nils-herrmann/clean_scirepeval huggingface dataset:
Improving agile requirements: the Quality User Story framework and tool
Venue: Requirements Engineering

User stories are a widely adopted requirements notation in agile development. Yet, user stories are too often poorly written in practice and exhibit inherent quality defects. Triggered by this observation, we propose the Quality User Story (QUS) framework, a set of 13 quality criteria that user story writers should strive to conform to. Based on QUS, we present the Automatic Quality User Story Artisan (AQUSA) software tool. Relying on natural language processing (NLP) techniques, AQUSA detects quality defects and suggest possible remedies. We describe the architecture of AQUSA, its implementation, and we report on an evaluation that analyzes 1023 user stories obtained from 18 software companies. Our tool does not yet reach the ambitious 100 recall that Daniel Berry and colleagues require NLP tools for RE to achieve. However, we obtain promising results and we identify some improvements that will substantially improve recall and precision.
The 'Incidentaloma' of the Pituitary Gland

We describe a series of 18 patients with an intrasellar mass incidentally discovered by computed tomography or magnetic resonance imaging. The average size of the mass was 13 mm, with a range from 5 to 25 mm. Initial ophthalmologic examination revealed bitemporal hemianopia in 2 patients. Results of routine endocrine testing showed partial hypopituitarism in 5 patients and growth hormone hypersecretion without signs and symptoms of acromegaly in 1 patient. Four patients underwent neurosurgery. Histologically, one chondroid chordoma and three pituitary adenomas were found. In the remaining 14 patients treated conservatively, repeated computed tomography and magnetic resonance imaging revealed no significant change in tumor size at the time of follow up (median, 22 months) Our results suggest that the "incidentaloma" of the pituitary gland is a benign condition that does not necessarily require neurosurgical intervention.
Anatomy, Physiology, and Laboratory Evaluation of the Pituitary Gland.
Venue: Otolaryngologic clinics of North America

The pituitary gland functions prominently in the control of most endocrine systems in the body. Diverse processes such as metabolism, growth, reproduction, and water balance are tightly regulated by the pituitary in conjunction with the hypothalamus and various downstream endocrine organs. Benign tumors of the pituitary gland are the primary cause of pituitary pathology and can result in inappropriate secretion of pituitary hormones or loss of pituitary function. First line management of clinically significant tumors often involves surgical resection. Understanding of normal pituitary physiology and basic testing strategies to assess for pituitary dysfunction should be familiar to any skull base surgeon.
Pituitary Gland: Pituitary Hormones

The human endocrine system forms a unique complex network that includes the hypothalamus, pituitary and pineal glands, adrenal gland, thyroid gland, parathyroid glands, ovaries, testes, thymus, and islet cells in the pancreas. The pituitary gland hypophysis as a 'master gland, regulates activity of other endocrine glands and affects a variety of physiological processes throughout the body. The regulation of the pituitary gland activity and its role as a complex endocrine center of body homeostasis are based on the interactions between the hypothalamus, pituitary cells, and downstream signaling neuronal networks to the endocrine gland, producing hormones that regulate and modulate a variety of the vital body functions.
PREMM: preterm early massage by the mother: protocol of a randomised controlled trial of massage therapy in very preterm infants
Venue: BMC Pediatrics

