nils-herrmann/clean_scirepeval huggingface dataset:
Camera angle affects dominance in video-mediated communication

Physical proximity and appearance guide people to interact with each other in different ways [1,6]. However, in Video-Mediated Communications (VMC), these are distorted in various ways. Monitors and camera zooms make people look close or far, monitors and camera angles can be high or low making people look tall or short, volume can be loud or soft, making people sound assertive or submissive, --all independent of the true physical characteristics or intentions of the participants. Here we test the apparent height of a person on how dominant they are in a group decision-making task. We found that the artificially tall people had more influence in the group decision than the artifically short people.
Neural Program Synthesis from Diverse Demonstration Videos

Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations. The code is available at https://shaohua0116.github.io/demo2program.
A soft suction-based end effector for endoluminal tissue manipulation

The trend towards reducing the invasiveness of surgical procedures has pushed research towards the development of smaller and smarter instrumentation, able to access remote body locations by passing through natural orifices or more convenient access points . Although a variety of flexible instruments have been proposed in literature, the endoscope remains the gold standard for diagnostic and therapeutics procedures in the gastrointestinal (GI) tract. Performing therapeutic procedures, such as removal of early stage cancer, through an endoscope introduces several challenges with current instrumentation in terms of instrument stability and the capability to provide accurate and repeatable dexterous motions at the surgical site . Techniques such as endoscopic submucosal dissection (ESD) have been proposed [3], but they require substantial learning curves. Different strategies have been proposed for augmenting the therapeutic capabilities of endoscopes by introducing manipulation aids or add-ons to the endoscope [4]-[6]. Embedding additional functionalities into a system that can be fixed at the tip of the endoscope represent a promising approach for improving current manipulation capabilities without disrupting the current procedure workflow. However, the design and fabrication of tools at these scales introduces several technical challenges, potentially limiting functionality. Recently, we have proposed a soft pop-up hybrid fabrication method as a promising approach for developing mm-scale mechanisms for minimally invasive surgery (MIS), and in particular in [7] we demonstrate a three degree of freedom (DoFs) arm actuated through embedded soft fluidic micro actuators that can be connected to the tip of an endoscope for manipulating endoluminal tissue . In this paper, we address the design of an end effector for manipulation of endoluminal tissue. Millimeter-scale end effectors that provide safe and effective manipulation presents a challenge for design and manufacturing. Previous work has primarily focused on jaw-like cable-actuated  or SMA-based grippers . We propose a soft suction-based gripper that can be integrated at the tip of an arm as represented in Fig.1. The use of vacuum grippers in laparoscopy has been successfully investigated in [10][11], showing reduced skill-dependent damage to the tissues. In addition, Yaw
A hybrid intrusion detection system design for computer network security

Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Intrusion detection systems can be misuse-detection or anomaly detection based. Misuse-detection based IDSs can only detect known attacks whereas anomaly detection based IDSs can also detect new attacks by using heuristic methods. In this paper we propose a hybrid IDS by combining the two approaches in one system. The hybrid IDS is obtained by combining packet header anomaly detection (PHAD) and network traffic anomaly detection (NETAD) which are anomaly-based IDSs with the misuse-based IDS Snort which is an open-source project. The hybrid IDS obtained is evaluated using the MIT Lincoln Laboratories network traffic data (IDEVAL) as a testbed. Evaluation compares the number of attacks detected by misusebased IDS on its own, with the hybrid IDS obtained combining anomaly-based and misusebased IDSs and shows that the hybrid IDS is a more powerful system.
Upper limb exoskeleton control based on sliding mode control and feedback linearization

Exoskeletons have potential to improve human quality of life by relieving loads on the human musculoskeletal system or by helping motor rehabilitation. Controllers that were initially developed for industrial applications are also applied to control an exoskeleton, despite of major differences in requirements. Nevertheless, which controller has better performance for this specific application remains an open question. This paper presents a comparison between sliding mode control and feedback linearization control. The implementation of the sliding mode controller assumes a complete measurement of the biological system's dynamics. On the other hand, the feedback linearization method does not need any information from the biological system. Our results indicate that a model combining biological control and dynamics could improve several characteristics of robotic exoskeleton, such as energy consumption.
A novel approach to online social influence maximization

Online social networks are becoming a true growth point of Internet. As individuals constantly desire to interact with each other, the ability for Internet to deliver this networking influence becomes much stronger. In this paper, we study the online social influence maximization problem, which is to find a small group of influential users that maximizes the spread of influence through networks. After a thorough analysis of existing models, especially two classical ones, namely Independent cascade and linear thresholds, we argue that their idea that each user can only be activated by its active neighbors is not applicable to online social networks, since in many applications there is no clear concept for the issue of "activation". In our proposed influence model, if there is a social influence path linking two nonadjacent individuals, the value of influence between these two individuals can be evaluated along the existing social path based on the influence transitivity property under certain constraints. To compute influence probabilities between two neighbors, we also develop a new method which leverages both structure of networks and historical data. With reasonably learned influence probabilities, we recast the problem of online influence maximization to a weighted maximum cut problem which analyzes the influence flow among graph vertices. By running a semidefinite program-based (GW) algorithm on a complete influence graph, an optimal seed set can be identified effectively. We also provide experimental results on real online social networks, showing that our algorithm significantly outperforms greedy methods.
Foot orthoses for the treatment of plantar fasciitis.

