nils-herrmann/clean_scirepeval huggingface dataset:
Proof-of-Personhood: Redemocratizing Permissionless Cryptocurrencies

Permissionless blockchain-based cryptocurrencies commonly use proof-of-work (PoW) or proof-of-stake (PoS) to ensure their security, e.g. to prevent double spending attacks. However, both approaches have disadvantages: PoW leads to massive amounts of wasted electricity and re-centralization, whereas major stakeholders in PoS might be able to create a monopoly. In this work, we propose proof-of-personhood (PoP), a mechanism that binds physical entities to virtual identities in a way that enables accountability while preserving anonymity. Afterwards we introduce PoPCoin, a new cryptocurrency, whose consensus mechanism leverages PoP to eliminate the dis-advantages of PoW and PoS while ensuring security. PoPCoin leads to a continuously fair and democratic wealth creation process which paves the way for an experimental basic income infrastructure.
Design and Analysis of the Millimeter-Wave SPDT Switch for TDD Applications

This paper presents a low-loss and high Tx-to-Rx isolation single-pole double-throw (SPDT) millimeter-wave switch for true time delay applications. The switch is designed based on matching-network and double-shunt transistors with quarter-wavelength transmission lines. The insertion loss and isolation characteristics of the switches are analyzed revealing that optimization of the transistor size with a matching-network switch on the receiver side and a double-shunt switch on the transmitter side can enhance the isolation performance with low loss. Implemented in 90-nm CMOS, the switch achieves a measured insertion loss and Tx-to-Rx isolation of 1.9 and 39 dB at 60 GHz, respectively. The input 1-dB gain compression point is 10 dBm at 60 GHz, and the return loss of the SPDT switch ports is greater than 10 dB at 48-67 GHz.
Slimness is associated with greater intercourse and lesser masturbation frequency.

I examined the relationship of recalled and diary recorded frequency of penile-vaginal intercourse (FSI), noncoital partnered sexual activity, and masturbation to measured waist and hip circumference in 120 healthy adults aged 19-38. Slimmer waist (in men and in the sexes combined) and slimmer hips (in men and women) were associated with greater FSI. Slimmer waist and hips were associated with rated importance of intercourse for men. Noncoital partnered sexual activity had a less consistent association with slimness. Slimmer waist and hips were associated with less masturbation (in men and in the sexes combined). I discuss the results in terms of differences between different sexual behaviors, attractiveness, emotional relatedness, physical sensitivity, sexual dysfunction, sociobiology, psychopharmacological aspects of excess fat and carbohydrate consumption, and implications for sex therapy.
Detecting metamorphic malwares using code graphs

Malware writers and detectors have been running an endless battle. Self-defense is the weapon most malware writers prepare against malware detectors. Malware writers have tried to evade the improved detection techniques of anti-virus(AV) products. Packing and code obfuscation are two popular evasion techniques. When these techniques are applied to malwares, they are able to change their instruction sequence while maintaining their intended function. We propose a detection mechanism defeating these self-defense techniques to improve malware detection. Since an obfuscated malware is able to change the syntax of its code while preserving its semantics, the proposed mechanism uses the semantic invariant. We convert the API call sequence of the malware into a graph, commonly known as a call graph, to extract the semantic of the malware. The call graph can be reduced to a code graph used for semantic signatures of the proposed mechanism. We show that the code graph can represent the characteristics of a program exactly and uniquely. Next, we evaluate the proposed mechanism by experiment. The mechanism has an 91% detection ratio of real-world malwares and detects 300 metamorphic malwares that can evade AV scanners. In this paper, we show how to analyze malwares by extracting program semantics using static analysis. It is shown that the proposed mechanism provides a high possibility of detecting malwares even when they attempt self-protection.
Intel® Software Guard Extensions (Intel® SGX) Software Support for Dynamic Memory Allocation inside an Enclave

