nils-herrmann/clean_scirepeval huggingface dataset:
Breast Cancer Image Enhancement using Median Filter and CLAHE

Aziz Makandar, Bhagirathi Halalli  Breat cancer is one of the major causes of death for woman in world wide. Mammography is the basic screening technique for early detection of tomour in the breast. The major objective of mammography is to detecting the small lumps at an earliest because they may lead for cancer. But it is difficult to identify the very small tomours in the conventional mammography because they are very noisy, low contrast, blur and fuzzy kind of images, it is necessary to enhance the mammography. Enhancement is done to bring out specific features of the mammography such as mass and microcalcification and to heighlight certain characteristics for early detection and easy diagnosis of breast cancer. This paper comprises the different denoising and cintrast enhancement techniques to recognize the suitable enhancement technique for mammography. Mammograms are denoised by linear and nonlinear filtering techniques and efficiency is measured by Root Meam Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) and then contrast of the image is enhanced by histogram based techniques.
Directly Addressable Variable-Length Codes

We introduce a symbol reordering technique that implicitly synchronizes variable-length codes, such that it is possible to directly access the i-th codeword without need of any sampling method. The technique is practical and has many applications to the representation of ordered sets, sparse bitmaps, partial sums, and compressed data structures for suffix trees, arrays, and inverted indexes, to name just a few. We show experimentally that the technique offers a competitive alternative to other data structures that handle this problem.
Interactive Multimodal Learning Environments Special Issue on Interactive Learning Environments : Contemporary Issues and Trends

What are interactive multimodal learning environments and how should they be designed to promote students’ learning? In this paper, we offer a cognitive–affective theory of learning with media from which instructional design principles are derived. Then, we review a set of experimental studies in which we found empirical support for five design principles: guided activity, reflection, feedback, control, and pretraining. Finally, we offer directions for future instructional technology research.
Learning to Map Vehicles into Bird's Eye View

Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies. This paper presents a way to learn a semantic-aware transformation which maps detections from a dashboard camera view onto a broader bird’s eye occupancy map of the scene. To this end, a huge synthetic dataset featuring 1M couples of frames, taken from both car dashboard and bird’s eye view, has been collected and automatically annotated. A deep-network is then trained to warp detections from the first to the second view. We demonstrate the effectiveness of our model against several baselines and observe that is able to generalize on real-world data despite having been trained solely on synthetic ones.
Representation Learning with Contrastive Predictive Coding

While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.
Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury

Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of executive dysfunction. We show for the first time that altered subcortical connectivity is associated with large-scale network disruption in traumatic brain injury and that this disruption is related to the cognitive impairments seen in these patients.
Optimizing power-accuracy trade-off in approximate adders

Approximate circuit design has gained significance in recent years targeting applications like media processing where full accuracy is not required. In this paper, we propose an approximate adder in which the approximate part of the sum is obtained by finding a single optimal level that minimises the mean error distance. Therefore hardware needed for the approximate part computation can be removed, which effectively results in very low power consumption. We compare the proposed adder with various approximate adders in the literature in terms of power and accuracy metrics. The power savings of our adder is shown to be 17% to 55% more than power savings of the existing approximate adders over a significant range of accuracy values. Further, in an image addition application, this adder is shown to provide the best trade-off between PSNR and power.
Exploring Relationships: A Systematic Review on Intimate Partner Violence and Attachment

Intimate Partner Violence (IPV) is an important public health challenge. In recent years, there has been a greater awareness concerning this phenomenon, its causes and consequences. Due to the relational nature of IPV, attachment theory (Bowlby, 1988) appears a useful framework to read the phenomenon and to better understand its components and its dynamics to provide more precise and tailored interventions in the future.  To summarize our knowledge of the research concerning IPV and attachment with an aim to better design and implement future research.  Computer database researches were conducted using the following databases: Psychinfo, Psycharticle, Medline, Scopus, Web of Science, and PubMed (all years to the 01/02/2018). Search terms were compiled into two concepts for all database namely Attachment and IPV.  After removing the duplicates, a total of 3,598 records was screened, resulting in the identification of 319 full-text articles to be further scrutinized. Upon closer examination, there was consensus that 113 of those studies met the study inclusion criteria. Data was organized considering specifically studies concerning (1) IPV victimization and attachment, (2) IPV perpetration and attachment (both these sections were articulated in Physical, Psychological, and Sexual IPV), and (3) New research (comprising same-sex couples, IPV and attachment in couple contexts and IPV profiles and attachment among perpetrators).  A number of studies failed to find significant associations between insecure attachment and IPV victimization or perpetration. Additional research is needed to provide a greater understanding of different IPV forms and to aid in the development of prevention and treatment interventions.
Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines

In this paper, we consider the problem of federated (or decentralized) learning using ADMM with multiple agents. We consider a scenario where a certain fraction of agents (referred to as Byzantines) provide falsified data to the system. In this context, we study the convergence behavior of the decentralized ADMM algorithm. We show that ADMM converges linearly to a neighborhood of the solution to the problem under certain conditions. We next provide guidelines for network structure design to achieve faster convergence. Next, we provide necessary conditions on the falsified updates for exact convergence to the true solution. To tackle the data falsification problem, we propose a robust variant of ADMM. We also provide simulation results to validate the analysis and show the resilience of the proposed algorithm to Byzantines.
SCAN: Learning Hierarchical Compositional Visual Concepts

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts.
Pattern Recognition: Possible Research Areas and Issues

Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervised, semi-supervised learning and key research areas such as grammar induction. Novel solutions to these possible problems could be well deployed for character recognition, speech analysis, man and machine diagnostics, person identification, industrial inspection and so on. The paper concludes with brief discussion on open issues that need to be addressed by future researchers.
Beamforming in Non-Regenerative Two-Way Multi-Antenna Relay Networks for Simultaneous Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beamforming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to maximize the sum rate of two-way relay network under the transmit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex procedure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-antenna sources is considered, we propose the alternating optimization based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.
Examining the Feasibilities of Industry 4 . 0 for the Hospitality Sector with the Lens of Management Practice

Industry 4.0 and its impact in the manufacturing sector are well documented. However, the service sector is understudied, and it is also facing the challenges of mass customization, digital enhancement, smart work environment, and efficient supply chain. The aim of this study is to fill this research gap by exploring the issues of Industry 4.0 in the service sector, with cases in the hospitality industry. All the challenges of Industry 4.0 require continuous innovation and learning, which is dependent on people and the enterprise’s capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and an effective learning and innovation environment. This paper proposes a framework of management practices which can promote the environment of innovation and learning in an organization, and hence facilitate business to match the pace of Industry 4.0 by facilitating technology acceptance e.g., digital enhancements and implementation of cyber physical systems (CPS). This study integrates the literature with logical beliefs to suggest the appropriate management practices for Industry 4.0. It represents one of the initial attempts to draw research attention towards the important role of management practices in Industry 4.0, as most of the recent studies have been restricted to the technological aspects. Semi-structured interviews of hospitality employees are conducted to explore the management practices suitable for meeting the challenges of Industry 4.0, specifically for informing the service sector.
Resolving attachment injuries in couples using emotionally focused therapy: steps toward forgiveness and reconciliation.

The goal of this study was to use task analysis to verify that the attachment injury resolution model described in this article discriminates resolved from nonresolved couples. Twenty-four couples with an attachment injury received, on average, 13 sessions of emotionally focused therapy (EFT). At the end of treatment, 15 of the 24 couples were identified as resolved. Segments of best sessions for all couples were transcribed and rated on 2 process measures. Resolved couples were found to be significantly more affiliative and achieved deeper levels of experiencing than nonresolved couples. They also showed significant improvements in dyadic satisfaction and forgiveness than nonresolved couples. The results support the attachment injury resolution model and suggest that resolution during EFT is beneficial to couples.
A case study of user participation in the information systems development process

There are many in the information systems discipline who believe that user participation is necessary for successful systems development. However, it has been suggested that this belief is neither grounded in theory nor substantiated by research data. This may indicate that researchers have not addressed fully the underlying complexity of the concept. If so, this is indicative of a deficiency in understanding user participation in information systems development as it occurs in organizations. In order to enhance the extant understanding of participative information systems development, the present study adopts a qualitative, case-based approach to research so as to provide an in-depth description of the complex social nature of the phenomenon as manifested in one organization. The results of the study illustrate that a high degree of direct and indirect user participation did not guarantee the successful implementation and use of information systems in the organization studied. Such participatory development practices did, however, result in the development of systems that adequately captured user requirements and hence satisfied user informational needs. It was clear that despite the perceived negative impact, which the new systems would have on user work-related roles and activities, the existence of an organization-wide participative policy, and associated participative structures, coupled with a favorable organization culture, generated a participatory development climate that was conducive to the successful development of information systems, while not guaranteeing it. That said, the central conclusion of this study was that user dissatisfaction with developed systems centered on the poor management of change in the organization.
A Survey of Parallelization Techniques for Multigrid Solvers

