nils-herrmann/clean_scirepeval huggingface dataset:
Intelligent injection liquid particle inspection machine based on two-dimensional Tsallis Entropy with modified pulse-coupled neural networks

The Automatic Liquid Particle Inspection Machine (AIM) using 2-D Tsallis Entropy with modified pulsecoupled neural networks (PCNN) is used in order to detect visible foreign particles within injection fluids. According to the motion of the particles in liquid, appropriate mechanisms are utilized which guarantees that the inspection machine will follow detection procedures: ‘‘Rotation, Abruptly Braking, Video Tracking’’ to extract tiny objects from complicated sequential images. In order to reduce the influence derived from air bubbles, improved spin/stop techniques are applied. The external capture mode of CCD cameras is used to avoid the possibility of omitting certain particles by trivial displacement. 2-D Tsallis Entropy with modified PCNN is applied in order to segment the difference images, and then to judge the existence of foreign particles according to the continuity and smoothness of their traces. Preliminary experimental results (125 ml 0.9% sodium chloride solution and 10% glucose as the samples) indicate that the inspection machine, which is superior to proficient inspectors, can detect the visible foreign particles effectively and that this detection speed and accuracy, as well as the correct detection rate can also facilitate the medicinal construction.
Probing the role of the vestibular system in motivation and reward-based attention

The vestibular system has widespread connections in the central nervous system. Several activation loci following vestibular stimulations have been notably reported in deep, limbic areas that are otherwise difficult to reach and modulate in healthy subjects. Following preliminary evidence, suggesting that such stimulations might affect mood and affective processing, we wondered whether the vestibular system is also involved in motivation. Evolutionary accounts suggest that visuo-vestibular mismatches might have a role in preventing the search for and exploitation of goods that previously resulted in aversive reactions, as they would be a fine warning signal which follows the contact with or ingestion of noxious neurotoxins. The first question was thus whether vestibular stimulation alters sensitivity to reward. Secondly, we sought to assess whether attention is allocated in space differently when cued by highly motivational stimuli, and if this interplay is further modulated by the vestibular system. In order to evaluate both motivational and attentional assets, we administered a Posner-like cueing task to 30 healthy subjects concurrently receiving sham or galvanic vestibular stimulation (GVS; Left-Anodal and Right-Anodal configurations). The participants had to discriminate targets appearing in either exogenously cued or uncued locations (50% validity); cues predicted the amount of points (0, 2, or 10) and thus money that they could earn for a correct response. The results highlight a robust inhibition of return (IOR) (faster responses for invalidly-cued targets) which was not modulated by different levels of reward or GVS. Across all stimulation sessions, rewards exerted a powerful beneficial effect over performance: reaction times were faster when rewards were at stake. However, this effect was largest in sham, but greatly reduced in GVS conditions, most notably with the Right-Anodal configuration. This is the first evidence for a decreased sensitivity to rewards causally induced by a perturbation of the vestibular system. While future studies will shed light on its neural underpinnings and clinical implications, here we argue that GVS could be a safe and promising way to enrich our understanding of reward processes and eventually tackle the management of patients with aberrant sensitivity to rewards.
RapidChain: A Fast Blockchain Protocol via Full Sharding

A major approach to overcoming the performance and scalability limitations of current blockchain protocols is to use sharding, which is to split the overheads of processing transactions among multiple, smaller groups of nodes. These groups work in parallel to maximize performance while requiring signi cantly smaller communication, computation, and storage per node, allowing the system to scale to large networks. However, existing sharding-based blockchain protocols still require a linear amount of communication (in the number of participants) per transaction, and hence, attain only partially the potential bene ts of sharding. We show that this introduces a major bottleneck to the throughput and latency of these protocols. Aside from the limited scalability, these protocols achieve weak security guarantees due to either a small fault resiliency (e.g., 1/8 and 1/4) or high failure probability, or they rely on strong assumptions (e.g., trusted setup) that limit their applicability to mainstream payment systems. We propose RapidChain, the rst sharding-based public blockchain protocol that is resilient to Byzantine faults from up to a 1/3 fraction of its participants, and achieves complete sharding of the communication, computation, and storage overhead of processing transactions without assuming any trusted setup. RapidChain employs an optimal intra-committee consensus algorithm that can achieve very high throughputs via block pipelining, a novel gossiping protocol for large blocks, and a provably-secure recon guration mechanism to ensure robustness. Using an e cient cross-shard transaction veri cation technique, RapidChain avoids gossiping transactions to the entire network. Our empirical evaluations suggest that RapidChain can process (and con rm) more than 7,300 tx/sec with an expected con rmation latency of roughly 8.7 seconds in a network of 4,000 nodes with an overwhelming time-to-failure of more than 4,500 years.
WashInDepth: Lightweight Hand Wash Monitor Using Depth Sensor

Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing WashInDepth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the gesture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS.
Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations

