nils-herrmann/clean_scirepeval huggingface dataset:
Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented.
Automatic Labelling of Topic Models Learned from Twitter by Summarisation

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA.
Joint time-frequency scattering for audio classification

We introduce the joint time-frequency scattering transform, a time shift invariant descriptor of time-frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time-frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time-frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.
Series-Fed Microstrip Antenna Array With Inclined-Slot Couplers as Three-Way Power Dividers

A novel approach to the design of series-fed antenna arrays has been presented, in which a modified three-way slot power divider is applied. In the proposed coupler, the power division is adjusted by changing the slot inclination with respect to the transmission line, whereas coupled transmission lines are perpendicular. The proposed modification reduces electrical length of the feeding line to <formula formulatype="inline"><tex Notation="TeX">$1 \lambda$</tex></formula>, hence results in dissipation losses' reduction. The theoretical analysis and measurement results of the 2<formula formulatype="inline"> <tex Notation="TeX">$\, \times \,$</tex></formula>8 microstrip antenna array operating within 10.5-GHz frequency range are shown in the letter, proving the novel inclined-slot power divider's capability to provide appropriate power distribution and its potential application in the large antenna arrays.
Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting

We propose improved Deep Neural Network (DNN) training loss functions for more accurate single keyword spotting on resource-constrained embedded devices. The loss function modifications consist of a combination of multi-task training and weighted cross entropy. In the multi-task architecture, the keyword DNN acoustic model is trained with two tasks in parallel the main task of predicting the keyword-specific phone states, and an auxiliary task of predicting LVCSR senones. We show that multi-task learning leads to comparable accuracy over a previously proposed transfer learning approach where the keyword DNN training is initialized by an LVCSR DNN of the same input and hidden layer sizes. The combination of LVCSRinitialization and Multi-task training gives improved keyword detection accuracy compared to either technique alone. We also propose modifying the loss function to give a higher weight on input frames corresponding to keyword phone targets, with a motivation to balance the keyword and background training data. We show that weighted cross-entropy results in additional accuracy improvements. Finally, we show that the combination of 3 techniques LVCSR-initialization, multi-task training and weighted cross-entropy gives the best results, with significantly lower False Alarm Rate than the LVCSR-initialization technique alone, across a wide range of Miss Rates.
Fast app launching for mobile devices using predictive user context

As mobile apps become more closely integrated into our everyday lives, mobile app interactions ought to be rapid and responsive. Unfortunately, even the basic primitive of launching a mobile app is sorrowfully sluggish: 20 seconds of delay is not uncommon even for very popular apps.
 We have designed and built FALCON to remedy slow app launch. FALCON uses contexts such as user location and temporal access patterns to predict app launches before they occur. FALCON then provides systems support for effective app-specific prelaunching, which can dramatically reduce perceived delay.
 FALCON uses novel features derived through extensive data analysis, and a novel cost-benefit learning algorithm that has strong predictive performance and low runtime overhead. Trace-based analysis shows that an average user saves around 6 seconds per app startup time with daily energy cost of no more than 2% battery life, and on average gets content that is only 3 minutes old at launch without needing to wait for content to update. FALCON is implemented as an OS modification to the Windows Phone OS.
Investigating app store ranking algorithms using a simulation of mobile app ecosystems

App stores are one of the most popular ways of providing content to mobile device users today. But with thousands of competing apps and thousands new each day, the problem of presenting the developers' apps to users becomes nontrivial. There may be an app for everything, but if the user cannot find the app they desire, then the app store has failed. This paper investigates app store content organisation using AppEco, an Artificial Life model of mobile app ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. This paper uses AppEco to investigate how best to organise the Top Apps Chart and New Apps Chart in Apple's iOS App Store. We study the effects of different app ranking algorithms for the Top Apps Chart and the frequency of updates of the New Apps Chart on the download-to-browse ratio. Results show that the effectiveness of the shop front is highly dependent on the speed at which content is updated. A slowly updated New Apps Chart will impact the effectiveness of the Top Apps Chart. A Top Apps Chart that measures success by including too much historical data will also detrimentally affect app downloads.
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MTLfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator’s driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.
Processor Design in 3D Die-Stacking Technologies

Three-dimensional integration is an emerging fabrication technology that vertically stacks multiple integrated chips. The benefits include an increase in device density; much greater flexibility in routing signals, power, and clock; the ability to integrate disparate technologies; and the potential for new 3D circuit and microarchitecture organizations. This article provides a technical introduction to the technology and its impact on processor design. Although our discussions here primarily focus on high-performance processor design, most of the observations and conclusions apply to other microprocessor market segments.
Characterizing the workload of a netflix streaming video server

