nils-herrmann/clean_scirepeval huggingface dataset:
Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis
Venue: BIS
Year: 2020

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business, people facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real life purchase order process data.
Citation Recommendation based on Citation links Explanation
Year: 2021

With the rapid increase of scientific papers, it is difficult for researchers to obtain appropriate references. Due to the black box property of neural network, the existing content based citation recommendation methods are mainly embedding text into a low dimensional space, and citation links are not interpretable (e.g. paper A cites paper B because of topic X) To solve this problem, we propose a Weighted Citation Network with Citation Links Explanation (WCN CLE) algorithm. Because citation links mainly depends on the content, we need to explore textual reasons for citation links. Firstly, we extract word fragments through citation network information for each citation link as its explanation. Secondly, we construct a weighted HIN network with two kinds of vertex (paper and author) The weight of each edge is calculated by its word fragments. Then the query document is linked to the HIN network through author information and word fragments similarity with candidate papers. Finally, each vertex's feature representation is obtained by network representation learning method, and recommend papers through vector similarity. Experimental results on two real world datasets show that WCN CLE has better performance due to its integration of structural information and semantic information.
A hybrid broadband millimeter wave diode ring mixer with advanced IF extraction technique
Venue: 2007 European Microwave Conference
Year: 2007

We report on a hybrid octave bandwidth millimeter wave diode mixer with advanced IF extraction. The design uses a reproducible, low cost hybrid technology including thin film circuit and flip chip mounted diode chips. Side coupled Marchand baluns with IF tap provide balanced RF and LO signals as well as serve for extracting the IF signal. Measurement results for fixed, low IF frequency show a conversion loss around 10 dB from 35 to 67 GHz. Furthermore the mixer is suitable for IF frequencies up to 23 GHz. These features allow very flexible applications of the presented mixer in broadband test and measurement systems.
Atrial Fibrillation in WPW Syndrome
Year: 2015

In large scale general population studies involving children and adults, the prevalence of WPW is estimated to be 1 3 in 1000 individuals. At present, it is estimated that approximately 65% of adolescents and 40% of individuals over 30 years with WPW pattern on a resting ECG are asymptomatic. An incidence of 4.5 episodes of sudden death, including resuscitated sudden cardiac death (SCD) per 1000 patient years was recently reported. The mechanism of sudden cardiac death in patients with WPW is thought to be associated with atrial fibrillation Case Illustration We reported a case of 45 year old female came with unstable irregular wide complex tachycardia in the form of pre excited AF. Cardioversion successfully terminate the tachyaarhythmias. ECG in sinus rhythm revealed WPW pattern ECG. Patient then referred for catheter ablation. Successful ablation was done resulting a normal pattern ECG Conclusion AF in WPW syndrome could lead to devastating events such as cardiac arrest. ECG recognition at the first place is very important for early management. Early stratification and management in patients with WPW is important to diminished the risk of AF occurence and SCD.
The role of delayed elasticity and stress relaxation in Sn Bi Cu lead free solders solidified under permanent magnet stirring
Year: 2018

This paper develops methods to explore the influence of permanent magnet stirring (PMS) on solidification microstructure, thermal behavior and creep parameters of Sn 20Bi 0.4Cu alloys predicted from the primary creep stage at 25 degC. A series of short term stress relaxation test (SRT) and strain relaxation recovery test (SRRT) are conducted to assess the viscoelastic behavior of tested samples. A three components consisting of elastic strain ee, delayed elastic strain ed and viscous strain ev are developed and characterized from SRRT for anticipated creep type constitutive equation. Significant improvement of delayed elastic strain, about 46% of the elastic strain, has been detected after applying PMS. The relative contribution of dislocation mechanism with n values of 5.4 7.7 is discussed in terms of imposed strain and the role of PMS on promoting the columnar to equiaxed transition (CET) of lamellar b Sn dendrites. Moreover, n value is found to increase with increasing the ratio of ed/ev and decrease with the amount of ev. The PMS also significantly decreased the undercooling, pasty range, onset temperature, while maintaining the peak temperature at the same level. These effects have good prospects for lower temperature processed electronic interconnections.
Realizing n Type Field Effect Performance via Introducing Trifluoromethyl Groups into the Donor Acceptor Copolymer Backbone
Venue: Macromolecules
Year: 2019

