nils-herrmann/clean_scirepeval huggingface dataset:
Convolutional neural network architectures for predicting DNA–protein binding

Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.



We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology.



All the models analyzed are available at http://cnn.csail.mit.edu


CONTACT
gifford@mit.edu



Supplementary data are available at Bioinformatics online.
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space and ranks them based on their semantic connections from their knowledge graph embedding. Experiments demonstrate ESR’s ability in improving Semantic Scholar’s online production system, especially on hard queries where word-based ranking fails.
SMV-Hunter: Large Scale, Automated Detection of SSL/TLS Man-in-the-Middle Vulnerabilities in Android Apps

Many Android apps use SSL/TLS to transmit sensitive information securely. However, developers often provide their own implementation of the standard SSL/TLS certificate validation process. Unfortunately, many such custom implementations have subtle bugs, have built-in exceptions for self-signed certificates, or blindly assert all certificates are valid, leaving many Android apps vulnerable to SSL/TLS Man-in-the-Middle attacks. In this paper, we present SMV-HUNTER, a system for the automatic, large-scale identification of such vulnerabilities that combines both static and dynamic analysis. The static component detects when a custom validation procedure has been given, thereby identifying potentially vulnerable apps, and extracts information used to guide the dynamic analysis, which then uses user interface enumeration and automation techniques to trigger the potentially vulnerable code under an active Man-in-the-Middle attack. We have implemented SMV-HUNTER and evaluated it on 23,418 apps downloaded from the Google Play market, of which 1,453 apps were identified as being potentially vulnerable by static analysis, with an average overhead of approximately 4 seconds per app, running on 16 threads in parallel. Among these potentially vulnerable apps, 726 were confirmed vulnerable using our dynamic analysis, with an average overhead of about 44 seconds per app, running on 8 emulators in parallel.
Combining active and semi-supervised learning for spoken language understanding

In this paper, we describe active and semi-supervised learning methods for reducing the labeling effort for spoken language understanding. In a goal-oriented call routing system, understanding the intent of the user can be framed as a classification problem. State of the art statistical classification systems are trained using a large number of human-labeled utterances, preparation of which is labor intensive and time consuming. Active learning aims to minimize the number of labeled utterances by automatically selecting the utterances that are likely to be most informative for labeling. The method for active learning we propose, inspired by certainty-based active learning, selects the examples that the classifier is the least confident about. The examples that are classified with higher confidence scores (hence not selected by active learning) are exploited using two semi-supervised learning methods. The first method augments the training data by using the machine-labeled classes for the unlabeled utterances. The second method instead augments the classification model trained using the human-labeled utterances with the machine-labeled ones in a weighted manner. We then combine active and semi-supervised learning using selectively sampled and automatically labeled data. This enables us to exploit all collected data and alleviates the data imbalance problem caused by employing only active or semi-supervised learning. We have evaluated these active and semi-supervised learning methods with a call classification system used for AT&T customer care. Our results indicate that it is possible to reduce human labeling effort significantly.
Differentially Private Data Release through Multidimensional Partitioning

Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release based on an interactive differential privacy interface. We propose two multidimensional partitioning strategies including a baseline cell-based partitioning and an innovative kd-tree based partitioning. In addition to providing formal proofs for differential privacy and usefulness guarantees for linear distributive queries , we also present a set of experimental results and demonstrate the feasibility and performance of our method.
Approximation Algorithms for the Firefighter Problem: Cuts over Time and Submodularity

We provide approximation algorithms for several variants of the Firefighter problem on general graphs. The Firefighter problem models the case where an infection or another diffusive process (such as an idea, a computer virus, or a fire) is spreading through a network, and our goal is to stop this infection by using targeted vaccinations. Specifically, we are allowed to vaccinate at most B nodes per time-step (for some budget B), with the goal of minimizing the effect of the infection. The difficulty of this problem comes from its temporal component, since we must choose nodes to vaccinate at every time-step while the infection is spreading through the network, leading to notions of “cuts over time”. We consider two versions of the Firefighter problem: a “non-spreading” model, where vaccinating a node means only that this node cannot be infected; and a “spreading” model where the vaccination itself is an infectious process, such as in the case where the infection is a harmful idea, and the vaccine to it is another infectious idea. We give complexity and approximation results for problems on both models.
Direct analysis of protein complexes using mass spectrometry

