LLukas22/scidocs huggingface dataset:
A Survey of software Reuse Libraries

The study of storage and retrieval methods of software assets in software libraries gives rise to a number of paradoxes: While this subject has been under investigation for nearly two decades, it still remains an active area of research in software reuse and software engineering; this can be explained by the observation that new technologies (such as the internet, the world wide web, object-oriented programming) keep opening new opportunities for better asset packaging, better library organizations, and larger scale libraries – thereby posing new technical challenges. Also, while many sophisticated solutions have been proposed to this problem, the state of the practice in software reuse is characterized by the use of ad-hoc, low-tech methods; this can be explained by the observation that most existing solutions are either too ineffective to be useful or too intractable to be usable. Finally, while it is difficult to imagine a successful software reuse program without a sophisticated, well-tuned, systematic procedure for software component storage and retrieval, it seems many successful software reuse experiments rely on trivial methods of component storage and retrieval; this can be explained by the observation that, in the current state of the practice, software libraries are not the bottleneck of the software reuse process. This paper presents a survey of methods of storage and retrieval of software assets in software libraries. In addition to a review of existing research efforts, the paper makes two contributions. First, a definition of (presumably) orthogonal attributes of storage and retrieval methods; these attributes are used, in turn, to classify existing methods into six broad classes. Second, a definition of (presumably) orthogonal assessment criteria, which include technical, managerial and human factors; these criteria afford us an exhaustive and uniform basis for assessing and comparing individual methods and classes of methods.
Power reflection coefficient analysis for complex impedances in RFID tag design

Kurokawa's method of calculating the power reflection coefficient from the Smith chart in the situation when one complex impedance is directly connected to another is applied to passive RFID tag design, where power reflection is important, as it determines the tag characteristics. The performance analysis of a specific RFID tag is presented together with experimental data, which is in close agreement with the theory.
Therapeutic strategies in rheumatoid arthritis over a 40-year period.

OBJECTIVE
To examine trends in therapeutic strategies and to identify the determinants of starting disease modifying antirheumatic drug (DMARD) therapy over a 40-year period in a population based inception cohort of patients with rheumatoid arthritis (RA).


METHODS
A population based inception cohort was assembled from among all Rochester, Minnesota, residents aged > or = 18 years who were first diagnosed with RA (1987 American College of Rheumatology criteria) between January 1, 1955, and January 1, 1995. All subjects were followed longitudinally through their complete medical records until death, migration from Olmsted County, or date of abstraction (January 1, 2001, to January 1, 2003). Drug exposure data were collected on all DMARD and corticosteroid regimens. Time to DMARD initiation was examined using the Kaplan-Meier method. The influence of calendar time and disease characteristics on time from incidence to first DMARD therapy and the number of DMARD regimens were analyzed using Cox regression and proportional odds models, respectively.


RESULTS
The study population comprised 603 patients (73% female) with a mean age of 58 years and a mean followup of 15 years. At 2 years after RA onset, 26% of patients in the 1955-74 cohort, 40% in the 1975-84 cohort, and 70% in the 1985-94 cohort had received a DMARD (log-rank p < 0.001). Age, rheumatoid factor (RF) positivity, erythrocyte sedimentation rate, large joint swelling, rheumatoid nodules, and destructive changes on radiographs were significantly associated with time to first DMARD regimen after adjustment for calendar time and sex. Patients who were older and RF positive and who did not receive CS were more likely to have received more DMARD regimens.


CONCLUSION
Time to initiation of DMARD therapy has shortened markedly over the past 3-4 decades. These changes in management of early RA provide evidence for the translation of scientific evidence into clinical practice in rheumatology. Age and various disease characteristics are significantly associated with initiation and the number of DMARD regimens used. These should be considered as confounders when examining the effect of early DMARD treatment on disease progression and mortality.
Mobile peer-to-peer video streaming over information-centric networks

Information Centric Networking (ICN) is a network paradigm alternative to the classic host-centric communication model: it provides users with content exposed as names, instead of providing communication channels between hosts. In this paper, we present a peer-to-peer application for live streaming of video content encoded at multiple bit rates. The application enables a small set of neighbouring cellular/Wi-Fi devices to increase the quality of video playback by using the Wi-Fi network to share the portion of the live stream downloaded by each peer via the cellular network. The application exploits the main functionalities of ICN: routing by name, in network caching and multicast delivery. Our work includes the implementation of a Java prototype of the application on a test-bed composed by Linux machines running the CCNx tool and streaming MPEG-DASH videos. We measured the performance of our solution and verified on the field that ICN simplifies the development of applications, as it provides built-in functions, which would be much more difficult to implement by relying on classical TCP/IP tools only. 2015 Elsevier B.V. All rights reserved.
Trial of CBT for impulse control behaviors affecting Parkinson patients and their caregivers.

