nils-herrmann/clean_scirepeval huggingface dataset:
Systematic review of success factors and barriers for software process improvement in global software development
Venue: IET Softw.

Nowadays, many software development organisations are globalising their work throughout the world. The motivation behind the software globalisation is competitive advantage and business profits that it yields to an organisation. Besides, these organisations are facing various challenges due to the distributed nature of the software development. However, software process improvement (SPI) is one of the biggest issues in the domain of global software development (GSD) The aim of this research study is to identify success factors and barriers to assist GSD organisations for successful implementation of SPI program. Accordingly, a systematic literature review approach was adopted to identify the success factors and barriers. A total of nine success factors and six barriers were identified that could impact SPI. Using the criteria of the factors having a frequency >50% as critical, total four success factors were ranked as critical success factors, i.e. management commitment, staff involvement, allocated resources and pilot projects. Moreover, total four barriers were classified as critical barriers, i.e. lack of resources, inexperienced staff, organisational politics and time pressure.
ANALISIS BUTIR SOAL ULANGAN AKHIR SEMESTER (UAS) BIOLOGI TAHUN PELAJARAN 2015/2016 KELAS X DAN XI PADA MAN SAMPIT

Soal UAS Biologi buatan guru di MAN Sampit belum pernah dianalisis, baik secara kualitas dan kuantitas yang meliputi tingkat kesukaran, daya pembeda, efektifitas pengecoh, validitas, dan reliabilitas. Penelitian ini bertujuan untuk mengetahui kualitas soal UAS buatan guru biologi semester genap tahun pelajaran 2015/2016 berdasarkan kesesuaiannya dengan dimensi kognitif taksonomi Bloom, tingkat kesukaran, daya pembeda, efektifitas pengecoh, validitas, dan reliabilitas. Hasil penelitian menunjukkan bahwa soal biologi kelas X dan kelas XI sebagai berikut: (1) Kualitas soal Ulangan Akhir Semester (UAS) Biologi tahun pelajaran 2015/2016 kelas X dan XI di MAN Sampit memiliki kualitas cukup baik, karena sudah sesuai dengan soal standar, tetapi perlu perbaikan aspek materi dan konstruksi pada beberapa soal, (2) Tingkat kesukaran butir soal biologi kelas X sebanyak 3 soal kategori sukar, 3 soal kategori sedang, dan 34 soal kategori mudah, sedangkan pada kelas XI bahwa sebanyak 8 soal kategori sukar, 9 soal kategori sedang, dan 23 soal kategori mudah, (3) Daya pembeda butir soal biologi kelas X, soal yang dinyatakan kategori sangat baik tidak ada, kategori baik 2 soal, kategori cukup 11 soal, dan kategori jelek 27 soal, sedangkan pada kelas XI soal dinyatakan kategori sangat baik tidak ada, kategori baik 2 soal, kategori cukup 12 soal, dan kategori jelek 26 soal, (4) Efektifitas pengecoh butir soal biologi kelas X dari 40 soal terdapat 2 soal termasuk kriteria baik, 10 soal kriteria cukup, 18 soal kriteria kurang baik, dan 10 soal kriteria tidak baik, pada kelas XI terdapat 3 soal kriteria sangat baik, 6 soal kriteria baik, 12 soal kriteria cukup, 14 soal kriteria kurang baik, dan 5 soal kriteria tidak baik, (5) Validitas butir soal biologi kelas X dari 40 soal terdapat 21 soal yang dinyatakan valid sedangkan soal yang dinyatakan tidak valid sebanyak 19 soal, pada kelas XI dari 40 soal terdapat 16 soal yang dinyatakan valid sedangkan soal yang dinyatakan tidak valid sebanyak 24 soal,dan (6) Reliabilitas butir soal biologi memiliki tingkat reliabilitas yang tinggi atau reliabel yakni 0,731 pada kelas X dan 0,667 pada kelas XI. Kata kunci: analisis soal, taksonomi Bloom, program SPSS 21
Knowledge Gaps in Cardiovascular Care of the Older Adult Population: A Scientific Statement From the American Heart Association, American College of Cardiology, and American Geriatrics Society.
Venue: Journal of the American College of Cardiology

