nils-herrmann/clean_scirepeval huggingface dataset:
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations

Many machine vision applications, such as compression, pictorial database querying and image understanding, often need to analyze in detail only a representative subset of the image that may be arranged into sequence of loci called regions-of-interest, ROIs. We haveinvestigated and developed a methodology that serves to automaticaJly identify such a subset of aROIo (olgorithmically detected ROIs) using different image processing algorithms and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences ofeye movements to fixate on similar sequencesof hROIs
GraphSum: Discovering correlations among multiple terms for graph-based summarization

Graph-based summarization entails extracting a worthwhile subset of sentences from a collection of textual documents by using a graph-based model to represent the correlations between pairs of document terms. However, since the high-order correlations among multiple terms are disregarded during graph evaluation, the summarization performance could be limited unless integrating ad-hoc language-dependent or semantics-based analysis. This paper presents a novel and general-purpose graph-based summarizer, namely GraphSum (Graph-based Summarizer). It discovers and exploits association rules to represent the correlations among multiple terms that have been neglected by previous approaches. The graph nodes, which represent combinations of two or more terms, are first ranked by means of a PageRank ∗Corresponding author.  Email addresses: elena.baralis@polito.it (Elena Baralis), luca.cagliero@polito.it (Luca Cagliero), naeem.mahoto@polito.it (Naeem Mahoto), alessandro.fiori@ircc.it (Alessandro Fiori) Preprint submitted to strategy that discriminates between positive and negative term correlations. Then, the produced node ranking is used to drive the sentence selection process. The experiments performed on benchmark and real-life documents demonstrate the effectiveness of the proposed approach compared to many stateof-the-art summarizers.
Citeology: visualizing paper genealogy

Citeology is an interactive visualization that looks at the relationships between research publications through their use of citations. The sample corpus uses all 3,502 papers published at ACM CHI and UIST between 1982 and 2010, and the 11,699 citations between them. A connection is drawn between each paper and all papers which it referenced from the collection. For an individual paper, the resulting visualization represents a "family tree" of sorts, showing multiple generations of referenced papers which the target paper built upon, and all descendant generations of future papers.
A Finite-State Turn-Taking Model for Spoken Dialog Systems

This paper introduces the Finite-State TurnTaking Machine (FSTTM), a new model to control the turn-taking behavior of conversational agents. Based on a non-deterministic finite-state machine, the FSTTM uses a cost matrix and decision theoretic principles to select a turn-taking action at any time. We show how the model can be applied to the problem of end-of-turn detection. Evaluation results on a deployed spoken dialog system show that the FSTTM provides significantly higher responsiveness than previous approaches.
The Benefit of Pets and Animal-Assisted Therapy to the Health of Older Individuals

Many studies utilizing dogs, cats, birds, fish, and robotic simulations of animals have tried to ascertain the health benefits of pet ownership or animal-assisted therapy in the elderly. Several small unblinded investigations outlined improvements in behavior in demented persons given treatment in the presence of animals. Studies piloting the use of animals in the treatment of depression and schizophrenia have yielded mixed results. Animals may provide intangible benefits to the mental health of older persons, such as relief social isolation and boredom, but these have not been formally studied. Several investigations of the effect of pets on physical health suggest animals can lower blood pressure, and dog walkers partake in more physical activity. Dog walking, in epidemiological studies and few preliminary trials, is associated with lower complication risk among patients with cardiovascular disease. Pets may also have harms: they may be expensive to care for, and their owners are more likely to fall. Theoretically, zoonotic infections and bites can occur, but how often this occurs in the context of pet ownership or animal-assisted therapy is unknown. Despite the poor methodological quality of pet research after decades of study, pet ownership and animal-assisted therapy are likely to continue due to positive subjective feelings many people have toward animals.
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing

