nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature

The mentions of human health perturbations such as the diseases and adverse effects denote a special entity class in the biomedical literature. They help in understanding the underlying risk factors and develop a preventive rationale. The recognition of these named entities in texts through dictionary-based approaches relies on the availability of appropriate terminological resources. Although few resources are publicly available, not all are suitable for the text mining needs. Therefore, this work provides an overview of the well known resources with respect to human diseases and adverse effects such as the MeSH, MedDRA, ICD-10, SNOMED CT, and UMLS. Individual dictionaries are generated from these resources and their performance in recognizing the named entities is evaluated over a manually annotated corpus. In addition, the steps for curating the dictionaries, rule-based acronym disambiguation and their impact on the dictionary performance is discussed. The results show that the MedDRA and UMLS achieve the best recall. Besides this, MedDRA provides an additional benefit of achieving a higher precision. The combination of search results of all the dictionaries achieve a considerably high recall. The corpus is available on http://www.scai.fraunhofer.de/disease-ae-corpus.html
Periosteal ganglion: a report of three new cases including MRI findings and a review of the literature

Objective. To clarify the clinicopathological features of periosteal ganglion.  Three patients with periosteal ganglion were studied clinicopathologically.  One patient was selected from the files of our institute and two from a consultation file.  All three lesions were located over the medial aspect of the tibia. Plain radiographs showed cortical erosions of varying degrees and mild periosteal reaction of the medial side of the tibia. MR images demonstrated well-circumscribed lesions overlying the cortical bone of the tibia, shown as low-intensity areas on T1-weighted images. On T2-weighted images, lesions were homogeneous, lobulated, and showed a characteristic markedly increased signal intensity. These findings are helpful in making a diagnosis of periosteal ganglion. Each patient had an uneventful clinical course after an excision involving the wall of the ganglion, the adjoining periosteum, and the underlying sclerotic cortical bone.
A spaceborne telemetry loaded bifilar helical antenna for LEO satellites

In this paper we describe the design, manufacturing and measurement of an antenna with an isoflux type radiation pattern used in the space segment TT&C subsystem. In particular, the antenna corresponds to the flight model of mission SAC-D/AQUARIUS, which is a LEO earth observation satellite. The chosen solution is a loaded bifilar helical antenna, which presents a higher gain than a typical hemispherical antenna but which doesn't require the use of a beam forming network like the classical quadrifilar Kilgus type antenna. This reduces the number of required parts, and due to its loading, smaller dimension and higher structural stiffness are achieved.
Blockchain network slice broker in 5G: Slice leasing in factory of the future use case

5G Network Slice Broker concept aims to enable mobile virtual network operators, over-the-top providers, and industry vertical market players to request and lease resources from infrastructure providers dynamically according to needs. In the future digital factory, the leasing of resources could also happen autonomously by manufacturing equipment. This paper presents Blockchain Slice Leasing Ledger Concept and analysis of its applicability in the Factory of the Future. The novel concept utilizes 5G Network Slice Broker in a Blockchain to reduce service creation time and enable manufacturing equipment autonomously and dynamically acquire the slice needed for more efficient operations.
Semi-supervised learning for image classification

Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling procedure), or to large datasets that are more representative but also add more label noise. Therefore, semi-supervised learning is a promising direction. It requires only few labels while simultaneously making use of the vast amount of images available today. We address object class recognition with semi-supervised learning. These algorithms depend on the underlying structure given by the data, the image description, and the similarity measure, and the quality of the labels. This insight leads to the main research questions of this thesis: “Is the structure given by labeled and unlabeled data more important than the algorithm itself?”, “Can we improve this neighborhood structure by a better similarity metric or with more representative unlabeled data?”, and “Is there a connection between the quality of labels and the overall performance and how can we get more representative labels?”. We answer all these questions, i.e., we provide an extensive evaluation, we propose several graph improvements, and we introduce a novel active learning framework to get more representative labels.
A novel dual-slope mm-Wave channel model based on 3D ray-tracing in urban environments

To solve mobile traffic crunch, the usage of enormous bandwidth in millimeter wave (mm-Wave) is under discussion. In this paper, we investigate radio channel characteristics of mm-Wave frequency in the downtown area of Ottawa using 3D ray-tracing technique. In the results, important parameters of the radio channel model, such as path loss exponent, shadow fading, delay spread and angle spread, are provided. Especially, in case of path loss model in non-line of sight, a novel dual-slope approach is proposed for two conventional deployment scenarios. Comparing to traditional single-slope path loss model, the proposed method has smaller RMS errors in terms of local mean of path loss observations. We believe that the proposed method is appropriate to evaluate performance of the mm-Wave system in dense urban environments.
Multimodal feature fusion for CNN-based gait recognition: an empirical comparison