BackgroundPreterm infants follow an altered neurodevelopmental trajectory compared to their term born peers as a result of the influence of early birth, and the altered environment. Infant massage in the preterm infant has shown positive effects on weight gain and reduced length of hospital stay. There is however, limited current evidence of improved neurodevelopment or improved attachment, maternal mood or anxiety. The aim of this study is to investigate the effects of infant massage performed by the mother in very preterm (VPT) infants. Effects on the infant will be assessed at the electrophysiological, neuroradiological and clinical levels. Effects on maternal mood, anxiety and mother infant attachment will also be measured. randomised controlled trial to investigate the effect of massage therapy in VPT infants. Sixty VPT infants, born at 28 to 32 weeks and 6 days gestational age, who are stable, off supplemental oxygen therapy and have normal cranial ultrasounds will be recruited and randomised to an intervention (infant massage) group or a control (standard care) group. Ten healthy term born infants will be recruited as a reference comparison group. The intervention group will receive standardised massage therapy administered by the mother from recruitment, until term equivalent age (TEA) The control group will receive care as usual (CAU) Infants and their mothers will be assessed at baseline, TEA, 12 months and 24 months corrected age (CA) with a battery of clinical, neuroimaging and electrophysiological measures, as well as structured questionnaires, psychoanalytic observations and neurodevelopmental assessments.DiscussionOptimising preterm infant neurodevelopment is a key aim of neonatal research, which could substantially improve long term outcomes and reduce the socio economic impact of VPT birth. This study has the potential to give insights into the mother baby relationship and any positive effects of infant massage on neurodevelopment. An early intervention such as massage that is relatively easy to administer and could alter the trajectory of preterm infant brain development, holds potential to improve neurodevelopmental outcomes in this vulnerable population.
A Deep Learning Approach for Network Intrusion Detection System
Venue: EAI Endorsed Trans. Security Safety

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self taught Learning (STL) a deep learning based technique, on NSL KDD a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f measure values.
Host derived salivary biomarkers in diagnosing periodontal disease: systematic review and meta analysis.
Venue: Journal of clinical periodontology

To systematically evaluate the accuracy of host derived salivary biomarkers in the diagnosis of periodontal disease based on the given sensitivity and specificity information.  Studies were eligible for inclusion if they had compared the diagnostic application of salivary biomarkers with clinical examination of periodontal disease. A detailed search was performed in five databases without restrictions on subject age, chronology, or language. Additionally, a partial grey literature search was conducted. The revised Quality Assessment of Diagnostic Accuracy Studies tool and Meta analysis were used to evaluate the selected studies.  From the 905 screened studies, four were included in the qualitative and quantitative analysis. One biomarker, macrophage inflammatory protein 1a (MIP 1a) had excellent diagnostic accuracy and two, interleukin 1b (IL 1b) and interleukin 6 (IL 6) showed acceptable diagnostic values. However, the only biomarker considered excellent was evaluated in a single study, which may reduce the robustness of the results.  There is currently limited evidence to confirm the diagnostic capability of salivary biomarkers in the clinical assessment of periodontal disease. Notwithstanding, the summary findings showed the growing importance of salivary biomarker, and can guide larger, well controlled, diagnostic accuracy studies. Likewise, although not conclusive, MIP 1a, IL 1b, and IL 6 may be promising biomarkers for future studies.
JUSTICE SCALIA AND THE RULE OF LAW: ORIGINALISM VS. THE LIVING CONSTITUTION

What secret knowledge, one must wonder, is breathed into lawyers when they become Justices of this Court, that enables them to discern that a practice which the text of the Constitution does not clearly proscribe, and which our people have regarded as constitutional for 200 years, is in fact unconstitutional? The Court must be living in another world. Day by day, case by case, it is busy designing a Constitution for a country I do not
Apparent Age Estimation from Face Images Combining General and Children Specialized Deep Learning Models
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

This work describes our solution in the second edition of the ChaLearn LAP competition on Apparent Age Estimation. Starting from a pretrained version of the VGG 16 convolutional neural network for face recognition, we train it on the huge IMDB Wiki dataset for biological age estimation and then fine tune it for apparent age estimation using the relatively small competition dataset. We show that the precise age estimation of children is the cornerstone of the competition. Therefore, we integrate a separate "children" VGG 16 network for apparent age estimation of children between 0 and 12 years old in our final solution. The "children" network is fine tuned from the "general" one. We employ different age encoding strategies for training "general" and "children" networks: the soft one (label distribution encoding) for the "general" network and the strict one (0/1 classification encoding) for the "children" network. Finally, we highlight the importance of the state of the art face detection and face alignment for the final apparent age estimation. Our resulting solution wins the 1st place in the competition significantly outperforming the runner up.
Adopting Privacy Regulations in a Data Warehouse A Case of the Anonimity versus Utility Dilemma
Venue: KDIR

