nils-herrmann/clean_scirepeval huggingface dataset:
Dolores: a model that predicts football match outcomes from all over the world

The paper describes Dolores, a model designed to predict football match outcomes in one country by observing football matches in multiple other countries. The model is a mixture of two methods: (a) dynamic ratings and (b) Hybrid Bayesian Networks. It was developed as part of the international special issue competition Machine Learning for Soccer. Unlike past academic literature which tends to focus on a single league or tournament, Dolores is trained with a single dataset that incorporates match outcomes, with missing data (as part of the challenge), from 52 football leagues from all over the world. The challenge involved using a single model to predict 206 future match outcomes from 26 different leagues, played from March 31 to April 9 in 2017. Dolores ranked 2nd in the competition with a predictive error 0.94% higher than the top and 116.78% lower than the bottom participants. The paper extends the assessment of the model in terms of profitability against published market odds. Given that the training dataset incorporates a number of challenges as part of the competition, the results suggest that the model generalised well over multiple leagues, divisions, and seasons. Furthermore, while detailed historical performance for each team helps to maximise predictive accuracy, Dolores provides empirical proof that a model can make a good prediction for a match outcome between teams x and y even when the prediction is derived from historical match data that neither x nor y participated in. While this agrees with past studies in football and other sports, this paper extends the empirical evidence to historical training data that does not just include match results from a single competition but contains results spanning different leagues and divisions from 35 different countries. This implies that we can still predict, for example, the outcome of English Premier League matches, based on training data from Japan, New Zealand, Mexico, South Africa, Russia, and other countries in addition to data from the English Premier league.
DNS tunneling detection through statistical fingerprints of protocol messages and machine learning

The use of covert-channel methods to bypass security policies has increased considerably in the recent years. Malicious users neutralize security restriction by encapsulating protocols like peer-to-peer, chat or http proxy into other allowed protocols like Domain Name Server (DNS) or HTTP. This paper illustrates a machine learning approach to detect one particular covert-channel technique: DNS tunneling. Despite packet inspection may guarantee reliable intrusion detection in this context, it may suffer of scalability performance when a large set of sockets should be monitored in real time. Detecting the presence of DNS intruders by an aggregation-based monitoring is of main interest as it avoids packet inspection, thus preserving privacy and scalability. The proposed monitoring mechanism looks at simple statistical properties of protocol messages, such as statistics of packets inter-arrival times and of packets sizes. The analysis is complicated by two drawbacks: silent intruders (generating small statistical variations of legitimate traffic) and quick statistical fingerprints generation (to obtain a detection tool really applicable in the field). Results from experiments conducted on a live network are obtained by replicating individual detections over successive samples over time and by making a global decision through a majority voting scheme. The technique overcomes traditional classifier limitations. An insightful analysis of the performance leads to discover a unique intrusion detection tool, applicable in the presence of different tunneled applications.
Standard of Care Versus Metastases-directed Therapy for PET-detected Nodal Oligorecurrent Prostate Cancer Following Multimodality Treatment: A Multi-institutional Case-control Study.

Most prostate cancer (PCa) patients with a biochemical failure following primary multimodality treatment (surgery and postoperative radiotherapy) relapse in the nodes.



To perform a matched-case analysis in men with lymph node recurrent PCa comparing standard of care (SOC) with metastasis-directed therapy (MDT).



PCa patients with a prostate-specific antigen (PSA) progression following multimodality treatment were included in this retrospective multi-institutional analysis.



The SOC cohort (n=1816) received immediate or delayed androgen deprivation therapy administered at PSA progression. The MDT cohort (n=263) received either salvage lymph node dissection (n=166) or stereotactic body radiotherapy (n=97) at PSA progression to a positron emission tomography-detected nodal recurrence.



The primary endpoint, cancer-specific survival (CSS), was analyzed using the Kaplan-Meier method, log-rank test, Cox proportional hazards models, and propensity score-matched analyses.