The incidence and prevalence of most cardiovascular disorders increase with age, and cardiovascular disease is the leading cause of death and major disability in adults =75 years of age; however, despite the large impact of cardiovascular disease on quality of life, morbidity, and mortality in older adults, patients aged =75 years have been markedly underrepresented in most major cardiovascular trials, and virtually all trials have excluded older patients with complex comorbidities, significant physical or cognitive disabilities, frailty, or residence in a nursing home or assisted living facility. As a result, current guidelines are unable to provide evidence based recommendations for diagnosis and treatment of older patients typical of those encountered in routine clinical practice. The objectives of this scientific statement are to summarize current guideline recommendations as they apply to older adults, identify critical gaps in knowledge that preclude informed evidence based decision making, and recommend future research to close existing knowledge gaps. To achieve these objectives, we conducted a detailed review of current American College of Cardiology/American Heart Association and American Stroke Association guidelines to identify content and recommendations that explicitly targeted older patients. We found that there is a pervasive lack of evidence to guide clinical decision making in older patients with cardiovascular disease, as well as a paucity of data on the impact of diagnostic and therapeutic interventions on key outcomes that are particularly important to older patients, such as quality of life, physical function, and maintenance of independence. Accordingly, there is a critical need for a multitude of large population based studies and clinical trials that include a broad spectrum of older patients representative of those seen in clinical practice and that incorporate relevant outcomes important to older patients in the study design. The results of these studies will provide the foundation for future evidence based guidelines applicable to older patients, thereby enhancing patient centered evidence based care of older people with cardiovascular disease in the United States and around the world.
Knowledge Gaps in Cardiovascular Care of the Older Adult Population: A Scientific Statement From the American Heart Association, American College of Cardiology, and American Geriatrics Society.
Venue: Circulation

The incidence and prevalence of most cardiovascular disorders increase with age, and cardiovascular disease is the leading cause of death and major disability in adults =75 years of age; however, despite the large impact of cardiovascular disease on quality of life, morbidity, and mortality in older adults, patients aged =75 years have been markedly underrepresented in most major cardiovascular trials, and virtually all trials have excluded older patients with complex comorbidities, significant physical or cognitive disabilities, frailty, or residence in a nursing home or assisted living facility. As a result, current guidelines are unable to provide evidence based recommendations for diagnosis and treatment of older patients typical of those encountered in routine clinical practice. The objectives of this scientific statement are to summarize current guideline recommendations as they apply to older adults, identify critical gaps in knowledge that preclude informed evidence based decision making, and recommend future research to close existing knowledge gaps. To achieve these objectives, we conducted a detailed review of current American College of Cardiology/American Heart Association and American Stroke Association guidelines to identify content and recommendations that explicitly targeted older patients. We found that there is a pervasive lack of evidence to guide clinical decision making in older patients with cardiovascular disease, as well as a paucity of data on the impact of diagnostic and therapeutic interventions on key outcomes that are particularly important to older patients, such as quality of life, physical function, and maintenance of independence. Accordingly, there is a critical need for a multitude of large population based studies and clinical trials that include a broad spectrum of older patients representative of those seen in clinical practice and that incorporate relevant outcomes important to older patients in the study design. The results of these studies will provide the foundation for future evidence based guidelines applicable to older patients, thereby enhancing patient centered evidence based care of older people with cardiovascular disease in the United States and around the world.
Attention Based Convolutional Neural Networks for Sentence Classification
Venue: INTERSPEECH

Sentence classification is one of the foundational tasks in spoken language understanding (SLU) and natural language processing (NLP) In this paper we propose a novel convolutional neural network (CNN) with attention mechanism to improve the performance of sentence classification. In traditional CNN, it is not easy to encode long term contextual information and correlation between non consecutive words effectively. In contrast, our attention based CNN is able to capture these kinds of information for each word without any external features. We conducted experiments on various public and inhouse datasets. The experimental results demonstrate that our proposed model significantly outperforms the traditional CNN model and achieves competitive performance with the ones that exploit rich syntactic features.
Framing responsibility for depression: how U.S. news media attribute causal and problem solving responsibilities when covering a major public health problem

This paper analyzes how major U.S. print and broadcast news media framed depression causal and problem solving responsibilities at individual and societal levels over the past three decades. Results from the content analysis showed that the media placed more causal and problem solving responsibilities on individuals than the society. However, references to societal solutions increased moderately over time. Organizational differences emerged in news attribution of responsibilities, as print media presented more individual level causes while broadcast media focused more on solutions at both individual and societal levels. Additionally, local newspapers put more problem solving responsibilities on individuals than national newspapers, while a cable news channel allocated more time to the discussion of overall depression responsibilities than network TV. Findings are discussed in the context of cultural orientations, organizational constraints, changing practice and trends in health news reporting, and the broader political/social environment in which the news media operate. Practical implications for health journalism, mental health communication and advocacy, and public health policy making are discussed.
How we think about depression: The role of linguistic framing

Descriptions of emotional disorders vary according to cultural and historical context. Framing mental illness as a disease as opposed to being a consequence of psychosocial factors has been proposed as a strategy to fight stigma in recent years. Here we combine two studies, a corpus analysis and an experimental survey, to explore this issue in the case of Spanish. First, we conducted a corpus analysis to investigate the patterns of linguistic framing of depression including disease like descriptions and metaphorical frames using data from Latin American countries. Two main patterns were identified: (1) depression is frequently framed as a brain disease. In line with medicalization trends observed worldwide, this pattern has increased over time. (2) The data showed that depression is also metaphorically construed as a place in space or as an opponent. Second, we investigated whether the instantiation of subtle linguistic cues influences people's perception of a description of a hypothetical case of depression. A survey experiment conducted among Colombian students revealed that when depression was framed as a disease, the participants' perception of the depressed person's responsibility was reduced. Moreover, disease like descriptions and metaphorical frames influenced participants' initial interpretations of the role of social causal factors.
Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities
Venue: Environmental Science and Pollution Research

