nils-herrmann/clean_scirepeval huggingface dataset:
On the Use of Soft-Decision Error-Correction Codes in nand Flash Memory

As technology continues to scale down, NAND Flash memory has been increasingly relying on error-correction codes (ECCs) to ensure the overall data storage integrity. Although advanced ECCs such as low-density parity-check (LDPC) codes can provide significantly stronger error-correction capability over BCH codes being used in current practice, their decoding requires soft-decision log-likelihood ratio (LLR) information. This results in two critical issues. First, accurate calculation of LLR demands fine-grained memory-cell sensing, which nevertheless tends to incur implementation overhead and access latency penalty. Hence, it is critical to minimize the fine-grained memory sensing precision. Second, accurate calculation of LLR also demands the availability of a memory-cell threshold-voltage distribution model. As the major source for memory-cell threshold-voltage distribution distortion, cell-to-cell interference must be carefully incorporated into the model. However, these two critical issues have not been ever addressed in the open literature. This paper attempts to address these open issues. We derive mathematical formulations to approximately model the threshold-voltage distribution of memory cells in the presence of cell-to-cell interference, based on which the calculation of LLRs is mathematically formulated. This paper also proposes a nonuniform memory sensing strategy to reduce the memory sensing precision and, thus, sensing latency while still maintaining good error-correction performance. In addition, we investigate these design issues under the scenario when we can also sense interfering cells and hence explicitly estimate cell-to-cell interference strength. We carry out extensive computer simulations to demonstrate the effectiveness and involved tradeoffs, assuming the use of LDPC codes in 2-bits/cell NAND Flash memory.
CNN-based modulation classification in the complicated communication channel

Machine Learning are gradually applied in Automatic Modulation Classification (AMC) domain these years. However, in the past researches, the experiment conditions are mostly idealized or simplified. This paper proposes a modified structure of Convolutional Neural Network (CNN), which is proved to have strong capability of classification. The wavelet denoising technology is exploited to restrain the high frequency noise for input signal. As an analysis of the feasibility of this method for complicated channel, we compare the proposed method with the commonly used AMC methods through a large number of signal sequences, taking consider of various distractors. The result shows that the method proposed outperforms on most rugged conditions.
Estimating surface normals in noisy point cloud data

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in R2 or a smooth surface in R3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.
Leveraging social grouping for trust building in foreign electronic commerce firms: An exploratory study

Internet development has fueled e-commerce firms’ globalization efforts, but many have met with only limited success. This often stems from the foreign firms’ limited understanding of a focal country’s local culture and idiosyncrasies. Foreign firms are usually viewed as out-group entities, which lowers consumers’ trust in them. The extent of such a phenomenon varies. In locations where people are more skeptical of out-groups, a critical question is whether it is possible to transform such foreign out-group firms into in-groups, specifically with the support of popular social networking media. Based on Social Identity Theory and Trust Transference Process, five strategies leveraging social grouping and social ties to build trust for foreign electronic commerce firms were proposed. A survey was conducted to examine their effectiveness. The results suggest that social-grouping strategies are useful for in-grouping foreign out-group entities to build trust, and the effectiveness of strategies is determined by the social similarity and psychological distance between the consumer and the endorser. This has important implications for scholars and practitioners, both local and abroad, to leverage social grouping to boost Internet sales.
Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers

1 We work on improving the speech recognition system using more linguistic features. We use a discriminative classifier with our new features and show that our system performs better than state-of-the-art techniques.  We work on a new regularizer for discriminative classifiers. Our system performs better than the existing systems on the speech recognition task.2 We work on a new regularizer for discriminative classifiers. Our system performs better than the existing systems on the speech recognition task. Focus is different even though technique and domain are same! A DOMAIN for me is a TECHNIQUE for you .. AND VICE VERSA • Part-of-speech tagging uses word segmentation and HMM  TECHNIQUE: word segmentation; HMM  DOMAIN: part-of-speech tagging • Parsing uses part-of-speech tagging as an intermediate tool  TECHNIQUE: part-of-speech tagging  DOMAIN: parsing Why BoW based techniques fail? • Bag-of-Words techniques assume words are independent  Cannot tell whether a phrase is a FOCUS, a TECHNIQUE, or a DOMAIN • Topic models (e.g. LDA) give higher level topics, like, ‘parsing’, ‘semantics’ • Our approach: Information extraction using dependency patterns Our approach: Dependency Patterns • Find patterns in dependency graph of sentences  In first iteration, 13 patterns for FOCUS, 7 for TECHNIQUE and 15 for DOMAIN Learn new patterns using bootstrapping! direct-object propose <phrase tree> prep_on work <phrase tree> direct-object use <phrase tree> prep_of task <phrase tree> prep_for algorithm <phrase tree> direct-object apply <phrase tree> FOCUS DOMAIN
Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.
Decentralized SINR Balancing in Cognitive Radio Networks

This paper considers the downlink of a cognitive radio (CR) network formed by multiple primary and secondary transmitters, where each multiantenna transmitter serves a preknown set of single-antenna users. This paper assumes that the secondary and primary transmitters can simultaneously transmit their data over the same frequency bands to achieve high system spectrum efficiency. This paper considers the downlink balancing problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary transmitters subject to both the total power constraint of the secondary transmitters and the maximum interference constraint at each primary user due to secondary transmissions. This paper proposes solving the problem using the alternating direction method of multipliers, which leads to a distributed implementation through limited information exchange across the coupled secondary transmitters. This paper additionally proposes a solution that guarantees feasibility at each iteration. Simulation results demonstrate that the proposed solution converges to the centralized solution in a reasonable number of iterations.
Depth map generation by image classification

This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as indoor, outdoor with geometric elements or outdoor without geometric elements. Using the information collected in the classification step a suitable depth map is estimated. The proposed technique is fully unsupervised and is able to generate depth map from a single view of the scene, requiring low computational resources.
Survey of fraud detection techniques

Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields. Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of different techniques to detect frauds.
Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization

We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence and its constituents (e.g., phrases) in the parsing tree. This enables us to draw on word-level to sentence-level supervisions derived from reference summaries. In addition, recursive neural networks are used to automatically learn ranking features over the tree, with hand-crafted feature vectors of words as inputs. Hierarchical regressions are then conducted with learned features concatenating raw features. Ranking scores of sentences and words are utilized to effectively select informative and nonredundant sentences to generate summaries. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that R2N2 outperforms state-of-the-art extractive summarization approaches.
Energy efficiency of 2-step charging power-clock for adiabatic logic

The generation of power-clocks in adiabatic integrated circuits is investigated. Specifically, we consider the energy efficiency of a 2-step charging strategy based on a single tank-capacitor circuit. We have investigated the impact of various parameters such as tank-capacitance to load capacitance ratio, ramping time, transistors sizing and power supply voltage scaling on energy recovery achievable in the 2-step charging circuit. We show that energy recovery achievable in the 2-step charging circuit depends on the tank-capacitor and load capacitor size concluding that tank-capacitance (CT) versus load capacitance (CL) is the significant parameter. We also show that the energy performance depends on the ramping time and improves for higher ramping times (lower frequencies). Energy recovery also improves if the transistors sizes in the step charging circuit are sized at their minimum dimensions. Lastly, we show that energy recovery decreases as the power supply voltage is scaled down. Specifically, the decrease in the energy recovery with decreasing power supply is significant for lower ramping times (higher frequencies). We propose that a Ct/Cl ratio of 10, keeping the width of the transistors in the step charging circuit minimum, can be chosen as a convenient ‘rule-of-thumb’ in practical designs.
An Active Testing Model for Tracking Roads in Satellite Images

