nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Text Structuring and Retrieval: Experiments in Automatic Encyclopedia Searching

Many conventional approaches to text analysis and information retrieval prove ineffective when large text collections must be processed in heterogeneous subject areas. An alternative text manipulation system is outlined useful for the retrieval of large heterogeneous texts, and for the recognition of content similarities between text excerpts, based on flexible text matching procedures carried out in several contexts of different scope. The methods are illustrated by search experiments performed with the 29-volume Funk and Wagnalls encyclopedia. Approaches to Text Retrieval We are addressing the problem of text retrieval from large, heterogeneous text databases, where the vocabulary varies widely and the subject matter is unrestricted. This includes newspaper articles, newswire dispatches, textbooks, dictionaries and encyclopedias, manuals, magazine articles, and so on. Necessarily, the retrieval operations must be preceded by some form of text analysis, or text indexing operation, leading to the assignment to query and document texts of appropriate content identifications. When content identifiers are attached to text items, the text retrieval operations can be carried out by comparing the identifications assigned to the various text items with the query identifications, and retrieving texts that appear sufficiently similar to the corresponding queries.  ● Department of Computer Science, Cornell University, Ithaca, NY 14853-7501. This study was supported in part by the National Science Foundation under grant IRI 89-15847. Permission to copy without fee all or part of this material ia granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice ie given that copying is by permieaion of the Association for Computing Machinery. To copy otherwiee, or to republish, requiree a fee and/or specific permission. @ 1991 ACM 0.89791.448-1/91/0009/0021 ,.,$1.50 In discussing the text analysis operation, it is customary to contrast the so-called keyword approach with more sophisticated procedures based in part on linguistic and logical considerations. In the keyword approach, single-term concepts, known as keywords,
Multi-Player Alpha-Beta Pruning

Korf, R.E., Multi-player alpha-beta pruning (Research Note), Artificial Intelligence 48 (1991) 99-111. We consider the generalization of minimax search with alpha-beta pruning to non-cooperative, perfect-information games with more than two players. The minimax algorithm was generalized in  to the maxn algorithm applied to vectors of n-tuples representing the evaluations for each of the players. If we assume an upper bound on the sum of the evaluations for each player, and a lower bound on each individual evaluation, then shallow alpha-beta pruning is possible, but not deep pruning. In the best case, the asymptotic branching factor is reduced to (1 + 4bv'~b-"s~-3)/2. In the average case, however, pruning does not reduce the asymptotic branching factor. Thus, alpha-beta pruning is found to be effective only in the special case of two-player games. In addition, we show that it is an optimal directional algorithm for two players.
Improving SAT-solving with Machine Learning

In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial, we fixed the values of a certain ratio of randomly selected variables, and calculated the confidence that the resulting sub-formula is satisfiable with our logistic regression model. The initial value of each variable was set based on the mean confidence scores of the trials that started from the literals of that variable. We were particularly interested in setting the initial values of the backbone variables correctly, which are variables that have the same value in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78% of the backbones correctly. Excluding the preprocessing time, compared with the default setting of Minisat, the runtime of Minisat for satisfiable formulae decreased by 23%. However, our method did not outperform vanilla Minisat in runtime, as the decrease in the conflicts was outweighed by the long runtime of the preprocessing period.
Load-Adaptive Modulation of a Series-Resonant Inverter for All-Metal Induction Heating Applications

A conventional induction heating (IH) system has been developed to heat the pot of ferromagnetic materials because the small resistance of nonferromagnetic materials induces large resonant current to the power switch of a series-resonant IH inverter. However, the heating capability for various materials is the most important to improve the functionality and the usability of IH products. In this paper, a load-adaptive modulation (LAM) method is proposed to heat the pot made from nonferromagnetic and ferromagnetic materials. The LAM method can change the magnitude of the input voltage of the IH coil and the operating frequency of the series-resonant IH inverter according to the resistance of the pot. The operational principle and the design method are analyzed to implement the proposed LAM method and its power control. The validity of the LAM method is experimentally verified using a 2-kW prototype series-resonant IH inverter.
Fabrication of microneedles precisely imitating mosquito's proboscis by nanoscale tree dimensional laser lithography and its characterization

Aiming at painless medical applications, a hollow microneedle imitating mosquito's proboscis is developed using three-dimensional laser lithography. Unlike other reported needles, presented microneedle is divided into two half parts, each is shaped like eaves-through and dependently moved. The tip of each part is three-dimensionally sharpened, and several jagged edges project to the outside. The combined halves act as one hollow microneedle, which can draw up human blood by capillary action. The microneedle can puncture an artificial skin without buckling. The resistive force during insertion can be reduced when the two halves are alternately advanced, which mimics the motion of mosquito's proboscis.
Aromas of rosemary and lavender essential oils differentially affect cognition and mood in healthy adults.

