nils-herrmann/clean_scirepeval huggingface dataset:
Costs and obstacles encountered in technical debt management A case study
Venue: J. Syst. Softw.

Various costs and a cost pattern of TD management were identified.TD management has a large up front cost, with very low ongoing costs over time.Obstacles to TD management, such as lack of process integration, were discovered.Strategies are proposed to improve adoption of TD management in software projects.Insights are also provided for research on TD management. Technical debt (TD) is a metaphor that characterizes the effect of immature software artifacts. The costs and benefits of TD, along with the uncertainty of its interest repayment, provide leverage for software managers, but also could lead to problems such as increased costs and lower quality during maintenance if it is left unattended. Therefore, effective approaches to TD management are needed by software practitioners. As one of our series of studies on TD management, this study was originally designed to reveal the cost side of explicit TD management. The study design required applying a simple proposed TD management approach to the subject project and then collecting cost information. Not surprisingly, we observed some deviation of the actual management process from our proposed one, which provided us with an opportunity to investigate the obstacles to explicitly managing TD. We also identified some costs and cost patterns related to TD management. Based on the insights gained from this study, we further propose strategies to overcome the obstacles and improve the application of TD management in practice.
A Flowchart for Rapid Technical Debt Management Decision Making
Venue: J. Softw.

Technical debt is known as delaying certain software maintenance tasks during software development life cycle to meet development goals in a short run. Such compromise increases maintenance cost in a way like growing financial interest in later development of software life cycle. Despite all the negative impact induced by technical debt, enterprise architects have to leverage between incurring technical debts and meeting short term customer and financial related goals while making the decision for whether to pay the technical debt. However, the decision making, management, and tracing for technical debt is never formalized and surprisingly done by ad hoc. This study is aimed for constructing a step by step decision making map which helps the decision makers consider all possible impact factors. More importantly, such standardized process would improve the efficiency, consistency, stakeholders' communication, and documentation for technical debt handling throughout the software life cycle. In order to evaluate the effectiveness of the proposed decision making map and the according practice, a survey is composed as the appendix of this research report. This preliminary work, although with resource constraint, is a great foundation to build on for further study.
Multi Dimensional, Phrase Based Summarization in Text Cubes
Venue: IEEE Data Eng. Bull.

To systematically analyze large numbers of textual documents, it is often desirable to manage documents (and their metadata) in a multi dimensional text database (Text Cube) Such structure provides flexibility of understanding local information with different granularities. Moreover, the contextualized analysis derived from cube structure often yields comparative insights. To quickly digest the content of subsets of documents in the multi dimensional context, we study the problem of phrase based summarization of a subset of documents of interest. We propose a new phrase ranking measure to leverage the relation between document subsets induced by multi dimensional context and identify phrases that truly distinguish the queried subset of documents from neighboring subsets (i.e. background) Our quality evaluation suggests the new measure involving dynamic, query dependent background generation is more effective than previous measures using the whole corpus as a static background for finding representative phrases. Computing this measure is more expensive due to the need of access to many subsets of documents to answer one query. We develop a cube based analytical platform that implements an efficient solution by materializing a deliberately selected part of statistics, and using these statistics to perform online query processing within a constant latency constraint. Our experiments in a large news dataset demonstrate the efficiency in both query processing time and storage cost.
A two tier network based intrusion detection system architecture using machine learning approach
Venue: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

Intrusion detection systems are systems that can detect any kind of malicious attacks, corrupted data or any kind of intrusion that can pose threat to our systems. In our paper, we would like to present a novel approach to build a network based intrusion detection system using machine learning approach. We have proposed a two tier architecture to detect intrusions on network level. Network behaviour can be classified as misuse detection and anomaly detection. As our analysis depends on the network behaviour, we have considered data packets of TCP/IP as our input data. After, pre processing the data by parameter filtering, we build a autonomous model on training set using hierarchical agglomerative clustering. Further, data gets classified as regular traffic pattern or intrusions using KNN classification. This reduces cost overheads. Misuse detection is conducted using MLP algorithm. Anomaly detection is conducted using Reinforcement algorithm where network agents learn from the environment and take decisions accordingly. The TP rate of our architecture is 0.99 and false positive rate is 0.01. Thus, our architecture provides a high level of security by providing high TP and low false positive rate. And, it also analyzes the usual network patterns and learns incrementally (to build autonomous system) to separate normal data and threats.
Modeling Multi factor Sequential User Behavior Data over Social Networks

Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time varying user behavior is commonly influenced by multiple factors: user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesian model HES to model dynamic user behavior data. We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.
Engineering Parallel Algorithms for Community Detection in Massive Networks
Venue: IEEE Transactions on Parallel and Distributed Systems

The amount of graph structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering) Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism will be necessary to scale to the data volume of real world applications. We address the deficit in computing capability by a flexible and extensible community detection framework with shared memory parallelism. Within this framework we design and implement efficient parallel community detection heuristics: A parallel label propagation scheme; the first large scale parallelization of the well known Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. We also compare our implementations with state of the art competitors. The processing rate of our fastest algorithm often reaches 50 M edges/second. We recommend the parallel Louvain method and our variant with refinement as both qualitatively strong and fast. Our methods are suitable for massive data sets with billions of edges.
Parallel algorithms for tensor completion in the CP format
Venue: Parallel Comput.