In this paper we characterize the workload of a Netflix streaming video web server. Netflix is a widely popular subscription service with over 81 million global subscribers [24]. The service streams professionally produced TV shows and movies over the Internet to an extremely diverse and representative set of playback devices over broadband, DSL, WiFi and cellular connections. Characterizing this type of workload is an important step to understanding and optimizing the performance of the servers used to support the growing number of streaming video services. We focus on the HTTP requests observed at the server from Netflix client devices by analyzing anonymized log files obtained from a server containing a portion of the Netflix catalog. We introduce the notion of chains of sequential requests to represent the spatial locality of the workload and find that despite servicing clients that adapt to changes in network and server conditions, and despite the fact that the majority of chains are short (60% are no longer than 1 MB), the vast majority of the bytes requested are sequential. We also observe that during a viewing session, client devices behave in recognizable patterns. We characterize sessions using transient, stable and inactive phases. We find that playback sessions are surprisingly stable; across all sessions 5% of the total session time is spent in transient phases, 79% in stable phases and 16% in inactive phases, and the average duration of a stable phase is 8.5 minutes. Finally we analyze the chains to evaluate different prefetch algorithms and show that by exploiting knowledge about workload characteristics, the workload can be serviced with 13% lower hard drive utilization or 30% less system memory compared to a prefetch algorithm that makes no use of workload characteristics.
EMC compliant LIN transceiver

This paper introduces an integrated Local Interconnect Network (LIN) transceiver which sets a new performance benchmark in terms of electromagnetic compatibility (EMC). The proposed topology succeeds in an extraordinary high robustness against RF disturbances which are injected into the BUS and in very low electromagnetic emissions (EMEs) radiated by the LIN network without adding any external components for filtering. In order to evaluate the circuits superior EMC performance, it was designed using a HV-BiCMOS technology for automotive applications, the EMC behavior was measured and the results were compared with a state of the art topology.
Human mobility models for opportunistic networks

Mobile ad hoc networks enable communications between clouds of mobile devices without the need for a preexisting infrastructure. One of their most interesting evolutions are opportunistic networks, whose goal is to also enable communication in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronously in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (e.g., smartphones and tablets) carried by human users, which is the typical OppNets scenario. In such a network where the movements of the communicating devices mirror those of their owners, finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this article we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start by reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobility. We discuss the shortcomings of the existing knowledge about human movements and extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that provides the basis for a discussion on open problems and further directions for research on modeling human mobility.
A Novel Location-Centric IoT-Cloud Based On-Street Car Parking Violation Management System in Smart Cities

Nowadays, in big cities, parking management is a critical issue from both the driver's side and the city government's side. From the driver's side, how to find an available parking lot in a city is a considerable concern. As a result, smart parking systems recently have received great interest, both in academia and industry. From the city government's side, how to manage and distribute such a limited public parking resource efficiently to give every visitor a fair chance of finding an on-street parking lot is also a considerable concern. However, existing studies of smart parking management focus only on assisting the driver's side to find available parking spaces. This study aims to raise a new perspective on such smart parking management and to propose a novel location-centric IoT-cloud-based parking violation management system. The system is designed to assist authoritative officers in finding parking violations easily and recommends the least cost path for officers so that officers can achieve their highest productivity in finding parking violations and issuing parking tickets. Experimental results show that the system not only improves the productivity of officers in finding parking violations and issuing tickets, but also helps reduce the traveling cost of officers and to reduce the average violation period of violating cars considerably.
Design and implementation of a smartphone-based portable ultrasound pulsed-wave doppler device for blood flow measurement

Blood flow measurement using Doppler ultrasound has become a useful tool for diagnosing cardiovascular diseases and as a physiological monitor. Recently, pocket-sized ultrasound scanners have been introduced for portable diagnosis. The present paper reports the implementation of a portable ultrasound pulsed-wave (PW) Doppler flowmeter using a smartphone. A 10-MHz ultrasonic surface transducer was designed for the dynamic monitoring of blood flow velocity. The directional baseband Doppler shift signals were obtained using a portable analog circuit system. After hardware processing, the Doppler signals were fed directly to a smartphone for Doppler spectrogram analysis and display in real time. To the best of our knowledge, this is the first report of the use of this system for medical ultrasound Doppler signal processing. A Couette flow phantom, consisting of two parallel disks with a 2-mm gap, was used to evaluate and calibrate the device. Doppler spectrograms of porcine blood flow were measured using this stand-alone portable device under the pulsatile condition. Subsequently, in vivo portable system verification was performed by measuring the arterial blood flow of a rat and comparing the results with the measurement from a commercial ultrasound duplex scanner. All of the results demonstrated the potential for using a smartphone as a novel embedded system for portable medical ultrasound applications.
Trust as a Commodity