This paper investigates how privacy can be protected in a data warehouse while, at the same time, an organisation tries to be as open as possible. First, we perform a literature review on relevant techniques and methods to preserve privacy and show that k anonimity can be applied to comply with an organisation's requirements. Then, we propose two design strategies to adopt privacy regulations within a data warehouse. The first proposal shows that during the ETL process a data transformation can be performed to effectively realise anonimised records in a business intelligence environment. The second proposal shows that with views and a having clause, anonimisation can be arranged as well.
Determinants of Internal Auditors Behavior in Whistle Blowing With Formal Retaliation And Structural Anonimity Line As Moderating Variables (Empirical Study At State Owned Enterprises In Indonesia)

The research was aimed to test the determinant of auditors internal behavior in whistle blowing with formal retaliation and structural anonimity line as moderating variables. The population of research was 735 internal auditors of entire State Owned Enterprises in Indonesia. Census methods was applied to collect the data and the samples were 197 among them. Structural Equation Modelling (SEM) aided by Warp PLS 5,0 was used for data processing. The conclusion was that auditors attitude on whistle blowing, subjective norms, and perceived behavioral control positively influenced whistle blowing intention. Whistle blowing intention positively influenced whistle blowing behavior. Structural anonimity line reinforced the relationship between whistle blowing intention and whistle blowing behavior. Formal retaliation weakened the relationship between whistle blowing intention and whistle blowing behavior
Evaluation of the effectiveness of remedial reading courses at community colleges

Limited research is available on the effectiveness of remedial college courses. The present study evaluated the effectiveness of two instructional approaches for developmental reading courses at a community college in the southwestern United States. The instructional approaches were traditional textbook based instruction and strategic reading instruction. The sample consisted of 64 participants. Half (n 32) of the participants were in the control group (n 32) and received traditional textbook based instruction; the other half (n 32) were in the experimental group and received strategic reading instruction. All participants completed the Nelson Denny Reading Form G at the beginning of the semester and then again 12 weeks later, at the end of the semester. The data were then statistically analyzed to identify any relationships between the type of instruction and the differences between the students' pretest and posttest scores. The null hypotheses for H1 and H2 were rejected because the results of the paired t tests indicated that both traditional textbook based instruction and strategic reading instruction have a statistically significant positive effect on students' performance on the test. A one way ANOVA was conducted to determine whether the pretest/posttest difference scores varied based on the type of instruction. The findings showed that both methods of instruction were equally effective in improving the reading comprehension skills of community college students in a developmental reading course. Based on the findings, community college leaders are encouraged to assess the effectiveness of the instructional methods used in developmental courses to ensure at risk community college students are receiving the most beneficial instruction.
DISPELLING THE MALE ONLY MYTH: CHICANAS AND CALO

Existing primarily throughout the Southwest in urban and rural areas, the Chicano1 com munity is heterogeneous, complex, and dynamic. Within this macroscopic entity there exist various subcultures such as the Pachucos. This subculture is characterized as having its own ethos, lifestyle, and even mode of dress, and it is this group which will be the primary focus of this study. The Pachuco is especially noted for possessing a unique value system and  This language has been classified by sociolinguists as "calo" or "pachuco Spanish."3 Since the 1940s, the members of this subculture have been the focus of numerous studies dealing with the sociological as well as the linguistic aspects of this group. In addition to the diverse elements that made each study a significant contribution to the database of information on this topic, they have had at least three elements in common: (1) the target groups of study were primarily male Chicano youths; (2) most of the studies were conducted by male researchers; and (3) they con sistently overlooked the woman's role within the pachuco social structure, and more specifically her own linguistic competence as well as her attitudes toward calo. Existing literature and studies on calo and its users do indeed reveal that they have been con ducted from a male perspective with a primary focus on male subjects. There is a noticeable lack of research on the Chicana, and those studies that briefly touched on women as users of calo la beled them as prostitutes, barmaids, or mates of gang members. Because sociolinguistic research concerning the Chicana has remained an untapped source of information, this study has proposed to look at a select group of Chicanas in order to obtain in sight into:
An Ethical Evaluation of Human Robot Relationships
Venue: Int. J. Soc. Robotics