Developing a new strategy to obtain n type organic semiconductors is crucial for the advance of organic electronics. We herein report the synthesis and investigation of a series of donor acceptor type diazaisoindigo based copolymers, named PAIID TFBVB C1, PAIID TFBVB C3, and PAIID BVB C3. After introduction of the trifluoromethyl groups with strong electron withdrawing ability into the molecular backbone of PAIID BVB C3 containing stilbene units, the obtained polymer PAIID TFBVB C3 shows obvious blue shifted UV vis absorption profiles because of the enlarged band gap revealed by the theoretical simulation. Meanwhile, both the energy levels of the lowest unoccupied molecular orbital (LUMO) of PAIID TFBVB C1 and PAIID TFBVB C3 with trifluoromethyl groups are lower than that of PAIID BVB C3 without trifluoromethyl groups, reducing the energy difference between the LUMO energy level and the work function of the source drain electrode and further facilitating electron injection. Consequently, both the PAIID TF.
SPOVC: a scalable RDF store using horizontal partitioning and column oriented DBMS
Venue: SWIM '12
Year: 2012

Organizing and indexing RDF data for efficient evaluation of SPARQL queries has been attracting a lot of attention in the recent past. Most of the techniques proposed in this context leverage the existing RDBMS or column oriented DB technologies. In this paper, we propose an organization SPOVC that uses five indexes, namely, Subject, Predicate, Object, Value and Class, on top of any column oriented DB. The main techniques used by the proposed scheme are horizontal partitioning of the logical indices and special indices for values and classes. The SPOVC approach has the advantage of delivering better performance if the underlying column store technology improves. The proposed approach is conceptually much simpler than the state of the art native storage based proposals and roughly gives the same performance. Our proposal extends an existing approach, SW Store, that uses column oriented DBs and vertical partitioning and obtains a two/three fold performance improvement. In addition, the proposed system is the only system that can effectively tackle SPARQL queries with filter patterns having range conditions and regular expressions.
Patterns of seasonal occurrence of sympatric killer whale lineages in waters off Southern Vancouver Island and Washington state, as determined by passive acoustic monitoring
Year: 2012

Supervisory Committee Dr. N. Ross Chapman (School of Earth and Ocean Sciences) Supervisor Dr. Richard K. Dewey (School of Earth and Ocean Sciences) Departmental Member Dr. S. Kim Juniper (School of Earth and Ocean Sciences) Departmental Member Dr. John K. B. Ford (Pacific Biological Station) Additional Member Killer whales inhabiting coastal waters of the northeastern Pacific are listed under the Canadian Species at Risk Act, which requires the identification of critical habitats for the recovery of their populations. Little is known about their distribution during the winter and what areas are important for their survival during these months. Passive acoustic monitoring is a valuable complementary method to traditional visual and photographic surveys although it has seldom been used to study killer whales and there are limitations in practice. There is a need to develop tools and protocols to maximize the efficiency of such studies. In this thesis, long term acoustic data collected with autonomous recorders were analyzed 1) to assess the performance of two types of analysis (Manual and Long Term Spectral Averages) for detecting and identifying killer whale calls and to compare the effects of using two different duty cycles (1/3 and 2/3) and 2) to investigate the seasonal occurrence of different killer whale populations at two sites off the west coasts of Vancouver Island and Washington: Swiftsure Bank and Cape Elizabeth. Both the use of Long Term Spectral Averages and a lower duty cycle resulted in a decrease in call detection and resolution of call identification, leading to underestimations of the amount of time the whales spent at the site. A compromise between a lower resolution data processing method
Anti Microbial, Anti Oxidant, and a Amylase Inhibitory Activity of Traditionally Used Medicinal Herbs: A Comparative Analyses of Pharmacology, and Phytoconstituents of Regional Halophytic Plants' Diaspora
Venue: Molecules
Year: 2020