Intel® Software Guard Extensions (Intel® SGX) SGX2 extends the Intel® Software Guard Extensions (SGX) instruction set and enables software developers to dynamically manage memory within the SGX environment. This paper reviews the current SGX Software RunTime Environment and proposes additions to the framework which will allow developers to benefit from features enabled by SGX2 such as dynamic heap management, stack expansion, and thread context creation.
“Positive” Results Increase Down the Hierarchy of the Sciences

The hypothesis of a Hierarchy of the Sciences with physical sciences at the top, social sciences at the bottom, and biological sciences in-between is nearly 200 years old. This order is intuitive and reflected in many features of academic life, but whether it reflects the "hardness" of scientific research--i.e., the extent to which research questions and results are determined by data and theories as opposed to non-cognitive factors--is controversial. This study analysed 2434 papers published in all disciplines and that declared to have tested a hypothesis. It was determined how many papers reported a "positive" (full or partial) or "negative" support for the tested hypothesis. If the hierarchy hypothesis is correct, then researchers in "softer" sciences should have fewer constraints to their conscious and unconscious biases, and therefore report more positive outcomes. Results confirmed the predictions at all levels considered: discipline, domain and methodology broadly defined. Controlling for observed differences between pure and applied disciplines, and between papers testing one or several hypotheses, the odds of reporting a positive result were around 5 times higher among papers in the disciplines of Psychology and Psychiatry and Economics and Business compared to Space Science, 2.3 times higher in the domain of social sciences compared to the physical sciences, and 3.4 times higher in studies applying behavioural and social methodologies on people compared to physical and chemical studies on non-biological material. In all comparisons, biological studies had intermediate values. These results suggest that the nature of hypotheses tested and the logical and methodological rigour employed to test them vary systematically across disciplines and fields, depending on the complexity of the subject matter and possibly other factors (e.g., a field's level of historical and/or intellectual development). On the other hand, these results support the scientific status of the social sciences against claims that they are completely subjective, by showing that, when they adopt a scientific approach to discovery, they differ from the natural sciences only by a matter of degree.
BodyVis: A New Approach to Body Learning Through Wearable Sensing and Visualization

Internal organs are hidden and untouchable, making it difficult for children to learn their size, position, and function. Traditionally, human anatomy (body form) and physiology (body function) are taught using techniques ranging from worksheets to three-dimensional models. We present a new approach called BodyVis, an e-textile shirt that combines biometric sensing and wearable visualizations to reveal otherwise invisible body parts and functions. We describe our 15-month iterative design process including lessons learned through the development of three prototypes using participatory design and two evaluations of the final prototype: a design probe interview with seven elementary school teachers and three single-session deployments in after-school programs. Our findings have implications for the growing area of wearables and tangibles for learning.
Mirror mirror on the ceiling: flexible wireless links for data centers

Modern data centers are massive, and support a range of distributed applications across potentially hundreds of server racks. As their utilization and bandwidth needs continue to grow, traditional methods of augmenting bandwidth have proven complex and costly in time and resources. Recent measurements show that data center traffic is often limited by congestion loss caused by short traffic bursts. Thus an attractive alternative to adding physical bandwidth is to augment wired links with wireless links in the 60 GHz band.
 We address two limitations with current 60 GHz wireless proposals. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. We propose and evaluate a new wireless primitive for data centers, 3D beamforming, where 60 GHz signals bounce off data center ceilings, thus establishing indirect line-of-sight between any two racks in a data center. We build a small 3D beamforming testbed to demonstrate its ability to address both link blockage and link interference, thus improving link range and number of concurrent transmissions in the data center. In addition, we propose a simple link scheduler and use traffic simulations to show that these 3D links significantly expand wireless capacity compared to their 2D counterparts.
FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation

is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close to the image sensor that can enable a thin system. We employ a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of the FlatCam design using two prototypes: one at visible wavelengths and one at infrared wavelengths.
Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer

This paper presents advances in Google’s hidden Markov model (HMM)-driven unit selection speech synthesis system. We describe several improvements to the run-time system; these include minimal latency, high-quality and fast refresh cycle for new voices. Traditionally unit selection synthesizers are limited in terms of the amount of data they can handle and the real applications they are built for. That is even more critical for reallife large-scale applications where high-quality is expected and low latency is required given the available computational resources. In this paper we present an optimized engine to handle a large database at runtime, a composite unit search approach for combining diphones and phrase-based units. In addition a new voice building strategy for handling big databases and keeping the building times low is presented.
Student attendance monitoring at the university using NFC