This paper surveys the techniques that are necessary for constructing computationally efficient parallel multigrid solvers. Both geometric and algebraic methods are considered. We first cover the sources of parallelism, including traditional spatial partitioning and more novel additive multilevel methods. We then cover the parallelism issues that must be addressed: parallel smoothing and coarsening, operator complexity, and parallelization of the coarsest grid solve.
A Study on Threads Detection and Tracking Systems for Military Applications using WSNs

In Wireless Sensor Network (WSN) applications it is critical to accurately determine the location of the distributed sensor nodes in order to report the data that is geographically meaningful. Since localization and tracking algorithms have been attracting research and development attention recently, a wide range of existing approaches regarding this topic have emerged. Tracking and localization algorithms have been proposed for different WSN applications including civilian, industrial and safety applications. A few research studies focused on tracking threads through military applications, such as detecting and tracking threads through border security area. Therefore this paper studies and investigates the existing WSN based tracking and localization algorithms and summarizes the potential requirements for localizing and tracking threads through military applications. The existing systems are categorized and discussed. A critical analysis is found in this paper, in order to guide the developer to design and implement a WSN-based tracking system for military applications.
A Security approach for Data Migration in Cloud Computing

Cloud computing is a new paradigm that combines several computing concepts and technologies of the Internet creating a platform for more agile and cost-effective business applications and IT infrastructure. The adoption of Cloud computing has been increasing for some time and the maturity of the market is steadily growing. Security is the question most consistently raised as consumers look to move their data and applications to the cloud. I justify the importance and motivation of security in the migration of legacy systems and I carry out an approach related to security in migration processes to cloud with the aim of finding the needs, concerns, requirements, aspects, opportunities and benefits of security in the migration process of legacy systems.
A Frequency Selective Radome With Wideband Absorbing Properties

A frequency selective radome is presented, acting as a pass band filter at a given frequency band, while behaving as an absorber above the transmission band. The pass band behavior is obtained by a metallic FSS realized through a compact interdigitated Jerusalem cross element characterized by a very large rejection band. The metallic FSS is used as the ground plane of a thin wideband absorber based on resistive high-impedance surfaces within the total reflection band. The outer absorber reduces the signature of the antenna system when the radome is illuminated by out of band signals. The resistive FSS which comprises the absorber is designed so to minimize losses within the transmitting band of the radome. The composite structure is thoroughly analyzed by an efficient equivalent circuit approach and by full-wave numerical simulations.
Skintillates: Designing and Creating Epidermal Interactions

Skintillates is a wearable technology that mimics tattoos - the oldest and most commonly used on-skin displays in human culture. We demonstrate that by fabricating electrical traces and thin electronics on temporary tattoo paper, a wide array of displays and sensors can be created. Just like the traditional temporary tattoos often worn by children and adults alike, Skintillates flex naturally with the user's skin. Our simple fabrication technique also enables users to freely design and print with a full range of colors to create application-specific customized designs. We demonstrate the technical capabilities of Skintillates as sensors and as expressive personal and private displays through a series of application examples. Finally, we detail the results of a set of user studies that highlight the user experience, comfort, durability, acceptability, and application potential for Skintillates.
Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model

Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs— ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a reliable and robust model. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made in the context of explaining consumer intention to use on-line shopping. Besides ease of use and usefulness, compatibility, privacy, security, normative beliefs, and selfefficacy are included in an augmented TAM. A test of this model, with data collected from 281 consumers, show support for seven of nine research hypotheses. Specifically, compatibility, usefulness, ease of use, and security were found to be significant predictors of attitude towards on-line shopping, but privacy was not. Further, intention to use on-line shopping was strongly influenced by attitude toward on-line shopping, normative beliefs, and self-efficacy.