nils-herrmann/clean_scirepeval huggingface dataset:
Synthesizing Programs for Images using Reinforced Adversarial Learning

Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator’s output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, OMNIGLOT, CELEBA) and synthetic 3D datasets. A video of the agent can be found at https://youtu.be/iSyvwAwa7vk.
Seven V's of Big Data understanding Big Data to extract value

Big Data has shown lot of potential in real world industry and research community. We support the power and potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 7 V's. 7th V as `Value' is desired output for industry challenges and issues. We provide a brief survey study of 7 Vs of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
ArticulatedFusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera

This paper proposes a real-time dynamic scene reconstruction method capable of reproducing the motion, geometry, and segmentation simultaneously given live depth stream from a single RGB-D camera. Our approach fuses geometry frame by frame and uses a segmentationenhanced node graph structure to drive the deformation of geometry in registration step. A two-level node motion optimization is proposed. The optimization space of node motions and the range of physically-plausible deformations are largely reduced by taking advantage of the articulated motion prior, which is solved by an efficient node graph segmentation method. Compared to previous fusion-based dynamic scene reconstruction methods, our experiments show robust and improved reconstruction results for tangential and occluded motions.
Spectral–Spatial Classification and Shape Features for Urban Road Centerline Extraction

This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.
The internet of energy: a web-enabled smart grid system

The quest for sustainable energy models is the main factor driving research on smart grid technology. SGs represent the bridging paradigm to enable highly efficient energy production, transport, and consumption along the whole chain, from the source to the user. Although this concept promises to be very fruitful, the research on how to deploy it in the real world has just begun. A discussion on the enabling technologies for SGs and a possible roadmap for the profitable evolution thereof is the focus of this article. After introducing the recent trends that are pushing the SG paradigm, we will discuss various key scenarios for the SG, and briefly introduce some of its key requirements. We will then provide an analysis of how current and future standard solutions in the areas of communications and networking can be engineered into a system that fulfills the needs of the SG vision. We advocate the use of small, cheap, and resource-constrained devices with pervasive computing capabilities as the key component to deploy a ubiquitous energy control system. To this end, the recent efforts carried out by Internet standardization bodies such as the IETF and W3C toward the vision of the Internet of Things (IoT) are especially relevant. The various components of the proposed solution have been successfully showcased in real-world implementations, and relevant actors such as ETSI, ZigBee, and IPSO are already evaluating their potential for future IoT applications, making the Internet-based smart grid vision considered in this article practically achievable in the not too distant future.
Towards a Distributed Large-Scale Dynamic Graph Data Store

In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.
Describing Videos using Multi-modal Fusion

Describing videos with natural language is one of the ultimate goals of video understanding. Video records multi-modal information including image, motion, aural, speech and so on. MSR Video to Language Challenge provides a good chance to study multi-modality fusion in caption task. In this paper, we propose the multi-modal fusion encoder and integrate it with text sequence decoder into an end-to-end video caption framework. Features from visual, aural, speech and meta modalities are fused together to represent the video contents. Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) are then used as the decoder to generate natural language sentences. Experimental results show the effectiveness of multi-modal fusion encoder trained in the end-to-end framework, which achieved top performance in both common metrics evaluation and human evaluation.
Efficient in-memory indexing of network-constrained trajectories

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.
Cancer Metabolomics and the Human Metabolome Database.

The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.
The design of a ZigBee-based greenhouse monitoring system

With the technological trends moving towards the Internet of Things (IoT), most of the continuous monitoring applications are migrating from wired networks to wireless low power short range communications. In a greenhouse, the continuous monitoring and management of the climatic conditions is of crucial importance. The paper presents an approach to the design and performance analysis of a flexible greenhouse monitoring wireless sensor module, based on general purpose microcontrollers and low power ZigBee communication modules. The results shows good performance in terms of functionality, accuracy, and overall cost. However, the measurements show that there is still space for system improvement in the sense of power consumption.
Graphene oxide dispersions in organic solvents.

