nils-herrmann/clean_scirepeval huggingface dataset:
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

With the booming of deep learning in the recent decade, deep neural network has achieved state-of-art performances on many machine learning tasks and has been applied to more and more research fields. Stock market prediction is an attractive research topic since the successful prediction on the market’s future movement leads to significant profit. In this thesis, we investigate to combine the conventional stock analysis techniques with the popular deep learning together and study the impact of deep neural network on stock market prediction. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily returns. Whereas financial news also contains useful information on public companies and the market. In this thesis we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.
Query-Based Abstractive Summarization Using Neural Networks

Creating short summaries of documents with respect to a query has applications in for example search engines, where it may help inform users of the most relevant results. Constructing such a summary automatically, with the potential expressiveness of a human-written summary, is a difficult problem yet to be fully solved. In this thesis, a neural network model for this task is presented. We adapt an existing dataset of news article summaries for the task and train a pointer-generator model using this dataset to summarize such articles. The generated summaries are then evaluated by measuring similarity to reference summaries. We observe that the generated summaries exhibit abstractive properties, but also that they have issues, such as rarely being truthful. However, we show that a neural network summarization model, similar to existing neural network models for abstractive summarization, can be constructed to make use of queries for more targeted summaries.
Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
Total productive maintenance : literature review and directions

The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation.  The paper systematically categorizes the published literature and then analyzes and reviews it methodically.  The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here.  The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a large number of papers in this field and presents the overview of various TPM implementation practices demonstrated by manufacturing organizations globally. It also highlights the approaches suggested by various researchers and practitioners and critically evaluates the reasons behind failure of TPM programmes in the organizations. Further, the enablers and success factors for TPM implementation have also been highlighted for ensuring smooth and effective TPM implementation in the organizations.  The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. It will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the significance of TPM.
How do job characteristics contribute to burnout ? Exploring the distinct mediating roles of perceived autonomy , competence , and relatedness

This study aimed to better understand the psychological mechanisms, referred to in the job demands–resources model as the energetic and motivational processes, that can explain relationships between job demands (role overload and ambiguity), job resources (job control and social support), and burnout (emotional exhaustion, depersonalization, and personal accomplishment). Drawing on self-determination theory, we examined whether psychological resources (perceived autonomy, competence, and relatedness) act as specific mediators between particular job demands and burnout as well as between job resources and burnout. Participants were 356 school board employees. Results of the structural equation analyses provide support for our hypothesized model, which proposes that certain job demands and resources are involved in both the energetic and motivational processes—given their relationships with psychological resources—and that they distinctively predict burnout components. Implications for burnout research and management practices are discussed.
Synchronized video and motion analysis for the assessment of procedures in the operating theater.

Objective assessment of surgical skill has recently been shown to be possible through the use of dexterity-based and video analysis systems. The aim of this study was to synchronize these 2 modalities to produce a comprehensive surgical assessment tool.



The Imperial College Surgical Assessment Device is a dexterity-based motion analysis device that has been developed in the Department of Surgical Oncology and Technology by the Surgical Computing and Imaging Research Group. Further advances to this system have been made to enable synchronized acquisition of hand kinematics and video from real procedures, and their concurrent analysis. To test the feasibility of the system, 10 laparoscopic cholecystectomies performed by 5 different surgeons on consenting patients were recorded. Analysis focused on the entire procedure and also on specific parts of the operation such as the clipping and cutting of the cystic duct and artery.



Dexterity analysis was performed using the objective measures of time, path length, number of movements, velocities, and trajectories. Comparative analysis of a surgeon's dexterity was carried out on the whole procedure and by using the synchronized zoom facility in the software. Kinematic signals revealed rapid changes in velocity caused by alternating between different instruments or occurring after complications such as bleeding.



This new motion analysis system has been shown to be an effective tool for the comprehensive assessment of operative procedures.
Title : Evaluation of in-line spatial filter velocimetry as pat monitoring tool for particle growth during fluid bed granulation

In this study, the feasibility of spatial filter velocimetry (SFV) as Process Analytical Technology tool for the in-line monitoring of the particle size distribution during top spray fluidized bed granulation was examined. The influence of several process (inlet air temperature during spraying and drying) and formulation variables (HPMC and Tween 20 concentration) upon the particle size distribution during processing, and the end product particle size distribution, tapped density and Hausner ratio was examined using a design of experiments (DOE) (2-level full factorial design, 19 experiments). The trend in end granule particle size distributions of all DOE batches measured with in-line SFV was similar to the offline laser diffraction (LD) data. Analysis of the DOE results showed that mainly the HPMC concentration and slightly the inlet air temperature during drying had a positive effect upon the average end granule size. The in-line SFV particle size data, obtained every 10 s during processing, further allowed to explain and better understand the (in)significance of the studied DOE variables, which was not possible based on the LD data as this technique only supplied end granule size information. The variation in tapped density and Hausner ratio among the end granules of the different DOE batches could be explained by their difference in average end granule size. Univariate, multivariate PLS and multiway N-PLS models were built to relate these end granule properties to the in-line measured particle size distribution. The multivariate PLS tapped density model and the multiway N-PLS Hausner ratio model showed the highest R values in combination with the lowest RMSEE values (R of 82% with an RMSEE of 0.0279 for tapped density and an R of 52% with an RMSEE of 0.0268 for Hausner ratio, respectively).
Caring for oneself to care for others: physicians and their self-care.