There are several complex business processes in the higher education. As the number of university students has been tripled in Hungary the automation of these task become necessary. The Near Field Communication (NFC) technology provides a good opportunity to support the automated execution of several education related processes. Recently a new challenge is identified at the Budapest University of Technology and Economics. As most of the lecture notes had become available in electronic format the students especially the inexperienced freshman ones did not attend to the lectures significantly decreasing the rate of successful exams. This drove to the decision to elaborate an accurate and reliable information system for monitoring the student's attendance at the lectures. Thus we have developed a novel, NFC technology based business use case of student attendance monitoring. In order to meet the requirements of the use case we have implemented a highly autonomous distributed environment assembled by NFC enabled embedded devices, so-called contactless terminals and a scalable backoffice. Beside the opportunity of contactless card based student identification the terminals support biometric identification by fingerprint reading. These features enable the implementation of flexible and secure identification scenarios. The attendance monitoring use case has been tested in a pilot project involving about 30 access terminals and more that 1000 students. In this paper we are introducing the developed attendance monitoring use case, the implemented NFC enabled system, and the experiences gained during the pilot project.
Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation

We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary’s representative and discriminative power. In addition, we propose a probabilistic model over the sparse codes of input signals, which allows us to expand the labeled set. As a consequence, the dictionary and the classifier learned from the enlarged labeled set yield lower generalization error on unseen data. Our approach learns a single dictionary and a predictive linear classifier jointly. Experimental results demonstrate the effectiveness of our approach in face and object category recognition applications.
Influence of strength training on sprint running performance. Current findings and implications for training.

Today, it is generally accepted that sprint performance, like endurance performance, can improve considerably with training. Strength training, especially, plays a key role in this process. Sprint performance will be viewed multidimensionally as an initial acceleration phase (0 to 10 m), a phase of maximum running speed (36 to 100 m) and a transition phase in between. Immediately following the start action, the powerful extensions of the hip, knee and ankle joints are the main accelerators of body mass. However, the hamstrings, the m. adductor magnus and the m. gluteus maximus are considered to make the most important contribution in producing the highest levels of speed. Different training methods are proposed to improve the power output of these muscles. Some of them aim for hypertrophy and others for specific adaptations of the nervous system. This includes general (hypertrophy and neuronal activation), velocity specific (speed-strength) and movement specific (sprint associated exercises) strength training. In developing training strategies, the coach has to keep in mind that strength, power and speed are inherently related to one another, because they are all the output of the same functional systems. As heavy resistance training results in a fibre type IIb into fibre type IIa conversion, the coach has to aim for an optimal balance between sprint specific and nonspecific training components. To achieve this they must take into consideration the specific strength training demands of each individual, based on performance capacity in each specific phase of the sprint.
Learning Styles and Learning Spaces : Enhancing Experiential Learning in Higher Education

CONCEPTUALIZATION This content downloaded from 206.87.46.46 on Wed, 26 Mar 2014 12:01:22 PM All use subject to JSTOR Terms and Conditions 2005 Kolb and Kolb 199 Is learning style a fixed trait or dynamic state? ELT clearly defines learning style as a dynamic state arising from an individual's preferential resolution of the dual dialectics of experiencing/conceptualizing and acting/reflecting. The stability and endurance of these states in individuals comes not solely from fixed genetic qualities or characteristics of human beings: nor, for that matter, does it come from the stable fixed demands of environmental circumstances. Rather, stable and enduring patterns of human individuality arise from consistent patterns of transaction between the individual and his or her environment . . . The way we process the possibilities of each new emerging event determines the range of choices and decisions we see. The choices and decisions we make to some extent determine the events we live through, and these events influence our future choices. Thus, people create themselves through the choice of actual occasions they live through (Kolb 1984: 63-64). Nonetheless, in practice and research there is a marked tendency to treat learning style as a fixed personality trait (e.g., Garner, 2000). Individuals often refer to themselves and others as though learning style was a fixed characteristic: "I have trouble making decisions because I am a diverger." "He likes to work alone because he is an assimilator." To emphasize the dynamic nature of learning style, the latest version of the LSI has changed the style names from diverger to diverging, and so on.
Ontological inference for image and video analysis

