nils-herrmann/clean_scirepeval huggingface dataset:
Terahertz band communications: Applications, research challenges, and standardization activities

Terahertz frequency band, 0.1–10THz, is envisioned as one of the possible resources to be utilized for wireless communications in networks beyond 5G. Communications over this band will be feature a number of attractive properties, including potentially terabit-per-second link capacities, miniature transceivers and, potentially, high energy efficiency. Meanwhile, a number of specific research challenges have to be addressed to convert the theoretical estimations into commercially attractive solutions. Due to the diversity of the challenges, the research on THz communications at its early stages was mostly performed by independent communities from different areas. Therefore, the existing knowledge in the field is substantially fragmented. In this paper, an attempt to address this issue and provide a clear and easy to follow introduction to the THz communications is performed. A review on the state-of-the-art in THz communications research is given by identifying the target applications and major open research challenges as well as the recent achievements by industry, academia, and the standardization bodies. The potential of the THz communications is presented by illustrating the basic tradeoffs in typical use cases. Based on the given summary, certain prospective research directions in the field are identified.
Personality development: continuity and change over the life course.

The development of personality across the human life course may be observed from three different standpoints: the person as actor (behaving), agent (striving), and author (narrating). Evident even in infancy, broad differences in social action patterns foreshadow the long-term developmental elaboration of early temperament into adult dispositional traits. Research on personal strivings and other motivational constructs provides a second perspective on personality, one that becomes psychologically salient in childhood with the consolidation of an agentic self and the articulation of more-or-less stable goals. Layered over traits and goals, internalized life stories begin to emerge in adolescence and young adulthood, as the person authors a narrative identity to make meaning out of life. The review traces the development of traits, goals, and life stories from infancy through late adulthood and ends by considering their interplay at five developmental milestones: age 2, the transition to adolescence, emerging adulthood, midlife, and old age.
IoT Interoperability—On-Demand and Low Latency Transparent Multiprotocol Translator

In the Industrial Internet of Things (IIoT) there is a clear need for a high level of interoperability between independently developed systems, often from different vendors. Traditional methods of interoperability including protocol gateways and adapters are often used at the network layer. Recent work on application interoperability has emphasized the use of middleware or protocol proxy/gateway. However, middleware tends to move the interoperability problem rather than solving it, and there are scalability issues with increasing the number of proxies, reconfiguration effort, and required bandwidth and processing overheads. This paper proposes a secure, on-demand, and transparent protocol translator for the IIoT. Targeting the challenge of interoperability between Internet protocol-based communication protocols, this paper analyzes current solutions and develops a set of requirements to be met by IoT protocol interoperability. The proposed protocol translator is not a middleware, it is a service-oriented architecture-based participant, it is used on-demand when needed, it does not introduce design time dependencies, it operates transparently, it supports low-latency, and it is secured through the use of Arrowhead authorization and authentication.
Segmentation Guided Attention Networks for Visual Question Answering

In this paper we propose to solve the problem of Visual Question Answering by using a novel segmentation guided attention based network which we call SegAttendNet. We use image segmentation maps, generated by a Fully Convolutional Deep Neural Network to refine our attention maps and use these refined attention maps to make the model focus on the relevant parts of the image to answer a question. The refined attention maps are used by the LSTM network to learn to produce the answer. We presently train our model on the visual7W dataset and do a category wise evaluation of the 7 question categories. We achieve state of the art results on this dataset and beat the previous benchmark on this dataset by a 1.5% margin improving the question answering accuracy from 54.1% to 55.6% and demonstrate improvements in each of the question categories. We also visualize our generated attention maps and note their improvement over the attention maps generated by the previous best approach.
Power efficiency optimization of charge pumps in embedded low voltage NOR flash memory

A new concept for improving efficiency of a charge pump system is presented. The concept is based on a set of elementary charge pumps connected in parallel in addition to the detection of the number of memory cells to be programmed at the same time. The proposed solution is able to supply current to a memory cell, while operating with maximum efficiency by activating one, two or more charge pumps. The proposed system is implemented using a 55 nm UMC High Voltage CMOS technology with a power supply of 1.2V. In the worst case where only one cell is to be programmed, simulation results show an improvement of power efficiency by a factor 2. The area penalty of the proposed system is only 3.8%.
The Most Uncreative Examinee: A First Step toward Wide Coverage Natural Language Math Problem Solving

We report on a project aiming at developing a system that solves a wide range of math problems written in natural language. In the system, formal analysis of natural language semantics is coupled with automated reasoning technologies including computer algebra, using logic as their common language. We have developed a prototype system that accepts as its input a linguistically annotated problem text. Using the prototype system as a reference point, we analyzed real university entrance examination problems from the viewpoint of end-to-end automated reasoning. Further, evaluation on entrance exam mock tests revealed that an optimistic estimate of the system’s performance already matches human averages on a few test sets.
Contextual and Structural Language Understanding in Dialogues