The literature suggests mechanical interventions such as foot orthoses and night splints are effective in reducing pain from plantar fasciitis. There is, however, a lack of controlled trials. We studied the effects of foot orthoses and night splints, alone or combined, in a prospective, randomized trial with 1-year followup.



Forty-three patients (34 women and nine men with a mean age of 46 years) with plantar fasciitis were randomized to receive foot orthoses (n = 13), foot orthoses and night splints (n = 15), or night splints alone (n = 15). Data were available for 34 (79%) patients after treatment (12 weeks), and for 38 (88%) at 1-year followup. Pain, functional limitations, and quality of life were evaluated with the Foot and Ankle Outcome Score.



All groups improved significantly in all outcomes evaluated across all times (p < 0.04). At 12 weeks, pain reduction of 30% to 50% compared to baseline were seen (p < 0.03). At 52 weeks, pain reduction of 62% was seen in the two groups using foot orthoses compared to 48% in the night splint only group (p < 0.01). Better compliance and fewer side effects were reported for orthosis use. At 12 months, 19 of 23 patients reported still using foot orthoses compared to 1 of 28 still using the night splint.



Foot orthoses and anterior night splints were effective both short-term and long-term in treating pain from plantar fasciitis. Parallel improvements in function, foot-related quality of life, and a better compliance suggest that a foot orthosis is the best choice for initial treatment plantar fasciitis.
Formal Verification of CNN-based Perception Systems

We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on reachability analysis for feed-forward neural networks and relies on MILP encodings of both the CNNs and transformations under question. We present an implementation and discuss the experimental results obtained for a CNN trained from the MNIST data set.
An ubiquitous miniaturized android based ECG monitoring system

Miniaturization of devices has brought a lot of improvements in the healthcare delivery as a result of advancenments in Integrated circuit technologies. As a result, proper and effective way of analyzing various body conditions and diseases can be achieved. When the heart muscles repolarize and depolarize, it generates electrical impulses which could help the doctor or the health professional to diagnose various heart abnormalities. The aim of this project is to design and develop a miniaturized ECG system displayed on both PC and Tablet for preliminary diagnoses of the heart. This system utilizes a programmable single-chip microcontroller for analysing the biosignals to indicate the condition of the heart. The system is incomporated with alarm systems which prompt the doctor if there is any abnormality from a patient's heart. These abnormalities could be bradycardia or tachycardia. This system is built with a provision of enabling wireless transmission of ECG signals to a PC or TABLET through BLUETOOTH and ANDROID PLATFORM. The android application can be installed and tested on any smart phone or tablet and screen resolution can be adjusted later as per requirement. This helps the doctor to have visual description of the patient ECG without the need of mounted monitors. The experimental results show that device is clinically useful, compact, cost effective and user friendly.
Formal Design and Verification of Self-Adaptive Systems with Decentralized Control

Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded.
 This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines, that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.
Mining program workflow from interleaved traces

Successful software maintenance is becoming increasingly critical due to the increasing dependence of our society and economy on software systems. One key problem of software maintenance is the difficulty in understanding the evolving software systems. Program workflows can help system operators and administrators to understand system behaviors and verify system executions so as to greatly facilitate system maintenance. In this paper, we propose an algorithm to automatically discover program workflows from event traces that record system events during system execution. Different from existing workflow mining algorithms, our approach can construct concurrent workflows from traces of interleaved events. Our workflow mining approach is a three-step coarse-to-fine algorithm. At first, we mine temporal dependencies for each pair of events. Then, based on the mined pair-wise tem-poral dependencies, we construct a basic workflow model by a breadth-first path pruning algorithm. After that, we refine the workflow by verifying it with all training event traces. The re-finement algorithm tries to find out a workflow that can interpret all event traces with minimal state transitions and threads. The results of both simulation data and real program data show that our algorithm is highly effective.
Today the Earwig, Tomorrow Man?

