nils-herrmann/clean_scirepeval huggingface dataset:
DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization

The large amount of videos popping up every day, make it is more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. In this paper, we propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve framelevel video summarization. Given a video, it can select a set of key frames, which contains the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced for enhancing temporal representation capturing. The generator aims to select key frames by using DTR units to effectively exploit global multi-scale temporal context and to complement the commonly used Bi-LSTM. To ensure that the summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The three-player loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on two public datasets SumMe and TVSum show the superiority of our DTR-GAN over the stateof-the-art approaches.
CMOS-Compatible Vertical-Silicon-Nanowire Gate-All-Around p-Type Tunneling FETs With $\leq 50$-mV/decade Subthreshold Swing

We present a vertical-silicon-nanowire-based p-type tunneling field-effect transistor (TFET) using CMOS-compatible process flow. Following our recently reported n-TFET , a low-temperature dopant segregation technique was employed on the source side to achieve steep dopant gradient, leading to excellent tunneling performance. The fabricated p-TFET devices demonstrate a subthreshold swing (SS) of 30 mV/decade averaged over a decade of drain current and an Ion/Ioff ratio of >; 105. Moreover, an SS of 50 mV/decade is maintained for three orders of drain current. This demonstration completes the complementary pair of TFETs to implement CMOS-like circuits.
High-frequency induction heating for small-foreign-metal particles Using SiC-MOSFETs inverter

This paper presents a detection method of small-foreign-metal particles using a 400 kHz SiC-MOSFETs high-frequency inverter. A 400 kHz SiC-MOSFETs high-frequency inverter is developed and applied to the small-foreign-metal particles detection on high-performance chemical films (HPCFs). HPCFs are manufactured with continuous production lines in industries. A new arrangement of IH coils are proposed, which is applicable for the practical production-lines of HPCFs. A prototype experimental model is constructed and tested. Experimental results demonstrate that the newly proposed IH coils with the constructed 400 kHz SiC-MOSFETs can heat small-foreign-metal particles and the heated small-foreign-metal particles can be detected by a thermographic camera. Experimental results with a new arrangement of IH coils also demonstrate that the proposed detection method of small-foreign-metal particles using 400 kHz SiC-MOSFETs high-frequency inverter can be applicable for the practical production lines of HPCFs.
NON INVASIVE BLOOD GLUCOMETER

Diabetes is a metabolic pathological condition of concern, which affects vital organs of body if not diagnosed and treated on time. Regular monitoring of blood glucose is important to avoid complication of diabetes. Commonly used glucose measurement methods are invasive which generally involves finger puncturing. These methods are painful and frequent pricking cause calluses on the skin and have risk of spreading infectious diseases. Therefore there is need to develop a non-invasive monitoring system which can measure blood glucose continuously without much problem. The present work is focused on development of non-invasive blood glucose measurement sensor system using Near-infrared (NIR) technique. In this study, near infrared optical measurement is applied to overcome the invasive issues. The designed device consists of an infrared emitter which placed over the fingertip for measurement of blood glucose optically. The near-infrared light will be sensed by the photo diode which having a certain wavelength. The intensity of received light is depending on the glucose molecules inside the blood. By analyzing the variation in voltages received after reflection in the cases the current diabetic condition as well as the approximate glucose level of the individual is going to be predicted. The obtained results are going to be validated with glucose meter readings and statistical analysis of the readings is to be done.
Faster, Cheaper, Better { a Hybridization Methodology to Develop Linear Algebra Software for GPUs