Classical approaches to clean data have relied on using integrity constraints, statistics, or machine learning. These approaches are known to be limited in the cleaning accuracy, which can usually be improved by consulting master data and involving experts to resolve ambiguity. The advent of knowledge bases KBs both general-purpose and within enterprises, and crowdsourcing marketplaces are providing yet more opportunities to achieve higher accuracy at a larger scale. We propose KATARA, a knowledge base and crowd powered data cleaning system that, given a table, a KB, and a crowd, interprets table semantics to align it with the KB, identifies correct and incorrect data, and generates top-k possible repairs for incorrect data. Experiments show that KATARA can be applied to various datasets and KBs, and can efficiently annotate data and suggest possible repairs.
A federated architecture for database systems

The contemporary approach to database system architecture requires the complete integration of data into a single, centralized database; while multiple logical databases can be supported by current database management software, techniques for relating these databases are strictly ad hoc. This problem is aggravated by the trend toward networks of small to medium size computer systems, as opposed to large, stand-alone main-frames. Moreover, while current research on distributed databases aims to provide techniques that support the physical distribution of data items in a computer network environment, current approaches require a distributed database to be logically centralized.
Improving routing in networks of UAVs via scoped flooding and mobility prediction

Due to their specific characteristics, Unmanned Aeronautical Ad-hoc Networks (UAANETs) can be classified as a special kind of mobile ad-hoc networks. Due to the high mobility of Unmanned Aerial Vehicles (UAVs), designing a good routing protocol for UAANETs is challenging. Recently, a new protocol called Reactive-Greedy-Reactive (RGR)  has been proposed as a promising routing protocol in high mobility and density-variable scenarios. Although the RGR protocol improves the packet delivery ratio, the overhead and delay are higher when compared to AODV . In this paper, a scoped flooding and mobility prediction based RGR protocol is proposed to improve the performance of RGR in UAANETs. Simulation results show that the new protocol can effectively enhance the performance of the RGR protocol in terms of packet delivery ratio, overhead, and delay.
Underactuated five-finger prosthetic hand inspired by grasping force distribution of humans

Simple grippers with one or two degrees of freedom are commercially available prosthetic hands; these pinch type devices cannot grasp small cylinders and spheres because of their small degree of freedom. This paper presents the design and prototyping of underactuated five-finger prosthetic hand for grasping various objects in daily life. Underactuated mechanism enables the prosthetic hand to move fifteen compliant joints only by one ultrasonic motor. The innovative design of this prosthetic hand is the underactuated mechanism optimized to distribute grasping force like those of humans who can grasp various objects robustly. Thanks to human like force distribution, the prototype of prosthetic hand could grasp various objects in daily life and heavy objects with the maximum ejection force of 50 N that is greater than other underactuated prosthetic hands.
A Resolver Angle Estimator Based on Its Excitation Signal

A resolver generates a pair of signals proportional to the sine and cosine of the angular position of its shaft. A new low-cost method for converting the amplitudes of these sine/cosine transducer signals into a measure of the input angle without using lookup tables is proposed. The new method takes advantage of the components used to operate the resolver, the excitation (carrier) signal in particular. This is a feedforward method based on comparing the amplitudes of the resolver signals to those of the excitation signal together with another shifted by pi/2. A simple method is then used to estimate the shaft angle through this comparison technique. The poor precision of comparison of the signals around their highly nonlinear peak regions is avoided by using a simple technique that relies only on the alternating pseudolinear segments of the signals. This results in a better overall accuracy of the converter. Beside simplicity of implementation, the proposed scheme offers the advantage of robustness to amplitude fluctuation of the transducer excitation signal.
ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning and Snapshot Ensembling

ViZDoom is a robust, first-person shooter reinforcement learning environment, characterized by a significant degree of latent state information. In this paper, double-Q learning and prioritized experience replay methods are tested under a certain ViZDoom combat scenario using a competitive deep recurrent Q-network (DRQN) architecture. In addition, an ensembling technique known as snapshot ensembling is employed using a specific annealed learning rate to observe differences in ensembling efficacy under these two methods. Annealed learning rates are important in general to the training of deep neural network models, as they shake up the statusquo and counter a model’s tending towards local optima. While both variants show performance exceeding those of built-in AI agents of the game, the known stabilizing effects of double-Q learning are illustrated, and priority experience replay is again validated in its usefulness by showing immediate results early on in agent development, with the caveat that value overestimation is accelerated in this case. In addition, some unique behaviors are observed to develop for priority experience replay (PER) and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves a valuable method for improving performance of the ViZDoom Marine.
Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks

Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on full-flows or the first few packets of flows. In contrast, many real-world scenarios require a classification decision well before a flow has finished even if the flow's beginning is lost. This implies classification must be achieved using statistics derived from the most recent N packets taken at any arbitrary point in a flow's lifetime. We propose training the classifier on a combination of short sub-flows (extracted from full-flow examples of the target application's traffic). We demonstrate this optimisation using the naive Bayes ML algorithm, and show that our approach results in excellent performance even when classification is initiated mid-way through a flow with windows as small as 25 packets long. We suggest future use of unsupervised ML algorithms to identify optimal sub-flows for training
Wireless attendance management system based on iris recognition

Iris recognition verification is one of the most reliable personal identification methods in Biometrics. With the rapid development of iris recognition verification, a number of its applications have been proposed until now including time attendance system etc. In this paper, a wireless iris recognition attendance management system is designed and implemented using Daugman’s algorithm (Daugman, 2003). This system based biometrics and wireless technique solves the problem of spurious attendance and the trouble of laying the corresponding network. It can make the users’ attendances more easily and effectively.
RDMA over Commodity Ethernet at Scale

Over the past one and half years, we have been using RDMA over commodity Ethernet (RoCEv2) to support some of Microsoft's highly-reliable, latency-sensitive services. This paper describes the challenges we encountered during the process and the solutions we devised to address them. In order to scale RoCEv2 beyond VLAN, we have designed a DSCP-based priority flow-control (PFC) mechanism to ensure large-scale deployment. We have addressed the safety challenges brought by PFC-induced deadlock (yes, it happened!), RDMA transport livelock, and the NIC PFC pause frame storm problem. We have also built the monitoring and management systems to make sure RDMA works as expected. Our experiences show that the safety and scalability issues of running RoCEv2 at scale can all be addressed, and RDMA can replace TCP for intra data center communications and achieve low latency, low CPU overhead, and high throughput.
Hidden Markov models based on multi-space probability distribution for pitch pattern modeling

This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widelyused statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional discrete or continuous HMMs to pitch pattern modeling since the observation sequence of pitch pattern is composed of one-dimensional continuous values and a discrete symbol which represents “unvoiced”. MSD-HMM includes discrete HMM and continuous mixture HMM as special cases, and further can model the sequence of observation vectors with variable dimension including zero-dimensional observations, i.e., discrete symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.
Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99

KDD 99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection and is the only labeled dataset publicly available. Numerous researchers employed the datasets in KDD 99 intrusion detection competition to study the utilization of machine learning for intrusion detection and reported detection rates up to 91% with false positive rates less than 1%. To substantiate the performance of machine learning based detectors that are trained on KDD 99 training data; we investigate the relevance of each feature in KDD 99 intrusion detection datasets. To this end, information gain is employed to determine the most discriminating features for each class.
Collusion-Secure Fingerprinting for Digital Data

This paper discusses methods for assigning codewords for the purpose of ngerprinting digital data (e.g., software, documents, and images). Fingerprinting consists of uniquely marking and registering each copy of the data. This marking allows a distributor to detect any unauthorized copy and trace it back to the user. This threat of detection will hopefully deter users from releasing unauthorized copies. A problem arises when users collude: For digital data, two di erent ngerprinted objects can be compared and the di erences between them detected. Hence, a set of users can collude to detect the location of the ngerprint. They can then alter the ngerprint to mask their identities. We present a general ngerprinting solution which is secure in the context of collusion. In addition, we discuss methods for distributing ngerprinted data.
The NS Manual (Formerly NS Notes and Documentation

ns c is LBNL’s Network Simulator [20]. The simulator is written in C++; it uses OTcl a s command and configuration interface.nsv2 has three substantial changes from nsv1: (1) the more complex objects in nsv1 have been decomposed into simpler components for greater flexibility and composabili ty; (2) the configuration interface is now OTcl, an object ori ented version of Tcl; and (3) the interface code to the OTcl interpr te is separate from the main simulator. Ns documentation is available in html, Postscript, and PDF f ormats. Seehttp://www.isi.edu/nsnam/ns/ns-documentation. html for pointers to these.
Rule-based information extraction from patients' clinical data

