nils-herrmann/clean_scirepeval huggingface dataset:
Sybil-Resistant Mixing for Bitcoin

A fundamental limitation of Bitcoin and its variants is that the movement of coin between addresses can be observed by examining the public block chain. This record enables adversaries to link addresses to individuals, and to identify multiple addresses as belonging to a single participant. Users can try to hide this information by mixing, where a participant exchanges the funds in an address coin-for-coin with another participant and address. In this paper, we describe the weaknesses of extant mixing protocols, and analyze their vulnerability to Sybil-based denial-of-service and inference attacks. As a solution, we propose Xim, a two-party mixing protocol that is compatible with Bitcoin and related virtual currencies. It is the first decentralized protocol to simultaneously address Sybil attackers, denial-of-service attacks, and timing-based inference attacks. Xim is a multi-round protocol with tunably high success rates. It includes a decentralized system for anonymously finding mix partners based on ads placed in the block chain. No outside party can confirm or find evidence of participants that pair up. We show that Xim's design increases attacker costs linearly with the total number of participants, and that its probabilistic approach to mixing mitigates Sybil-based denial-of-service attack effects. We evaluate protocol delays based on our measurements of the Bitcoin network.
Robust Hash Functions for Digital Watermarking

Digital watermarks have recently been proposed for authentication of both video data and still images and for integrity verification of visual multimedia. In such applications, the watermark has to depend on a secret key and on the original image. It is important that the dependence on the key be sensitive, while the dependence on the image be continuous (robust). Both requirements can be satisfied using special image digest functions that return the same bit-string for a whole class of images derived from an original image using common processing operations. It is further required that two completely different images produce completely different bit-strings. In this paper, we discuss methods how such robust hash functions can be built. We describe an algorithm and evaluate its performance. We also show how the hash bits As another application, the robust image digest can be used as a search index for an efficient image database
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems

Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we see that achieving universally secure, private, and fair systems is practically impossible. We have seen for example how generative adversarial networks can be used to learn about the expected private training data; how the exploitation of additional data can reveal private information in the original one; and how what looks like unrelated features can teach us about each other. Confronted with this challenge, in this paper we open a new line of research, where the security, privacy, and fairness is learned and used in a closed environment. The goal is to ensure that a given entity (e.g., the company or the government), trusted to infer certain information with our data, is blocked from inferring protected information from it. For example, a hospital might be allowed to produce diagnosis on the patient (the positive task), without being able to infer the gender of the subject (negative task). Similarly, a company can guarantee that internally it is not using the provided data for any undesired task, an important goal that is not contradicting the virtually impossible challenge of blocking everybody from the undesired task. We design a system that learns to succeed on the positive task while simultaneously fail at the negative one, and illustrate this with challenging cases where the positive task is actually harder than the negative one being blocked. Fairness, to the information in the negative task, is often automatically obtained as a result of this proposed approach. The particular framework and examples open the door to security, privacy, and fairness in very important closed scenarios, ranging from private data accumulation companies like social networks to law-enforcement and hospitals. J. Sokolić and M. R. D. Rodrigues are with the Department of Electronic and Electrical Engineering, Univeristy College London, London, UK (e-mail: {jure.sokolic.13, m.rodrigues}@ucl.ac.uk). Q. Qiu and G. Sapiro are with the Department of Electrical and Computer Engineering, Duke University, NC, USA (e-mail: {qiang.qiu, guillermo.sapiro}@duke.edu). The work of Guillermo Sapiro was partially supported by NSF, ONR, ARO, NGA. May 24, 2017 DRAFT ar X iv :1 70 5. 08 19 7v 1 [ st at .M L ] 2 3 M ay 2 01 7
Deep belief network based semantic taggers for spoken language understanding

This paper investigates the use of deep belief networks (DBN) for semantic tagging, a sequence classification task, in spoken language understanding (SLU). We evaluate the performance of the DBN based sequence tagger on the well-studied ATIS task and compare our technique to conditional random fields (CRF), a state-of-the-art classifier for sequence classification. In conjunction with lexical and named entity features, we also use dependency parser based syntactic features and part of speech (POS) tags . Under both noisy conditions (output of automatic speech recognition system) and clean conditions (manual transcriptions), our deep belief network based sequence tagger outperforms the best CRF based system described in  by an absolute 2% and 1% F-measure, respectively.Upon carrying out an analysis of cases where CRF and DBN models made different predictions, we observed that when discrete features are projected onto a continuous space during neural network training, the model learns to cluster these features leading to its improved generalization capability, relative to a CRF model, especially in cases where some features are either missing or noisy.
Motif-Based Hyponym Relation Extraction from Wikipedia Hyperlinks