This study was designed to assess the olfactory impact of the essential oils of lavender (Lavandula angustifolia) and rosemary (Rosmarlnus officinalis) on cognitive performance and mood in healthy volunteers. One hundred and forty-four participants were randomly assigned to one of three independent groups, and subsequently performed the Cognitive Drug Research (CDR) computerized cognitive assessment battery in a cubicle containing either one of the two odors or no odor (control). Visual analogue mood questionnaires were completed prior to exposure to the odor, and subsequently after completion of the test battery. The participants were deceived as to the genuine aim of the study until the completion of testing to prevent expectancy effects from possibly influencing the data. The outcome variables from the nine tasks that constitute the CDR core battery feed into six factors that represent different aspects of cognitive functioning. Analysis of performance revealed that lavender produced a significant decrement in performance of working memory, and impaired reaction times for both memory and attention based tasks compared to controls. In contrast, rosemary produced a significant enhancement of performance for overall quality of memory and secondary memory factors, but also produced an impairment of speed of memory compared to controls. With regard to mood, comparisons of the change in ratings from baseline to post-test revealed that following the completion of the cognitive assessment battery, both the control and lavender groups were significantly less alert than the rosemary condition; however, the control group was significantly less content than both rosemary and lavender conditions. These findings indicate that the olfactory properties of these essential oils can produce objective effects on cognitive performance, as well as subjective effects on mood.
Learning to Detect Rooftops in Aerial Images

In this paper, we examine the use of machine learning to improve the robustness of systems for image analysis on the task of roof detection. We review the problem of analyzing aerial photographs, and describe an existing vision system that attempts to automate the identiication of buildings in aerial images. After this, we brieey review several well-known learning algorithms that represent a wide variety of inductive biases. We report three experiments designed to illuminate facets of applying machine learning methods to the image analysis task; one experiment focuses on within-image learning, another deals with the cost of diierent errors, and a third addresses between-image learning. Experimental results demonstrate that machine-learned classiiers meet or exceed the accuracy of handcrafted solutions and that useful generalization occurs when training and testing on data derived from diierent images.
Robotic excavation in construction automation

C onstruction is of prime economic significance to many industry sectors. Intense competition, shortages of skilled labor, and technological advances are forcing rapid change in the construction industry, thus motivating construction automation . Earthmoving machines, such as bulldozers, wheel loaders, excavators, scrapers, and graders, are common in construction. Excavationbased operations are used in general earthmoving, digging, and sheet-piling for displacing large amounts of material. On a smaller scale, operations such as trenching and footing formation require precisely controlled excavation. Although the fully automated construction site is still a dream of some civil engineers, research developments have shown the promise of robotics and automation in construction . Despite the apparent economic importance of excavation in construction, there have been few implementations of autonomous or teleoperated excavators. A number of researchers have investigated the feasibility of automating excavation. Many of these studies have addressed the possible use of autonomous excavators during unmanned phases of establishing manned Lunar or Martian research stations , . Much of the work on terrestrial excavation has focused on teleoperation, rather than on the system requirements for autonomous operation. Although there have been a number of valuable theoretical and experimental contributions to the field of autonomous, robotic or teleoperated excavation [5]-[8], autonomous operation of a full-scale excavator has not been commercially demonstrated. Many of the experimental studies reported in the literature involve using conventional industrial robots fitted with buckets to excavate in a bed of loose sand. While there are parallels between “classical” robotics and robotic excavation, there are also some pronounced differences. In particular, an excavator is not fixed relative to the work piece; it plastically deforms the work piece by applying large forces and is caused to move relative to the soil by the same large forces. Furthermore, strategic and bucket trajectory planning must necessarily occur in a dynamic environment; if the excavator is not changing the profile of the soil being worked, it is not doing useful work. This article presents some results of the autonomous excavation project conducted at the Australian Centre for Field Robotics (ACFR) with a focus on construction automation. The application of robotic technology and computer control is one key to construction industry automation. Excavation automation is a multidisciplinary task, encompassing a broad area of research and development planning monitoring environment sensing and modeling navigation machine modeling and control. The ultimate goal of the ACFR excavation project is to demonstrate fully autonomous execution of excavation tasks in
Digital Signatures Out of Second-Preimage Resistant Hash Functions