Natural language understanding (NLU) is a core component of a dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module ignores the contexts of utterances and tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, contexts and linguistic properties provide informative cues for better understanding. This paper introduces a novel model, a generalization of RNN to additionally incorporate 1) contextual utterances and 2) non-flat network topologies guided by prior knowledge. The model automatically figures out the salient and accurate contexts and substructures that are essential to predict the semantic tags of the given sentences, resulting in better understanding. The experiments showed the significant improvement on contextual and structural language understanding scenarios.
Exemplar-Based Portrait Style Transfer

Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits. Previous methods use facial landmarks to densely align the content face with the style face to reduce the artifacts. However, they can only handle the facial region. As for the whole image, building the dense correspondence is difficult and may easily introduce errors. In this paper, we propose a robust approach for portrait style transfer that gets rid of dense correspondence. Our approach is based on the guided image synthesis framework. We propose three novel guidance maps for the synthesis process. Contrary to former methods, these maps do not require the dense correspondence between content image and style image, which allows our method to handle the whole portrait photo instead of facial region only. In comparison with recent neural style transfer methods, our method achieves more pleasing results and preserves more texture details. Extensive experiments demonstrate our advantage over former methods on portrait style transfer.
An Accurate Multi-Row Panorama Generation Using Multi-Point Joint Stitching

Most of the existing panorama generation tools require the input images to be captured along one direction, and yield a narrow strip panorama. To generate a large viewing field panorama, this paper proposes a multi-row panorama generation (MRPG) method. For a pan/tilt camera whose scanning path covers a wide range of horizontal and vertical views, the image frames in different views correspond to different coordinate benchmarks and different projections. And the image frame should not only be aligned with the continuous frames in timeline but also be aligned with other frames in spatial neighborhood even with long time intervals. For these problems, MRPG first designs an optimal scanning path to cover the large viewing field, and chooses the center frame as the reference frame to start to stitch. The stitching order of multi-frame is arranged in first-column and second-row to ensure a small alignment error. Moreover, MRPG proposes a multi-point joint stitching method to eliminate the seams and correct the distortions, which makes the current frame accurately integrated into the panoramic canvas from all directions. Experimental results show that MRPG can generate a more accurate panorama than other state-of-the-art image stitching methods, and give a better visual effect for a large viewing field panorama.
Information extraction and text mining of Ancient Vattezhuthu characters in historical documents using image zoning

The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient characters conversion still poses a big challenge. Because image recognition technology has reached near-perfection when it comes to scanning English and other language text. But optical character recognition (OCR) software capable of digitizing printed Tamil text with high levels of accuracy is still elusive. Only a few people are familiar with the ancient characters and make attempts to convert them into written documents manually. The proposed system overcomes such a situation by converting all the ancient historical documents from inscriptions and palm manuscripts into Tamil digital text format. It converts the digital text format using Tamil unicode. Our algorithm comprises different stages: i) image preprocessing, ii) feature extraction, iii) character recognition and iv) digital text conversion. The first phase conversion accuracy of the Brahmi script rate of our algorithm is 91.57% using the neural network and image zoning method. The second phase of the Vattezhuthu character set is to be implemented. Conversion accuracy of Vattezhuthu is 89.75%.
REALTIME COMPOSITING OF PROCEDURAL FACADE TEXTURES ON THE GPU

The real time rendering of complex virtual city models has become more important in the last few years for many practical applications like realistic navigation or urban planning. For maximum rendering performance, the complexity of the geometry or textures can be reduced by decreasing the resolution until the data set can fully reside on the memory of the graphics card. This typically results in a low quality of the virtual city model. Alternatively, a streaming algorithm can load the high quality data set from the hard drive. However, this approach requires a large amount of persistent storage providing several gigabytes of static data. We present a system that uses a texture atlas containing atomic tiles like windows, doors or wall patterns, and that combines those elements on-the-fly directly on the graphics card. The presented approach benefits from a sophisticated randomization approach that produces lots of different facades while the grammar description itself remains small. By using a ray casting apporach, we are able to trace through transparent windows revealing procedurally generated rooms which further contributes to the realism of the rendering. The presented method enables real time rendering of city models with a high level of detail for facades while still relying on a small memory footprint.
Shoulder impingement revisited: evolution of diagnostic understanding in orthopedic surgery and physical therapy