Amazon Mechanical Turk (MTurk) is becoming a prevalent source of quick and cost effective data for organizational research, but there are questions about the appropriateness of the platform for organizational research. To answer these questions, we conducted an integrative review based on 75 papers evaluating the MTurk platform and 250 MTurk samples used in organizational research. This integrative review provides four contributions: (1) we analyze the trends associated with the use of MTurk samples in organizational research; (2) we develop a systems perspective (recruitment system, selection system, and work management system) to synthesize and organize the key factors influencing data collected on MTurk that may affect generalizability and data quality; (3) within each factor, we also use available MTurk samples from the organizational literature to analyze key issues (e.g., sample characteristics, use of attention checks, payment); and (4) based on our review, we provide specific recommendations and a checklist for data reporting in order to improve data transparency and enable further research on this issue.
PCDS: A new approach for the development of circuit generators in analog IC design

While digital IC design is highly automated, analog circuits are still handcrafted in a time-consuming, manual fashion today. This paper introduces a novel Parameterized Circuit Description Scheme (PCDS) for the development of procedural analog schematic generators as parameterized circuits. Circuit designers themselves can use PCDS to create circuit automatisms which capture valuable expert knowledge, offer full topological flexibility, and enhance the re-use of well-established topologies. The generic PCDS concept has been successfully implemented and employed to create parameterized circuits for a broad range of use cases. The achieved results demonstrate the efficiency of our PCDS approach and the potential of parameterized circuits to increase automation in circuit design, also to benefit physical design by promoting the common schematic-driven-layout flow, and to enhance the applicability of circuit synthesis approaches.
Matching Curves to Imprecise Point Sets using Fréchet Distance

Let P be a polygonal curve in R of length n, and S be a point-set of size k. The Curve/Point Set Matching problem consists of finding a polygonal curve Q on S such that the Fréchet distance from P is less than a given ε. We consider eight variations of the problem based on the distance metric used and the omittability or repeatability of the points. We provide closure to a recent series of complexity results for the case where S consists of precise points. More importantly, we formulate a more realistic version of the problem that takes into account measurement errors. This new problem is posed as the matching of a given curve to a set of imprecise points. We prove that all three variations of the problem that are in P when S consists of precise points become NP-complete when S consists of imprecise points. We also discuss approximation re-
Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware

Embedded devices have become ubiquitous, and they are used in a range of privacy-sensitive and security-critical applications. Most of these devices run proprietary software, and little documentation is available about the software’s inner workings. In some cases, the cost of the hardware and protection mechanisms might make access to the devices themselves infeasible. Analyzing the software that is present in such environments is challenging, but necessary, if the risks associated with software bugs and vulnerabilities must be avoided. As a matter of fact, recent studies revealed the presence of backdoors in a number of embedded devices available on the market. In this paper, we present Firmalice, a binary analysis framework to support the analysis of firmware running on embedded devices. Firmalice builds on top of a symbolic execution engine, and techniques, such as program slicing, to increase its scalability. Furthermore, Firmalice utilizes a novel model of authentication bypass flaws, based on the attacker’s ability to determine the required inputs to perform privileged operations. We evaluated Firmalice on the firmware of three commercially-available devices, and were able to detect authentication bypass backdoors in two of them. Additionally, Firmalice was able to determine that the backdoor in the third firmware sample was not exploitable by an attacker without knowledge of a set of unprivileged credentials.
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.
tmChem: a high performance approach for chemical named entity recognition and normalization

Chemical compounds and drugs are an important class of entities in biomedical research with great potential in a wide range of applications, including clinical medicine. Locating chemical named entities in the literature is a useful step in chemical text mining pipelines for identifying the chemical mentions, their properties, and their relationships as discussed in the literature. We introduce the tmChem system, a chemical named entity recognizer created by combining two independent machine learning models in an ensemble. We use the corpus released as part of the recent CHEMDNER task to develop and evaluate tmChem, achieving a micro-averaged f-measure of 0.8739 on the CEM subtask (mention-level evaluation) and 0.8745 f-measure on the CDI subtask (abstract-level evaluation). We also report a high-recall combination (0.9212 for CEM and 0.9224 for CDI). tmChem achieved the highest f-measure reported in the CHEMDNER task for the CEM subtask, and the high recall variant achieved the highest recall on both the CEM and CDI tasks. We report that tmChem is a state-of-the-art tool for chemical named entity recognition and that performance for chemical named entity recognition has now tied (or exceeded) the performance previously reported for genes and diseases. Future research should focus on tighter integration between the named entity recognition and normalization steps for improved performance. The source code and a trained model for both models of tmChem is available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmChem. The results of running tmChem (Model 2) on PubMed are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator.
Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls

Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.
Theoretical Comparison of Testing Methods

Comparison of software testing methods is meaningful only if sound theory relates the properties compared to actual software quality. Existing comparisons typically use anecdotal foundations with no necessary relationship to quality, comparing methods on the basis of technical terms the methods themselves define. In the most seriously flawed work, one method whose efficacy is unknown is used as a standard for judging other methods! Random testing, as a method that can be related to quality (in both the conventional sense of statistical reliability, and the more stringent sense of software assurance), offers the opportunity for valid comparison.
Computational Higher Type Theory IV: Inductive Types