Halophytes are the category of plants growing under harsh conditions of super salinity, and are wide spread in the coastal Mediterranean climatic conditions and desert oasis. They are adept at surviving through maintaining excessive production of enzymatic, and non enzymatic secondary metabolites, especially phenolics and flavonoids that primarily work as anti oxidants and phytoalexins. Five major halophyte species growing in the kingdom's Qassim's high salted desert regions were investigated for confirming their traditionally used biological activity of sugar control and anti infectious properties. In this context, the comparative presence of phenolics, and flavonoids together with anti microbial, anti oxidants, and the anti diabetic potentials of the plants' extracts were investigated through the a amylase inhibition method. The highest concentrations of phenolics and flavonoids were detected in Salsola imbricata (360 mg/g of the extract as Gallic Acid Equivalents/GAE, and 70.5 mg/g of the extract as Rutin Equivalents/RE) In contrast, the lowest concentrations of phenolics and flavonoids were detected in Salsola cyclophylla (126.6 mg/g GAE, and 20.5 mg/g RE) The halophytes were found rich in trace elements, a factor for water retention in high salinity plants, wherein iron and zinc elements were found comparatively in higher concentrations in Aeluropus lagopoides (4113 ug/kg, and 40.1 ug/kg, respectively) while the copper was detected in higher concentration (11.1 ug/kg) in S. imbricata, analyzed through Inductively Coupled Plasma Optical Emission Spectrometric (ICP OES) analysis. The anti oxidant potentials and a amylase enzyme inhibition based anti diabetic activity of S. imbricata was significantly higher than the other halophytes under study, wherein S. cyclophylla exhibited the lowest level of a amylase inhibition. The maximum DPPH radicals' (52.47 mg/mL) and a amylase inhibitions (IC50 22.98 ug/mL) were detected in A. lagopoides. The anti microbial activity against the Methicillin Resistant Staphylococcus aureus was strongly exhibited by Zygophyllum simplex (33 mm Inhibition Zone Diameter, 50 ug/mL Minimum Inhibitory Concentration) while Escherichia coli, Enterococcus faecalis, and Candida albicans growths were moderately inhibited by Tamarix aphylla. The current findings exhibited significant differences among the locally distributed halophytic plants species with regards to their bioactivity levels, anti oxidant potentials, and the presence of trace elements. The ongoing data corroborated the plants' traditional uses in infections and diabetic conditions. The enhanced local distribution of the plants' diaspora and higher density of occurrence of these plants species in this region, in comparison to their normal climatic condition's counterparts, seemed to be affected by humans' use of the species as part of the traditional and alternative medicine over a period of long time.
Bootstrapped Q learning with Context Relevant Observation Pruning to Generalize in Text based Games
Venue: EMNLP
Year: 2020

We show that Reinforcement Learning (RL) methods for solving Text Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalization. Our method first trains a base model using Q learning, which typically overfits the training games. The base model's action token distribution is used to perform observation pruning that removes irrelevant tokens. A second bootstrapped model is then retrained on the pruned observation text. Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x 20x fewer training games compared to previous state of the art methods despite requiring less number of training episodes.
An exploratory study of industry 4.0 in Malaysia: a case study of higher education institution in Malaysia
Venue: Indonesian Journal of Electrical Engineering and Computer Science
Year: 2019

Industry 4.0 is defining as a smart factory through the emerging technology of the virtual and physical world as a collective term of technologies and concepts of value chain organization. The lack of understanding about Industry 4.0 concepts in Malaysia, especially from Higher Education Institution perspective, becoming an issue in Malaysia. Furthermore, industries in Malaysia are still relying on external expertise to implement Industry 4.0 in their environment with the expectation of an increase in productivity, efficiency, quality and to move away from foreign workers. This research aims to describe the relationship between technologies within Industry 4.0, people and the environment from the perspective of the Higher Education Institution in Malaysia and identify the problem of applying Industry 4.0 at the Higher Education Institution. Qualitative interviews were conducted with participants from an academic area and Industry 4.0 expert who has experience in both industry and academia. The Work System Framework is adopted in this research as a theoretical lens to extract the relevant information and deepen the understanding of applying Industry 4.0 at the Higher Education Institution in Malaysia. Results of this research reveal the problems faced by the Higher Education Institution in Malaysia towards applying Industry 4.0 in teaching and learning based on the relationship of people, technology and the environment. The results may contribute knowledge to the universities in preparation for future ready graduates or talents as human capital for Industry 4.0 in Malaysia.
Impact of Performance Appraisal System on Job Satisfaction, Employee Engagement, Organizational Citizenship Behavior and Productivity
Year: 2011

Performance appraisal is a formal system that evaluates the quality of an employee's performance. Perfor mance appraisal is one of the most complex and controversial human resource techniques. In many organiza tions performance appraisal systems are used to identify the better performing employees who should get the majority of available merit pay increases, bonuses, and promotions Performance appraisal system is important to any organizational work performance; it determines the organization's success or failure. This paper summarizes the conceptual foundation of performance appraisal system and its relationship between Job Satisfaction, Organization Culture, Organization Citizenship Behavior, Employee Engagement and thus with Productivity.
Scaling up forest landscape restoration in Canada in an era of cumulative effects and climate change
Year: 2020