Novel parallel algorithms for tensor completion problems, with applications to recommender systems and function learning.Parallelization strategy offers greatly reduced memory requirements compared to previously published matrix equivalents.Convergence results for both alternating least squares and cyclic coordinate descent. Low rank tensor completion addresses the task of filling in missing entries in multi dimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large scale datasets and applications that feature high dimensions, the development of distributed algorithms is central. In this work, we propose novel, highly scalable algorithms based on a combination of the canonical polyadic (CP) tensor format with block coordinate descent methods. Although similar algorithms have been proposed for the matrix case, the case of higher dimensions gives rise to a number of new challenges and requires a different paradigm for data distribution. The convergence of our algorithms is analyzed and numerical experiments illustrate their performance on distributed memory architectures for tensors from a range of different applications.
Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is productbased sentiment summarization of multi documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets" We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state of the art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features. Sentiment classification aims to automatically predict sentiment polarity (e.g. positive or negative) of users publishing sentiment data (e.g. reviews, blogs) Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time consuming and ex pensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain.
Energy Efficient Associative Memory Based on Neural Cliques
Venue: IEEE Transactions on Circuits and Systems II: Express Briefs

Traditional memories use an address to index the stored data. Associative memories rely on a different principle: Part of previously stored data are used to retrieve the remaining part. They are widely used, for instance, in network routers for packet forwarding. A classical way to implement such memories is content addressable memory (CAM) Since its operation is fully parallel, the response is obtained in a single clock cycle. However, this comes at the cost of energy consumption. This brief proposes to use a recent type of neural networks as a novel way to implement associative memories. Owing to an efficient retrieval algorithm guided by the information being searched, they are a good candidate for low power associative memory. Compared to the CAM based system, the analog implementation of 12 kb neuro inspired memory designed for 65 nm CMOS technology offers 48% energy savings.
Depth Transfer: Depth Extraction from Videos Using Nonparametric Sampling

In this chapter, a technique that automatically generates plausible depth maps from videos using nonparametric depth sampling is discussed. We demonstrate this method in cases where existing methods fail (nontranslating cameras and dynamic scenes) This technique is applicable to single images as well as videos. For videos, local motion cues are used to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, a Microsoft Kinect based system is developed to collect a large dataset containing stereoscopic videos with known depths, and this depth estimation technique outperforms the state of the art on benchmark databases. This method can be used to automatically convert a monoscopic video into stereo for 3D visualization demonstrated through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
CryptoNets: applying neural networks to encrypted data with high throughput and accuracy
Venue: ICML 2016

Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud based machine learning solutions for such tasks. In this work, we will present a method to convert learned neural networks to CryptoNets, neural networks that can be applied to encrypted data. This allows a data owner to send their data in an encrypted form to a cloud service that hosts the network. The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form. These encrypted predictions can be sent back to the owner of the secret key who can decrypt them. Therefore, the cloud service does not gain any information about the raw data nor about the prediction it made. We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make around 59000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions.
A fully differential OTA with dynamic offset cancellation in 28nm FD SOI process
Venue: Electron Technology Conference

This papers presents a classic fully differential operational transconductance amplifier (FDOTA) implemented in industrial 28 nm FD SOI (Fully Depleted SOI) technology. A novel approach to minimized the FDOTA offset voltage is proposed. The solution employs the unique feature of FD SOI technology back gate biasing combined with modern compensation methodology. The proposed method results in considerable design overhead. However, this offset cancellation approach is very effective and allows to improve FDOTA performance when classic techniques reach their limits.
Teachers' Perceptions of the Relationship between Inclusive Education and Distributed Leadership in two Primary Schools in Slovakia and New South Wales (Australia)

The academic literature on the practice of inclusive education presents diverse and at times contradictory perspectives in how it is connected to practices of distributed leadership. Depending on the approach, on the one hand, inclusive educational practice may enable distributed school leadership, while on the other hand, it may allow for hierarchical management styles if staff members do not implement inclusive practices. This paper explores how school staff members perceive and understand the relationship between practices of inclusive education and distributed leadership in two public primary schools: one in New South Wales (Australia) and one in Slovakia. These two schools were identified by external informants as good practice examples of inclusive education. Using qualitative research methods based on interviews, this paper identifies two main understandings of this relationship. First, although distributed leadership may encourage the goals of inclusive education, it may in some circumstances also hinder their achievement. Second, distributed leadership can be constructed as an indispensable component of inclusive education, and this has implications for how the target groups of inclusive education are conceptualised. This paper also discusses the wider social and political contexts of the two primary schools and how in each case context significantly constrained and shaped understandings and practices of inclusion and distributed leadership in the practice of teachers and principals.
Natural Fibres for Sustainable Development in Fashion Industry