It is well known that clinicians experience distress and grief in response to their patients' suffering. Oncologists and palliative care specialists are no exception since they commonly experience patient loss and are often affected by unprocessed grief. These emotions can compromise clinicians' personal well-being, since unexamined emotions may lead to burnout, moral distress, compassion fatigue, and poor clinical decisions which adversely affect patient care. One approach to mitigate this harm is self-care, defined as a cadre of activities performed independently by an individual to promote and maintain personal well-being throughout life. This article emphasizes the importance of having a self-care and self-awareness plan when caring for patients with life-limiting cancer and discusses validated methods to increase self-care, enhance self-awareness and improve patient care.
Conglobation in the Pill Bug, Armadillidium vulgare, as a Water Conservation Mechanism

Water balance of the terrestrial isopod, Armadillidium vulgare, was investigated during conglobation (rolling-up behavior). Water loss and metabolic rates were measured at 18 +/- 1 degrees C in dry air using flow-through respirometry. Water-loss rates decreased 34.8% when specimens were in their conglobated form, while CO2 release decreased by 37.1%. Water loss was also measured gravimetrically at humidities ranging from 6 to 75 %RH. Conglobation was associated with a decrease in water-loss rates up to 53 %RH, but no significant differences were observed at higher humidities. Our findings suggest that conglobation behavior may help to conserve water, in addition to its demonstrated role in protection from predation.
ENGINEERED CEMENTITIOUS COMPOSITES (ECC) – TAILORED COMPOSITES THROUGH MICROMECHANICAL MODELING

This article provides a brief overview of several classes of fiber reinforced cement based composites and suggests future directions in FRC development. Special focus is placed on micromechanics based design methodology of strain-hardening cement based composites. As example, a particular engineered cementitious composite newly developed at the ACE-MRL at the University of Michigan is described in detail with regard to its design, material composition, processing, and mechanical properties. Three potential applications which utilize the unique properties of such composites are cited in this paper, and future research needs are identified.  To appear in Fiber Reinforced Concrete: Present and the Future,
Joint Entity and Relation Extraction Using Card-Pyramid Parsing

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. We present a new method for joint entity and relation extraction using a graph we call a “card-pyramid.” This graph compactly encodes all possible entities and relations in a sentence, reducing the task of their joint extraction to jointly labeling its nodes. We give an efficient labeling algorithm that is analogous to parsing using dynamic programming. Experimental results show improved results for our joint extraction method compared to a pipelined approach.
Medical 3D Printing for the Radiologist.

While use of advanced visualization in radiology is instrumental in diagnosis and communication with referring clinicians, there is an unmet need to render Digital Imaging and Communications in Medicine (DICOM) images as three-dimensional (3D) printed models capable of providing both tactile feedback and tangible depth information about anatomic and pathologic states. Three-dimensional printed models, already entrenched in the nonmedical sciences, are rapidly being embraced in medicine as well as in the lay community. Incorporating 3D printing from images generated and interpreted by radiologists presents particular challenges, including training, materials and equipment, and guidelines. The overall costs of a 3D printing laboratory must be balanced by the clinical benefits. It is expected that the number of 3D-printed models generated from DICOM images for planning interventions and fabricating implants will grow exponentially. Radiologists should at a minimum be familiar with 3D printing as it relates to their field, including types of 3D printing technologies and materials used to create 3D-printed anatomic models, published applications of models to date, and clinical benefits in radiology. Online supplemental material is available for this article.
Survey of machine learning techniques for malware analysis

Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind such complexity, and to develop technologies to keep pace with malware evolution. This survey aims at providing an overview on the way machine learning has been used so far in the context of malware analysis in Windows environments, i.e. for the analysis of Portable Executables. We systematize surveyed papers according to their objectives (i.e., the expected output), what information about malware they specifically use (i.e., the features), and what machine learning techniques they employ (i.e., what algorithm is used to process the input and produce the output). We also outline a number of issues and challenges, including those concerning the used datasets, and identify the main current topical trends and how to possibly advance them. In particular, we introduce the novel concept of malware analysis economics, regarding the study of existing trade-offs among key metrics, such as analysis accuracy and economical costs.
Virtual autopsy using imaging: bridging radiologic and forensic sciences. A review of the Virtopsy and similar projects