At a median follow-up of 70 (interquartile range: 48-98) mo, MDT was associated with an improved CSS on univariate (p=0.029) and multivariate analysis (hazard ratio: 0.33, 95% confidence interval [CI]: 0.17-0.64) adjusted for the year of radical prostatectomy (RP), age at RP, PSA at RP, time from RP to PSA progression, Gleason score, surgical margin status, pT- and pN-stage. In total, 659 men were matched (3:1 ratio). The 5-yr CSS was 98.6% (95% CI: 94.3-99.6) and 95.7% (95% CI: 93.2-97.3) for MDT and SOC, respectively (p=0.005, log-rank). The main limitations of our study are its retrospective design and lack of standardization of systemic treatment in the SOC cohort.



MDT for nodal oligorecurrent PCa improves CSS as compared with SOC. These retrospective data from a multi-institutional pooled analysis should be considered as hypothesis-generating and inform future randomized trials in this setting.



Prostate cancer patients experiencing a lymph node recurrence might benefit from local treatments directed at these lymph nodes.
A Framework for Effective Big data Analytics for Decision Support Systems

Supporting decision makers requires a good understanding of the various elements that affect the outcomes of a decision. Decision Support Systems have provided decision makers with such insights throughout its history of usage with varying degrees of success. The availability of data sources was a main limitation to what decision support systems can do. Therefore, with the advent of improved analytical methods for Big data sources new opportunities have emerged that can possibly enhance how decision makers analyze their problem and arrive at decisions using information systems. This paper analyzed current related works on both Big data and decision support systems to identify clear elements and factors relevant to the subject and identifying possible ways to enhance their joint usage. Finally, the paper proposes a framework that integrates the key components needed to ensure the quality and relevance of data being analyzed by decision support systems while providing the benefits of insights generated over time from past decisions and positive
Minimal Sufficient Explanations for Factored Markov Decision Processes

Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for courseadvising through a user study involving students.
Sorted random projections for robust rotation-invariant texture classification

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS. & 2011 Elsevier Ltd. All rights reserved.
An Efficient and Reliable Reservation Algorithm for Mobile Transactions

In a mobile computing environment, a user carrying a portable computer can execute a mobile t11m,,· action by submitting the ope.rations of the transaction to distributed data servers from different locations. M a result of this mobility, the operations of the transaction may be executed at different servers. The distribution oC operations implies that the transmission of messages (such as those involved in a two phase commit protocol) may be required among these data servers in order to coordinate the execution ofthese operations. In this paper, we will address the distribution oC operations that update partitioned data in mobile environments. We show that, for operations pertaining to resource allocation, the message overhead (e.g., for a 2PC protocol) introduced by the distribution of operations is undesirable and unnecessary. We introduce a new algorithm, the RenlnJation Algorithm (RA), that does not necessitate the incurring of message overheads Cor the commitment of mobile transactions. We address two issues related to the RA algorithm: a termination protocol and a protocol for non_partition.commutotive operation". We perform a comparison between the proposed RA algorithm and existing solutions that use a 2PC protocol.
The neonatal cerebellum: the highest level of glucocorticoid receptors in the brain.

We investigated the postnatal development of glucocorticoid receptors (GR) in rat cerebral cortex, hippocampus, olfactory bulbs and cerebellum during a period when cell proliferation, including neurogenesis in the latter 3 brain parts, is intensive. The concentration of GR was determined as the specific binding of [3H]dexamethasone to the cytosol stabilized against the inactivation of unoccupied receptor molecules by sodium molybdate. Unoccupied GR levels were found to increase during the first week, most rapidly in the cerebellum, where it reached the maximum during the second week. Specific binding of [3H]dexamethasone to the cerebellar nuclear fraction has already been present at days 8 and 15. By the end of the second week the cytosolic unoccupied GR begin to decline in the cerebellum. Total receptor binding, however, estimated in adrenalectomized animals does not decline. On the other hand, at day 8 adrenalectomy did not reveal more receptors than obtained in intact animals. Large numbers of GR in the neonatal cerebellum may specifically mediate the adverse effects of glucocorticoids on the cerebellar development during the period when the intensive morphogenetic processes take place in this part of the brain.
A Double Error Correction Code for 32-Bit Data Words With Efficent Decoding

