nils-herrmann/clean_scirepeval huggingface dataset:
On a 3D analogue of the first Hu moment invariant and a family of shape ellipsoidness measures

In this paper we show that a 3D analogue for the first Hu moment invariant, originally introduced for planar shapes, is minimized by a sphere. By exploiting this fact we define a new compactness measure for 3D shapes. The new compactness measure indicates how much a given shape differs from a sphere, which is assumed to be the most compact 3D shape. The new measure is invariant with respect to the similarity transformation and takes the maximum value if and only if the shape considered is a sphere. The methodology, used to derive the theoretical framework necessary for the definition and explanation of the behavior of the new compactness measure, is further extended and a family of 3D ellipsoidness measures is obtained. These ellipsoidness measures are also invariant with respect to similarity transformations and are maximized with certain ellipsoids only (these ellipsoids have the specific ratios between ellipsoid axes). The measures from the family distinguish among ellipsoids whose axes ratios differ—not all the ellipsoids have the same certain ellipsoidness measure. All the new measures are straightforward and fast to compute, e.g. within an asymptotically optimal $$\mathcal{O}(n)$$ O ( n ) time, if n is the number of voxels of the shape considered, or n is the number of triangles in the object surface triangular mesh. Several experiments, on the well-known McGill 3D Benchmark Data Set, are provided to illustrate the behavior of the new measures.
Speech restoration based on deep learning autoencoder with layer-wised pretraining

Neural network can be used to “remember” speech patterns by encoding speech statistical regularity in network parameters. Clean speech can be “recalled” when noisy speech is input to the network. Adding more hidden layers can increase network capacity. But when the hidden layer size increases (deep network), the network is easily to be trapped to a local solution when traditional training strategy is used. Therefore, the performance of using a deep network sometimes is even worse than using a shallow network. In this study, we explore the greedy layer-wised pretraining strategy to train a deep autoencoder (DAE) for speech restoration, and apply the restored speech for noisy robust speech recognition. The DAE is first pretrained using quasi-Newton optimization algorithm layer by layer in which each layer is regarded as a shallow autoencoder. And the output of the preceding layer is served as the input to the next layer. The pretrained layers are stacked and “unrolled” to be a DAE. The pretrained parameters are served as initial parameters of the DAE which are used to refine training. The trained DAE is used as a filter for speech restoration when noisy speech is given. Noisy robust speech recognition experiments were done to examine the performance of the trained deep network. Experimental results show that the DAE trained with pretraining process significantly improved the performance of speech restoration from noisy input.
Combinatorial portfolio selection with the ELECTRE III method: Case study of the Stock Exchange of Thailand (SET)

Various techniques of portfolio selection are applied to interpret the status of the market and predict the market's future trend, but they are not beneficial to small investors because these techniques should be administered by an expert. In addition, these techniques desire accumulation of data about the market and complicated calculations, which is too much effort for individual small investors. Therefore, portfolio selection with two significant financial ratios using the ELECTRE III method is proposed for these investors to make trading decisions. In order to demonstrate the effectiveness of this new method, it is compared to the situation where a fix percentage allocation existed and data was collected from the Stock Exchange of Thailand (SET).
Adding navigation to the equation: Turning decisions for end-to-end vehicle control

Navigation and obstacle avoidance are two problems that are not easily incorporated into direct control of autonomous vehicles solely based on visual input. However, they are required if lane following given proper lane markings is not enough to incorporate trained systems into larger architectures. We present a method to allow for obstacle avoidance while driving using a single, front-facing camera as well as navigation capabilities such as taking turns at junctions and lane changes by feeding turn indicator signals into a Convolutional Neural Network. Both situations share the difficulty intrinsic to single camera setups of limited field of views. This problem is handled by using a spatial history of input images to extend the field of view regarding static obstacles. The trained model, referred to as DriveNet, is evaluated in real world driving scenarios, using the same model for lateral vehicle control to both dynamically drive around obstacles as well as perform lane changing and turning in intersections.
Artificial Intelligence in Sports Prediction

