nils-herrmann/clean_scirepeval huggingface dataset:
Design of a Passively-Powered, Programmable Sensing Platform for UHF RFID Systems

This paper presents a wireless, battery-free, platform for sensing and computation that is powered and read by a standards compliant ultra-high frequency (UHF) radio frequency identification (RFID) reader. The WISP (wireless identification and sensing platform) includes a fully-programmable 16 bit microcontroller with analog-to-digital converter. The microcontroller firmware implements portions of the electronic product code (EPC) class 1 generation 1 protocol. When queried, the platform communicates arbitrary sensor data by emulating an EPC tag whose ID encodes the desired sensor data; the required 16-bit CRC is computed dynamically by the microcontroller. The RFID reader reports the received tag ID to application software which can interpret the information contained in the tag ID. The programmability of the WISP along with its implementation as a PCB allows for flexible integration of arbitrary low-power sensors. Furthermore, sensors are also exclusively powered from the RFID reader resulting in a completely battery free device. Sensors integrated into the WISP platform so far include light, temperature, and rectified voltage, and are shown experimentally to have an operating range of up to 4.5m. To the authors' knowledge, WISP is the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary, multi-bit data in a single response packet.
Are Metamorphic Viruses Really Invincible?

Metamorphic viruses enjoy the apparent invincibility because a virus writer has the advantage of knowing the weak spots of AV technologies. We could turn the tables if we can identify similar weak spots in a metamorphic virus. Indeed, Lakhotia and Singh close their otherwise gloomy article with one bright spot: " The good news is that a virus writer is confronted with the same theoretical limit as anti-virus technologies… It may be worth contemplating how this could be used to the advantage of anti-virus technologies. "  In the game of " hide and seek, " where a virus tries to hide and the AV scanners tries to seek, the winner is the one that can take advantage of the other's weak spot. So far a virus writer has enjoyed the upper hand for she could exploit the limitations of AV technologies. Metamorphic viruses are particularly insidious in taking such advantage. A metamorphic virus thwarts detection by signature-based (static) AV technologies by morphing its code as it propagates. A virus can also thwart detection by emulation-based (dynamic) technologies. To do so it needs to detect whether it is running in an emulator and change its behavior. So are metamorphic viruses invincible? This paper investigates the above remark and identifies what promises to be the Achilles' heel of a metamorphic virus. The key observation is that in order to mutate its code, generations after generations, a metamorphic virus must analyze its own code. Thus, it too must face the limits of static and dynamic analyses. Beyond that a metamorphic virus has another constraint: it must be able to re-analyze the mutated code that it generates. Thus, the analysis within the virus, of how to transform the code in current generation, depends upon the complexity of transformations in the previous generation. To overcome the challenges of static and dynamic analyses, the virus has the following options: do not obfuscate the transformed code in any generation; use some coding conventions that can aid it in detecting its own obfuscations; or develop smart algorithms to detect its specific obfuscations. This paper uncovers the Achilles' heel of a metamorphic virus.
Accurate Scale Estimation for Robust Visual Tracking

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation. Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8% in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-theart trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6% in median distance precision, while operating at real-time.
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract the high-level information needed by mobile apps. It is critical that the gains in inference accuracy that deep models afford become embedded in future generations of mobile apps. In this work, we present the design and implementation of DeepX, a software accelerator for deep learning execution. DeepX signif- icantly lowers the device resources (viz. memory, computation, energy) required by deep learning that currently act as a severe bottleneck to mobile adoption. The foundation of DeepX is a pair of resource control algorithms, designed for the inference stage of deep learning, that: (1) decompose monolithic deep model network architectures into unit- blocks of various types, that are then more efficiently executed by heterogeneous local device processors (e.g., GPUs, CPUs); and (2), perform principled resource scaling that adjusts the architecture of deep models to shape the overhead each unit-blocks introduces. Experiments show, DeepX can allow even large-scale deep learning models to execute efficently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.
Using Big Data as a window into consumers’ psychology

The rise of ‘Big Data’ had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information – with the help of new analytical techniques – can be translated into valuable insights on consumers’ psychological states and traits that can, in turn, be used to inform marketing strategy. Finally, we discuss opportunities and challenges related to the use of Big Data as a window into consumers’ psychology, and provide recommendations for how to implement related technologies in a way that benefits both businesses and consumers.
Benchmarking NFV Software Dataplanes

A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental and critical performance metrics. Specifically, we compare two state-of-the-art open-source NFV dataplanes, SoftNIC and ClickOS, using commodity 10GbE NICs under various typical workloads. Our key observations are that (1) both dataplanes have performance issues processing small (≤128B) packets; (2) it is not always best to put all VMs of a service chain on one server due to NUMA effect. We propose resource allocation strategies to remedy the problems, including carefully adding CPU cores and vNICs to VMs, and spreading VMs of a service chain to separate servers. To fundamentally address these problems and scale their performance, SoftNIC and ClickOS could improve the support for NIC queues and multiple cores.
Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval.

