nils-herrmann/clean_scirepeval huggingface dataset:
You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code

Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.
Reader-Aware Multi-Document Summarization via Sparse Coding

We propose a new MDS paradigm called readeraware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RAMDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.
DFD: Efficient Functional Dependency Discovery

The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.
History of CRISPR-Cas from Encounter with a Mysterious Repeated Sequence to Genome Editing Technology.

Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems are well-known acquired immunity systems that are widespread in archaea and bacteria. The RNA-guided nucleases from CRISPR-Cas systems are currently regarded as the most reliable tools for genome editing and engineering. The first hint of their existence came in 1987, when an unusual repetitive DNA sequence, which subsequently was defined as a CRISPR, was discovered in the Escherichia coli genome during an analysis of genes involved in phosphate metabolism. Similar sequence patterns were then reported in a range of other bacteria as well as in halophilic archaea, suggesting an important role for such evolutionarily conserved clusters of repeated sequences. A critical step toward functional characterization of the CRISPR-Cas systems was the recognition of a link between CRISPRs and the associated Cas proteins, which were initially hypothesized to be involved in DNA repair in hyperthermophilic archaea. Comparative genomics, structural biology, and advanced biochemistry could then work hand in hand, not only culminating in the explosion of genome editing tools based on CRISPR-Cas9 and other class II CRISPR-Cas systems but also providing insights into the origin and evolution of this system from mobile genetic elements denoted casposons. To celebrate the 30th anniversary of the discovery of CRISPR, this minireview briefly discusses the fascinating history of CRISPR-Cas systems, from the original observation of an enigmatic sequence in E. coli to genome editing in humans.
Software-Defined “Hardware” Infrastructures: A Survey on Enabling Technologies and Open Research Directions

This paper provides an overview of software-defined “hardware” infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today’s physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server’s chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure’s layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.
MetaCluster 4.0: A Novel Binning Algorithm for NGS Reads and Huge Number of Species

Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets.
Gain-Enhanced Patch Antenna Without Enlarged Size via Loading of Slot and Shorting Pins

A patch antenna with loading of slot and shorting pins is proposed in this paper to enhance the radiation gain without increasing the patch size. At first, a straight slot is transversely introduced in the central line of a patch. For the dominant TM01 mode, this central line behaves as a virtual electric wall with zero electric field and maximum surface current underneath and on the patch, respectively. By doing so, the central slot serves as an extra radiator so that this slot-loaded patch produces radiation as an equivalent three-slot array. Meanwhile, a pair of shorting pins is longitudinally placed at the two symmetrical sides of this central slot to maintain the resonant frequency. Due to the out of phase between the electric fields in the central slot and two sided slots, the far-field radiated by the three slots can be canceled with each other in specific directions away from broadside. Thus, this cancellation results in sidelobe of low level in the E-plane due to emergence of radiation nulls, and further makes the main radiation beam more directionally selective. Accordingly, the directivity and gain of this proposed patch antenna are enhanced with no need of enlarging the overall size of patch. Finally, the simulated and measured results are provided to validate the operating principle and show that the directivity is increased by about 1.7 dB increment.
Self-Similarity Driven Color Demosaicking

Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green, or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state-of-the-art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels. In such a case, which occurs when thin structures or fine periodic patterns were present in the original, state-of-the-art methods can create disturbing artifacts, known as zipper effect, blur, and color spots. The aim of this paper is to show that these artifacts can be avoided by involving the image self-similarity to infer missing colors. Detailed experiments show that a satisfactory solution can be found, even for the most critical cases. Extensive comparisons with state-of-the-art algorithms will be performed on two different classic image databases.
Mining Needle in a Haystack: Classifying Rare Classes via Two-phase Rule Induction

Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed two-phase rule induction method in the context of learning complete and precise signatures of rare classes. The key feature of our method is that it separately conquers the objectives of achieving high recall and high precision for the given target class. The first phase of the method aims for high recall by inducing rules with high support and a reasonable level of accuracy. The second phase then tries to improve the precision by learning rules to remove false positives in the collection of the records covered by the first phase rules. Existing sequential covering techniques try to achieve high precision for each individual disjunct learned. In this paper, we claim that such approach is inadequate for rare classes, because of two problems: splintered false positives and error-prone small disjuncts. Motivated by the strengths of our two-phase design, we design various synthetic data models to identify and analyze the situations in which two state-of-the-art methods, RIPPER and C4.5 rules, either fail to learn a model or learn a very poor model. In all these situations, our two-phase approach learns a model with significantly better recall and precision levels. We also present a comparison of the three methods on a challenging real-life network intrusion detection dataset. Our method is significantly better or comparable to the best competitor in terms of achieving better balance between recall and precision.
Optimal path planning for a mobile robot using cuckoo search algorithm

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The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the conclusions that were derived, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions.  Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.
Ranking fraud detection for mobile apps: a holistic view

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App develops to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the Apple's App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
Deep learning for visual understanding: A review

Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-theart in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks. & 2015 Elsevier B.V. All rights reserved.
#greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter

Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-theart face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.
Face recognition by stepwise nonparametric margin maximum criterion

Linear discriminant analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper; a new nonparametric linear feature extraction method, stepwise nonparametric margin maximum criterion (SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the non- singularity of the within-class scatter matrix neither. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression
Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals

Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of <inline-formula><tex-math notation="LaTeX">$\text{15}\%\pm \text{6.3}$</tex-math> </inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) and <inline-formula><tex-math notation="LaTeX">$\text{22}\%\pm \text{5.7}$</tex-math></inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) for subject and event classifications, respectively.
The Relay–Eavesdropper Channel: Cooperation for Secrecy

This paper establishes the utility of user cooperation in facilitating secure wireless communications. In particular, the four-terminal relay-eavesdropper channel is introduced and an outer-bound on the optimal rate-equivocation region is derived. Several cooperation strategies are then devised and the corresponding achievable rate-equivocation region are characterized. Of particular interest is the novel noise-forwarding (NF) strategy, where the relay node sends codewords independent of the source message to confuse the eavesdropper. This strategy is used to illustrate the deaf helper phenomenon, where the relay is able to facilitate secure communications while being totally ignorant of the transmitted messages. Furthermore, NF is shown to increase the secrecy capacity in the reversely degraded scenario, where the relay node fails to offer performance gains in the classical setting. The gain offered by the proposed cooperation strategies is then proved theoretically and validated numerically in the additive white Gaussian noise (AWGN) channel.
Excellent resistive switching properties of atomic layer-deposited Al2O3/HfO2/Al2O3 trilayer structures for non-volatile memory applications

We have demonstrated a flexible resistive random access memory unit with trilayer structure by atomic layer deposition (ALD). The device unit is composed of Al2O3/HfO2/Al2O3-based functional stacks on TiN-coated Si substrate. The cross-sectional HRTEM image and XPS depth profile of Al2O3/HfO2/Al2O3 on TiN-coated Si confirm the existence of interfacial layers between trilayer structures of Al2O3/HfO2/Al2O3 after 600°C post-annealing. The memory units of Pt/Al2O3/HfO2/Al2O3/TiN/Si exhibit a typical bipolar, reliable, and reproducible resistive switching behavior, such as stable resistance ratio (>10) of OFF/ON states, sharp distribution of set and reset voltages, better switching endurance up to 10(3) cycles, and longer data retention at 85°C over 10 years. The possible switching mechanism of trilayer structure of Al2O3/HfO2/Al2O3 has been proposed. The trilayer structure device units of Al2O3/HfO2/Al2O3 on TiN-coated Si prepared by ALD may be a potential candidate for oxide-based resistive random access memory.