nils-herrmann/clean_scirepeval huggingface dataset:
On the Power of Over-parametrization in Neural Networks with Quadratic Activation

We provide new theoretical insights on why overparametrization is effective in learning neural networks. For a k hidden node shallow network with quadratic activation and n training data points, we show as long as k ≥ √ 2n, over-parametrization enables local search algorithms to find a globally optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when k ≥ √ 2n, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks.
Hybrid Beamforming for Large Antenna Arrays With Phase Shifter Selection

This paper proposes an asymptotically optimal hybrid beamforming solution for large antenna arrays by exploiting the properties of the singular vectors of the channel matrix. It is shown that the elements of the channel matrix with Rayleigh fading follow a normal distribution when large antenna arrays are employed. The proposed beamforming algorithm is effective in both sparse and rich propagation environments, and is applicable for both point-to-point and multiuser scenarios. In addition, a closed-form expression and a lower bound for the achievable rates are derived when analog and digital phase shifters are employed. It is shown that the performance of the hybrid beamformers using phase shifters with more than 2-bit resolution is comparable with analog phase shifting. A novel phase shifter selection scheme that reduces the power consumption at the phase shifter network is proposed when the wireless channel is modeled by Rayleigh fading. Using this selection scheme, the spectral efficiency can be increased as the power consumption in the phase shifter network reduces. Compared with the scenario that all of the phase shifters are in operation, the simulation results indicate that the spectral efficiency increases when up to 50% of phase shifters are turned OFF.
Interpretable and Informative Explanations of Outcomes

In this paper, we solve the following data summarization problem: given a multi-dimensional data set augmented with a binary attribute, how can we construct an interpretable and informative summary of the factors affecting the binary attribute in terms of the combinations of values of the dimension attributes? We refer to such summaries as explanation tables. We show the hardness of constructing optimally-informative explanation tables from data, and we propose effective and efficient heuristics. The proposed heuristics are based on sampling and include optimizations related to computing the information content of a summary from a sample of the data. Using real data sets, we demonstrate the advantages of explanation tables compared to related approaches that can be adapted to solve our problem, and we show significant performance benefits of our optimizations.
Safe for Generations to Come: Considerations of Safety for Millimeter Waves in Wireless Communications

With the increasing demand for higher data rates and more reliable service capabilities for wireless devices, wireless service providers are facing an unprecedented challenge to overcome a global bandwidth shortage. Early global activities on beyond fourth-generation (B4G) and fifth-generation (5G) wireless communication systems suggest that millimeter-wave (mmWave) frequencies are very promising for future wireless communication networks due to the massive amount of raw bandwidth and potential multigigabit-per-second (Gb/s) data rates [1]?[3]. Both industry and academia have begun the exploration of the untapped mmWave frequency spectrum for future broadband mobile communication networks. In April 2014, the Brooklyn 5G Summit , sponsored by Nokia and the New York University (NYU) WIRELESS research center, drew global attention to mmWave communications and channel modeling. In July 2014, the IEEE 802.11 next-generation 60-GHz study group was formed to increase the data rates to over 20 Gb/s in the unlicensed 60-GHz frequency band while maintaining backward compatibility with the emerging IEEE 802.11ad wireless local area network (WLAN) standard .
An Android Application Protection Scheme against Dynamic Reverse Engineering Attacks