3 4 for (n = 0 ; n < nt ; n++) // l oop on c o l s 5 for (m = 0 ; m < mt ; m++) // l oop on rows 6 s t a r pu ma t r i x da t a r e g i s t e r (& t i l e h a nd l e [m] [ n ] , 0 , 7 &t i l e [m] [ n ] , M, M, N, s izeof ( f loat ) ) ;  Registration of the tiles as handles of matrix data type. Initialization. When initializing StarPU with starpu_init, StarPU automatically detects the topology of the machine and launches one thread per processing unit to execute the tasks. Data registration. Each tile is registered into StarPU to be associated to a handle. As shown in Figure 3, the tile_handle[m][n] StarPU abstraction is obtained from each actual memory pointer, tile[m][n]. Several data types are pre-defined for the handles. Here, tiles are registered as matrices since a submatrix is itself a matrix. Codelets definition. As shown at lines 38-42 for the sgemm_codelet in Figure 4, a codelet is a structure that describes a multi-versioned kernel (sgemm here). It contains pointers to the functions that implement the kernel on the different types of units: lines 1-14 for the CPU and 1630 for the GPU. The prototype of these functions is fixed: an array of pointers to the data interfaces that describe the local data replicates, followed by a pointer to some user-provided argument for the codelet. The STARPU_MATRIX_GET_PTR is a helper function that takes a data interface in the matrix format and returns the address of the local copy. Function starpu_unpack_cl_args is also a helper function that retrieves the arguments stacked in the cl_arg pointer by the application. Those arguments are passed when the tasks are inserted. Tasks insertion. In StarPU, a task consists of a codelet working on a list of handles. The access mode (e.g., read-write) of each handle is also required so that the runtime can compute the dependences between tasks. A task may also take values as arguments (passed through pointers). A task is inserted with the starpu_insert_Task function. 1 Lines 33-41 in Figure 5 shows how the sgemm task is inserted. The first argument is the codelet, sgemm_codelet. The following arguments are either values (keyword VALUE) or handles (when an access mode is specified). For instance, a value is specified at line 34, corresponding to the content of the notrans Other interfaces not discussed here are available.
Bangladeshi Vehicle Digital License Plate Recognition for Metropolitan Cities Using Support Vector Machine

Bangladeshi vehicle digital license plate recognition system using support vector machine for metropolitan cities (i.e. Dhaka, Chittagong) is presented in this paper. The proposed system divided into three major partslicense plate detection, plate character segmentation and character recognition. Experiments have been done for this proposed framework. More than 1000 images taken from various scenes are used, including diverse angles, different lightening conditions and complex scenes. In the first phase, Sobel operator and histogram analysis is used to detect the license plate region. Then, connected component labeling and bounding box technique used to segment the characters of detected license plate region. After that, Gabor filter is applied on the segmented characters to acquire desired character features. Since feature vector obtained using Gabor filter is in a high dimension, to reduce the dimensionality a nonlinear dimensionality reduction technique that is Kernel PCA has been used. Finally, Support Vector Machine has been used for classification. The experimental results show that proposed method can correctly recognize the license plate characters. vehicle digital license plate detection; morphology; Sobel operator; connected component labeling; Gabor filter; Kernel PCA; Support Vector Machine.
Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles

The performance of a vehicle control strategy, in terms of fuel economy improvement and emission reduction, is strongly influenced by driving conditions and drivers' driving styles. The term of ‘driving conditions’ here means the traffic conditions and road type, which is usually indicated by standard driving cycles, say FTP 75 and NEDC; the term of ‘driving styles’ here relates to the drivers' behavior, especially how drivers apply pressure on acceleration and brake pedal. To realize optimal fuel economy, it is ideal to obtain the information of future driving conditions and drivers' driving styles. This paper summarizes the methods and parameters that have been utilized to attain this end as well as the results. Based on this study, methods and parameters can be better selected for further improvement of driving conditions prediction and driving style recognition based hybrid electric vehicle control strategy.
Geometric Loss Functions for Camera Pose Regression with Deep Learning

Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet  is a deep convolutional neural network which learns to regress the 6-DOF camera pose from a single image. It learns to localize using high level features and is robust to difficult lighting, motion blur and unknown camera intrinsics, where point based SIFT registration fails. However, it was trained using a naive loss function, with hyper-parameters which require expensive tuning. In this paper, we give the problem a more fundamental theoretical treatment. We explore a number of novel loss functions for learning camera pose which are based on geometry and scene reprojection error. Additionally we show how to automatically learn an optimal weighting to simultaneously regress position and orientation. By leveraging geometry, we demonstrate that our technique significantly improves PoseNets performance across datasets ranging from indoor rooms to a small city.
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs). Using a sequence-tosequence model, and some trivial preprocessing and postprocessing of AMRs, we obtain a baseline accuracy of 53.1 (F-score on AMR-triples). We examine five different approaches to improve this baseline result: (i) reordering AMR branches to match the word order of the input sentence increases performance to 58.3; (ii) adding part-of-speech tags (automatically produced) to the input shows improvement as well (57.2); (iii) So does the introduction of super characters (conflating frequent sequences of characters to a single character), reaching 57.4; (iv) optimizing the training process by using pre-training and averaging a set of models increases performance to 58.7; (v) adding silver-standard training data obtained by an off-the-shelf parser yields the biggest improvement, resulting in an F-score of 64.0. Combining all five techniques leads to an F-score of 71.0 on holdout data, which is state-of-the-art in AMR parsing. This is remarkable because of the relative simplicity of the approach.
Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation

In this paper, a new approach to adaptive underfrequency load shedding (UFLS), a procedure for protecting electric power systems from dynamic instability and frequency collapse, is presented. It consists of two main stages. In the first stage, the frequency and the rate of frequency change are estimated by the nonrecursive Newton-type algorithm. By using the simplest expression of the generator swing equation, in the second algorithm stage, the magnitude of the disturbance is determined. The UFLS plan is adapted to the magnitude estimated, obtaining in this way a more efficient system operation during emergency conditions. Results of procedure testing are demonstrated through the dynamic simulations by using: 1) a simple three-machine test system and 2) a ten-machine New England system
CIPA: A collaborative intrusion prevention architecture for programmable network and SDN

Coordinated intrusion, like DDoS, Worm outbreak and Botnet, is a major threat to network security nowadays and will continue to be a threat in the future. To ensure the Internet security, effective detection and mitigation for such attacks are indispensable. In this paper, we propose a novel collaborative intrusion prevention architecture, i.e. CIPA, aiming at confronting such coordinated intrusion behavior. CIPA is deployed as a virtual network of an artificial neural net over the substrate of networks. Taking advantage of the parallel and simple mathematical manipulation of neurons in a neural net, CIPA can disperse its lightweight computation power to the programmable switches of the substrate. Each programmable switch virtualizes one to several neurons. The whole neural net functions like an integrated IDS/IPS. This allows CIPA to detect distributed attacks on a global view. Meanwhile, it does not require high communication and computation overhead. It is scalable and robust. To validate CIPA, we have realized a prototype on Software-Defined Networks. We also conducted simulations and experiments. The results demonstrate that CIPA is effective.
Deep learning for tactile understanding from visual and haptic data

Robots which interact with the physical world will benefit from a fine-grained tactile understanding of objects and surfaces. Additionally, for certain tasks, robots may need to know the haptic properties of an object before touching it. To enable better tactile understanding for robots, we propose a method of classifying surfaces with haptic adjectives (e.g., compressible or smooth) from both visual and physical interaction data. Humans typically combine visual predictions and feedback from physical interactions to accurately predict haptic properties and interact with the world. Inspired by this cognitive pattern, we propose and explore a purely visual haptic prediction model. Purely visual models enable a robot to “feel” without physical interaction. Furthermore, we demonstrate that using both visual and physical interaction signals together yields more accurate haptic classification. Our models take advantage of recent advances in deep neural networks by employing a unified approach to learning features for physical interaction and visual observations. Even though we employ little domain specific knowledge, our model still achieves better results than methods based on hand-designed features.
Stack4Things as a fog computing platform for Smart City applications

Fog computing envisions computation logic to be moved at the edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed. Delegating to the Cloud the whole burden of applications could not be efficient indeed, for example in case of workload bursts. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. It enables developers and users to manage an IoT infrastructure, remotely controlling nodes as well as virtualizing their functions and creating network overlays among them, implementing a provisioning model for Cyber-Physical Systems. Moreover, it provides mechanisms to scatter the application logic on top of the involved smart objects and to choose with fine granularity which specific tasks to delegate to centralized Cloud infrastructure. In this paper, we show the core Stack4Things mechanisms implementing a Fog computing approach towards a run-time “rewireable” Smart City paradigm. We demonstrate its effectiveness in a smart mobility scenario where vehicles interact with City-level smart objects to provide end users with highly responsive geolocalised services.
Electronic Shepherd - a Low-Cost, Low-Bandwidth, Wireless Network System