Discovering hyponym relations among domain-specific terms is a fundamental task in taxonomy learning and knowledge acquisition. However, the great diversity of various domain corpora and the lack of labeled training sets make this task very challenging for conventional methods that are based on text content. The hyperlink structure of Wikipedia article pages was found to contain recurring network motifs in this study, indicating the probability of a hyperlink being a hyponym hyperlink. Hence, a novel hyponym relation extraction approach based on the network motifs of Wikipedia hyperlinks was proposed. This approach automatically constructs motif-based features from the hyperlink structure of a domain; every hyperlink is mapped to a 13-dimensional feature vector based on the 13 types of three-node motifs. The approach extracts structural information from Wikipedia and heuristically creates a labeled training set. Classification models were determined from the training sets for hyponym relation extraction. Two experiments were conducted to validate our approach based on seven domain-specific datasets obtained from Wikipedia. The first experiment, which utilized manually labeled data, verified the effectiveness of the motif-based features. The second experiment, which utilized an automatically labeled training set of different domains, showed that the proposed approach performs better than the approach based on lexico-syntactic patterns and achieves comparable result to the approach based on textual features. Experimental results show the practicability and fairly good domain scalability of the proposed approach.
Remembering the past and imagining the future: a neural model of spatial memory and imagery.

The authors model the neural mechanisms underlying spatial cognition, integrating neuronal systems and behavioral data, and address the relationships between long-term memory, short-term memory, and imagery, and between egocentric and allocentric and visual and ideothetic representations. Long-term spatial memory is modeled as attractor dynamics within medial-temporal allocentric representations, and short-term memory is modeled as egocentric parietal representations driven by perception, retrieval, and imagery and modulated by directed attention. Both encoding and retrieval/imagery require translation between egocentric and allocentric representations, which are mediated by posterior parietal and retrosplenial areas and the use of head direction representations in Papez's circuit. Thus, the hippocampus effectively indexes information by real or imagined location, whereas Papez's circuit translates to imagery or from perception according to the direction of view. Modulation of this translation by motor efference allows spatial updating of representations, whereas prefrontal simulated motor efference allows mental exploration. The alternating temporal-parietal flows of information are organized by the theta rhythm. Simulations demonstrate the retrieval and updating of familiar spatial scenes, hemispatial neglect in memory, and the effects on hippocampal place cell firing of lesioned head direction representations and of conflicting visual and ideothetic inputs.
A frequency-shift CMOS magnetic biosensor array with single-bead sensitivity and no external magnet

Future Point-of-Care (PoC) molecular-level diagnosis requires advanced biosensing systems that can achieve high sensitivity and portability at low power consumption levels, all within a low price-tag for a variety of applications such as in-field medical diagnostics, epidemic disease control, biohazard detection, and forensic analysis. Magnetically labeled biosensors are proposed as a promising candidate to potentially eliminate or augment the optical instruments used by conventional fluorescence-based sensors. However, magnetic biosensors developed thus far require externally generated magnetic biasing fields [1–4] and/or exotic post-fabrication processes [1,2]. This limits the ultimate form-factor of the system, total power consumption, and cost. To address these impediments, we present a low-power scalable frequency-shift magnetic particle biosensor array in bulk CMOS, which provides single-bead detection sensitivity without any (electrical or permanent) external magnets.
Real time hand gesture recognition for computer interaction

Hand gesture recognition is a natural and intuitive way to interact with the computer, since interactions with the computer can be increased through multidimensional use of hand gestures as compare to other input methods. The purpose of this paper is to explore three different techniques for HGR (hand gesture recognition) using finger tips detection. A new approach called “Curvature of Perimeter” is presented with its application as a virtual mouse. The system presented, uses only a webcam and algorithms which are developed using computer vision, image and the video processing toolboxes of Matlab.
Moving object detection using background subtraction for a moving camera with pronounced parallax