We propose a new construction for Merkle authentication trees which does not require collision resistant hash functions; in contrast with previous constructions that attempted to avoid the dependency on collision resistance, our technique enjoys provable security assuming the well-understood notion of second-preimage resistance. The resulting signature scheme is existentially unforgeable when the underlying hash function is second-preimage resistant, yields shorter signatures, and is affected neither by birthday attacks nor by the recent progresses in collision-finding algorithms.
Semantics-Based Online Malware Detection: Towards Efficient Real-Time Protection Against Malware

Recently, malware has increasingly become a critical threat to embedded systems, while the conventional software solutions, such as antivirus and patches, have not been so successful in defending the ever-evolving and advanced malicious programs. In this paper, we propose a hardware-enhanced architecture, GuardOL, to perform online malware detection. GuardOL is a combined approach using processor and field-programmable gate array (FPGA). Our approach aims to capture the malicious behavior (i.e., high-level semantics) of malware. To this end, we first propose the frequency-centric model for feature construction using system call patterns of known malware and benign samples. We then develop a machine learning approach (using multilayer perceptron) in FPGA to train classifier using these features. At runtime, the trained classifier is used to classify the unknown samples as malware or benign, with early prediction. The experimental results show that our solution can achieve high classification accuracy, fast detection, low power consumption, and flexibility for easy functionality upgrade to adapt to new malware samples. One of the main advantages of our design is the support of early prediction-detecting 46% of malware within first 30% of their execution, while 97% of the samples at 100% of their execution, with <;3% false positives.
Changes in structural and functional connectivity among resting-state networks across the human lifespan

At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.
Opportunistic Computation Offloading in Mobile Edge Cloud Computing Environments

The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.
Impingement of lesser trochanter on ischium as a potential cause for hip pain

The bony anatomy of the hip leads to a limited array of impingement syndromes, more frequently resulting from abnormal contact between the femoral neck and acetabulum. We report an unusual case of osseous impingement between the lesser trochanter and ischium, with involvement of the intervening quadratus femoris muscle. While the prevalence and etiology of this finding is unclear, it may represent a cause for hip pain.
Latent Variable PixelCNNs for Natural Image Modeling

We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous stateof-the-art probabilistic models.
Privacy preserving EHR system using attribute-based infrastructure

Secure management of Electronic Health Records (EHR) in a distributed computing environment such as cloud computing where computing resources including storage is provided by a third party service provider is a challenging task. In this paper, we explore techniques which guarantees security and privacy of medical data stored in the cloud. We show how new primitives in attribute-based cryptography can be used to construct a secure and privacy-preserving EHR system that enables patients to share their data among healthcare providers in a flexible, dynamic and scalable manner.
RFID Application in Hospitals: A Case Study on a Demonstration RFID Project in a Taiwan Hospital

After manufacturing and retail marketing, healthcare is considered the next home for Radio Frequency Identification (RFID). Although in its infancy, RFID technology has great potential in healthcare to significantly reduce cost, and improve patient safety and medical services. However, the challenge will be to incorporate RFID into medical practice, especially when relevant experience is limited. To explore this issue, we conducted a case study that demonstrated RFID integration into the medical world at one Taiwan hospital. The project that was studied, the Location-based Medicare Service project, was partially subsidized by the Taiwan government and was aimed at containing SARS, a dangerous disease that struck Taiwan in 2003. Innovative and productive of several significant results, the project established an infrastructure and platform allowing for other applications. In this paper we describe the development strategy, design, and implementation of the project. We discuss our findings pertaining to the collaborative development strategy, device management, data management, and value generation. These findings have important implications for developing RFID applications in healthcare organizations.
Wireless powering for a self-propelled and steerable endoscopic capsule for stomach inspection.

This paper describes the integration of an active locomotion module in a wirelessly powered endoscopic capsule. The device is a submersible capsule optimized to operate in a fluid environment in a liquid-distended stomach. A 3D inductive link is used to supply up to 400mW to the embedded electronics and a set of 4 radio-controlled motor propellers. The design takes advantage of a ferrite-core in the receiving coil-set. This approach significantly improves the coupling with the external field source with respect to earlier work by the group. It doubles the power that can be received with a coreless coil-set under identical external conditions. The upper limit of the received power was achieved complying with the strict regulations for safe exposure of biological tissue to variable magnetic fields. The wireless transferred power was proven to be sufficient to achieve the speed of 7cm/s in any directions. An optimized locomotion strategy was defined which limits the power consumption by running only 2 motors at a time. A user interface and a joystick controller allow to fully drive the capsule in an intuitive manner. The device functionalities were successfully tested in a dry and a wet environment in a laboratory set-up.
Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.

Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.