nils-herrmann/clean_scirepeval huggingface dataset:
Software Crash Analysis for Automatic Exploit Generation on Binary Programs

This paper presents a new method, capable of automatically generating attacks on binary programs from software crashes. We analyze software crashes with a symbolic failure model by performing concolic executions following the failure directed paths, using a whole system environment model and concrete address mapped symbolic memory in S2 E. We propose a new selective symbolic input method and lazy evaluation on pseudo symbolic variables to handle symbolic pointers and speed up the process. This is an end-to-end approach able to create exploits from crash inputs or existing exploits for various applications, including most of the existing benchmark programs, and several large scale applications, such as a word processor (Microsoft office word), a media player (mpalyer), an archiver (unrar), or a pdf reader (foxit). We can deal with vulnerability types including stack and heap overflows, format string, and the use of uninitialized variables. Notably, these applications have become software fuzz testing targets, but still require a manual process with security knowledge to produce mitigation-hardened exploits. Using this method to generate exploits is an automated process for software failures without source code. The proposed method is simpler, more general, faster, and can be scaled to larger programs than existing systems. We produce the exploits within one minute for most of the benchmark programs, including mplayer. We also transform existing exploits of Microsoft office word into new exploits within four minutes. The best speedup is 7,211 times faster than the initial attempt. For heap overflow vulnerability, we can automatically exploit the unlink() macro of glibc, which formerly requires sophisticated hacking efforts.
A Survey of Data Mining and Deep Learning in Bioinformatics

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes.
Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA

Convolutional neural network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many computer vision algorithms. But the computation complexity of CNN is much higher than traditional algorithms. With the help of GPU acceleration, CNN-based applications are widely deployed in servers. However, for embedded platforms, CNN-based solutions are still too complex to be applied. Various dedicated hardware designs on field-programmable gate arrays (FPGAs) have been carried out to accelerate CNNs, while few of them explore the whole design flow for both fast deployment and high power efficiency. In this paper, we investigate state-of-the-art CNN models and CNN-based applications. Requirements on memory, computation and the flexibility of the system are summarized for mapping CNN on embedded FPGAs. Based on these requirements, we propose Angel-Eye, a programmable and flexible CNN accelerator architecture, together with data quantization strategy and compilation tool. Data quantization strategy helps reduce the bit-width down to 8-bit with negligible accuracy loss. The compilation tool maps a certain CNN model efficiently onto hardware. Evaluated on Zynq XC7Z045 platform, Angel-Eye is <inline-formula> <tex-math notation="LaTeX">$6 {\times }$ </tex-math></inline-formula> faster and <inline-formula> <tex-math notation="LaTeX">$5{\times }$ </tex-math></inline-formula> better in power efficiency than peer FPGA implementation on the same platform. Applications of VGG network, pedestrian detection and face alignment are used to evaluate our design on Zynq XC7Z020. NIVIDA TK1 and TX1 platforms are used for comparison. Angel-Eye achieves similar performance and delivers up to <inline-formula> <tex-math notation="LaTeX">$16 {\times }$ </tex-math></inline-formula> better energy efficiency.
Robot hands and the mechanics of manipulation

What do you do to start reading robot hands and the mechanics of manipulation? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this robot hands and the mechanics of manipulation.
Botta: An Arabic Dialect Chatbot

This paper presents BOTTA, the first Arabic dialect chatbot. We explore the challenges of creating a conversational agent that aims to simulate friendly conversations using the Egyptian Arabic dialect. We present a number of solutions and describe the different components of the BOTTA chatbot. The BOTTA database files are publicly available for researchers working on Arabic chatbot technologies. The BOTTA chatbot is also publicly available for any users who want to chat with it online.
Coordinated Allocation of Service Function Chains

Network Functions Virtualization (NFV) is an emerging initiative to overcome increasing operational and capital costs faced by network operators due to the need to physically locate network functions in specific hardware appliances. In NFV, standard IT virtualization evolves to consolidate network functions onto high volume servers, switches and storage that can be located anywhere in the network. Services are built by chaining a set of Virtual Network Functions (VNFs) deployed on commodity hardware. The implementation of NFV leads to the challenge: How several network services (VNF chains) are optimally orchestrated and allocated on the substrate network infrastructure? In this paper, we address this problem and propose CoordVNF, a heuristic method to coordinate the composition of VNF chains and their embedding into the substrate network. CoordVNF aims to minimize bandwidth utilization while computing results within reasonable runtime.
Metabolic syndrome: pathophysiology and implications for management of cardiovascular disease.