While the global restoration movement is rapidly gaining momentum, understanding the concept and benefits of forest and landscape restoration (FLR) is paramount to safeguarding the natural capital of Canada's forests. In the face of increasing cumulative effects, we investigated the opportunities for scaling up FLR efforts in Canadian forests. The pace of industrial natural resource extraction developments (logging, agriculture, mining, and energy sector) and their overlapping in time and space with the impacts of climate change have resulted in ecosystem function and services alteration, as well as changes in natural disturbance regimes (e.g. wildland fire and pests) These dramatic and synergistic changes to environmental, socio economic and cultural values occurring in the landscape need to be considered in land use planning but are highly variable and uncertain. We suggested that adding FLR to the land use agenda could offer a clear pathway to transform degraded landscapes into functional landscapes that deliver co benefits and livelihoods to multiple land users. Supported by existing policies, the practices and goals of FLR initiatives dovetail with Canadian national conservation priorities such as national woodland caribou recovery as well as the development of Indigenous protected areas and nature based climate solutions. At the international level, involvement in FLR initiatives such as the Bonn Challenge could advance Canadian climate change and ecosystem degradation commitments identified in the 2030 Sustainable Development Goals as well as in the Convention on Biological Diversity. To inform policymakers and practitioners, we suggested key collaborative actions to move towards a national restoration framework. Given competing land uses and values, a successful FLR strategy will require a multi stakeholder governance approach supported by the different levels of government as well as other land users including extractive industries and Indigenous communities. Assessing socio economic trade offs around planning, funding, implementing, and monitoring restoration efforts is also crucial.
A Theory of Fermat Paths for Non Line Of Sight Shape Reconstruction
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

We present a novel theory of Fermat paths of light between a known visible scene and an unknown object not in the line of sight of a transient camera. These light paths either obey specular reflection or are reflected by the object's boundary, and hence encode the shape of the hidden object. We prove that Fermat paths correspond to discontinuities in the transient measurements. We then derive a novel constraint that relates the spatial derivatives of the path lengths at these discontinuities to the surface normal. Based on this theory, we present an algorithm, called Fermat Flow, to estimate the shape of the non line of sight object. Our method allows, for the first time, accurate shape recovery of complex objects, ranging from diffuse to specular, that are hidden around the corner as well as hidden behind a diffuser. Finally, our approach is agnostic to the particular technology used for transient imaging. As such, we demonstrate mm scale shape recovery from pico second scale transients using a SPAD and ultrafast laser, as well as micron scale reconstruction from femto second scale transients using interferometry. We believe our work is a significant advance over the state of the art in non line of sight imaging.
Comparative Effectiveness of Kinesiotaping and Low Dye Taping In Improving Pain and Disability in Subjects with Plantar Fasciitis
Year: 2019

PF also known as the painful heel syndrome occurs in both males and females is primarily an overuse injury involving the inflammation of the fascia on the plantar surface of the foot and microtears of the plantar fascia at its insertion on the calcaneus. Conservative treatment involving the use of strapping or structural correction is a first line intervention for PF treatment. The knowledge gained on the effectiveness of Kinesio taping versus Low Dye taping, and which is the superior form of strapping for PF, may assist the practitioner in achieving a better and comprehensive treatment plan of PF.  To find out the comparative effectiveness of kinesiotaping in conjunction with conventional treatment and low dye taping in conjunction with conventional treatment for improving heel pain and disability in subjects with Plantar fasciitis.  Randomized control trial Setting: Participants were enrolled from Safdarjung Hospital Sports Injury Centre, Pandit Deendayal Upadhyaya National Institute for Persons with Physical Disabilities, ISIC Institute of Rehabilitation Sciences Patients: 45 subjects who were diagnosed with plantar fasciitis were included in the study Intervention: All the three groups received home exercises and supervised conventional exercise programme. The two experimental groups received Kinesiotaping and Low Dye Taping respectively.  Outcome measures included Numeric Pain Rating Scale (NPRS 11) Revised Foot Function Index (RFFI) Visual Analogue Scale (VAS)  There was significant improvement in Numeric Pain Rating Scale (NPRS 11) Revised Foot Function Index (RFFI) Visual Analogue Scale (VAS) within the groups at 1 week post intervention. Between groups analysis showed significant improvement in Visual Analogue Scale (VAS)  Small sample size (n=45).no follow ups done.  Addition of Kinesiotaping to the conventional group brought significant difference from conventional therapy group alone in improving first step pain (VAS) after one week of intervention but there was significant improvement in each group after one week post intervention.
Probabilistic Regression for Visual Tracking
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore crucial to represent the uncertainty in the target estimation. Although current prominent paradigms rely on estimating a state dependent confidence score, this value lacks a clear probabilistic interpretation, complicating its use. In this work, we therefore propose a probabilistic regression formulation and apply it to tracking. Our network predicts the conditional probability density of the target state given an input image. Crucially, our formulation is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task. The regression network is trained by minimizing the Kullback Leibler divergence. When applied for tracking, our formulation not only allows a probabilistic representation of the output, but also substantially improves the performance. Our tracker sets a new state of the art on six datasets, achieving 59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are available at https:/github.com/visionml/pytracking.