OBJECTIVE
To test the effects of a novel cognitive-behavioral therapy (CBT)-based intervention delivered by a nurse therapist to patients with Parkinson disease (PD) with clinically significant impulse control behaviors (ICB).


METHODS
This was a randomized controlled trial comparing up to 12 sessions of a CBT-based intervention compared to a waiting list control condition with standard medical care (SMC). A total of 27 patients were randomized to the intervention and 17 to the waiting list. Patients with a Mini-Mental State Examination score of <24 were excluded. The coprimary outcomes were overall symptom severity and neuropsychiatric disturbances in the patients and carer burden and distress after 6 months. Secondary outcome measures included depression and anxiety, marital satisfaction, and work and social adjustment in patients plus general psychiatric morbidity and marital satisfaction in carers.


RESULTS
There was a significant improvement in global symptom severity in the CBT intervention group vs controls, from a mean score consistent with moderate to one of mild illness-related symptoms (χ(2) = 16.46, p < 0.001). Neuropsychiatric disturbances also improved significantly (p = 0.03), as did levels of anxiety and depression and adjustment. Measures of carer burden and distress showed changes in the desired direction in the intervention group but did not change significantly. General psychiatric morbidity did improve significantly in the carers of patients given CBT.


CONCLUSIONS
This CBT-based intervention is the first to show efficacy in ICB related to PD in terms of patient outcomes. The hoped-for alleviation of carer burden was not observed. The study demonstrates the feasibility and potential benefit of a psychosocial treatment approach for these disturbances at least in the short term, and encourages further larger-scale clinical trials.


CLASSIFICATION OF EVIDENCE
The study provides Class IV evidence that CBT plus SMC is more effective than SMC alone in reducing the severity of ICB in PD, based upon Clinical Global Impression assessment (χ(2) = 16.46, p < 0.001): baseline to 6-month follow-up, reduction in symptom severity CBT group, 4.0-2.5; SMC alone group, 3.7-3.5.
Detecting opinion spams and fake news using text classification

*Correspondence Traore, Issa, ECE Department, University of Victoria, Victoria, BC, Canada. Email: itraore@ece.uvic.ca In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n-gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state-of-the-art methods.
Deep Learning in Software Engineering

Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future. Introduction Driven by the success of deep learning in data mining and pattern recognition, recent years have witnessed an increasing trend for industrial practitioners and academic researchers to integrate deep learning into SE tasks [1]-[3]. For typical SE tasks, deep learning helps SE participators extract requirements from natural language text [1], generate source code [2], predict defects in software [3], etc. As an initial statistics of research papers in SE in this study, deep learning has achieved competitive performance against previous algorithms on about 40 SE tasks. There are at least 98 research papers published or accepted in 66 venues, integrating deep learning into SE tasks. Despite the encouraging amount of papers and venues, there exists little overview analysis on deep learning in SE, e.g., the common way to integrate deep learning into SE, the SE phases facilitated by deep learning, the interests of SE practitioners on deep learning, etc. Understanding these questions is important. On the one hand, it helps practitioners and researchers get an overview understanding of deep learning in SE. On the other hand, practitioners and researchers can develop more practical deep learning models according to the analysis. For this purpose, this study conducts a bibliography analysis on research papers in the field of SE that use deep learning techniques. In contrast to literature reviews,
Cooperative learning techniques in CS1: design and experimental evaluation

A set of cooperative learning exercises were designed for use in a Java-based CS1 course. The exercises used specific roles to focus students' attention on key concepts of the Java language, and on key mental processes of programming and problem solving. A controlled experiment was conducted to evaluate the effectiveness of this approach. The results show that the cooperative learning experience had a significant positive effect on student performance, and may have been of special benefit to women and minority students.
Sensor fusion-based lane detection for LKS+ACC system

−This paper discusses the market trends and advantages of a safety system integrating LKS (Lane Keeping System) and ACC (Adaptive Cruise Control), referred to as the LKS+ACC system, and proposes a method utilizing the range data from ACC for the sake of lane detection. The overall structure of lane detection is the same as the conventional method using monocular vision: EDF (Edge Distribution Function)-based initialization, sub-ROI (Region Of Interest) for left/right and distance-based layers, steerable filter-based feature extraction, and model fitting in each sub-ROI. The proposed method adds only the system for confining lane detection ROI to free space that is established by range data. Experimental results indicate that such a simple adaptive ROI can overcome occlusion of lane markings and disturbance of neighboring vehicles.
HEF1, a novel target of Wnt signaling, promotes colonic cell migration and cancer progression