This paper reports a new novel low-cost, wireless communication network system, called the "Electronic Shepherd" (ES). The system is innovative in the way that it supports flock behavior, meaning that a flock leader monitors the state of the other elements in the flock using low-cost radio communication equipment. The paper addresses both details of the terminal devices and communication protocols, as well as testing of the system in a real environment. The ES system was originally made to address special needs for sheep and reindeer farmers who were seeking a system to keep track of their animals during the grazing season. The system, including GPS receivers, UHF radio communication transceivers and GPRS modems, contributes a new approach for low-cost networking and service implementation, not only for the purpose of animal tracking, but also for other applications where objects are to be monitored at a low cost.
AutoEpsDBSCAN : DBSCAN with Eps Automatic for Large Dataset

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is a primary method for database mining . It is either used as a stand-alone tool to get insight into the distribution of a data set or as a pre-processing step for other algorithms operating on the detected clusters. Almost all of the wellknown clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately [1], . DBSCAN (Density Based Spatial Clustering of Application with Noise)  is a base algorithm for density based clustering techniques. This paper gives a survey of density based clustering algorithms with the proposed enhanced algorithm that automatically selects the input parameters along with its implementation and comparison with the existing DBSCAN algorithm. The experimental results shows that the proposed algorithm can detect the clusters of varied density with different shapes and sizes from large amount of data which contains noise and outliers, requires only one input parameters and gives better output then the DBSCAN algorithm. KeywordsClustering Algorithms, Data mining, DBSCAN, Density, Eps, Minpts, and VDBSCAN.
From Feedforward to Recurrent LSTM Neural Networks for Language Modeling

Language models have traditionally been estimated based on relative frequencies, using count statistics that can be extracted from huge amounts of text data. More recently, it has been found that neural networks are particularly powerful at estimating probability distributions over word sequences, giving substantial improvements over state-of-the-art count models. However, the performance of neural network language models strongly depends on their architectural structure. This paper compares count models to feedforward, recurrent, and long short-term memory (LSTM) neural network variants on two large-vocabulary speech recognition tasks. We evaluate the models in terms of perplexity and word error rate, experimentally validating the strong correlation of the two quantities, which we find to hold regardless of the underlying type of the language model. Furthermore, neural networks incur an increased computational complexity compared to count models, and they differently model context dependences, often exceeding the number of words that are taken into account by count based approaches. These differences require efficient search methods for neural networks, and we analyze the potential improvements that can be obtained when applying advanced algorithms to the rescoring of word lattices on large-scale setups.
Named entity recognition on Indonesian microblog messages

This paper describes a model to address the task of named-entity recognition on Indonesian microblog messages due to its usefulness for higher-level tasks or text mining applications on Indonesian microblogs. We view our task as a sequence labeling problem using machine learning approach. We also propose various word-level and orthographic features, including the ones that are specific to the Indonesian language. Finally, in our experiment, we compared our model with a baseline model previously proposed for Indonesian formal documents, instead of microblog messages. Our contribution is two-fold: (1) we developed NER tool for Indonesian microblog messages, which was never addressed before, (2) we developed NER corpus containing around 600 Indonesian microblog messages available for future development.
Assessing children's inference generation: what do tests of reading comprehension measure?

Previous research suggests that children with specific comprehension difficulties have problems with the generation of inferences. This raises important questions as to whether poor comprehenders have poor comprehension skills generally, or whether their problems are confined to specific inference types.



The main aims of the study were (a) using two commonly used tests of reading comprehension to classify the questions requiring the generation of inferences, and (b) to investigate the relative performance of skilled and less-skilled comprehenders on questions tapping different inference types.


SAMPLE
The performance of 10 poor comprehenders (mean age 110.06 months) was compared with the performance of 10 normal readers (mean age 112.78 months) on two tests of reading comprehension.



A qualitative analysis of the NARA II (form 1) and the WORD comprehension subtest was carried out. Participants were then administered the NARA II, WORD comprehension subtest and a test of non-word reading.



The NARA II was heavily reliant on the generation of knowledge-based inferences, while the WORD comprehension subtest was biased towards the retention of literal information. Children identified by the NARA II as having comprehension difficulties performed in the normal range on the WORD comprehension subtests. Further, children with comprehension difficulties performed poorly on questions requiring the generation of knowledge-based and elaborative inferences. However, they were able to answer questions requiring attention to literal information or use of cohesive devices at a level comparable to normal readers.



Different reading tests tap different types of inferencing skills. Lessskilled comprehenders have particular difficulty applying real-world knowledge to a text during reading, and this has implications for the formulation of effective intervention strategies.