nils-herrmann/clean_scirepeval huggingface dataset:
Healing on the cloud: Secure cloud architecture for medical wireless sensor networks

There has been a host of research works on wireless sensor networks (WSN) for medical applications. However, the major shortcoming of these efforts is a lack of consideration of data management. Indeed, the huge amount of high sensitive data generated and collected by medical sensor networks introduces several challenges that existing architectures cannot solve. These challenges include scalability, availability and security. Furthermore, WSNs for medical applications provide useful and real information about patients’ health state. This information should be available for healthcare providers to facilitate response and to improve the rescue process of a patient during emergency. Hence, emergency management is another challenge for medical wireless sensor networks. In this paper, we propose an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. Our architecture overcomes all the aforementioned challenges and makes easy information sharing between healthcare professionals in normal and emergency situations. Furthermore, we propose an effective and flexible security mechanism that guarantees confidentiality, integrity as well as fine grained access control to outsourced medical data. This mechanism relies on Ciphertext Policy Attribute-based Encryption (CP-ABE) to achieve high flexibility and performance. Finally, we carry out extensive simulations that allow showing that our scheme provides an efficient, fine-grained and scalable access control in normal and emergency situations.
Smart garbage monitoring and clearance system using internet of things

The increase in population, has led to tremendous degradation in the state of affairs of hygiene with respect to waste management system. The spillover of waste in civic areas generates the polluted condition in the neighboring areas. It may aggravate numerous severe diseases for the nearby people. This will humiliate the appraisal of the affected area. For eliminating or mitigating the garbage's and maintains the cleanness, it requires 'smartness based waste management system. This paper is proposed IOT based smart waste clean management system which checks the waste level over the dustbins by using Sensor systems. Once it detected immediately this system altered to concern authorized through GSM/GPRS. For this system used Microcontroller as an interface between the sensor system and GSM/GPRS system. To monitor and integrate an android application is developed for the desired information which is related to the various level of waste in different locations. This is ensued the greenish in the environment and support for swachh bharat for cleanness.
Measures for textual patent similarities: a guided way to select appropriate approaches

The measurement of textual patent similarities is crucial for important tasks in patent management, be it prior art analysis, infringement analysis, or patent mapping. In this paper the common theory of similarity measurement is applied to the field of patents, using solitary concepts as basic textual elements of patents. After unfolding the term ‘similarity’ in a content and formal oriented level and presenting a basic model of understanding, a segmented approach to the measurement of underlying variables, similarity coefficients, and the criteria-related profiles of their combinations is lined out. This leads to a guided way to the application of textual patent similarities, interesting both for theory and practice.
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning

Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities. The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application. Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV, which take place by means of frequency division duplex via orthogonal or nonorthogonal multiple access schemes. The problem of jointly optimizing the bit allocation for uplink and downlink communications as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation strategies. Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.
Learning Discriminative Representations from RGB-D Video Data

Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGBD video data for visual recognition tasks. We address this as an optimization problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGBD video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task. The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSRDailyActivity3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art handcrafted and machine-learned features.
PCT: Partial Co-Alignment of Social Networks

People nowadays usually participate in multiple online social networks simultaneously to enjoy more social network services. Besides the common users, social networks providing similar services can also share many other kinds of information entities, e.g., locations, videos and products. However, these shared information entities in different networks are mostly isolated without any known corresponding connections. In this paper, we aim at inferring such potential corresponding connections linking multiple kinds of shared entities across networks simultaneously. Formally, the problem is referred to as the network “Partial Co-alignmenT” (PCT) problem. PCT is an important problem and can be the prerequisite for many concrete cross-network applications, like social network fusion, mutual information exchange and transfer. Meanwhile, the PCT problem is also very challenging to address due to various reasons, like (1) the heterogeneity of social networks, (2) lack of training instances to build models, and (3) one-to-one constraint on the correspondence connections. To resolve these challenges, a novel unsupervised network alignment framework, UNICOAT (), is introduced in this paper. Based on the heterogeneous information, UNICOAT transforms the PCT problem into a joint optimization problem. To solve the objective function, the one-to-one constraint on the corresponding relationships is relaxed, and the redundant non-existing corresponding connections introduced by such a relaxation will be pruned with a novel network co-matching algorithm proposed in this paper. Extensive experiments conducted on real-world coaligned social network datasets demonstrate the effectiveness of UNICOAT in addressing the PCT problem.
JPE 11-5-2 A Current-Fed Parallel Resonant Push-Pull Inverter with a New Cascaded Coil Flux Control for Induction Heating Applications