This paper presents an extension of earlier work in the use of artificial intelligence for prediction of sporting outcomes. An expanded model is described, as well as a broadening of the area of application of the original work. The model used is a form of multi-layer perceptron and it is presented with a number of features which attempt to capture the quality of various sporting teams. The system performs well and compares favourably with human tipsters in several environments. A study of less rigid "World Cup" formats appears, along with extensive live testing results in a major international tipping competition.
Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera

2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping.
Challenges with applying vulnerability prediction models

Vulnerability prediction models (VPM) are believed to hold promise for providing software engineers guidance on where to prioritize precious verification resources to search for vulnerabilities. However, while Microsoft product teams have adopted defect prediction models, they have not adopted vulnerability prediction models (VPMs). The goal of this research is to measure whether vulnerability prediction models built using standard recommendations perform well enough to provide actionable results for engineering resource allocation. We define 'actionable' in terms of the inspection effort required to evaluate model results. We replicated a VPM for two releases of the Windows Operating System, varying model granularity and statistical learners. We reproduced binary-level prediction precision (~0.75) and recall (~0.2). However, binaries often exceed 1 million lines of code, too large to practically inspect, and engineers expressed preference for source file level predictions. Our source file level models yield precision below 0.5 and recall below 0.2. We suggest that VPMs must be refined to achieve actionable performance, possibly through security-specific metrics.
Efficient Authentication and Signing of Multicast Streams over Lossy Channels

Multicast stream authentication and signing is an important and challenging problem. Applications include the continuous authentication of radio and TV Internet broadcasts, and authenticated data distribution by satellite. The main challenges are fourfold. First, authenticity must be guaranteed even when only the sender of the data is trusted. Second, the scheme needs to scale to potentially millions of receivers. Third, streamed media distribution can have high packet loss. Finally, the system needs to be efficient to support fast packet rates. We propose two efficient schemes, TESLA and EMSS, for secure lossy multicast streams. TESLA, short for Timed Efficient Stream Loss-tolerant Authentication, offers sender authentication, strong loss robustness, high scalability, and minimal overhead, at the cost of loose initial time synchronization and slightly delayed authentication. EMSS, short for Efficient Multi-chained Stream Signature, provides nonrepudiation of origin, high loss resistance, and low overhead, at the cost of slightly delayed verification. This work began in Summer 1999 when Adrian Perrig and Dawn Song were visiting the IBM T. J. Watson research lab. Initial research on stream authentication was done during Summer 1999 by Ran Canetti, Adrian Perrig, and Dawn Song at IBM. Additional improvements were suggested by J. D. Tygar in Fall 1999 at UC Berkeley. Implementation was done in Fall 1999 by Adrian Perrig at UC Berkeley. The work on stream signatures was done by J. D. Tygar, Adrian Perrig, and Dawn Song at UC Berkeley. Additional work was performed by Ran Canetti in Spring 2000. Ran Canetti is at IBM T. J. Watson Research Center, and Adrian Perrig, Dawn Song, and J. D. Tygar are at the Computer Science Division, UC Berkeley. This research was suported in part by the Defense Advanced Research Projects Agency under DARPA contract N6601-99-28913 (under supervision of the Space and Naval Warfare Systems Center San Diego), by the National Science foundation under grant FD99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C-3513. Views and conclusions contained in this document are those of the authors and do not necessarily represent the official opinion or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS, or IBM.
Scaling personalized web search

Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.
Achieving high utilization with software-driven WAN

We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.
Visual Speech Recognition