There has been recent interest on designing double error correction (DEC) codes for 32-bit data words that support fast decoding as they can be useful to protect memories. To that end, solutions based on orthogonal Latin square codes have been recently presented that achieve fast decoding but require a large number of parity check bits. In this letter, a DEC code derived from difference set codes is presented. The proposed code is able to reduce the number of parity check bits needed at the cost of a slightly more complex decoding. Therefore, it provides memory designers with an additional option that can be useful when making trade-offs between memory size and speed.
Giant Condyloma Acuminatum of Vulva in an HIV-Infected Woman

First described in 1925, giant condyloma acuminatum also known as Buschke-Löwenstein tumor (BLT) is a benign, slow-growing, locally destructive cauliflower-like lesion usually in the genital region. The disease is usually locally aggressive and destructive with a potential for malignant transformation. The causative organism is human papilloma virus. The most common risk factor is immunosuppression with HIV; however, any other cause of immunodeficiency can be a predisposing factor. We present a case of 33-year-old female patient, a known HIV patient on antiretroviral therapy for ten months. She presented with seven-month history of an abnormal growth in the genitalia that was progressive accompanied with foul smelling yellowish discharge and friable. Surgical excision was performed successfully. Pap smear of the excised tissue was negative. Despite being a rare condition, giant condyloma acuminatum is relatively common in HIV-infected patients.
Research Directions on Semantic Web and Education

Educational systems are gradually incorporating semantic web technologies aiming to provide a more adaptable, personalized and intelligent learning environment. In fact, there is a significant interest within the AIED community about the evolution of e-learning in this direction. This is driven by the hope that the use of Semantic Web (SW) technologies in educational systems can help the accomplishment of AAAL: Anytime, Anywhere, Anybody Learning where most of the WWW resources are reusable learning objects supported by standard technologies and learning is facilitated by intelligent pedagogical agents. Motivated by this trend, our paper presents an overview of the field of the Semantic Web in Education considering some of the main research challenges, architecture and standards, and showing how the use of ontologies can enhance the potential for making AAAL really meaningful to the learners in the SW era.
SLAW: A New Mobility Model for Human Walks

Simulating human mobility is important in mobile networks because many mobile devices are either attached to or controlled by humans and it is very hard to deploy real mobile networks whose size is controllably scalable for performance evaluation. Lately various measurement studies of human walk traces have discovered several significant statistical patterns of human mobility. Namely these include truncated power-law distributions of flights, pause-times and inter-contact times, fractal way-points, and heterogeneously defined areas of individual mobility. Unfortunately, none of existing mobility models effectively captures all of these features. This paper presents a new mobility model called SLAW (Self-similar Least Action Walk) that can produce synthetic walk traces containing all these features. This is by far the first such model. Our performance study using using SLAW generated traces indicates that SLAW is effective in representing social contexts present among people sharing common interests or those in a single community such as university campus, companies and theme parks. The social contexts are typically common gathering places where most people visit during their daily lives such as student unions, dormitory, street malls and restaurants. SLAW expresses the mobility patterns involving these contexts by fractal waypoints and heavy-tail flights on top of the waypoints. We verify through simulation that SLAW brings out the unique performance features of various mobile network routing protocols.
Face Recognition and Privacy in the Age of Augmented Reality