This paper proposes a moving object detection algorithm which can handle videos taken by a moving camera in the presence of pronounced parallax. The paper considers the idea that objects in a image can be considered to be spatially distributed across multiple planes, the movement of each of which can be estimated using a Visual Odometry (VO) algorithm. For each plane, a Homography matrix between consecutive frames is calculated to facilitate motion compensation. The approach also identifies regions where parallax is pronounced and where the most recent data are then assumed to provide the background. Moving objects can then be extracted by an existing background subtraction algorithm. Since the camera states are known (from the VO), the detections on the image can be projected to a global space and tracked using a nearly constant velocity model. This approach is evaluated using a simulated video of a city and real video from an outdoor benchmark.
Resilience Processes in Development : Four Waves of Research on Positive Adaptation in the Context of Adversity

How do children and adolescents “make it” when their development is threatened by poverty, neglect, maltreatment, war, violence, or exposure to oppression, racism, and discrimination? What protects them when their parents are disabled by substance abuse, mental illness, or serious physical illness? How do we explain the phenomenon of resilience—children succeeding in spite of serious challenges to their development—and put this knowledge to work for the bene fi t of children and society? The scienti fi c study of resilience emerged around 1970 when a group of pioneering researchers began to notice the phenomenon of positive adaptation among subgroups of children who were considered “at risk” for developing later psychopathology (Masten, 2001, 2012 ) . The resilience research pioneers led a revolution in thinking about the origins and treatment of psychopathology. The primary focus of earlier clinical research on children at high risk for psychopathology had been either to observe the consequences of adversity or the unfolding of risk processes accounting for the etiology of disorders. Research efforts were directed towards understanding pathology and de fi cits, rather than on how problems were averted, resolved, or transcended. The fi eld of mental health at the time was dominated by psychoanalytic theory and a disease-oriented biomedical model that located the source of illness within the individual. However, the fi rst investigators to explore the phenomenon of resilience realized that models based primarily on predicting psychopathology were limited in scope and usefulness, providing little understanding of how good outcomes were achieved by many of the children identi fi ed as “at risk.” Such information was vital to the goal of intervening to improve the odds of good developmental outcomes among children at risk. One of the great contributions of the early investigators was their recognition and championing of the idea that understanding positive developmental pathways in the context of adversity is fundamentally important for preventing and treating problems, particularly among children at risk for psychopathology. The study of resilience has advanced in four major waves of research. In this chapter we highlight the concepts and fi ndings resulting from these waves to date, as they have shaped an emerging resilience framework for research and practice. The fi rst wave of work yielded good descriptions of resilience phenomena, along with basic concepts and methodologies, and focused on the individual. The second wave yielded a more dynamic accounting of resilience, adopting a developmental systems approach to theory and research on positive adaptation in the context of adversity or risk, and focused on the transactions among individuals and the many systems in which their development is embedded. The third wave focused on creating resilience by intervention Resilience Processes in Development: Four Waves of Research on Positive Adaptation in the Context of Adversity
A Dynamic Secret-Based Encryption Scheme for Smart Grid Wireless Communication

Integrating information network into power system is the key for realizing the vision of smart grid, but also introduces many security problems. Wireless communication offers the benefits of low cost, rapid deployment, shared communication medium, and mobility; at the same time, it causes many security and privacy challenges. In this paper, the concept of dynamic secret is applied to design an encryption scheme for smart grid wireless communication. Between two parties of communication, the previous packets are coded as retransmission sequence, where retransmitted packet is marked as “1” and the other is marked as “0.” During the communication, the retransmission sequence is generated at both sides to update the dynamic encryption key. Any missing or misjudging in retransmission sequence would prevent the adversary from achieving the keys. In our experiments, a smart grid platform is built, employing the ZigBee protocol for wireless communication. And a dynamic secret-based encryption demo system is designed based on this platform. The experiment results show that the retransmission and packet loss in ZigBee communication are inevitable and unpredictable, and it is impossible for the adversary to track the updating of the dynamic encryption key.
SILK: An Implementation of a Buffer Insertion Ring

Ring topology local area networks (LAN’s) using the “buffer insertion” access method have as yet received relatively little attention. In this paper we present details of a LAN of this.-, called SILK-system for integrated local communication (in German, “Kommunikation”). Sections of the paper describe the synchronous transmission technique of the ring channel, the time-multiplexed access of eight ports at each node, the “braided” interconnection for bypassing defective nodes, and the role of interface transformation units and user interfaces, as well as some traffic,characteristics and reliability aspects. SILK’S modularity and open system concept are demonstrated by the already implemented applications such as distributed text editing, local telephone or teletex exchange, and process control in a TV studio.
Using imagination to understand the neural basis of episodic memory.