Misregulation of the canonical Wnt/β-catenin pathway and aberrant activation of Wnt signaling target genes are common in colorectal cancer (CRC) and contribute to cancer progression. Altered expression of human enhancer of filamentation 1 (HEF1; also known as NEDD9 or Cas-L) has been implicated in progression of melanoma, breast, and CRC. However, the regulation of HEF1 and the role of HEF1 in CRC tumorigenesis are not fully understood. We here identify HEF1 as a novel Wnt signaling target. The expression of HEF1 was upregulated by Wnt-3a, β-catenin, and Dvl2 in a dose-dependent manner, and was suppressed following β-catenin downregulation by shRNA. In addition, elevated HEF1 mRNA and protein levels were observed in CRC cell lines and primary tumor tissues, as well as in the colon and adenoma polyps of ApcMin/+ mice. Moreover, HEF1 levels in human colorectal tumor tissues increased with the tumor grade. Chromatin immunoprecipitation (ChIP) assays and promoter analyses revealed three functional T-cell factor (TCF)-binding sites in the promoter of HEF1 responsible for HEF1 induction by Wnt signaling. Ectopic expression of HEF1 increased cell proliferation and colony formation, while downregulation of HEF1 in SW480 cells by shRNA had the opposite effects and inhibited the xenograft tumor growth. Furthermore, overexpression of HEF1 in SW480 cells promoted cell migration and invasion. Together, our results determined a novel role of HEF1 as a mediator of the canonical Wnt/β-catenin signaling pathway for cell proliferation, migration, and tumorigenesis, as well as an important player in colorectal tumorigenesis and progression. HEF1 may represent an attractive candidate for drug targeting in CRC.
Major eras of Design for Test

From writing functional tests to managing design groups to managing major businesses, Dr. Rhines has been personally involved in the evolution of Design for Test. From this perspective, he will describe the driving forces and technologies that changed the way we design products to make them testable, and what will drive change in the future.
Privacy-preserving generative deep neural networks support clinical data sharing

Though it is widely recognized that data sharing enables faster scientific progress, the sensible need to protect participant privacy hampers this practice in medicine. We train deep neural networks that generate synthetic subjects closely resembling study participants. Using the SPRINT trial as an example, we show that machine-learning models built from simulated participants generalize to the original dataset. We incorporate differential privacy, which offers strong guarantees on the likelihood that a subject could be identified as a member of the trial. Investigators who have compiled a dataset can use our method to provide a freely accessible public version that enables other scientists to perform discovery-oriented analyses. Generated data can be released alongside analytical code to enable fully reproducible workflows, even when privacy is a concern. By addressing data sharing challenges, deep neural networks can facilitate the rigorous and reproducible investigation of clinical datasets. Introduction Sharing individual-level data from clinical studies remains challenging. The status quo often requires scientists to establish a formal collaboration and execute extensive data usage agreements before sharing data. These requirements slow or even prevent data sharing between researchers in all but the closest collaborations. Recent initiatives have begun to address cultural challenges around data sharing. The New England Journal of Medicine recently held the Systolic Blood Pressure Trial (SPRINT) Data Analysis Challenge to examine possible benefits of clinical trial data sharing (1, 2). The SPRINT clinical trial examined the efficacy of intensive lowering of systolic blood pressure (<120 mmHg) compared with treatment to a standard systolic blood pressure goal (<140 mmHg). Intensive blood pressure lowering resulted in fewer cardiovascular events and the trial was stopped early. Reanalysis of the challenge data led to the development of personalized treatment scores (3) and decision support systems (4), in addition to a more specific analysis of blood pressure management in participants with chronic kidney disease (5). Initiatives such as the SPRINT Data Analysis Challenge have begun to address cultural norms. Even for this effort which focused on data sharing, investigators were required to execute data use agreements that included clauses to maintain security and prohibit re-identification or sharing. . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/159756 doi: bioRxiv preprint first posted online Jul. 5, 2017;
Metastatic Crohn's disease involving the penis