In recent years, massive amounts of identified and unidentified facial data have become available—often publicly so—through Web 2.0 applications. So have also the infrastructure and technologies necessary to navigate through those data in real time, matching individuals across online services, independently of their knowledge or consent. In the literature on statistical re-identification [5, 6], an identified database is pinned against an unidentified database in order to recognize individuals in the latter and associate them with information from the former. Many online services make available to visitors identified facial images: social networks such as Facebook and LinkedIn, online services such as Amazon.com profiles, or organizational rosters. Consider Facebook, for example. Most active Facebook users (currently estimated at 1.35 billion monthly active users worldwide [7], with over 250 billion photos uploaded photos ) use photos of themselves as their primary profile image. These photos are often identifiable: Facebook has pursued a ‘real identity’ policy, under which members are expected to appear on the network under their real names under penalty of account cancellation . Using tagging features and login security questions, Facebook has encouraged users to associate their and their friends’ names to uploaded photos. Facebook photos are also frequently publicly available. Primary profile photos must be shared with strangers un-
Systematic Testing of Convolutional Neural Networks for Autonomous Driving

We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles. Our analysis procedure comprises an image generator that produces synthetic pictures by sampling in a lower dimension image modification subspace and a suite of visualization tools. The image generator produces images which can be used to test the CNN and hence expose its vulnerabilities. The presented framework can be used to extract insights of the CNN classifier, compare across classification models, or generate training and validation datasets.
Improving Recurrent Neural Networks For Sequence Labelling

In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.
The Impact of Data Aggregation in Wireless Sensor Networks

Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Datacentric mechanisms that perform in-network aggregation of data are needed in this setting for energy-efficient information flow. In this paper we model data-centric routing and compare its performance with traditional end-toend routing schemes. We examine the impact of sourcedestination placement and communication network density on the energy costs and delay associated with data aggregation. We show that data-centric routing offers significant performance gains across a wide range of operational scenarios. We also examine the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.
Blob detection and filtering for character segmentation of license plates

This paper presents a character segmentation method to address automatic number plate recognition problem. The method considered pixel intensity, character appearance, and arrangement of characters altogether to segment character regions. The method firstly discovers candidate blobs of characters by using connected component analysis and appearance-based character detection. A character recognizer is used for removing redundant and noisy blobs. Then, a trained classifier selects character blobs among the candidates by examining arrangement of the blobs. Experimental results show an achievement of 98.3% of segmentation rate, which prove the effectiveness of our method.
Coulomb crystallization of highly charged ions

Control over the motional degrees of freedom of atoms, ions, and molecules in a field-free environment enables unrivalled measurement accuracies but has yet to be applied to highly charged ions (HCIs), which are of particular interest to future atomic clock designs and searches for physics beyond the Standard Model. Here, we report on the Coulomb crystallization of HCIs (specifically 40Ar13+) produced in an electron beam ion trap and retrapped in a cryogenic linear radiofrequency trap by means of sympathetic motional cooling through Coulomb interaction with a directly laser-cooled ensemble of Be+ ions. We also demonstrate cooling of a single Ar13+ ion by a single Be+ ion—the prerequisite for quantum logic spectroscopy with a potential 10−19 accuracy level. Achieving a seven-orders-of-magnitude decrease in HCI temperature starting at megakelvin down to the millikelvin range removes the major obstacle for HCI investigation with high-precision laser spectroscopy.
Software Pipelined Execution of Stream Programs on GPUs

The StreamIt programming model has been proposed to exploit parallelism in streaming applications on general purpose multi-core architectures. This model allows programmers to specify the structure of a program as a set of filters that act upon data, and a set of communication channels between them. The StreamIt graphs describe task, data and pipeline parallelism which can be exploited on modern Graphics Processing Units (GPUs), as they support abundant parallelism in hardware. In this paper, we describe the challenges in mapping StreamIt to GPUs and propose an efficient technique to software pipeline the execution of stream programs on GPUs. We formulate this problem --- both scheduling and assignment of filters to processors --- as an efficient Integer Linear Program (ILP), which is then solved using ILP solvers. We also describe a novel buffer layout technique for GPUs which facilitates exploiting the high memory bandwidth available in GPUs. The proposed scheduling utilizes both the scalar units in GPU, to exploit data parallelism, and multiprocessors, to exploit task and pipeline parallelism. Further it takes into consideration the synchronization and bandwidth limitations of GPUs, and yields speedups between 1.87X and 36.83X over a single threaded CPU.