Early neuroimaging studies often failed to obtain evidence of medial temporal lobe (MTL) activation during episodic encoding or retrieval, but a growing number of studies using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have provided such evidence. We review data from fMRI studies that converge on the conclusion that posterior MTL is associated with episodic encoding; too few fMRI studies of retrieval have reported MTL activations to allow firm conclusions about their exact locations. We then turn to a recent meta-analysis of PET studies (Lepage et al., Hippocampus 1998;8:313-322) that appears to contradict the fMRI encoding data. Based on their analysis of the rostrocaudal distribution of activations reported during episodic encoding or retrieval, Lepage et al. (1998) concluded that anterior MTL is strongly associated with episodic encoding, whereas posterior MTL is strongly associated with episodic retrieval. After considering the evidence reviewed by Lepage et al. (1998) along with additional studies, we conclude that PET studies of encoding reveal both anterior and posterior MTL activations. These observations indicate that the contradiction between fMRI and PET studies of encoding was more apparent than real. However, PET studies have reported anterior MTL encoding activations more frequently than have fMRI studies. We consider possible sources of these differences.
An Internet-Wide View of Internet-Wide Scanning

While it is widely known that port scanning is widespread, neither the scanning landscape nor the defensive reactions of network operators have been measured at Internet scale. In this work, we analyze data from a large network telescope to study scanning activity from the past year, uncovering large horizontal scan operations and identifying broad patterns in scanning behavior. We present an analysis of who is scanning, what services are being targeted, and the impact of new scanners on the overall landscape. We also analyze the scanning behavior triggered by recent vulnerabilities in Linksys routers, OpenSSL, and NTP. We empirically analyze the defensive behaviors that organizations employ against scanning, shedding light on who detects scanning behavior, which networks blacklist scanning, and how scan recipients respond to scans conducted by researchers. We conclude with recommendations for institutions performing scans and with implications of recent changes in scanning behavior for researchers and network operators.
Policy Distillation

Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent.
Hippocampal Contributions to Control: The Third Way

Recent experimental studies have focused on the specialization of different neural structures for different types of instrumental behavior. Recent theoretical work has provided normative accounts for why there should be more than one control system, and how the output of different controllers can be integrated. Two particlar controllers have been identified, one associated with a forward model and the prefrontal cortex and a second associated with computationally simpler, habitual, actor-critic methods and part of the striatum. We argue here for the normative appropriateness of an additional, but so far marginalized control system, associated with episodic memory, and involving the hippocampus and medial temporal cortices. We analyze in depth a class of simple environments to show that episodic control should be useful in a range of cases characterized by complexity and inferential noise, and most particularly at the very early stages of learning, long before habitization has set in. We interpret data on the transfer of control from the hippocampus to the striatum in the light of this hypothesis.
The Internet of Things in an Enterprise Context

This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.
Realization of a motion control IC for electric-powered wheelchair

This work is mainly to implement a motion control IC for electric wheelchair based on the novel FPGA (Field Programmable Gate Array) technology, which a Nios ll soft core processor can be embedded into FPGA to construct an SoPC (System on a Programmable Chip) environment. This electric wheelchair herein is driven by two DC rim motors and a fuzzy controller is applied in speed loop of each motor drive to cope with the system uncertainty and external load problems. The motion control IC has two modules. One is Nios II processor IP (Intelligent properties) which is used to perform the function of motion trajectory for electric-powered wheelchair and the data communication with control panel; and the other is an application IP which is utilized to execute the function of PWM (Pulse width modulation) generation, QEP (Quadrature encoder pulse) signal detection, and speed loop fuzzy controller for two DC rim motors. Therefore, a fully digital motion controller for electric wheelchair can be realized within one FPGA chip. Finally, an experimental system, which is composed of the DE2 (CycloneII EP2C35F672C6) with FPGA basis, two inverters, and one platform for electric wheelchair, is set up to prove the efficiency and correctness of the proposed motion control IC.
Effects of in-season short-term plyometric training program on leg power, jump- and sprint performance of soccer players.