Metastatic Crohn's disease is a rare complication in Crohn's disease and there have been only several cases of metastatic Crohn's disease involving the penis. We report one such case. A 22-year-old male student developed anal pain and alternative constipation and diarrhea in December, 1985, followed by diarrhea and lower abdominal pain in January, 1986. He was diagnosed as having Crohn's disease of ileocolitis type. He was admitted to our hospital in July, 1987 because of exacerbation of Crohn's disease. He had anal tags. Soon after admission, two red swollen lesions with central ulcer and erosions were demonstrated at the eversion of the foreskin adjacent to coronal sulcus. Histology of the lesions revealed granulomas with epithelioid cells and giant cells. The lesion responded to a topical steroid. Eight cases of metastatic Crohn's disease involving the penis are briefly reviewed.
Transcriptome analysis reveals global regulation in response to CO2 supplementation in oleaginous microalga Coccomyxa subellipsoidea C-169

BACKGROUND
Microalgae are emerging as suitable feedstock for renewable biofuel production and providing a promising way to alleviate green house gas CO2. Characterizing the metabolic pathways involved in the biosynthesis of energy-rich compounds and their global regulation upon elevated CO2 is necessary to explore the mechanism underlying rapid growth and lipid accumulation, so as to realize the full potential of these organisms as energy resources.


RESULTS
In the present study, 2 and 5 % CO2 increased growth rate and lipid accumulation in autotrophically cultured green alga Coccomyxa subellipsoidea C-169. Overall biomass productivity as 222 mg L(-1) day(-1) and fatty acid content as 48.5 % dry cell weight were attained in 2 % CO2, suggesting C-169 as a great candidate for lipid production via CO2 supplementation. Transcriptomic analysis of 2 % against 0.04 % CO2-cultured C-169 unveiled the global regulation of important metabolic processes. Other than enhancing gene expression in the Calvin cycle, C-169 upregulated the expression of phosphoenolpyruvate carboxylase, pyruvate carboxylase and carbamoyl-phosphate synthetase II to enhance the anaplerotic carbon assimilation reactions upon elevated CO2. Upregulation of ferredoxin and ferredoxin-NADP(+) reductase implied that plentiful energy captured through photosynthesis was transferred through ferredoxin to sustain rapid growth and lipid accumulation. Genes involved in the glycolysis, TCA cycle and oxidative phosphorylation were predominantly upregulated presumably to provide abundant intermediates and metabolic energy for anabolism. Coordinated upregulation of nitrogen acquisition and assimilation genes, together with activation of specific carbamoyl-phosphate synthetase and ornithine pathway genes, might help C-169 to maintain carbon/nitrogen balance upon elevated CO2. Significant downregulation of fatty acid degradation genes, as well as the upregulation of fatty acid synthesis genes at the later stage might contribute to the tremendous lipid accumulation.


CONCLUSION
Global and collaborative regulation was employed by C-169 to assimilate more carbon and maintain carbon/nitrogen balance upon elevated CO2, which provide abundant carbon skeleton and affluent metabolic energy to sustain rapid growth and lipid accumulation. Data here for the first time bring significant insights into the regulatory profile of metabolism and acclimation to elevated CO2 in C-169, which provide important information for future metabolic engineering in the development of sustainable microalgae-based biofuels.
Global event properties in proton-proton physics with ALICE

ALICE is a unique opportunity for studying low Pt physics and minimum bias events, and consequently for hunting substructures in strong interactions. The general question concerning global event properties in pp and ppbar physics is indeed whether substructures can be seen in the data in a model-independent way. In other terms, can we identify the interface between perturbative and non-perturbative regimes?
Higher dose imatinib for children with de novo chronic phase chronic myelogenous leukemia: a report from the Children's Oncology Group.

PURPOSE
To determine the efficacy of imatinib in children with newly diagnosed chronic phase (CP) chronic myelogenous leukemia (CML).


METHODS
This was an open label, multi-center phase II clinical trial. Courses were defined as consecutive 28-day intervals. Oral imatinib was administered daily at 340 mg/m² without interruption in the absence of toxicity.


RESULTS
Fifty-one children received 978 28-day courses of imatinib. The most common toxicities encountered were hematologic. Forty-one patients (80%) achieved a complete hematologic response by the end of course 2. Nineteen children (38%) obtained a complete cytogenetic response (CCyR) at the end of course 3. Overall, 72% achieved CCyR at a median time of 5.6 months. The rate of complete molecular response (>3 log reduction) was 27%. Progression-free and overall survival at 3 years were 72%  ± 6.4% and 92%  ± 3.9%, respectively.


CONCLUSIONS
Daily oral imatinib at a dose of 340 mg/m² is well tolerated in children. In addition, imatinib therapy is effective in inducing a high percent of hematologic, cytogenetic and molecular responses, comparable to adults with CML. (This study was registered at ClinicalTrials.gov under identifier NCT00030394.).