The paper describes a rule-based information extraction (IE) system developed for Polish medical texts. We present two applications designed to select data from medical documentation in Polish: mammography reports and hospital records of diabetic patients. First, we have designed a special ontology that subsequently had its concepts translated into two separate models, represented as typed feature structure (TFS) hierarchies, complying with the format required by the IE platform we adopted. Then, we used dedicated IE grammars to process documents and fill in templates provided by the models. In particular, in the grammars, we addressed such linguistic issues as: ambiguous keywords, negation, coordination or anaphoric expressions. Resolving some of these problems has been deferred to a post-processing phase where the extracted information is further grouped and structured into more complex templates. To this end, we defined special heuristic algorithms on the basis of sample data. The evaluation of the implemented procedures shows their usability for clinical data extraction tasks. For most of the evaluated templates, precision and recall well above 80% were obtained.
Detecting energy-greedy anomalies and mobile malware variants

Mobile users of computation and communication services have been rapidly adopting battery-powered mobile handhelds, such as PocketPCs and SmartPhones, for their work. However, the limited battery-lifetime of these devices restricts their portability and applicability, and this weakness can be exacerbated by mobile malware targeting depletion of battery energy. Such malware are usually difficult to detect and prevent, and frequent outbreaks of new malware variants also reduce the effectiveness of commonly-seen signature-based detection. To alleviate these problems, we propose a power-aware malware-detection framework that monitors, detects, and analyzes previously unknown energy-depletion threats. The framework is composed of (1) a power monitor which collects power samples and builds a power consumption history from the collected samples, and (2) a data analyzer which generates a power signature from the constructed history. To generate a power signature, simple and effective noise-filtering and data-compression are applied, thus reducing the detection overhead. Similarities between power signatures are measured by the χ2-distance, reducing both false-positive and false-negative detection rates. According to our experimental results on an HP iPAQ running a Windows Mobile OS, the proposed framework achieves significant (up to 95%) storage-savings without losing the detection accuracy, and a 99% true-positive rate in classifying mobile malware.
Reinforcement Learning based Control of Traffic Lights in Non-stationary Environments: A Case Study in a Microscopic Simulator

Coping with dynamic changes in traffic volume has been the object of recent publications. Recently, a method was proposed, which is capable of learning in non-stationary scenarios via an approach to detect context changes. For particular scenarios such as the traffic control one, the performance of that method is better than a greedy strategy, as well as other reinforcement learning approaches, such as Q-learning and Prioritized Sweeping. The goal of the present paper is to assess the feasibility of applying the above mentioned approach in a more realistic scenario, implemented by means of a microscopic traffic simulator. We intend to show that to use of context detection is suitable to deal with noisy scenarios where non-stationarity occurs not only due to the changing volume of vehicles, but also because of the random behavior of drivers in what regards the operational task of driving (e.g. deceleration probability). The results confirm the tendencies already detected in the previous paper, although here the increase in noise makes the learning task much more difficult, and the correct separation of contexts harder.
Concept Based Query Expansion Using WordNet

Query expansion is a widely studied technique for improving information retrieval effectiveness. In this paper we proposed a new query expansion technique using the comprehensive thesaurus WordNet and its semantic relatedness measure modules. Word sense disambiguation are performed on original query sentence, yielding the concept of each term in the query. Based on those recovered concepts, expanded query terms are generated from WordNet lexical database. The proposed method has been evaluated in document retrieval on the Web using query sentence. Our extensive experimental results demonstrate a 7% precision improvement over retrieval methods not employing query expansion techniques.
Stochastic Gradient VB and the Variational Auto-Encoder

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.