E.C. is a 53-year-old postmenopausal female, referred for treatment of hypertension, with a family history of type 2 diabetes, hypertension, and coronary heart disease (CHD). Until learning that her blood pressure was “too high” during a routine physical examination, she felt well, and her postmenopausal symptoms had responded to hormone replacement therapy. She was not overweight (her body mass index [BMI] was 23.7 kg/m), and the only abnormality on physical examination was a blood pressure of 145/95 RAR. Laboratory results revealed a normal blood count and urinalysis, with the following fasting plasma concentrations of relevant metabolic variables (in mg/dL): glucose 102, triglycerides (TG) 238, low-density lipoprotein cholesterol (LDL-C) 147, and high-density lipoprotein cholesterol (HDL-C) 52. E.C. is hypertensive and hypertriglyceridemic and at increased risk for CHD. Less obvious is that these metabolic abnormalities are highly likely to be the manifestations of a more fundamental defect—resistance to insulin-mediated glucose disposal and compensatory hyperinsulinemia, changes that greatly increase CHD risk.1,2 The importance of insulin resistance as a CHD risk factor was first explicated in 1998, and the cluster of abnormalities likely to appear as manifestations of the defect in insulin action designated as syndrome X.1 Support for this notion has grown almost as fast as the names used to describe the phenomenon. The Adult Treatment Panel III (ATP III) has recently3 recognized the importance as CHD risk factors of a “constellation of lipid and nonlipid risk factors of metabolic origin,” designated this cluster of abnormalities as “the metabolic syndrome,” and indicated that “this syndrome is closely linked to insulin resistance.” Table 1 lists the criteria the ATP III stipulated be used to diagnose the metabolic syndrome, and a recent report4 has applied these criteria to the database of the Third National Health and Nutrition Examination Survey (NHANES III) and estimated that 1 out of 4 adults living in the United States merits this diagnosis. The goal of this presentation is to put into perspective the importance of insulin resistance and compensatory hyperinsulinemia in the pathogenesis and clinical course of CHD, as well as the implications of both the ATP III guidelines concerning the diagnosis of the metabolic syndrome.
Dilated Residual Networks

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the models depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (degridding), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation.
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resourcelimited scenarios. A widely-used practice in relevant work assumes that a smallernorm parameter or feature plays a less informative role at the inference time. In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs) that does not critically rely on this assumption. Instead, it focuses on direct simplification of the channel-tochannel computation graph of a CNN without the need of performing a computationally difficult and not-always-useful task of making high-dimensional tensors of CNN structured sparse. Our approach takes two stages: first to adopt an end-toend stochastic training method that eventually forces the outputs of some channels to be constant, and then to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned. Our approach is mathematically appealing from an optimization perspective and easy to reproduce. We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and competitive performance.
Scan Detection: A Data Mining Approach

A precursor to many attacks on networks is often a reconnaissance operation, more commonly referred to as a scan. Despite the vast amount of attention focused on methods for scan detection, the state-ofthe-art methods suffer from high rate of false alarms and low rate of scan detection. In this paper, we formalize the problem of scan detection as a data mining problem. We show how the network traffic data sets can be converted into a data set that is appropriate for running off-the-shelf classifiers on. Our method successfully demonstrates that data mining models can encapsulate expert knowledge to create an adaptable algorithm that can substantially outperform state-ofthe-art methods for scan detection in both coverage and precision.
Principles of Smart Home Control

Seeking to be sensitive to users, smart home researchers have focused on the concept of control. They attempt to allow users to gain control over their lives by framing the problem as one of end-user programming. But families are not users as we typically conceive them, and a large body of ethnographic research shows how their activities and routines do not map well to programming tasks. End-user programming ultimately provides control of devices. But families want more control of their lives. In this paper, we explore this disconnect. Using grounded contextual fieldwork with dual-income families, we describe the control that families want, and suggest seven design principles that will help end-user programming systems deliver that control.
Building an Internet culture