Ample numbers of natural fibres are available in nature from plants, animals, insects, and minerals. Each fibre is different from others, and that causes difference in their properties. Accordingly, these fibres alone, or in admixture with other fibres, are used in the design and development of specific fashion products. With time, different fashion industries have been developed all over the world for manufacturing different fibre based fashion products. There is a huge potential of value addition in these fashion industries with the intervention of newer product design. Again, in this fashion industry, apart from newer design, avoiding the use of common natural fibres (cotton, wool, silk, etc. commands more profit. Recent trends also show the use of these natural fibres for sustainable growth in this fashion industry. This chapter also deals with future aspects of the use of uncommon natural fibre for sustainable fashion industry.
Implementing intelligent physical exercise training at the workplace: health effects among office workers a randomized controlled trial
Venue: European Journal of Applied Physiology

PurposeThe aim was to assess 1 year cardiovascular health effects of Intelligent Physical Exercise Training, IPET.MethodsOffice workers from six companies were randomized 1:1 to a training group, TG (N 194) or a control group, CG (N 195) TG received 1 h supervised high intensity IPET every week within working hours for 1 year, and was recommended to perform 30 min of moderate intensity physical activity 6 days a week during leisure. The training program was based on baseline health check measures of cardiorespiratory fitness (CRF) body composition, blood pressure, blood profile, and musculoskeletal health.ResultsThere were no baseline differences between groups. CRF assessed as VO2max in absolute values and relative to body weight was (mean SD) 3.0 0.8 l/min and 35.4 10.9 ml/min/kg for females, 3.9 1.0 l/min and 37.9 11.79 ml/min/kg for males. Intention to treat analysis demonstrated a significant almost 5 increase in VO2max in TG compared with CG. A per protocol analysis of those with an adherence of =70 demonstrated a significant increase in CRF of more than 10 compared with CG, and a significant reduction in systolic blood pressure 5.3 13.7 mm Hg) compared with CG.ConclusionHigh intensity IPET combined with the recommendations of moderate intensity physical activity demonstrated significant clinical relevant improvements in CRF and systolic blood pressure. This underlines the effectiveness of health promotion by implementing physical exercise training at the workplace.
Effects on muscle strength, maximal jump height, flexibility and postural sway after soccer and Zumba exercise among female hospital employees: a 9 month randomised controlled trial
Venue: Journal of sports sciences

This 9 month randomised controlled workplace physical activity trial investigated the effects of soccer and Zumba exercise, respectively, on muscle strength, maximal jump height, sit and reach flexibility and postural sway among female workers. A total of 107 female hospital employees aged 25 63 were cluster randomised to a soccer group, a Zumba group or a control group. Training was conducted outside working hours as two to three 1 h weekly sessions the first 3 months and once a week the last 6 months. Tests were conducted at baseline, after 3 and 9 months. The soccer group improved maximal neck extension strength both after 3 (1.2 kg; P 0.05) and 9 months (1.7 kg; P 0.01) compared to the control group. The Zumba group improved maximal trunk extension strength (3.1 kg; P 0.04) after 3 months, with improvements in postural sway velocity moment 9.2 mm2/s; P 0.05) and lower limb lean mass (0.4 kg; P 0.05) after 9 months. No significant intervention effects were revealed in vertical jump height or sit and reach flexibility. The present study indicates that workplace initiated soccer and Zumba exercise may be beneficial for improvement of the neck and trunk strength, which may have preventive effects with regard to future perceived muscle pain in the respective body regions. Furthermore, the Zumba group revealed positive effects on lower limb lean mass and postural sway compared to the control group.
Cancer statistics, 2016
Venue: CA: a cancer journal for clinicians

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program) the Centers for Disease Control and Prevention (National Program of Cancer Registries) and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2016, 1,685,210 new cancer cases and 595,690 cancer deaths are projected to occur in the United States. Overall cancer incidence trends (13 oldest SEER registries) are stable in women, but declining by 3.1% per year in men (from 2009 2012) much of which is because of recent rapid declines in prostate cancer diagnoses. The cancer death rate has dropped by 23% since 1991, translating to more than 1.7 million deaths averted through 2012. Despite this progress, death rates are increasing for cancers of the liver, pancreas, and uterine corpus, and cancer is now the leading cause of death in 21 states, primarily due to exceptionally large reductions in death from heart disease. Among children and adolescents (aged birth 19 years) brain cancer has surpassed leukemia as the leading cause of cancer death because of the dramatic therapeutic advances against leukemia. Accelerating progress against cancer requires both increased national investment in cancer research and the application of existing cancer control knowledge across all segments of the population.