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing building area coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.
Accelerating Very Deep Convolutional Networks for Classification and Detection
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

This paper aims to accelerate the test time computation of convolutional neural networks (CNNs) especially very deep CNNs <xref ref type="bibr" rid="ref1"[1]/xref> that have substantially impacted the computer vision community. Unlike previous methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We develop an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD) More importantly, while previous methods mainly focus on optimizing one or two layers, our nonlinear method enables an asymmetric reconstruction that reduces the rapidly accumulated error when multiple (e.g. <inline formula> <tex math notation="LaTeX"\ge$/tex math><alternatives><inline graphic xlink:type="simple" xlink:href="he ieq1 2502579.gif" /alternatives>/inline formula>10) layers are approximated. For the widely used very deep VGG 16 model <xref ref type="bibr" rid="ref1"[1]/xref> our method achieves a whole model speedup of 4<inline formula><tex math notation="LaTeX"\times$/tex math> <alternatives><inline graphic xlink:type="simple" xlink:href="he ieq2 2502579.gif"/alternatives>/inline formula> with merely a 0.3 percent increase of top 5 error in ImageNet classification. Our 4<inline formula><tex math notation="LaTeX"\times$ /tex math><alternatives><inline graphic xlink:type="simple" xlink:href="he ieq3 2502579.gif"/alternatives>/inline formula> accelerated VGG 16 model also shows a graceful accuracy degradation for object detection when plugged into the Fast R CNN detector <xref ref type="bibr" rid="ref2"[2]/xref>
Food Image Recognition Using Very Deep Convolutional Networks
Venue: MADiMa ACM Multimedia

We evaluated the effectiveness in classifying food images of a deep learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine tuned this architecture for classifying food images from three well known food image datasets: ETH Food 101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28% 81.45% and 76.17% as top 1 accuracy and 96.88% 97.27% and 92.58% as top 5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors? Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.
Very Deep Convolutional Networks for Natural Language Processing
Venue: ArXiv

The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which are very successful in computer vision. We present a new architecture for text processing which operates directly on the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with the depth: using up to 29 convolutional layers, we report significant improvements over the state of the art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to NLP.
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Venue: International Journal of Computer Vision

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?" computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of $35$$35 objects, $26$$26 attributes, and $21$$21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.
Antecedent Factors Affecting Academic Performance of Graduate Students at the Nairobi Evangelical Graduate School of Theology

This paper reports the findings of a Master's level thesis work that was done in 1997 to assess the antecedent factors affecting the academic performance of graduate students at the Nairobi Evangelical School of Theology (N.E.G.S.T. which is currently Africa International University (AIU) The paper reviews the effect of lack of finance on academic performance; the effect of age on academic performance; the effect of language proficiency on academic performance; the effect of family responsibilities on academic performance; the effect of academic aspirations on academic performance; and the effect of student faculty interactions on academic performance. The study targeted twenty seven graduate students at the seminary. These respondents were sampled purposively. Data were collected through the use of a semi structured questionnaire. The items in the questionnaire were developed using a five point Likert scale. Open ended questions were coded and analyzed thematically. A Chi square correlation analysis was computed to establish the relationship between study variables. Teacher friendly interactions, inspiration for further studies, family responsibilities, language proficiency age and lack of finance were some of the factors identified to be affecting academic performance of students. The paper recommends that time management, regulation of work load and commitment to seeking academic mentorship, are significant components that students need to embrace to enhance their academic performance. As a lecturer a lecturer in the institution, the author observes the practical relevance of the findings despite many changes in the institution. Recommendation is given to conduct more replication studies to investigate factors affecting AIU's students' academic performance in relation to current changes such as cultural, academic and numerical.  Excellence, student academic performance, academic goals, education
The Relationship among Grit, Self efficacy, Achievement Goal, and Academic Self Regulation in Elementary School Children

This study examined the structural relationship among elementary students' grit, self efficacy, achievement goals (i.e. mastery goal, performance approach goal, performance avoidance goal) and academic self regulation. Additionally, we investigated how grit predicted academic self regulation via self efficacy and achievement goals. A total of 267 5th and 6th graders' responses were collected from elementary schools in Korea. Results showed that the hypothesized structural equation model fitted the data well based on fit indices. Grit had a positive effect on self efficacy, mastery goal, and performance approach goal. Specifically, the relative predictability of grit on self efficacy and mastery goal was greater than the one on performance approach goal. The effect of grit on performance avoidance goal was not statistically significant. Moreover, grit had a significant direct and indirect effect on academic self regulation. Mastery goal was found to partially mediate the relationship between grit and academic self regulation. This study investigated how grit influenced academic self regulation through motivational variables and provided empirical evidences.