The dispersion behavior of graphene oxide in different organic solvents has been investigated. As-prepared graphite oxide could be dispersed in N, N-dimethylformamide, N-methyl-2-pyrrolidone, tetrahydrofuran, and ethylene glycol. In all of these solvents, full exfoliation of the graphite oxide material into individual, single-layer graphene oxide sheets was achieved by sonication. The graphene oxide dispersions exhibited long-term stability and were made of sheets between a few hundred nanometers and a few micrometers large, similar to the case of graphene oxide dispersions in water. These results should facilitate the manipulation and processing of graphene-based materials for different applications.
A low profile 10:1 (200–2000 MHz) wide band long slot array

A low profile 10:1 band (200-2000 MHz) long slot array was developed for radar and EW applications. For performance demonstration, a prototype array of 11times11 elements (1.2 m times 1.2 m) was built and tested. It is backed by a ferrite loaded ground plane, separated from the aperture by 5-cm air gap. The antenna is ultra thin, with a total thickness of 8 cm including the ferrite tiles, about 5% of the wave length at 200 MHz. On average, the array has 2 dB loss over the 10:1 band. The antenna is designed for airborne missions that require very wide band and ultra-thin profile.
Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules

Extracting time expressions from free text is a fundamental task for many applications. We analyze time expressions from four different datasets and find that only a small group of words are used to express time information and that the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we propose a type-based approach named SynTime1 for time expression recognition. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related token regular expressions. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a lightweight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text from different domains and different text types. Experiments on benchmark datasets and tweets data show that SynTime outperforms state-of-the-art methods.
Textureless object detection using cumulative orientation feature

We propose a novel image feature for textureless object detection. The feature is based on quantized gradient orientations those have been shown to be robust to cluttered backgrounds and illumination changes. We make this feature robust to the appearance changes of a targeted object itself induced by its transformations and small deformations. In our proposed method, we add small random values to the similarity transformation parameters and synthesize many model images. Then quantized orientations are extracted on these images and the orientations are cumulated at each pixel. The frequencies of selected features are utilized as weights when calculating scores. Our proposed feature is evaluated on publicly available dataset and achieve top-class performance both in speed and detection accuracy compared to state-of-the-art techniques.
Mobile Based Attendance Marking System Using Android And Biometrics

We have seen over the years that the process of manual attendance has been carried out across almost all educational institutions. The process is not only time consuming but also sometimes inefficient resulting in the false marking of attendance. Today, we need not maintain pen and paper based attendance registers. Following this thought, we have proposed an attendance marking and calculation system which is implemented on Android mobile application integrating biometric scanner that communicates with the database and verification can be achieved. This Android application will give the students information on attendance and change in timetable if any whereas the biometric scanner is used for verification, authentication and to avoid proxy.
On-Street Parking Guidance with Real-Time Sensing Data for Smart Cities

On-street parking is an essential component of parking infrastructure for smart cities, which allows users to park near their destinations for short term. However, due to limited capacity, saturated on-street parking becomes a serious and widespread problem for urban transportation systems. Greedily searching for an on-street parking spot in a saturated area is often a frustrating task for drivers, and cruising for vacant parking spots results in additional delays and impaired local circulation. With the recent development of networked smart parking meter, real-time city-wide on- street parking information becomes available for more efficient parking management. In this paper, we design an online parking guidance system that recommends parking spots in real-time based on the parking availability prediction. With a receding horizon optimization framework, our solution minimizes the user's driving and walking cost by adapting the spatiotemporally dynamic supply and demand in the local area, significantly reducing parking competitions in a timely manner. We implement and evaluate our solution with a dataset of 13,503,655 parking records collected from 5228 in-ground sensors distributed in the Australian city Melbourne. The evaluation results show that our approach achieves up to 63.8% delay reduction compared with existing solutions.
Animals as Mobile Biological Sensors for Forest Fire Detection

This paper proposes a mobile biological sensor system that can assist in earlydetection of forest fires one of the most dreaded natural disasters on the earth. The main ideapresented in this paper is to utilize animals with sensors as Mobile Biological Sensors(MBS). The devices used in this system are animals which are native animals living inforests, sensors (thermo and radiation sensors with GPS features) that measure thetemperature and transmit the location of the MBS, access points for wireless communicationand a central computer system which classifies of animal actions. The system offers twodifferent methods, firstly: access points continuously receive data about animals' locationusing GPS at certain time intervals and the gathered data is then classified and checked tosee if there is a sudden movement (panic) of the animal groups: this method is called animalbehavior classification (ABC). The second method can be defined as thermal detection(TD): the access points get the temperature values from the MBS devices and send the datato a central computer to check for instant changes in the temperatures. This system may beused for many purposes other than fire detection, namely animal tracking, poachingprevention and detecting instantaneous animal death.
Evaluation of Business Processes for Business Process Standardization