Lip reading is used to understand or interpret speech without hearing it, a technique especially mastered by people with hearing difficulties. The ability to lip read enables a person with a hearing impairment to communicate with others and to engage in social activities, which otherwise would be difficult. Recent advances in the fields of computer vision, pattern recognition, and signal processing has led to a growing interest in automating this challenging task of lip reading. Indeed, automating the human ability to lip read, a process referred to as visual speech recognition (VSR) (or sometimes speech reading), could open the door for other novel related applications. VSR has received a great deal of attention in the last decade for its potential use in applications such as human-computer interaction (HCI), audio-visual speech recognition (AVSR), speaker recognition, talking heads, sign language recognition and video surveillance. Its main aim is to recognise spoken word(s) by using only the visual signal that is produced during speech. Hence, VSR deals with the visual domain of speech and involves image processing, artificial intelligence, object detection, pattern recognition, statistical modelling, etc. There are two different main approaches to the VSR problem, the visemic* approach and the holistic approach, each with its own strengths and weaknesses. The traditional and most common approaches to automatic lip reading are based on visemes. A Viseme is the mouth shapes (or appearances) or sequences of mouth dynamics that are required to generate a phoneme in the visual domain. However, several problems arise while using visemes in visual speech recognition systems such as the low number of visemes (between 10 and 14) compared to phonemes (between 45 and 53). Visemes cover only a small subspace of the mouth motions represented in the visual domain, and many other problems. These problems contribute to the bad performance of the traditional approaches; hence, the visemic approach is something like digitising the signal of the spoken word, and digitising causes a loss of information. The holistic approach such as the “visual words” (Hassanat, 2009) considers the signature of the whole word rather than only parts of it. This approach can provide a good alternative to the visemic approaches to automatic lip reading. The major problem that faces this approach is that for a complete English language lip reading system, we need to train the whole of the English language words in the dictionary! Or to train (at least) the distinct ones.
Modeling purposeful adaptive behavior with the principle of maximum causal entropy

Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Grünwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.
The Matrix Cookbook

We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishøj, Christian Schröppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jürgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barão, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.
Blockchain Technology: Applications in Health Care.

Blockchain technology has gained substantial attention in recent years with increased interest in several diverse fields, including the healthcare industry. Blockchain offers a secure, distributed database that can operate without a central authority or administrator. Blockchain uses a distributed, peer-to-peer network to make a continuous, growing list of ordered records called blocks to form a digital ledger. Each transaction, represented in a cryptographically signed block, is then automatically validated by the network itself. Blockchain has also garnered interest as a platform to improve the authenticity and transparency of healthcare data through many use cases, from maintaining permissions in electronic health records (EHR) to streamlining claims processing. In this article, we describe the basics of blockchain and illustrate current and future applications of this technology within the healthcare industry.
Mining Multi-label Data

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application. Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization into emotions [11, 12, 13, 14] and directed marketing . Table 1 presents a variety of applications that are discussed in the literature. This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can significantly expand the real-world applications of multi-label learning methods: a) exploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Section 7 presents the most frequently used evaluation measures for multi-label learn-
The roles of emojis in mobile phone notifications

The texts in mobile messages are not always easy to decipher since tone and body language is removed from the context. Emojis offer an attractive way to express emotions to avoid misunderstandings of message tone. In this paper we shed the light on the roles of Emojis in phone notification, we conducted an in-situ study to gather phone notification data. We outline the relationship between Emojis and various social network applications including WhatsApp, Facebook and Twitter. Early results allow us to draw several conclusions in relation to number, position, type and sentimental value of Emojis. It turns out that most popular Emojis in one social app is not as popular in the others. Emojis sentimental polarity in Twitter is high and overall number of Emojis is less than Facebook. The sentimental value of Emojis is more meaningful when there are multiple Emoji in one notification.
Evaluation methodologies for the NB-IOT system: Issues and ongoing efforts

The recently standardized Narrow Band Internet of Things (NB-IOT) is expected to become a disruptive technology in the IoT market. The low cost, manageability, and reliability offered by a Mobile Network Operator (MNO) can be the enabler for the widespread acceptance and deployment of IoT devices. In this market phase, network planning plays a crucial role in the possible services forecast, with obvious consequences on the market analysis by the MNO. Moreover, there are many possible improvements in the NB-IOT device management, which are intentionally left as implementation dependent elements. As a consequence, it is necessary to evaluate the performances of an NB-IOT system, both for scientific and deployment purposes. In this paper we will outline the various evaluation systems and the ongoing efforts to build a new and efficient NB-IOT simulation platform.
Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists-and probably the most crucial one-is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.