This paper presents a cascaded coil flux control based on a Current Source Parallel Resonant Push-Pull Inverter (CSPRPI) for Induction Heating (IH) applications. The most important problems associated with current source parallel resonant inverters are start-up problems and the variable response of IH systems under load variations. This paper proposes a simple cascaded control method to increase an IH system’s robustness to load variations. The proposed IH has been analyzed in both the steady state and the transient state. Based on this method, the resonant frequency is tracked using Phase Locked Loop (PLL) circuits using a Multiplier Phase Detector (MPD) to achieve ZVS under the transient condition. A laboratory prototype was built with an operating frequency of 57-59 kHz and a rated power of 300 W. Simulation and experimental results verify the validity of the proposed power control method and the PLL dynamics.
Accident Detection System using Image Processing and MDR

In this research, we suggested a vision-based traffic accident detection system for automatically detecting, recording, and reporting traffic accidents at intersections. This model first extracts the vehicles from the video image of CCD camera, tracks the moving vehicles, and extracts features such as the variation rate of the velocity, position, area, and direction of moving vehicles. The model then makes decisions on the traffic accident based on the extracted features. And we suggested and designed the metadata registry for the system to improve the interoperability. In the field test, 4 traffic accidents were detected and recorded by the system. The video clips are invaluable for intersection safety analysis.
SideNoter: Scholarly Paper Browsing System based on PDF Restructuring and Text Annotation

In this paper, we discuss our ongoing efforts to construct a scientific paper browsing system that helps users to read and understand advanced technical content distributed in PDF. Since PDF is a format specifically designed for printing, layout and logical structures of documents are indistinguishably embedded in the file. It requires much effort to extract natural language text from PDF files, and reversely, display semantic annotations produced by NLP tools on the original page layout. In our browsing system, we tackle these issues caused by the gap between printable document and plain text. Our system provides ways to extract natural language sentences from PDF files together with their logical structures, and also to map arbitrary textual spans to their corresponding regions on page images. We setup a demonstration system using papers published in ACL anthology and demonstrate the enhanced search and refined recommendation functions which we plan to make widely available to NLP researchers.
Steps Toward an Ecology of Infrastructure: Design and Access for Large Information Spaces

ion? Does it supplant the real or is there, in it, reality itself? Like so many true things, this one doesn't resolve itself to a black or a white. Nor is it gray. It is, along with the rest of life, black/white. Both/neither."  1.  People who study how technology affects organizational transformation increasingly recognize its dual, paradoxical nature. It is both engine and barrier for change; both customizable and rigid; both inside and outside organizational practices. It is product and process. Some authors have analyzed this seeming paradox as structuration: (after Giddens)—technological rigidities give rise to adaptations which in turn require calibration and standardization. Over time, structureagency relations re-form dialectically (Orlikowski 1991, Davies and Mitchell 1994, Korpela 1994). This paradox is integral to large scale, dispersed technologies (Brown 1047-7047/96/0701/0111$01.25
Identifying Image Tags from Instagram Hashtags Using the HITS Algorithm

Many automatic image annotation methods are based on the learning by example paradigm. Image tagging, through manual image inspection, is the first step towards this end. However, manual image annotation, even for creating the training sets, is time-consuming, complicated and contains human subjectivity errors. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are crucial. As we showed in one of our previous studies, tags accompanying photos in social media and especially the Instagram hashtags can be used for image annotation. However, it turned out that only a 20% of the Instagram hashtags are actually relevant to the content of the image they accompany. Identifying those hashtags through crowdsourcing is a plausible solution. In this work, we investigate the effectiveness of the HITS algorithm for identifying the right tags in a crowdsourced image tagging scenario. For this purpose, we create a bipartite graph in which the first type of nodes corresponds to the annotators and the second type to the tags they select, among the hashtags, to annotate a particular Instagram image. From the results, we conclude that the authority value of the HITS algorithm provides an accurate estimation of the appropriateness of each Instagram hashtag to be used as a tag for the image it accompanies while the hub value can be used to filter out the dishonest annotators.
Unified constant-frequency integration control of three-phase standard bridge boost rectifiers with power-factor correction

In this paper, a three-phase six-switch standard boost rectifier with unity-power-factor-correction is investigated. A general equation is derived that relates input phase voltage and duty ratios of switches in continuous conduction mode. Based on one of solutions and using One-Cycle Control, a Unified Constant-frequency Integration (UCI) controller for powerfactor-correction (PFC) is proposed. For the standard bridge boost rectifier, unity-power-factor and low total-harmonicdistortion (THD) can be realized in all three phases with a simple circuit that is composed of one integrator with reset along with several flips-flops, comparators, and some logic and linear components. It does not require multipliers and threephase voltage sensors, which are used in many other control approaches. In addition, it employs constant switching frequency modulation that is desirable for industrial applications. The proposed control approach is simple and reliable. Theoretical analysis is verified by simulation and experimental results.
The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization

Recently there has been quite a number of independent research activities that investigate the potentialities of integrating social networking concepts into Internet of Things (IoT) solutions. The resulting paradigm, named Social Internet of Things (SIoT), has the potential to support novel applications and networking services for the IoT in more effective and efficient ways. In this context, the main contributions of this paper are the following: i) we identify appropriate policies for the establishment and the management of social relationships between objects in such a way that the resulting social network is navigable; ii) we describe a possible architecture for the IoT that includes the functionalities required to integrate things into a social network; iii) we analyze the characteristics of the SIoT network structure by means of simulations.
Hopping in legged systems — Modeling and simulation for the two-dimensional one-legged case

A two-dimensional one-legged hopping machine is modeled and simulated in order to better understand legged systems that hop and run. The analysis is focused on balance, dynamic stability, and resonant oscillation for the planar case. A springy leg with nonzero mass, a simple body, and an actuated hinge-type hip are incorporated in the model. Control of the model is decomposed into a vertical hopping part, a horizontal velocity part, and a body attitude part. Estimates of total system energy are used in regulating hopping height in order to initiate hopping, to maintain level hopping, to change from one hopping height to another, and to terminate hopping. Balance and control of forward velocity are explored with three algorithms. The feasibility of decomposing the control of running into a height control part, a forward velocity control part, and an attitude control part is verified by simulations.
A Novel Approach to Structural Facial Volume Replacement

Improved understanding of the anatomy and physiology of the aging face has laid the foundation for adopting an earlier and more comprehensive approach to facial rejuvenation, shifting the focus from individual wrinkle treatment and lift procedures to a holistic paradigm that considers the entire face and its structural framework. This article presents an overview of a comprehensive method to address facial aging. The key components to the reported strategy for improving facial cosmesis include, in addition to augmentation of volume loss, protection with sunscreens and antioxidants; promotion of epidermal cell turnover with techniques such as superficial chemical peels; microlaser peels and microdermabrasion; collagen stimulation and remodeling via light, ultrasound, or radiofrequency (RF)-based methods; and muscle control with botulinum toxin. For the treatment of wrinkles and for the augmentation of pan-facial dermal lipoatrophy, several types of fillers and volumizers including hyaluronic acid (HA), autologous fat, and calcium hydroxylapatite (CaHA) or injectable poly-l-lactic acid (PLLA) are available. A novel bimodal, trivector technique to restore structural facial volume loss that combines supraperiosteal depot injections of volume-depleted fat pads and dermal/subcutaneous injections for panfacial lipoatrophy with PLLA is presented. The combination of treatments with fillers; toxins; light-, sound-, and RF-based technologies; and surgical procedures may help to forestall the facial aging process and provide more natural results than are possible with any of these techniques alone. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Hard Drive Side-Channel Attacks Using Smartphone Magnetic Field Sensors

In this paper we present a new class of side-channel attacks on computer hard drives. Hard drives contain one or more spinning disks made of a magnetic material. In addition, they contain different magnets which rapidly move the head to a target position on the disk to perform a write or a read. The magnetic fields from the disk’s material and head are weak and well shielded. However, we show that the magnetic field due to the moving head can be picked up by sensors outside of the hard drive. With these measurements, we are able to deduce patterns about ongoing operations. For example, we can detect what type of the operating system is booting up or what application is being started. Most importantly, no special equipment is necessary. All attacks can be performed by using an unmodified smartphone placed in proximity of a hard drive.
Lifted Inference and Learning in Statistical Relational Models

Statistical relationalmodels combine aspects of first-order logic andprobabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in themodel, and reasoning about groups of objects as awhole, lifted algorithms dramatically improve the run time of inference and learning. The thesis has five main contributions. First, we propose a new method for logical inference, called first-order knowledge compilation. We show that by compiling relational models into a new circuit language, hard inference problems become tractable to solve. Furthermore, we present an algorithm that compiles relational models into our circuit language. Second, we show how to use first-order knowledge compilation for statistical relational models, leading to a new state-of-the-art lifted probabilistic inference algorithm. Third, we develop a formal framework for exact lifted inference, including a definition in terms of its complexity w.r.t. the number of objects in the world. From this follows a first completeness result, showing that the two-variable class of statistical relational models always supports lifted inference. Fourth, we present an algorithm for
Clustering Appearances of Objects Under Varying Illumination Conditions

We introduce two appearance-based methods for clustering a set of images of 3-D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.
On Common Ground: Jost's (1897) law of forgetting and Ribot's (1881) law of retrograde amnesia.

T. Ribot's (1881) law of retrograde amnesia states that brain damage impairs recently formed memories to a greater extent than older memories, which is generally taken to imply that memories need time to consolidate. A. Jost's (1897) law of forgetting states that if 2 memories are of the same strength but different ages, the older will decay more slowly than the younger. The main theoretical implication of this venerable law has never been worked out, but it may be the same as that implied by Ribot's law. A consolidation interpretation of Jost's law implies an interference theory of forgetting that is altogether different from the cue-overload view that has dominated thinking in the field of psychology for decades.