Trust is central to all transactions and yet economists rarely discuss the notion. It is treated rather as background environment, present whenever called upon, a sort of ever-ready lubricant that permits voluntary participation in production and exchange. In the standard model of a market economy it is taken for granted that consumers meet their budget constraints: they are not allowed to spend more than their wealth. Moreover, they always deliver the goods and services they said they would. But the model is silent on the rectitude of such agents. We are not told if they are persons of honour, conditioned by their upbringing always to meet the obligations they have chosen to undertake, or if there is a background agency which enforces contracts, credibly threatening to mete out punishment if obligations are not fulfilled a punishment sufficiently stiff to deter consumers from ever failing to fulfil them. The same assumptions are made for producers. To be sure, the standard model can be extended to allow for bankruptcy in the face of an uncertain future. One must suppose that there is a special additional loss to becoming bankrupt a loss of honour when honour matters, social and economic ostracism, a term in a debtors’ prison, and so forth. Otherwise, a person may take silly risks or, to make a more subtle point, take insufficient care in managing his affairs, but claim that he ran into genuine bad luck, that it was Mother Nature’s fault and not his own lack of ability or zeal.
Creating a Natural Logic Inference System with Combinatory Categorial Grammar

This dissertation presents an integrated system for producing Natural Logic inferences, which are used in a wide variety of natural language understanding tasks. Natural Logic is the process of creating valid inferences by making incremental edits to natural language expressions with respect to a universal monotonicity calculus, without resorting to logical representation of the expressions (using First Order Logic for instance). The system generates inferences from surface forms using a three-stage process. First, each sentence is subjected to syntactic analysis, using a purpose-built syntactic parser. Then the rules of the monotonicity calculus are applied, specifying the directionality of entailment for each sentence constituents. A constituent can be either upward or downward entailing, meaning that we may replace it with a semantically broader or narrower term. Finally, we can find all suitable replacement terms for each target word by using the WordNet lexical database, which contains hypernymic and hyponymic relations. Using Combinatory Categorial Grammar, we were able to incorporate the monotonicity determination step in the syntactic derivation process. In order to achieve wide coverage over English sentences we had to introduce statistical models into our syntactic parser. At the current stage we have implemented a simple statistical model similar to those of Probabilistic Context-Free Grammars. The system is intended to provide input to “deep” reasoning engines, used for higher-level Natural Language Processing applications such as Recognising Textual Entailment. In addition to independently evaluating each component of the system, we present our comprehensive findings using Cyc, a large-scale knowledge base, and we outline a solution for its relatively limited concept coverage.
Periodic Performance Prediction for Real-time Business Process Monitoring

The purpose of this paper is to provide industrial managers with insight into the real-time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real-time business process monitoring.  In the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real-time progress in order to describe the ongoing status.  The proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.  The performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.  Whereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real-time indicator describing the current capability of ongoing process.
Cancer drug resistance: an evolving paradigm

Resistance to chemotherapy and molecularly targeted therapies is a major problem facing current cancer research. The mechanisms of resistance to 'classical' cytotoxic chemotherapeutics and to therapies that are designed to be selective for specific molecular targets share many features, such as alterations in the drug target, activation of prosurvival pathways and ineffective induction of cell death. With the increasing arsenal of anticancer agents, improving preclinical models and the advent of powerful high-throughput screening techniques, there are now unprecedented opportunities to understand and overcome drug resistance through the clinical assessment of rational therapeutic drug combinations and the use of predictive biomarkers to enable patient stratification.
An improved global feature selection scheme for text classification

Feature selection is known as a good solution to the high dimensionality of the feature space and mostly preferred feature selection methods for text classification are filter-based ones. In a common filter-based feature selection scheme, unique scores are assigned to features depending on their discriminative power and these features are sorted in descending order according to the scores. Then, the last step is to add top-N features to the feature set where N is generally an empirically determined number. In this paper, an improved global feature selection scheme (IGFSS) where the last step in a common feature selection scheme is modified in order to obtain a more representative feature set is proposed. Although feature set constructed by a common feature selection scheme successfully represents some of the classes, a number of classes may not be even represented. Consequently, IGFSS aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in IGFSS to label features according to their discriminative power on classes and these labels are used while producing the feature sets. Experimental results on well-known benchmark datasets with various classifiers indicate that IGFSS improves the performance of classification in terms of two widely-known metrics namely Micro-F1 and Macro-F1.