Functional MRI (fMRI) studies investigating the neural basis of episodic memory recall, and the related task of thinking about plausible personal future events, have revealed a consistent network of associated brain regions. Surprisingly little, however, is understood about the contributions individual brain areas make to the overall recollective experience. To examine this, we used a novel fMRI paradigm in which subjects had to imagine fictitious experiences. In contrast to future thinking, this results in experiences that are not explicitly temporal in nature or as reliant on self-processing. By using previously imagined fictitious experiences as a comparison for episodic memories, we identified the neural basis of a key process engaged in common, namely scene construction, involving the generation, maintenance and visualization of complex spatial contexts. This was associated with activations in a distributed network, including hippocampus, parahippocampal gyrus, and retrosplenial cortex. Importantly, we disambiguated these common effects from episodic memory-specific responses in anterior medial prefrontal cortex, posterior cingulate cortex and precuneus. These latter regions may support self-schema and familiarity processes, and contribute to the brain's ability to distinguish real from imaginary memories. We conclude that scene construction constitutes a common process underlying episodic memory and imagination of fictitious experiences, and suggest it may partially account for the similar brain networks implicated in navigation, episodic future thinking, and the default mode. We suggest that additional brain regions are co-opted into this core network in a task-specific manner to support functions such as episodic memory that may have additional requirements.
Privacy-Adversarial User Representations in Recommender Systems

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can’t enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.
Irregular lattices for complex shape grammar facade parsing

High-quality urban reconstruction requires more than multi-view reconstruction and local optimization. The structure of facades depends on the general layout, which has to be optimized globally. Shape grammars are an established method to express hierarchical spatial relationships, and are therefore suited as representing constraints for semantic facade interpretation. Usually inference uses numerical approximations, or hard-coded grammar schemes. Existing methods inspired by classical grammar parsing are not applicable on real-world images due to their prohibitively high complexity. This work provides feasible generic facade reconstruction by combining low-level classifiers with mid-level object detectors to infer an irregular lattice. The irregular lattice preserves the logical structure of the facade while reducing the search space to a manageable size. We introduce a novel method for handling symmetry and repetition within the generic grammar. We show competitive results on two datasets, namely the Paris 2010 and the Graz 50. The former includes only Hausmannian, while the latter includes Classicism, Biedermeier, Historicism, Art Nouveau and post-modern architectural styles.
[Oral hairy leukoplakia induced by topical steroids].

Oral hairy leukoplakia (OHL) is an EBV-associated condition of the oral mucosa, which is often painless. It is found predominantly in HIV-positive patients and is considered a clinical indicator of immunosuppression. OHL has rarely been described in HIV-negative patients, being found most often in association with iatrogenic immunosuppression. OHL induced by topical steroids remains extremely rare.



An 81-year-old HIV-negative woman, treated for 3 months with topical steroids for oral lichen planus, developed an asymptomatic white, corrugated, non-removable plaque with vertical folds on the lateral edge of the tongue. Associated oral candidiasis was noted. Based upon histological findings and in situ hybridisation showing numerous EBV-infected epithelial cells, a diagnosis of oral hairy leucoplakia was made.



To our knowledge, we report herein only the second recorded case of OHL induced strictly by topical steroids. Self-medication and poor adherence to dosage recommendations were noted in the patient's medical history. Physicians must be aware of the rare but nevertheless possible adverse events associated with topical steroid use, particularly when such medication is prescribed over a long period for inflammatory diseases of the oral mucosa.
A Survey of Radio Propagation Modeling for Tunnels

Radio signal propagation modeling plays an important role in designing wireless communication systems. The propagation models are used to calculate the number and position of base stations and predict the radio coverage. Different models have been developed to predict radio propagation behavior for wireless communication systems in different operating environments. In this paper we shall limit our discussion to the latest achievements in radio propagation modeling related to tunnels. The main modeling approaches used for propagation in tunnels are reviewed, namely, numerical methods for solving Maxwell equations, waveguide or modal approach, ray tracing based methods and two-slope path loss modeling. They are discussed in terms of modeling complexity and required information on the environment including tunnel geometry and electric as well as magnetic properties of walls.