We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy (\active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on \where to look next" and motivated by the \divide-and-conquer" strategy of parlor games such as \Twenty Questions." We choose \tests" (matched lters for short road segments) one at a time in order to remove as much uncertainty as possible about the \true hypothesis" (road position) given the results of the previous tests. The tests are chosen on-line based on a statistical model for the joint distribution of tests and hypotheses. The problem of minimizing uncertainty (measured by entropy) is formulated in simple and explicit analytical terms. To execute this entropy testing rule we then alternate between data collection and optimization: at each iteration new image data are examined and a new entropy minimization problem is solved (exactly), resulting in a new image location to inspect, and so forth. We report experiments using panchromatic SPOT satellite imagery with a ground resolution of ten meters: given a starting point and starting direction, we are able to rapidly track highways in southern France over distances on the order of one hundred kilometers without manual intervention. Une m ethode active pour le suivi de routes dans des images satellitaires R esum e : Nous pr esentons une approche originale pour suivre automatiquement une route dans une image de la Terre acquise par satellite. Ce faisant, nous illustrons une m ethodologie plus g en erale pour le probl eme du suivi de structures lin eiques et autres dans des images. Notre approche est li ee a des travaux r ecents en vision active concernant les strat egies de perception ainsi qu'' a la m ethode algorithmique "diviser pour r egner" dans le jeu des vingt questions. Nous choisissons des questions (d etecteur local pour la pr esence d'une route) s equentiel-lement, de mani ere a r eduire le plus possible l'incertitude sur l'inconnue (la localisation de la route dans l'image). Ce choix est bas e sur un mod ele statistique pour la distribution jointe de l'inconnue et des r eponses aux questions. Le probl eme de minimisation de l'incer-titude (mesur ee par l'entropie de C. Shannon) est formul e de mani ere analytique. Ex ecuter cette strat egie consiste donc a alterner entre …
Motion, emotion and empathy in esthetic experience

The implications of the discovery of mirroring mechanisms and embodied simulation for empathetic responses to images in general, and to works of visual art in particular, have not yet been assessed. Here, we address this issue and we challenge the primacy of cognition in responses to art. We propose that a crucial element of esthetic response consists of the activation of embodied mechanisms encompassing the simulation of actions, emotions and corporeal sensation, and that these mechanisms are universal. This basic level of reaction to images is essential to understanding the effectiveness both of everyday images and of works of art. Historical, cultural and other contextual factors do not preclude the importance of considering the neural processes that arise in the empathetic understanding of visual artworks.
The leadership of emergence : A complex systems leadership theory of emergence at successive organizational levels

Complexity science reframes leadership by focusing on the dynamic interactions between all individuals, explaining how those interactions can, under certain conditions, produce emergent outcomes. We develop a Leadership of Emergence using this approach, through an analysis of three empirical studies which document emergence in distinct contexts. Each of these studies identifies the same four “conditions” for emergence: the presence of a Dis-equilibrium state, Amplifying actions, Recombination/“Self-organization”, and Stabilizing feedback. From these studies we also show how these conditions can be generated through nine specific behaviors which leaders can enact, including: Disrupt existing patterns through embracing uncertainty and creating controversy, Encourage novelty by allowing experiments and supporting collective action, Provide sensemaking and sensegiving through the artful use of language and symbols, and Stabilize the system by Integrating local constraints. Finally, we suggest ways for advancing a meso-model of leadership, and show how our findings can improve complexity science applications in management.
Trajectory pattern mining

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.
Expression Cloning of TMEM16A as a Calcium-Activated Chloride Channel Subunit

Calcium-activated chloride channels (CaCCs) are major regulators of sensory transduction, epithelial secretion, and smooth muscle contraction. Other crucial roles of CaCCs include action potential generation in Characean algae and prevention of polyspermia in frog egg membrane. None of the known molecular candidates share properties characteristic of most CaCCs in native cells. Using Axolotl oocytes as an expression system, we have identified TMEM16A as the Xenopus oocyte CaCC. The TMEM16 family of "transmembrane proteins with unknown function" is conserved among eukaryotes, with family members linked to tracheomalacia (mouse TMEM16A), gnathodiaphyseal dysplasia (human TMEM16E), aberrant X segregation (a Drosophila TMEM16 family member), and increased sodium tolerance (yeast TMEM16). Moreover, mouse TMEM16A and TMEM16B yield CaCCs in Axolotl oocytes and mammalian HEK293 cells and recapitulate the broad CaCC expression. The identification of this new family of ion channels may help the development of CaCC modulators for treating diseases including hypertension and cystic fibrosis.
A Theory and Tools for Applying Sandboxes Effectively