Our hypothesis was that the addition of an 8-week lower limb plyometric training program (hurdle and depth jumping) to normal in-season conditioning would enhance measures of competitive potential (peak power output [PP], jump force, jump height, and lower limb muscle volume) in junior soccer players. The subjects (23 men, age 19 ± 0.7 years, body mass 70.5 ± 4.7 kg, height 1.75 ± 0.06 m, body fat 14.7 ± 2.6%) were randomly assigned to a control (normal training) group (Gc; n = 11) and an experimental group (Gex, n = 12) that also performed biweekly plyometric training. A force-velocity ergometer test determined PP. Characteristics of the squat jump (SJ) and the countermovement jump (CMJ) (jump height, maximal force and velocity before take-off, and average power) were determined by force platform. Video-camera kinematic analyses over a 40-m sprint yielded running velocities for the first step (VS), the first 5 m (V5m) and between 35 and 40 m (Vmax). Leg muscle volume was estimated using a standard anthropometric kit. Gex showed gains relative to controls in PP (p < 0.01); SJ (height p < 0.01; velocity p < 0.001), CMJ (height p < 0.001; velocity p < 0.001, average power p < 0.01) and all sprint velocities (p < 0.001 for V5m and Vmax, p < 0.01 for VS). There was also a significant increase (p < 0.05) in thigh muscle volume, but leg muscle volume and mean thigh cross-sectional area remain unchanged. We conclude that biweekly plyometric training of junior soccer players (including adapted hurdle and depth jumps) improved important components of athletic performance relative to standard in-season training. Accordingly, such exercises are highly recommended as part of an annual soccer training program.
Quantifying Mental Health from Social Media with Neural User Embeddings

Mental illnesses adversely affect a significant proportion of the population worldwide. However, the typical methods to estimate and characterize the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of these conditions are often years out of date. Automated approaches that supplement traditional methods with broad, aggregated information derived from social media provide a potential means of furnishing near real-time estimates at scale. These may in turn provide grist for supporting, evaluating and iteratively improving public health programs and interventions. We propose a novel approach for mental health quantification that leverages user embeddings induced from social media post histories. Recent work showed that learned user representations capture latent aspects of individuals (e.g., political leanings). This paper investigates whether these representations also correlate with mental health statuses. To this end, we induced embeddings for a set of users known to be affected by depression and post-traumatic stress disorder, and for a set of demographically matched ‘control’ users. We then evaluated the induced user representations with respect to: (i) their ability to capture homophilic relations with respect to mental health statuses; and (ii) their predictive performance in downstream mental health models. Our experimental results demonstrate that learned user embeddings capture relevant signals for mental health quantification.
Skinput: appropriating the body as an input surface

We present Skinput, a technology that appropriates the human body for acoustic transmission, allowing the skin to be used as an input surface. In particular, we resolve the location of finger taps on the arm and hand by analyzing mechanical vibrations that propagate through the body. We collect these signals using a novel array of sensors worn as an armband. This approach provides an always available, naturally portable, and on-body finger input system. We assess the capabilities, accuracy and limitations of our technique through a two-part, twenty-participant user study. To further illustrate the utility of our approach, we conclude with several proof-of-concept applications we developed.
Security Aspects in Software Defined Radio and Cognitive Radio Networks: A Survey and A Way Ahead

Software Defined Radio (SDR) and Cognitive Radio (CR) are promising technologies, which can be used to alleviate the spectrum shortage problem or the barriers to communication interoperability in various application domains. The successful deployment of SDR and CR technologies will depend on the design and implementation of essential security mechanisms to ensure the robustness of networks and terminals against security attacks. SDR and CR may introduce entirely new classes of security threats and challenges including download of malicious software, licensed user emulation and selfish misbehaviors. An attacker could disrupt the basic functions of a CR network, cause harmful interference to licensed users or deny communication to other CR nodes. The research activity in this area has started only recently and many challenges are still to be resolved. This paper presents a survey of security aspects in SDR and CR. We identify the requirements for the deployment of SDR and CR, the main security threats and challenges and the related protection techniques. This paper provides an overview of the SDR and CR certification process and how it is related to the security aspects. Finally, this paper summarizes the most critical challenges in the context of the future evolution of SDR/CR technologies.
A guided tour to approximate string matching

We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.
A series elastic actuator as a new load-sensitive continuously variable transmission mechanism for control actuation systems

Electrical motors are often used to actuate mechanisms with varying speed and under varying load e.g., missile control actuation systems or robot extremities. Sizing of the motor is critical in these applications where space is limited. Varying speed and load cause poor utilization of the motor if the reduction ratio is constant. This requires employment of a larger motor than the smallest possible with ideal variable transmission. To realize better utilization of the motor, we propose a series elastic actuator that works as a load sensitive continuously variable transmission system. This is achieved by combining a pin in slot mechanism with an elastic output shaft. The proposed mechanism is modeled and simulated using example missile flight data and a typical robot finger loading scenario. Results are compared to the constant reduction ratio case, and advantages of the proposed mechanism are shown as decreased actuator size and power.
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning

In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization. For datasets of size n, our approach requires O(n/ε) gradient evaluations to reach ε-accuracy and, when a certain error bound holds, the complexity improves to O(κn log(1/ε)). These rates are n times better than those achieved by typical, full gradient methods.
Issues in Stacked Generalization

Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classi cation tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We nd that best results are obtained when the higher-level model combines the con dence (and not just the predictions) of the lower-level ones. We demonstrate the e ectiveness of stacked generalization for combining three di erent types of learning algorithms for classi cation tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.