When people interact with socially interactive robots on a regular basis, it could be that people start developing some kind of relationship with such robots. People are able to get attached to several objects in our everyday world. However, the relationships we build with regular objects differ significantly from those we may build with socially interactive robots. In contrast to common nonhuman objects, socially interactive robots act autonomously, which increases people's expectations about its capacities. Moreover, human robot interactions are constructed according to the rules of human human interaction, inviting users to interact socially with robots. This paper fosters a discussion on the ethical considerations of human robot relationships and discusses whether these bonds between humans and robots could contribute to the good life. Research on people's interactions with and social reactions towards socially interactive robots is necessary to shape the ethical, societal and legal perspectives on these issues, and facilitates the design of responsible robotics and the successful introduction of robots into our society.
Heart Disease and Stroke Statistics 2016 Update: A Report From the American Heart Association
Venue: Circulation

Author(s) Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee
Executive Summary: Heart Disease and Stroke Statistics 2016 Update A Report From the American Heart Association
Venue: Circulation

Author(s) Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee
Attribute Based Hybrid Encryption with User Revocation in Data Outsourcing Systems

Cloud computing is an emerging computing paradigm, enabling users to remotely store their data in a server and provide services on demand. In cloud computing cloud users and cloud services providers are almost certain to be from different trusted domain. The confidentiality of the data is major problem when users use commercial cloud servers to store their records because it can be view by everyone, by assuring that the users control the access to their own records, it is a promising method to encrypt the files before outsourcing and access control should be enforced though cryptography instead of role based access control. Cipher text policy attribute based hybrid encryption is a promising cryptographic solution to these issues for enforcing access control policies defined by data owner on outsourced cloud data. The access control mechanism using cipher text policy attribute based hybrid encryption to enforce access control policies with efficient user attribute and user revocation capability. The fine grained access control can be achieved by dual encryption mechanism which takes advantages of the attribute based hybrid encryption and selective group key distribution in each attribute group. On demonstrating the proposed algorithm the confidential data is efficiently managed and secured in data outsourcing in cloud system.
Embedding lightweight proxy re encryption for efficient attribute revocation in cloud computing
Venue: Int. J. High Perform. Comput. Netw.

Cloud computing has emerged as a successful paradigm to deliver flexible and convenient data processing and sharing. Security and privacy management of outsourced data is among the top concerns for cloud adoption. To this end, attribute based encryption primitive has been recognised to achieve secure data access control with privacy preserving of data. However, dealing with the evolution of user attribute and policy update limits the efficiency of attribute based access control. In this paper, we propose a lightweight proxy re encryption L PRE technique to enable efficient attribute revocation in collaborative, multi owner, and multi authority cloud systems. Our novel L PRE optimises attribute revocation cost by outsourcing re encryption key generation to a delegated proxy server in a dynamic and secure manner. In addition, we also introduce key update algorithm to optimise cost for the update of non revoked users' keys. We also provide the proof to show that our proposed L PRE scheme is secure under general security model. Finally, a performance evaluation is performed to demonstrate the practicality and efficiency of our proposed scheme.
Entity Disambiguation by Knowledge and Text Jointly Embedding
Venue: CoNLL

For most entity disambiguation systems, the secret recipes are feature representations for mentions and entities, most of which are based on Bag of Words (BoW) representations. Commonly, BoW has several drawbacks: (1) It ignores the intrinsic meaning of words/entities; (2) It often results in high dimension vector spaces and expensive computation; (3) For different applications, methods of designing handcrafted representations may be quite different, lacking of a general guideline. In this paper, we propose a different approach named EDKate. We first learn low dimensional continuous vector representations for entities and words by jointly embedding knowledge base and text in the same vector space. Then we utilize these embeddings to design simple but effective features and build a two layer disambiguation model. Extensive experiments on real world data sets show that (1) The embedding based features are very effective. Even a single one embedding based feature can beat the combination of several BoW based features. (2) The superiority is even more promising in a difficult set where the mention entity prior cannot work well. (3) The proposed embedding method is much better than trivial implementations of some off the shelf embedding algorithms. (4) We compared our EDKate with existing methods/systems and the results are also positive.