It is more expensive and time consuming to build modern software without extensive supply chains. Supply chains decrease these development risks, but typically at the cost of increased security risk. In particular, it is often difficult to understand or verify what a software component delivered by a third party does or could do. Such a component could contain unwanted behaviors, vulnerabilities, or malicious code, many of which become incorporated in applications utilizing the component. Sandboxes provide relief by encapsulating a component and imposing a security policy on it. This limits the operations the component can perform without as much need to trust or verify the component. Instead, a component user must trust or verify the relatively simple sandbox. Given this appealing prospect, researchers have spent the last few decades developing new sandboxing techniques and sandboxes. However, while sandboxes have been adopted in practice, they are not as pervasive as they could be. Why are sandboxes not achieving ubiquity at the same rate as extensive supply chains? This thesis advances our understanding of and overcomes some barriers to sandbox adoption. We systematically analyze ten years (2004 – 2014) of sandboxing research from top-tier security and systems conferences. We uncover two barriers: (1) sandboxes are often validated using relatively subjective techniques and (2) usability for sandbox deployers is often ignored by the studied community. We then focus on the Java sandbox to empirically study its use within the open source community. We find features in the sandbox that benign applications do not use, which have promoted a thriving exploit landscape. We develop run time monitors for the Java Virtual Machine (JVM) to turn off these features, stopping all known sandbox escaping JVM exploits without breaking benign applications. Furthermore, we find that the sandbox contains a high degree of complexity benign applications need that hampers sandbox use. When studying the sandbox’s use, we did not find a single application that successfully deployed the sandbox for security purposes, which motivated us to overcome benignly-used complexity via tooling. We develop and evaluate a series of tools to automate the most complex tasks, which currently require error-prone manual effort. Our tools help users derive, express, and refine a security policy and impose it on targeted Java application JARs and classes. This tooling is evaluated through case studies with industrial collaborators where we sandbox components that were previously difficult to sandbox securely. Finally, we observe that design and implementation complexity causes sandbox developers to accidentally create vulnerable sandboxes. Thus, we develop and evaluate a sandboxing technique that leverages existing cloud computing environments to execute untrusted computations. Malicious outcomes produced by the computations are contained by ephemeral virtual machines. We describe a field trial using this technique with Adobe Reader and compare the new sandbox to existing sandboxes using a qualitative framework we developed.
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing

Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). Unfortunately, large scale systems cannot be easily machine-learned due to a lack of directly supervised data. We propose a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (e.g. WordNet) with learning from raw text. The model jointly learns representations of words, entities and MRs via a multi-task training process operating on these diverse sources of data. Hence, the system ends up providing methods for knowledge acquisition and wordsense disambiguation within the context of semantic parsing in a single elegant framework. Experiments on these various tasks indicate the promise of the approach.
Securing Web Service by Automatic Robot Detection

Web sites are routinely visited by automated agents known as Web robots, that perform acts ranging from the beneficial, such as indexing for search engines, to the malicious, such as searching for vulnerabilities, attemptin g to crack passwords, or spamming bulletin boards. Previous work to identify malicious robots has relied on ad-hoc signature matching and has been performed on a per-site basis. As Web robots evolve and diversify, these techniques have not been scaling. We approach the problem as a special form of the Turing test and defend the system by inferring if the traffic source is human or robot. By extracting the implicit patterns of human Web browsing, we develop simple yet effective algorithms to detect human users. Our experiments with the CoDeeN content distribution network show that 95% of human users are detected within the first 57 requests, and 80% can be identified in only 20 requests, with a maximum false positive rate of 2.4%. In the time that this system has been deployed on CoDeeN, robotrelated abuse complaints have dropped by a factor of 10.