Kirsh, D., Today the earwig, tomorrow man?, Artificial Intelligence 47 (1991) 161-184. A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.
Design and implementation of 0.7 to 7 GHz broadband double-ridged horn antenna

In this paper, simulation and measurement results of 0.7-7 GHz double-ridged guide horn (DRGH) antenna with coaxial input feed section is presented. This antenna, due to the large frequency band required by standards, is appropriate to be used in electromagnetic compatibility (EMC) testing and antenna measurement as a transmitter. A step by step method for designing DRGH antenna is given. A suitable taper for the ridges in the horn is designed and its impedance variations along the horn are shown. In addition, a new structure for the electrical field probe in the feed section is introduced by which a shift-down of lower frequency to 0.7 GHz is achieved. A sensitivity analysis is done on the parameters of the proposed structure. Other parameters of the feed section are also investigated and optimized values are obtained. Finally, the proposed antenna has been fabricated and measurement results show good agreement with the simulation.
Semantics-Enabled User Interest Detection from Twitter

Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, user interest detection from social networks has been the subject of increasing attention. Some recent works have proposed to enrich social posts by annotating them with unambiguous relevant ontological concepts extracted from external knowledge bases and model user interests as a bag of concepts. However, in the bag of concepts approach, each topic of interest is represented as an individual concept that is already predefined in the knowledge base. Therefore, it is not possible to infer fine-grained topics of interest, which are only expressible through a collection of multiple concepts or emerging topics, which are not yet defined in the knowledge base. To address these issues, we view each topic of interest as a conjunction of several concepts, which are temporally correlated on Twitter. Based on this, we extract active topics within a given time interval and determine a users inclination towards these active topics. We demonstrate the effectiveness of our approach in the context of a personalized news recommendation system. We show through extensive experimentation that our work is able to improve the state of the art.
Steganography-The Art of Hiding Data

Given the shear volume of data stored and transmitted electronically in the world today, it is no surprise that countless methods of protecting such data have evolved. One lesser-known but rapidly growing method is steganography, the art and science of hiding information so that it does not even appear to exist. Moreover, in an ideal world we would all be able to openly send encrypted email or files to each other with no fear of reprisals. However, there are often cases when this is not possible, either because you are working for a company that does not allow encrypted emails or perhaps the local government does not approve of encrypted communication. This is one of the cases where Steganography can help hide the encrypted messages, images, keys, secret data, etc. This paper discusses the purpose of steganography. Explains how steganography is related to cryptography as well as what it can and cannot be used for. It also discusses a brief history of steganography. In addition, some of the tools and software used in steganography are demonstrated and including some discussion of the most popular algorithms involved in these tools. This paper further explains the advantages and disadvantages, as well as, strengths and weaknesses in the use of steganography.
Iron loss analysis of interior permanent-magnet synchronous motors-variation of main loss factors due to driving condition

In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control
A novel two-switch active clamp forward converter for high input voltage applications

A novel two-switch active clamp forward converter suitable for high input voltage applications is proposed. It consists of two main switches, one auxiliary switch, one clamping capacitor, and two clamping diodes. The main advantage of the proposed converter, compared to the conventional active forward converters, is that circuit complexity is reduced and the voltage stress of the main switches is effectively clamped to either the input voltage or the clamping capacitor voltage by two clamping diodes without limiting the maximum duty ratio. Also, the clamping circuit does not include additional active switches, so a low cost can be achieved without degrading the efficiency. Therefore, the proposed converter can feature high efficiency and low cost for high input voltage applications. The operational principles, features, and design considerations of the proposed converter are presented in this paper. The validity of this study is confirmed by the experimental results from a prototype with 200 W, 375 V input, and 12 V output.
Detection and Analysis of Emotion From Speech Signals

Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.
Kylix: A Sparse Allreduce for Commodity Clusters

Allreduce is a basic building block for parallel computing. Our target here is "Big Data" processing on commodity clusters (mostly sparse power-law data). Allreduce can be used to synchronize models, to maintain distributed datasets, and to perform operations on distributed data such as sparse matrix multiply. We first review a key constraint on cluster communication, the minimum efficient packet size, which hampers the use of direct all-to-all protocols on large networks. Our allreduce network is a nested, heterogeneous-degree butterfly. We show that communication volume in lower layers is typically much less than the top layer, and total communication across all layers a small constant larger than the top layer, which is close to optimal. A chart of network communication volume across layers has a characteristic "Kylix" shape, which gives the method its name. For optimum performance, the butterfly degrees also decrease down the layers. Furthermore, to efficiently route sparse updates to the nodes that need them, the network must be nested. While the approach is amenable to various kinds of sparse data, almost all "Big Data" sets show power-law statistics, and from the properties of these, we derive methods for optimal network design. Finally, we present experiments showing with Kylix on Amazon EC2 and demonstrating significant improvements over existing systems such as PowerGraph and Hadoop.