This is the fourth in a series of papers extending Martin-Löf’s meaning explanation of dependent type theory to higher-dimensional types. In this installment, we show how to define cubical type systems supporting a general schema of indexed cubical inductive types whose constructors may take dimension parameters and have a specified boundary. Using this schema, we are able to specify and implement many of the higher inductive types which have been postulated in homotopy type theory, including homotopy pushouts, the torus, W-quotients, truncations, arbitrary localizations. By including indexed inductive types, we enable the definition of identity types. The addition of higher inductive types makes computational higher type theory a model of homotopy type theory, capable of interpreting almost all of the constructions in the HoTT Book [40] (with the exception of inductive-inductive types). This is the first such model with an explicit canonicity theorem, which specifies the canonical values of higher inductive types and confirms that every term in an inductive type evaluates to such a value.
Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs)

Slip and fall is one of the major causes for human injuries for elders and professional workers. Real-time detection and prediction of the foot slip is critical for developing effective assistive and rehabilitation devices to prevent falls and train balance disorder patients. This paper presents a novel real-time slip detection and prediction scheme with wearable inertial measurement units (IMUs). The slip-detection algorithm is built on a new dynamic model for bipedal walking with slips.
Interactive food and beverage marketing: targeting adolescents in the digital age.

Because of their avid use of new media and their increased spending power, adolescents have become primary targets of a new "Media and Marketing Ecosystem." Digital media resonate particularly well with many of the fundamental developmental tasks of adolescence by enabling instantaneous and constant contact with peers, providing opportunities for self-expression, identity exploration, and social interaction, and facilitating mobility and independence. Six key features of interactive media--ubiquitous connectivity, personalization, peer-to-peer networking, engagement, immersion, and content creation--are emblematic of the ways in which young people are both shaping and being shaped by this new digital culture. The advertising industry, in many instances led by food and beverage marketers, is purposefully exploiting the special relationship that teenagers have with new media, with online marketing campaigns that create unprecedented intimacies between adolescents and the brands and products that now literally surround them. Major food and beverage companies, including Coca-Cola, McDonald's, Burger King, and Kentucky Fried Chicken (KFC), have incorporated these elements into their interactive marketing strategies, posing particular risks to adolescents, who are not being addressed in the current U.S. policy and self-regulatory regimens. However, recent and emerging neuroscience and psychological research on adolescents suggests a need to revisit the traditional approach to regulation of advertising. Despite the growth of interactive marketing, academic research on the impact of digital advertising on children and youth remains underdeveloped. Additional research and policy initiatives are needed to address the growing health threat facing youth in the digital marketplace.
A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels

This paper presents an approach to automatic video game level design consisting of a computational model of player enjoyment and a generative system based on evolutionary computing. The model estimates the entertainment value of game levels according to the presence of “rhythm groups,” which are defined as alternating periods of high and low challenge. The generative system represents a novel combination of genetic algorithms (GAs) and constraint satisfaction (CS) methods and uses the model as a fitness function for the generation of fun levels for two different games. This top-down approach improves upon typical bottom-up techniques in providing semantically meaningful parameters such as difficulty and player skill, in giving human designers considerable control over the output of the generative system, and in offering the ability to create levels for different types of games.
Digital Forensics for Eucalyptus

Cloud computing is a computing paradigm that shifts drastically from traditional computing architecture. Although this new computing paradigm brings many advantages like utility computing model but the design in not flawless and hence suffers from not only many known computer vulnerabilities but also introduces unique information confidentiality, integrity and availability risks as well due its inherent design paradigm. As a result the digital forensics, which relies heavily on physical access to computing devices and applications logs, has become a biggest challenge in cloud environments due to the non-availability of physical access to computing devices and applications logs. As we will see this paper highlights many of the digital forensics issues in the cloud environments and tries to address some of these forensics issues by identifying possible Syslog or Snort logs that can help in detecting cloud attacks or conducting digital forensics in cloud environments by analyzing logs generated by an open source cloud computing Eucalyptus software. As we will see in the paper we neither had access to Eucalyptus logs dataset nor it was known that any such dataset exist that could be analyzed offline for digital forensics purposes. Thus we generated our own dataset by attacking Eucalyptus with many of the known cloud attacks and then analyzing the resultant dataset to identify possible log entries that could identify cloud attacks or help in conducting digital forensics in cloud environments.
UML Modeling of User and Database Interaction

In this paper, we will present a design technique for user and database interaction based on UML. User interaction will be modeled by means of UML state diagrams, and database interaction by means of UML sequence diagrams. The proposed design technique establishes how to integrate both diagrams in order to describe the user interface and database interaction of a business software system. A case study of an Internet Book Shopping system will be shown to illustrate the proposal.
Adipose Hypertrophy Following Cryolipolysis.

Cryolipolysis has become a popular non-invasive treatment for unwanted fatty collections. The benefits are minimal down time, short recovery time, and decreased pain, although pain is the most frequent complaint. The author recently treated a patient who presented with an enlarging adipose collection on his lower abdomen following cryolipolysis with CoolSculpting(®) (ZELTIQ Aesthetics, Inc., Pleasanton, CA).