We describe a rapid, sensitive process for comprehensively identifying proteins in macromolecular complexes that uses multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) to separate and fragment peptides. The SEQUEST algorithm, relying upon translated genomic sequences, infers amino acid sequences from the fragment ions. The method was applied to the Saccharomyces cerevisiae ribosome leading to the identification of a novel protein component of the yeast and human 40S subunit. By offering the ability to identify >100 proteins in a single run, this process enables components in even the largest macromolecular complexes to be analyzed comprehensively.
COSBench: A Benchmark Tool for Cloud Object Storage Services

With object storage services becoming increasingly accepted as replacements for traditional file or block systems, it is important to effectively measure the performance of these services. Thus people can compare different solutions or tune their systems for better performance. However, little has been reported on this specific topic as yet. To address this problem, we present COSBench (Cloud Object Storage Benchmark), a benchmark tool that we are currently working on in Intel for cloud object storage services. In addition, in this paper, we also share the results of the experiments we have performed so far.
Supply Chain Design and Analysis : Models and Methods

For years, researchers and practitioners have primarily investigated the various processes within manufacturing supply chains individually. Recently, however, there has been increasing attention placed on the performance, design, and analysis of the supply chain as a whole. This attention is largely a result of the rising costs of manufacturing, the shrinking resources of manufacturing bases, shortened product life cycles, the leveling of the playing field within manufacturing, and the globalization of market economies. The objectives of this paper are to: (1) provide a focused review of literature in multi-stage supply chain modeling and (2) define a research agenda for future research in this area.
Real time finger tracking and contour detection for gesture recognition using OpenCV

Gestures are important for communicating information among the human. Nowadays new technologies of Human Computer Interaction (HCI) are being developed to deliver user's command to the robots. Users can interact with machines through hand, head, facial expressions, voice and touch. The objective of this paper is to use one of the important modes of interaction i.e. hand gestures to control the robot or for offices and household applications. Hand gesture detection algorithms are based on various machine learning methods such as neural networks, support vector machine, and Adaptive Boosting (AdaBoost). Among these methods, AdaBoost based hand-pose detectors are trained with a reduced Haar-like feature set to make the detector robust. The corresponding context-free grammar based proposed method gives effective real time performance with great accuracy and robustness for more than four hand gestures. Rectangles are creating some problem due to that we have also implement the alternate representation method for same gestures i.e. fingertip detection using convex hull algorithm.
Preparing for the Unknown: Learning a Universal Policy with Online System Identification

We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training examples that are generated using a physical simulator. Our system is made of two components: a Universal Policy (UP) and a function for Online System Identification (OSI). We describe our control policy as universal because it is trained over a wide array of dynamic models. These variations in the dynamic model may include differences in mass and inertia of the robots components, variable friction coefficients, or unknown mass of an object to be manipulated. By training the Universal Policy with this variation, the control policy is prepared for a wider array of possible conditions when executed in an unknown environment. The second part of our system uses the recent state and action history of the system to predict the dynamics model parameters μ. The value of μ from the Online System Identification is then provided as input to the control policy (along with the system state). Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment. We have evaluated the performance of this system on a variety of tasks, including the problem of cart-pole swing-up, the double inverted pendulum, locomotion of a hopper, and block-throwing of a manipulator. UP-OSI is effective at these tasks across a wide range of dynamic models. Moreover, when tested with dynamic models outside of the training range, UP-OSI outperforms the Universal Policy alone, even when UP is given the actual value of the model dynamics. In addition to the benefits of creating more robust controllers, UP-OSI also holds out promise of narrowing the Reality Gap between simulated and real physical systems.
Non-invasive optical spectroscopy and imaging of human brain function

Brain activity is associated with changes in optical properties of brain tissue. Optical measurements during brain activation can assess haemoglobin oxygenation, cytochrome-c-oxidase redox state, and two types of changes in light scattering reflecting either membrane potential (fast signal) or cell swelling (slow signal), respectively. In previous studies of exposed brain tissue, optical imaging of brain activity has been achieved at high temporal and microscopical spatial resolution. Now, using near-infrared light that can penetrate biological tissue reasonably well, it has become possible to assess brain activity in human subjects through the intact skull non-invasively. After early studies employing single-site near-infrared spectroscopy, first near-infrared imaging devices are being applied successfully for low-resolution functional brain imaging. Advantages of the optical methods include biochemical specificity, a temporal resolution in the millisecond range, the potential of measuring intracellular and intravascular events simultaneously and the portability of the devices enabling bedside examinations.
Temporal Filtering of Depth Images using Optical Flow