Companies often implement multiple process variants in their organizations at the same time. Often these variants differ widely in terms of efficiency, quality and cycle time. In times of highly volatile global economic markets, companies cannot afford unnecessary redundant business processes. Business Process Standardization has therefore become a major topic in the field of Business Process Management, both in research and practice. Management decisions concerning standardization are difficult and complex, due to limited project budgets, organizational and technological changes as well as the increasing challenges of globalization. Choosing suitable processes for standardization is the essential precondition for streamlining business processes in the first place and therefore for reaping the full benefits of Business Process Standardization. However, there is hardly any tool available that supports the evaluation of business processes for standardization. To close this research gap, we develop an instrument for the evaluation of business processes in the context of Business Process Standardization with the aim to help practitioners in their day-to-day operations. Our research aims at providing the necessary preconditions for companies to profit from the advantages of business process standardization and to support decision-making.
A novel single switch transformerless quadratic DC/DC buck-boost converter

A novel quadratic buck-boost DC/DC converter is presented in this study. The proposed converter utilizes only one active switch and can step-up/down the input voltage, while the existing single switch quadratic buck/boost converters can only work in step-up or step-down mode. First, the proposed converter is analyzed in steady-state. Then, its performance is validated using simulations in MATLAB/Simulink software. Finally, an experimental prototype is built for further verification.
Lanelet2: A high-definition map framework for the future of automated driving

Although accurate and comprehensive maps are indispensable for highly automated driving, especially in complex urban scenarios, there are hardly any publications in which requirements for these maps are discussed. In our opinion, such maps must meet high demands in terms of accuracy, completeness, verifiability and extensibility, so that the resulting complexity can only be handled by an enclosing, carefully designed software framework. In this paper we therefore introduce the open-source map framework Lanelet2 implemented in C++ and explain the underlying concept. The goal of Lanelet2 is not only to be usable for typical, isolated applications such as localization or motion planning, but for various potential applications of maps for highly automated driving. On the basis of both abstract and real examples we show the concrete structure of Lanelet2 maps and its use for automated driving.
What do usability evaluators do in practice?: an explorative study of think-aloud testing

Think-aloud testing is a widely employed usability evaluation method, yet its use in practice is rarely studied. We report an explorative study of 14 think-aloud sessions, the audio recordings of which were examined in detail. The study shows that immediate analysis of observations made in the think-aloud sessions is done only sporadically, if at all. When testing, evaluators seem to seek confirmation of problems that they are already aware of. During testing, evaluators often ask users about their expectations and about hypothetical situations, rather than about experienced problems. In addition, evaluators learn much about the usability of the tested system but little about its utility. The study shows how practical realities rarely discussed in the literature on usability evaluation influence sessions. We discuss implications for usability researchers and professionals, including techniques for fast-paced analysis and tools for capturing observations during sessions.
Investigating the use of "Grounded Theory" in information systems research

"Grounded Theory" has been employed quite widely in studies of information systems (IS) phenomena. A survey of IS literature reveals conflict in the understanding and use of "grounded theory". The term "grounded theory" is often used as a catch phrase to denote usage of a grounded theory approach to conducting research. A variety of grounded theory approaches have been employed in IS research. The purpose of this investigation was to establish the alternative approaches employed, and the extent to which each was used. To achieve this purpose, a comprehensive review of IS studies that employed "grounded theory" was carried out. Articles from the commonly ranked top 50 IS-centric journals were used as the frame of reference. These journals most closely represent the status quo in IS research. Articles for the period 1985 to 2007 were examined. The analysis revealed four main grounded theory approaches in IS research. These can be classified as the "Glaserian" grounded theory approach, the "Straussian" grounded theory approach, the use of "grounded theory" as part of a mixed methodology, and the simple application of grounded theory techniques, typically for data analysis purposes. The latter has been the most common application of "grounded theory" in IS research. The "Glaserian" approach was the least often employed, with many studies opting for the "Straussian" approach. These and other findings are discussed and implications drawn.