“Impingement syndrome” is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that “impingement syndrome” is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of “impingement syndrome”. Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of “anterior shoulder pain”. While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of “impingement syndrome”. For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.
BotGuard: Lightweight real-time botnet detection in software defined networks

The distributed detection of botnets may induce heavy computation and communication costs to network devices. Each device in related scheme only has a regional view of Internet, so it is hard to detect botnet comprehensively. In this paper, we propose a lightweight real-time botnet detection framework called Bot-Guard, which uses the global landscape and flexible configurability of software defined network (SDN) to identify botnets promptly. SDN, as a new network framework, can make centralized control in botnet detection, but there are still some challenges in such detections. We give a convex lens imaging graph (CLI-graph) to depict the topology characteristics of botnet, which allows SDN controller to locate attacks separately and mitigate the burden of network devices. The theoretical and experimental results prove that our scheme is capable of timely botnet detecting in SDNs with the accuracy higher than 90% and the delay less than 56 ms.
A Randomized Singular Value Decomposition Algorithm for Image Processing Applications

The main contribution of this paper is to demonstrate that a new randomized SVD algorithm, proposed by Drineas et. al. in , is not only of theoretical interest but also a viable and fast alternative to traditional SVD algorithms in applications (e.g. image processing). This algorithm samples a constant number of rows (or columns) of the matrix, scales them appropriately to form a small matrix, say S, and then computes the SVD of S (which is a good approximation to the SVD of the original matrix). We experimentally evaluate the accuracy and speed of this algorithm for image matrices, using various probability distributions to perform the sampling.
Medical image registration using mutual information

Analysis of multispectral or multitemporal images requires proper geometric alignment of the images to compare corresponding regions in each image volume. Retrospective three-dimensional alignment or registration of multimodal medical images based on features intrinsic to the image data itself is complicated by their different photometric properties, by the complexity of the anatomical objects in the scene and by the large variety of clinical applications in which registration is involved. While the accuracy of registration approaches based on matching of anatomical landmarks or object surfaces suffers from segmentation errors, voxel-based approaches consider all voxels in the image without the need for segmentation. The recent introduction of the criterion of maximization of mutual information, a basic concept from information theory, has proven to be a breakthrough in the field. While solutions for intrapatient affine registration based on this concept are already commercially available, current research in the field focuses on interpatient nonrigid matching.
A novel bridgeless buck-boost PFC converter

Conventional cascade buck-boost PFC (CBB-PFC) converter suffers from the high conduction loss in the input rectifier bridge. To resolve the above problem, a novel bridgeless buck-boost PFC topology is proposed in this paper. The proposed PFC converter which removes the input rectifier bridge has three conduction semiconductors at every moment. Comparing with CBB-PFC topology, the proposed topology reduces the conduction semiconductors, reduces conduction losses effectively, improves the efficiency of converter and is suitable for use in the wide input voltage range. In this paper, the average current mode control was implemented with UC3854, the theoretical analysis and design of detection circuits was presented. The experimental prototype with 400 V/600 W output and line input voltage range from 220 VAC to 380 VAC was built. Experimental results show that the proposed converter can improve 0.8% efficiency comparing CBB-PFC converter.
Group Recommendation Based on Financial Social Network for Robo-Advisor

Robo-advisor is a financial advisor that can get help from machine-learning algorithms to automatically analyze financial product risk levels and provide portfolio recommendations. In the previous work, robo-advisor mainly focused on the basic information and investment preferences of individual users and often ignored the relationship between groups and the individual’s risk preference. In the actual environment, the individual investment behavior and the group’s social relations are inseparable. In order to solve this challenge, this paper proposes a group recommendation model based on financial social networks and collaborative filtering algorithms. Compared with the latest personalized recommendation system, it not only considers the asset status and risk level of individual investors but also considers social relationships and risk levels among groups. With experiments on benchmark and real-world datasets, we demonstrate that the proposed algorithm achieves the superior performance on both the tasks compared to the state-of-the-art methods.
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.
Deep neural network for manufacturing quality prediction

Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this issue, a deep neural network (DNN), consisting of a deep belief network (DBN) in the bottom and a regression layer on the top, is proposed in this paper. The DBN uses a greedy algorithm for unsupervised feature learning. It could learn effective features for manufacturing quality prediction in an unsupervised pattern which has been proven to be effective for many fields. Then the learned features are inputted into the regression tool, and the quality predictions are obtained. One type of manufacturing system with multi-parameter is investigated by the proposed DNN model. The experiments show that the DNN has good performance of the deep architecture, and overwhelms the peer shallow models. It is recommended from this study that the deep learning technique is more promising in manufacturing quality prediction.
Using Intermediate Representations to Solve Math Word Problems

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.