We present a novel depth image enhancement approach for RGB-D cameras such as the Kinect. Our approach employs optical flow of color images for refining the quality of corresponding depth images. We track every depth pixel over a sequence of frames in the temporal domain and use valid depth values of the same point for recovering missing and inaccurate information. We conduct experiments on different test datasets and present visually appealing results. Our method significantly reduces the temporal noise level and the flickering artifacts.
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task

This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.
Recognition of Tables and Forms

Tables and forms are a very common way to organize information in structured documents. Their recognition is fundamental for the recognition of the documents. Indeed, the physical organization of a table or a form gives a lot of information concerning the logical meaning of the content. This chapter presents the different tasks that are related to the recognition of tables and forms and the associated well-known methods and remaining B. Coüasnon ( ) IRISA/INSA de Rennes, Rennes Cedex, France e-mail: couasnon@irisa.fr A. Lemaitre IRISA/Université Rennes 2, Rennes Cedex, France e-mail:couasnon@irisa.fr D. Doermann, K. Tombre (eds.), Handbook of Document Image Processing and Recognition, DOI 10.1007/978-0-85729-859-1 20,  Coüasnon and A. Lemaitre challenges. Three main tasks are pointed out: the detection of tables in heterogeneous documents; the classification of tables and forms, according to predefined models; and the recognition of table and form contents. The complexity of these three tasks is related to the kind of studied document: image-based document or digital-born documents. At last, this chapter will introduce some existing systems for table and form analysis.
Learning semantic representations using convolutional neural networks for web search

This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. By using the convolution-max pooling operation, local contextual information at the word n-gram level is modeled first. Then, salient local fea-tures in a word sequence are combined to form a global feature vector. Finally, the high-level semantic information of the word sequence is extracted to form a global vector representation. The proposed models are trained on clickthrough data by maximizing the conditional likelihood of clicked documents given a query, us-ing stochastic gradient ascent. The new models are evaluated on a Web document ranking task using a large-scale, real-world data set. Results show that our model significantly outperforms other se-mantic models, which were state-of-the-art in retrieval performance prior to this work.
5G: Evolution of a secure mobile technology

The mobile technology is an ever evolving concept. The world has seen various generations of mobile technology be it 1G, 2G, 3G or 4G. The fifth generation of mobile technology i.e. 5G is seen as a futuristic notion that would help in solving the issues that are pertaining in the 4G. In this paper we have discussed various security issues of 4G with respect to Wi-max and long term evolution. These issues are discussed at MAC and physical layer level. The security issues are seen in terms of possible attacks, system vulnerabilities and privacy concerns. We have also highlighted how the notions of 5G can be tailored to provide a more secure mobile computing environment. We have considered the futuristic architectural framework for 5G networks in our discussion. The basic concepts and features of the fifth generation technology are explained here. We have also analyzed five pillars of strength for the 5G network security which would work in collaboration with each other to provide a secure mobile computing environment to the user.
Handwriting recognition in historical documents using very large vocabularies

Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.
Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs. FCMs are especially applicable to soft knowledge domains and several example FCMs are given. Causality is represented as a fuzzy relation on causal concepts. A fuzzy causal algebra for governing causal propagation on FCMs is developed. FCM matrix representation and matrix operations are presented in the Appendix.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification problems. In this paper, we study NAS for semantic image segmentation, an important computer vision task that assigns a semantic label to every pixel in an image. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Without any ImageNet pretraining, our architecture searched specifically for semantic image segmentation attains state-of-the-art performance.
Shoptalk: toward independent shopping by people with visual impairments

ShopTalk, a proof-of-concept system designed to assist individuals with visual impairments with finding shelved products in grocery stores, is built on the assumption that simple verbal route directions and layout descriptions can be used to leverage the O&M skills of independent visually impaired travelers to enable them to navigate the store and retrieve shelved products. This paper introduces ShopTalk and summarizes experiments performed in a real-world supermarket.