The Internet oers the hope of a more democratic society. By promoting a decentralized form of social mobilization, it is said, the Internet can help us to renovate our institutions and liberate ourselves from our authoritarian legacies. The Internet does indeed hold these possibilities, but they are hardly inevitable. In order for the Internet to become a tool for social progress, not a tool of oppression or another centralized broadcast medium or simply a waste of money, concerned citizens must understand the dierent ways in which the Internet can become embedded in larger social processes. In thinking about culturally appropriate ways of using technologies like the Internet, the best starting-point is with peopleÐcoherent communities of people and the ways they think together. Let us consider an example. A photocopier company asked an anthropologist named Julian Orr to study its repair technicians and recommend the best ways to use technology in supporting their work. Orr (1996) took a broad view of the technicians' lives, learning some of their skills and following them around. Each morning the technicians would come to work, pick up their company vehicles, and drive to customers' premises where photocopiers needed ®xing; each evening they would return to the company, go to a bar together, and drink beer. Although the company had provided the technicians with formal training, Orr discovered that they actually acquired much of their expertise informally while drinking beer together. Having spent the day contending with dicult repair problems, they would entertain one another with ``war stories'', and these stories often helped them with future repairs. He suggested, therefore, that the technicians be given radio equipment so that they could remain in contact all day, telling stories and helping each other with their repair tasks. As Orr's (1996) story suggests, people think together best when they have something important in common. Networking technologies can often be used to create a Telematics and Informatics 15 (1998) 231±234
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.
Tsunami Generation by Submarine Mass Failure . I : Modeling , Experimental Validation , and Sensitivity Analyses

Numerical simulations are performed with a two-dimensional 2D fully nonlinear potential flow FNPF model for tsunami generation by two idealized types of submarine mass failure SMF : underwater slides and slumps. These simulations feature rigid or deforming SMFs with a Gaussian cross section, translating down a plane slope. In each case, the SMF center of mass motion is expressed as a function of geometric, hydrodynamic, and material parameters, following a simple wavemaker formalism, and prescribed as a boundary condition in the FNPF model. Tsunami amplitudes and runup are obtained from computed free surface elevations. Model results are experimentally validated for a rigid 2D slide. Sensitivity studies are performed to estimate the effects of SMF–shape, type, and initial submergence depth—on the generated tsunamis. A strong SMF deformation during motion is shown to significantly enhance tsunami generation, particularly in the far-field. Typical slumps are shown to generate smaller tsunamis than corresponding slides. Both tsunami amplitude and runup are shown to depend strongly on initial SMF submergence depth. For the selected SMF idealized geometry, this dependence is simply expressed by power laws. Other sensitivity analyses are presented in a companion paper, and results from numerical simulations are converted into empirical curve fits predicting characteristic tsunami amplitudes as functions of nondimensional governing parameters. It should be stressed that these empirical formulas are only valid in the vicinity of the tsunami sources and, because of the complexity of the problem, many simplifications were necessary. It is further shown in the companion paper how 2D results can be modified to account for three-dimensional tsunami generation and used for quickly estimating tsunami hazard or for performing simple
Smart Cities at Risk!: Privacy and Security Borderlines from Social Networking in Cities

As smart cities infrastructures mature, data becomes a valuable asset which can radically improve city services and tools. Registration, acquisition and utilization of data, which will be transformed into smart services, are becoming more necessary than ever. Online social networks with their enormous momentum are one of the main sources of urban data offering heterogeneous real-time data at a minimal cost. However, various types of attacks often appear on them, which risk users' privacy and affect their online trust. The purpose of this article is to investigate how risks on online social networks affect smart cities and study the differences between privacy and security threats with regard to smart people and smart living dimensions.
Detection of breast cancer by mammogram image segmentation.

An important approach for describing a region is to quantify its structure content. In this paper the use of functions for computing texture based on statistical measures is prescribed. MPM (Maximizer of the posterior margins) algorithm is employed. The segmentation based on texture feature would classify the breast tissue under various categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image into important segments. Images from mini-MIAS data base (Mammogram Image Analysis Society database (UK)) have been considered to conduct our experiments. The segmentation thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This is our basic step for developing a computer aided detection (CAD) system for early detection of breast cancer.
Towards a new interpretation of separable convolutions

In recent times, the use of separable convolutions in deep convolutional neural network architectures has been explored. Several researchers, most notably and have used separable convolutions in their deep architectures and have demonstrated state of the art or close to state of the art performance. However, the underlying mechanism of action of separable convolutions is still not fully understood. Although, their mathematical definition is well understood as a depth-wise convolution followed by a point-wise convolution, “deeper” interpretations (such as the “extreme Inception”) hypothesis have failed to provide a thorough explanation of their efficacy. In this paper, we propose a hybrid interpretation that we believe is a better model for explaining the efficacy of separable convolutions.