The transdisciplinary research project Virtopsy is dedicated to implementing modern imaging techniques into forensic medicine and pathology in order to augment current examination techniques or even to offer alternative methods. Our project relies on three pillars: three-dimensional (3D) surface scanning for the documentation of body surfaces, and both multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) to visualise the internal body. Three-dimensional surface scanning has delivered remarkable results in the past in the 3D documentation of patterned injuries and of objects of forensic interest as well as whole crime scenes. Imaging of the interior of corpses is performed using MSCT and/or MRI. MRI, in addition, is also well suited to the examination of surviving victims of assault, especially choking, and helps visualise internal injuries not seen at external examination of the victim. Apart from the accuracy and three-dimensionality that conventional documentations lack, these techniques allow for the re-examination of the corpse and the crime scene even decades later, after burial of the corpse and liberation of the crime scene. We believe that this virtual, non-invasive or minimally invasive approach will improve forensic medicine in the near future.
Learning Disentangled Representations for Timber and Pitch in Music Audio

Timbre and pitch are the two main perceptual properties of musical sounds. Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the effect of the other. Researchers have managed to handcraft such timbre-invariant or pitch-invariant features using domain knowledge and signal processing techniques, but it remains difficult to disentangle them in the resulting feature representations. Drawing upon state-of-the-art techniques in representation learning, we propose in this paper two deep convolutional neural network models for learning disentangled representation of musical timbre and pitch. Both models use encoders/decoders and adversarial training to learn music representations, but the second model additionally uses skip connections to deal with the pitch information. As music is an art of time, the two models are supervised by frame-level instrument and pitch labels using a new dataset collected from MuseScore. We compare the result of the two disentangling models with a new evaluation protocol called “timbre crossover,” which leads to interesting applications in audiodomain music editing. Via various objective evaluations, we show that the second model can better change the instrumentation of a multi-instrument music piece without much affecting the pitch structure. By disentangling timbre and pitch, we envision that the model can contribute to generating more realistic music audio as well.
Real time fabric defect detection system on Matlab and C++/Opencv platforms

In industrial fabric productions, real time systems are needed to detect the fabric defects. This paper presents a real time defect detection approach which compares the time performances of Matlab and C++ programming languages. In the proposed method, important texture features of the fabric images are extracted using CoHOG method. Artificial neural network is used to classify the fabric defects. The developed method has been applied to detect the knitting fabric defects on a circular knitting machine. An overall defect detection success rate of 93% is achieved for the Matlab and C++ applications. To give an idea to the researches in defect detection area, real time operation speeds of Matlab and C++ codes have been examined. Especially, the number of images that can be processed in one second has been determined. While the Matlab based coding can process 3 images in 1 second, C++/Opencv based coding can process 55 images in 1 second. Previous works have rarely included the practical comparative evaluations of software environments. Therefore, we believe that the results of our industrial experiments will be a valuable resource for future works in this area.
Modern Face Recognition with Deep Learning

Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions.
Smart technologies for personalized experiences: a case study in the hospitality domain

Recent advances in the field of technology have led to the emergence of innovative technological smart solutions providing unprecedented opportunities for application in the tourism and hospitality industry. With intensified competition in the tourism market place, it has become paramount for businesses to explore the potential of technologies, not only to optimize existing processes but facilitate the creation of more meaningful and personalized services and experiences. This study aims to bridge the current knowledge gap between smart technologies and experience personalization to understand how smart mobile technologies can facilitate personalized experiences in the context of the hospitality industry. By adopting a qualitative case study approach, this paper makes a two-fold contribution; it a) identifies the requirements of smart technologies for experience creation, including information aggregation, ubiquitous mobile connectedness and real time synchronization and b) highlights how smart technology integration can lead to two distinct levels of personalized tourism experiences. The paper concludes with the development of a model depicting the dynamic process of experience personalization and a discussion of the strategic implications for tourism and hospitality management and research.
Using Geometry to Detect Grasp Poses in 3D Point Clouds

This paper proposes a new approach to using machine learning to detect grasp poses on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand poses that are expected to be good grasps. There are two main contributions. First, we identify a set of necessary conditions on the geometry of a grasp that can be used to generate a set of grasp hypotheses. This helps focus grasp detection away from regions where no grasp can exist. Second, we show how geometric grasp conditions can be used to generate labeled datasets for the purpose of training the machine learning algorithm. This enables us to generate large amounts of training data and it grounds our training labels in grasp mechanics. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at http://wiki.ros.org/agile_grasp.
Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.