This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach. These models can integrate different sources of information both horizontally (multi-modal and temporal fusion) and vertically (bottom–up, top–down) by incorporating prior hierarchical knowledge expressed as an extensible ontology. Two implementations of this approach are presented. The first consists of a content-based image retrieval system that allows users to search image databases using an ontological query language. Queries are parsed using a probabilistic grammar and Bayesian networks to map high-level concepts onto low-level image descriptors, thereby bridging the ‘semantic gap’ between users and the retrieval system. The second application extends the notion of ontological languages to video event detection. It is shown how effective high-level state and event recognition mechanisms can be learned from a set of annotated training sequences by incorporating syntactic and semantic constraints represented by an ontology.
Core Security Requirements Artefacts

Although security requirements engineering has recently attracted increasing attention, it has lacked a context in which to operate. A number of papers have described how security requirements may be violated, but apart from a few hints in the general literature, none have described satisfactorily what security requirements are. This paper proposes a framework of core security requirements artefacts, which unifies the concepts of the two disciplines of requirements engineering and security engineering. From requirements engineering it takes the concept of functional goals, which are operationalised into functional requirements, with appropriate constraints. From security engineering it takes the concept of assets, together with threats of harm to those assets. Security goals aim to protect from those threats, and are operationalised into security requirements, which take the form of constraints on the functional requirements. In addition we explore the consequences of the fact that security is concerned with the protection of assets, while computers only provide interfaces. We show how to specify the relationship between security requirements and the specification of software behaviour, using Jackson's Problem Frames approach.
Origami: A 803-GOp/s/W Convolutional Network Accelerator

An ever-increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art results in object recognition and detection, semantic segmentation, action recognition, optical flow, and super resolution. Hardware acceleration of these algorithms is essential to adopt these improvements in embedded and mobile computer vision systems. We present a new architecture, design, and implementation, as well as the first reported silicon measurements of such an accelerator, outperforming previous work in terms of power, area, and I/O efficiency. The manufactured device provides up to 196 GOp/s on 3.09  $\text {mm}^{2}$  of silicon in UMC 65-nm technology and can achieve a power efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it the first architecture scalable to TOp/s performance.
A study of forensic & analysis tools

There are a wide range of forensic and analysis tools to examine digital evidence in existence today. Traditional tool design examines each source of digital evidence as a BLOB (binary large object) and it is up to the examiner to identify the relevant items from evidence. In the face of rapid technological advancements we are increasingly confronted with a diverse set of digital evidence and being able to identify a particular tool for conducting a specific analysis is an essential task. In this paper, we present a systematic study of contemporary forensic and analysis tools using a hypothesis based review to identify the different functionalities supported by these tools. We highlight the limitations of the forensic tools in regards to evidence corroboration and develop a case for building evidence correlation functionalities into these tools.
Distributional Memory: A General Framework for Corpus-Based Semantics

Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this “one task, one model” approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of weighted word-link-word tuples arranged into a third-order tensor. Different matrices are then generated from the tensor, and their rows and columns constitute natural spaces to deal with different semantic problems. In this way, the same distributional information can be shared across tasks such as modeling word similarity judgments, discovering synonyms, concept categorization, predicting selectional preferences of verbs, solving analogy problems, classifying relations between word pairs, harvesting qualia structures with patterns or example pairs, predicting the typical properties of concepts, and classifying verbs into alternation classes. Extensive empirical testing in all these domains shows that a Distributional Memory implementation performs competitively against task-specific algorithms recently reported in the literature for the same tasks, and against our implementations of several state-of-the-art methods. The Distributional Memory approach is thus shown to be tenable despite the constraints imposed by its multi-purpose nature.