People identification in video based on the way they walk (i.e. gait) is a relevant task in computer vision using a non-invasive approach. Standard and current approaches typically derive gait signatures from sequences of binary energy maps of subjects extracted from images, but this process introduces a large amount of non-stationary noise, thus, conditioning their efficacy. In contrast, in this paper we focus on the raw pixels, or simple functions derived from them, letting advanced learning techniques to extract relevant features. Therefore, we present a comparative study of different Convolutional Neural Network (CNN) architectures on three low-level features (i.e. gray pixels, optical flow channels and depth maps) on two widely-adopted and challenging datasets: TUM-GAID and CASIA-B. In addition, we perform a comparative study between different early and late fusion methods used to combine the information obtained from each kind of low-level features. Our experimental results suggest that (i) the use of hand-crafted energy maps (e.g. GEI) is not necessary, since equal or better results can be achieved from the raw pixels; (ii) the combination of multiple modalities (i.e. gray pixels, optical flow and depth maps) from different CNNs allows to obtain state-of-the-art results on the gait task with an image resolution several times smaller than the previously reported results; and, (iii) the selection of the architecture is a critical point that can make the difference between state-of-the-art results or poor results.
Behavioral Hierarchy: Exploration and Representation

Behavioral modules are units of behavior providing reusable building blocks that can be composed sequentially and hierarchically to generate extensive ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focusses on two features of behavioral hierarchy that appear to be less well recognized: its influence on exploratory behavior and the opportunity it affords to reduce the representational challenges of planning and learning in large, complex domains. Four computational examples are described that use methods of hierarchical reinforcement learning to illustrate the influence of behavioral hierarchy on exploration and representation. Beyond illustrating these features, the examples provide support for the central role of behavioral hierarchy in development and learning for both artificial and
On the Cache Behavior of SPLASH-2 Benchmarks on ARM and ALPHA Processors in Gem5 Full System Simulator

Today cache size and hierarchy level of caches play an important role in improving computer performance. By using full system simulations of gem5, the variation in memory bandwidth, system bus throughput, L1 and L2 cache size misses are measured by running SPLASH-2 Benchmarks on ARM and ALPHA Processors. In this work we calculate cache misses, memory bandwidth and system bus throughput by running SPLASH2 benchmarks on gem5 Full System Mode. Our results show that L1 cache misses decrease as L1 cache size is varied from 16KB to 64KB. L1 cache misses are independent of L2 cache size after the program data resides in L2 cache. The memory bandwidth and system bus throughput decreases as L1 and L2 cache size increases.
Towards an OWL-based framework for extracting information from clinical texts

In this paper, we present our ongoing work towards an OWL-based framework for extracting a variety of information (including patient history) from clinical texts. Our framework integrates a well-known natural language processing (NLP) system by converting its ontology and output logical form interpretation into the Web Ontology Language (OWL). The OWL-based Semantic Query-Enhanced Web Rule Language (SQWRL) is then used as a platform for authoring Semantic Web-aware rules for extracting information of interest from the OWL knowledge based created from parsing a clinical report. We also describe our ongoing work on using this system for extracting a timeline-based patient medical record from the history of present illness section of clinical texts.
The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection

Wagstaff (2012) draws attention to the pervasiveness of abstract evaluation metrics that explicitly ignore or remove problem specifics. While such metrics allow practitioners to compare numbers across application domains, they offer limited insight into the impact of algorithmic decisions on humans and their perception of the algorithm's correctness. Even for problems that are mathematically the same, both the real-cost of (mathematically) identical errors, as well as their perceived-cost by users, may significantly vary according to the specifics of each problem domain, as well as of the user perceiving the result. While the real-cost of errors has been considered previously, little attention has been paid to the perceived-cost issue. We advocate for the inclusion of human-centered metrics that elicit error costs from humans from two perspectives: the nature of the error, and the user context. Focusing on hate speech detection on social media, we demonstrate that even when fixing the performance as measured by an abstract metric such as precision, user perception of correctness varies greatly depending on the nature of errors and user characteristics.
Approximate String Matching with q-grams and Maximal Matches

Ukkonen, E., Approximate string-matching with q-grams and maximal matches, Theoretical Computer Science 92 (1992) 191-211. We study approximate string-matching in connection with two string distance functions that are computable in linear time. The first function is based on the so-called q-grams. An algorithm is given for the associated string-matching problem that finds the locally best approximate occurrences of pattern P, IPI = m, in text T, 1 TI = n, in time O(n log@-q)). The occurrences with distance <k can be found in time O(nlog k). The other distance function is based on finding maximal common substrings and allows a form of approximate string-matching in time O(n). Both distances give a lower bound for the edit distance (in the unit cost model), which leads to fast hybrid algorithms for the edit distance based string-matching.
Web Content Mining Techniques Tools & Algorithms – A Comprehensive Study

Nowadays, the growth of World Wide Web has exceeded a lot with more expectations. Large amount of text documents, multimedia files and images were available in the web and it is still increasing in its forms. Data mining is the form of extracting data’s available in the internet. Web mining is a part of data mining which relates to various research communities such as information retrieval, database management systems and Artificial intelligence. The information’s in these forms are well structured from the ground principles. This Web mining adopts much of the data mining techniques to discover potentially useful information from web contents. In this paper, the concepts of web mining with its categories were discussed. The paper mainly focused on the Web Content mining tasks along with its techniques and algorithms.
Photovoltaic energy production forecast using support vector regression

Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15 min. The technique used is based on $$\nu $$ ν -SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$ R 2 coefficient exceeding 90 % . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$ R 2 value decreases if the predictions of irradiance and temperature are not very accurate.
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-ofthe-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.
The Role of Adult Attachment Styles in Psychopathology and Psychotherapy Outcomes

Attachment theory provides a model for understanding how the attachment styles formed in infancy systematically affect subsequent psychological functioning across the life span. Attachment styles provide the cognitive schemas, or working models, through which individuals perceive and relate to their worlds. In turn, these schemas predispose the development of psychopathologies and influence outcomes when people undergo psychotherapy. After reviewing recent empirical findings, the authors conclude that an understanding of attachment theory facilitates the conceptualization of clients’ problems and the selection of appropriate interventions. Accordingly, attachment styles should be assessed as a standard part of treatment planning. Furthermore, the authors propose that attachment styles should be assessed as individual difference variables in psychotherapy outcome research because adult attachment styles dictate how people perceive and respond to their environments and, therefore, how clients respond differentially to various treatments.
Machine Translation Approaches: Issues and Challenges

In the modern world, there is an increased need for language translations owing to the fact that language is an effective medium of communication. The demand for translation has become more in recent years due to increase in the exchange of information between various regions using different regional languages. Accessibility to web document in other languages, for instance, has been a concern for information Professionals. Machine translation (MT), a subfield under Artificial Intelligence, is the application of computers to the task of translating texts from one natural (human) language to another. Many approaches have been used in the recent times to develop an MT system. Each of these approaches has its own advantages and challenges. This paper takes a look at these approaches with the few of identifying their individual features, challenges and the best domain they are best suited to.
The Hidden Pitfalls of Kanban in Software Development

The objective of this thesis is to explore the hidden pitfalls of Kanban in software development. The aim is to discover the reasons behind the pitfalls and actions that nourish Kanban’s failure. Academic background and methodology Software development industry has been shifting towards more iterative, responsive and people-oriented development methods, which present the values of lean and agile thinking. Being characterized as the antidote for bureaucracy, the shift towards agile development methodologies has been one of the most significant factors affecting the software industry. Kanban as an agile change management framework has been perceived as the fairy godmother of software development making the reasons behind struggling Kanban projects particularly interesting. Thesis interviews five agile experts in software development and studies their experiences of unsuccessful Kanban implementations. Emphasis is given to similar experiences and perceptions on how Kanban fails to redeem its value proposition.  The primary finding of the study is that an organization that is unable to change its mindset to support Kanban is a pitfall affecting the whole project, inevitably causing Kanban to fail. This is a challenge that all agile methods have in common. Agile can flourish only when agile values are being appreciated. The secondary finding is that many software teams claiming to be using Kanban have implemented only a shallow imitation of the real method thus creating a superficial implementation, which prevents achieving Kanban induced benefits. Furthermore, the erroneous human interpretation of what Kanban really is and how to apply it correctly is a key factor contributing to the emerge of the pitfalls.
DFT Interpolation Algorithm for Kaiser–Bessel and Dolph–Chebyshev Windows

This paper describes the discrete Fourier transform (DFT) interpolation algorithm for arbitrary windows and its application and performance for optimal noncosine Kaiser-Bessel and Dolph-Chebyshev windows. The interpolation algorithm is based on the polynomial approximation of the window's spectrum that is computed numerically. Two- and three-point (2p and 3p) interpolations are considered. Systematic errors and noise sensitivity are analyzed for the chosen Kaiser-Bessel and Dolph-Chebyshev windows and compared with Rife-Vincent class I windows.
A New Low Power Dynamic Full Adder Cell Based on Majority Function

A new low power dynamic CMOS one bit full adder cell is presented. In this design the time consuming XOR gates are eliminated. It is based on Majority Function. This new cell is compared with two widely used dynamic adders as well as other conventional and recently proposed architectures. It is implemented in two level dynamic CMOS with zipper technique and the number of transistors, chip area and switching activity is significantly reduced.
Beyond pixels: Exploiting camera metadata for photo classification

Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. We consider three problems, indoor-outdoor classification, sunset detection, and manmade-natural classification. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for each problem. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the three problems using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.
Adaptive Transfer Learning

Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a “safe transfer” of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.
Inductance and Resistance Calculations in Three-Dimensional Packaging Using Cylindrical Conduction-Mode Basis Functions

For the successful electrical design of system-in-package, this paper proposes an efficient method for extracting wideband resistance and inductance from a large number of 3-D interconnections. The proposed method uses the modal equivalent network from the electric field integral equation with cylindrical conduction-mode basis function, which reduces the matrix size for large 3-D interconnection problems. Additional enhancement schemes proposed further reduce the cost for computing the partial inductances. Therefore, the method discussed in this paper can be used to construct accurate models of a large number of 3-D interconnection structures such as more than 100 bonding wires used for stacking chips.