Reverse engineering of Android applications is easy because the applications are written in the high level but simple bytecode language. Due to malicious reverse engineering attacks, many Android applications are tampered and repackaged into malicious applications. To protect Android applications from reverse engineering, many research studies have proposed and developed anti-reverse engineering techniques such as obfuscation, packing (packed executable), encryption, and anti-debugging. Obfuscation, packing and encryption are the defense techniques against static reverse engineering, which cannot prevent dynamic reverse engineering like memory dumping and runtime debugging. On the other hand, the existing defense techniques against dynamic reverse engineering have usually tried to protect applications by determining whether they are being executed on an emulationbased analysis environment and stopping their execution on the emulator. However, the protection techniques based on detecting the emulators become ineffective because attackers recently employ dynamic reverse engineering directly on real mobile devices. This paper presents a new protection scheme for Android applications against dynamic reverse engineering which can be applied on real mobile devices. Our scheme checks if a device on which the application is running is rooted and/or the application is being debugged. If so, our scheme stops the execution of the application. Our experiments demonstrate that the rooted/debugging environments detection techniques can be evaded by method hooking attacks and that the evasion attack, fortunately, can be detected by our scheme. One of the strength of our approach is that it is not implemented as a part of application’s source code but a separate executable. It can be applied to applications whose source code is not available.
Oral leukoedema with mucosal desquamation caused by toothpaste containing sodium lauryl sulfate.

E4 CUTIS WWW.CUTIS.COM  A 34-year-old woman presented for evaluation of dry mouth and painless peeling of the oral mucosa of 2 months’ duration. She denied any other skin eruptions, dry eyes, vulvar or vaginal pain, or recent hair loss. A recent antinuclear antibodies test was negative. The patient’s medical history was otherwise unremarkable and her current medications included multivitamins only. Oral examination revealed peeling gray-white tissue on the buccal mucosa and mouth floor . After the tissue was manually removed with a tongue blade, the mucosal base was normal in color and texture. The patient denied bruxism, biting of the mucosa or other oral trauma, or use of tobacco or nonsteroidal anti-inflammatory drugs. Biopsies from the buccal mucosa were performed to rule out erosive lichen planus and autoimmune blistering disorders. Microscopy revealed parakeratosis and intracellular edema of the mucosa. An intraepithelial cleft at the parakeratotic surface also was present . Minimal inflammation was noted. Fungal staining and direct immunofluorescence were negative. The gray-white clinical appearance of the oral mucosa resembled leukoedema, but the peeling phenomenon was uncharacteristic. Histologically, leukoedema typically has a parakeratotic and acanthotic epithelium with marked intracellular edema of the spinous layer.1,2 Our patient demonstrated
Unintended Consequences of Machine Learning in Medicine.

Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic  There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology). However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. Moreover, the introduction of new technologies in health care has not always been straightforward or without unintended and adverse effects.2 In this Viewpoint we consider the potential unintended consequences that may result from the application of ML-DSS in clinical practice.
New measurement scales for evaluating perceptions of the technology-mediated customer service experience

Service organizations are increasingly utilizing advanced information and communication technologies, such as the Internet, in hopes of improving the efficiency, cost-effectiveness, and/or quality of their customer-facing operations. More of the contact a customer has with the firm is likely to be with the back-office and, therefore, mediated by technology. While previous operations management research has been important for its contributions to our understanding of customer contact in face-to-facesettings, considerably less work has been done to improve our understanding of customer contact in what we refer to as technology-mediated settings (e.g., via telephone, instant messaging (IM), or email). This paper builds upon the service operations management (SOM) literature on customer contact by theoretically defining and empirically developing new multi-item measurement scales specifically designed for assessing tech ology-mediated customer contact. Seminal works on customer contact theory and its empirical measurement are employed to provide a foundation for extending these concepts to technology-mediated contexts. We also draw upon other important frameworks, including the Service Profit Chain, the Theory of Planned Behavior, and the concept of media/information richness, in order to identify and define our constructs. We follow a rigorous empirical scale development process to create parsimonious sets of survey items that exhibit satisfactory levels of reliability and validity to be useful in advancing SOM empirical research in the emerging Internet-enabled back-office.
Survey on distance metric learning and dimensionality reduction in data mining

Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as $$k$$ k -nearest neighbor classifier, hierarchical clustering and spectral clustering, heavily rely on the underlying distance metric for correctly measuring relations among input data. In recent years, many studies have demonstrated, either theoretically or empirically, that learning a good distance metric can greatly improve the performance of classification, clustering and retrieval tasks. In this survey, we overview existing distance metric learning approaches according to a common framework. Specifically, depending on the available supervision information during the distance metric learning process, we categorize each distance metric learning algorithm as supervised, unsupervised or semi-supervised. We compare those different types of metric learning methods, point out their strength and limitations. Finally, we summarize open challenges in distance metric learning and propose future directions for distance metric learning.
Reading the Videos: Temporal Labeling for Crowdsourced Time-Sync Videos Based on Semantic Embedding

Recent years have witnessed the boom of online sharing media contents, which raise significant challenges in effective management and retrieval. Though a large amount of efforts have been made, precise retrieval on video shots with certain topics has been largely ignored. At the same time, due to the popularity of novel time-sync comments, or so-called “bullet-screen comments”, video semantics could be now combined with timestamps to support further research on temporal video labeling. In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. To be specific, due to the informal expression of bullet-screen comments, we first propose a temporal deep structured semantic model (T-DSSM) to represent comments into semantic vectors by taking advantage of their temporal correlation. Then, video highlights are recognized and labeled via semantic vectors in a supervised way. Extensive experiments on a real-world dataset prove that our framework could effectively label video highlights with a significant margin compared with baselines, which clearly validates the potential of our framework on video understanding, as well as bullet-screen comments interpretation.
Chopper: partitioning models into 3D-printable parts

3D printing technology is rapidly maturing and becoming ubiquitous. One of the remaining obstacles to wide-scale adoption is that the object to be printed must fit into the working volume of the 3D printer. We propose a framework, called Chopper, to decompose a large 3D object into smaller parts so that each part fits into the printing volume. These parts can then be assembled to form the original object. We formulate a number of desirable criteria for the partition, including assemblability, having few components, unobtrusiveness of the seams, and structural soundness. Chopper optimizes these criteria and generates a partition either automatically or with user guidance. Our prototype outputs the final decomposed parts with customized connectors on the interfaces. We demonstrate the effectiveness of Chopper on a variety of non-trivial real-world objects.
Automated Identification of Cryptographic Primitives in Binary Code with Data Flow Graph Isomorphism

Softwares use cryptographic algorithms to secure their communications and to protect their internal data. However the algorithm choice, its implementation design and the generation methods of its input parameters may have dramatic consequences on the security of the data it was initially supposed to protect. Therefore to assess the security of a binary program involving cryptography, analysts need to check that none of these points will cause a system vulnerability. It implies, as a first step, to precisely identify and locate the cryptographic code in the binary program. Since binary analysis is a difficult and cumbersome task, it is interesting to devise a method to automatically retrieve cryptographic primitives and their parameters.
 In this paper, we present a novel approach to automatically identify symmetric cryptographic algorithms and their parameters inside binary code. Our approach is static and based on DFG isomorphism. To cope with binary codes produced from different source codes and by different compilers and options, the DFG is normalized using code rewrite mechanisms. Our approach differs from previous works, that either use statistical criteria leading to imprecise results, or rely on heavy dynamic instrumentation. To validate our approach, we present experimental results on a set of synthetic samples including several cryptographic algorithms, binary code of well-known cryptographic libraries and reference source implementation compiled using different compilers and options.
Open-Domain Name Error Detection using a Multi-Task RNN

Out-of-vocabulary name errors in speech recognition create significant problems for downstream language processing, but the fact that they are rare poses challenges for automatic detection, particularly in an open-domain scenario. To address this problem, a multi-task recurrent neural network language model for sentence-level name detection is proposed for use in combination with out-of-vocabulary word detection. The sentence-level model is also effective for leveraging external text data. Experiments show a 26% improvement in name-error detection F-score over a system using n-gram lexical features.
A New Coupling Mechanism for Circularly Polarized Annular-Ring Patch Antenna

