deep learning, most existing tools focus on simpler models. In this work, we present GAN Lab, the first interactive visualization tool designed for non-experts to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models. With GAN Lab, users can interactively train generative models and visualize the dynamic training process's intermediate results. GAN Lab tightly integrates an model overview graph that summarizes GAN's structure, and a layered distributions view that helps users interpret the interplay between submodels. GAN Lab introduces new interactive experimentation features for learning complex deep learning models, such as step-by-step training at multiple levels of abstraction for understanding intricate training dynamics. Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web browsers, without the need for installation or specialized hardware, overcoming a major practical challenge in deploying interactive tools for deep learning.
Enhancing purchase intention through social media brand community: the roles of social presence, interactivity, and peer motivation

By integrating the perspectives of social presence, interactivity, and peer motivation, this study developed a theoretical model to examine the factors affecting members' purchase intention in the context of social media brand community. Data collected from members of a fan page brand community on Facebook in Taiwan was used to test the model. The results also show that peer extrinsic motivation and peer intrinsic motivation have positive influences on purchase intention. The results also reveal that human-message interaction exerts significant influence on peer extrinsic motivation and peer intrinsic motivation, while human-human interaction has a positive effect on human-message interaction. Finally, the results report that awareness impacts human-message interaction significantly, whereas awareness, affective social presence, and cognitive social presence influence human-human interaction significantly.
Research Commentary - Platform Evolution: Coevolution of Platform Architecture, Governance, and Environmental Dynamics

T emergence of software-based platforms is shifting competition toward platform-centric ecosystems, although this phenomenon has not received much attention in information systems research. Our premise is that the coevolution of the design, governance, and environmental dynamics of such ecosystems influences how they evolve. We present a framework for understanding platform-based ecosystems and discuss five broad research questions that present significant research opportunities for contributing homegrown theory about their evolutionary dynamics to the information systems discipline and distinctive information technology-artifactcentric contributions to the strategy, economics, and software engineering reference disciplines.