A novel coupling technique for circularly polarized annular-ring patch antenna is developed and discussed. The circular polarization (CP) radiation of the annular-ring patch antenna is achieved by a simple microstrip feed line through the coupling of a fan-shaped patch on the same plane of the antenna. Proper positioning of the coupling fan-shaped patch excites two orthogonal resonant modes with 90 phase difference, and a pure circular polarization is obtained. The dielectric material is a cylindrical block of ceramic with a permittivity of 25 and that reduces the size of the antenna. The prototype has been designed and fabricated and found to have an impedance bandwidth of 2.3% and a 3 dB axial-ratio bandwidth of about 0.6% at the center frequency of 2700 MHz. The characteristics of the proposed antenna have been by simulation software HFSS and experiment. The measured and simulated results are in good agreement.
Substitutes for Leadership : Their Meaning and Measurement

Current theories and models of leadership seek to explain the influence of the hierarchical superior upon the satisfaction and performance of subordinates. While disagreeing with one another in important respects, these theories and models share an implicit assumption that while the style of leadership likely to be effective may vary according to the situation, some leadership style will be effective regardless of the situation. It has been found, however, that certain individual, task, and organizational variables act as "substitutes for leadership," negating the hierarchical superior's ability to exert either positive or negative influence over subordinate attitudes and effectiveness. This paper identifies a number of such substitutes for leadership, presents scales of questionnaire items for their measurement, and reports some preliminary tests.
Standard Function Blocks for Flexible IED in IEC 61850-Based Substation Automation

Flexible intelligent electronic devices (IEDs) are highly desirable to support free allocation of function to IED by means of software reconfiguration without any change of hardware. The application of generic hardware platforms and component-based software technology seems to be a good solution. Due to the advent of IEC 61850, generic hardware platforms with a standard communication interface can be used to implement different kinds of functions with high flexibility. The remaining challenge is the unified function model that specifies various software components with appropriate granularity and provides a framework to integrate them efficiently. This paper proposes the function-block (FB)-based function model for flexible IEDs. The standard FBs are established by combining the IEC 61850 model and the IEC 61499 model. The design of a simplified distance protection IED using standard FBs is described and investigated. The testing results of the prototype system in MATLAB/Simulink demonstrate the feasibility and flexibility of FB-based IEDs.
Is This a Joke? Detecting Humor in Spanish Tweets

While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics. There exist some previous works, but a characterization of humor that allows its automatic recognition and generation is far from being specified. In this work we build a crowdsourced corpus of labeled tweets, annotated according to its humor value, letting the annotators subjectively decide which are humorous. A humor classifier for Spanish tweets is assembled based on supervised learning, reaching a precision of 84% and a recall of 69%.
Localization in highly dynamic environments using dual-timescale NDT-MCL

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual-timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT-NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously proposed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.
Automatic Generation of XSS and SQL Injection Attacks with Goal-Directed Model Checking

Cross-site scripting (XSS) and SQL injection errors are two prominent examples of taint-based vulnerabilities that have been responsible for a large number of security breaches in recent years. This paper presents QED, a goal-directed model-checking system that automatically generates attacks exploiting taint-based vulnerabilities in large Java web applications. This is the first time where model checking has been used successfully on real-life Java programs to create attack sequences that consist of multiple HTTP requests. QED accepts any Java web application that is written to the standard servlet specification. The analyst specifies the vulnerability of interest in a specification that looks like a Java code fragment, along with a range of values for form parameters. QED then generates a goal-directed analysis from the specification to perform session-aware tests, optimizes to eliminate inputs that are not of interest, and feeds the remainder to a model checker. The checker will systematically explore the remaining state space and report example attacks if the vulnerability specification is matched. QED provides better results than traditional analyses because it does not generate any false positive warnings. It proves the existence of errors by providing an example attack and a program trace showing how the code is compromised. Past experience suggests this is important because it makes it easy for the application maintainer to recognize the errors and to make the necessary fixes. In addition, for a class of applications, QED can guarantee that it has found all the potential bugs in the program. We have run QED over 3 Java web applications totaling 130,000 lines of code. We found 10 SQL injections and 13 cross-site scripting errors.
Estimation of Human Body Shape and Posture Under Clothing

Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model [2, 18] as statistical model.