nils-herrmann/clean_scirepeval huggingface dataset:
GORAM – Group ORAM for Privacy and Access Control in Outsourced Personal Records 1

Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems. To tackle this problem, we present GORAM, a cryptographic system that protects the secrecy and integrity of outsourced data with respect to both an untrusted server and malicious clients, guarantees the anonymity and unlinkability of accesses to such data, and allows the data owner to share outsourced data with other clients, selectively granting them read and write permissions. GORAM is the first system to achieve such a wide range of security and privacy properties for outsourced storage. In the process of designing an efficient construction, we developed two new, generally applicable cryptographic schemes, namely, batched zero-knowledge proofs of shuffle and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented GORAM in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.
TinyVM, an efficient virtual machine infrastructure for sensor networks

We present TinyVM, a Virtual Machine (VM) for nesC and C applications on sensor motes. TinyVM executes compressed bytecode on-the-fly to conserve memory. To facilitate creation of application-specific VMs, partitioning of applications into bytecode, VM instruction set extensions and machine-code is supported. We provide experimental evidence for the efficiency of TinyVM on Atmega128-based motes and on the Intel iMote2. TinyVM also runs on Windows and Linux, and we are currently porting TinyVM to Telos-based motes.
The structure of musical preferences: a five-factor model.

Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners' affective reactions to excerpts of music from a wide variety of musical genres. The findings from 3 independent studies converged to suggest that there exists a latent 5-factor structure underlying music preferences that is genre free and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as (a) a Mellow factor comprising smooth and relaxing styles; (b) an Unpretentious factor comprising a variety of different styles of sincere and rootsy music such as is often found in country and singer-songwriter genres; (c) a Sophisticated factor that includes classical, operatic, world, and jazz; (d) an Intense factor defined by loud, forceful, and energetic music; and (e) a Contemporary factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and the auditory characteristics of the music.
Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space

The paper outlines a framework for autonomous control of a CRM (customer relationship management) system. First, it explores how a modified version of the widely accepted Recency-Frequency-Monetary Value system of metrics can be used to define the state space of clients or donors. Second, it describes a procedure to determine the optimal direct marketing action in discrete and continuous action space for the given individual, based on his position in the state space. The procedure involves the use of model-free Q-learning to train a deep neural network that relates a client’s position in the state space to rewards associated with possible marketing actions. The estimated value function over the client state space can be interpreted as customer lifetime value, and thus allows for a quick plug-in estimation of CLV for a given client. Experimental results are presented, based on KDD Cup 1998 mailing dataset of donation solicitations.
Modeling Cellular-to-UAV Path-Loss for Suburban Environments

Operating unmanned aerial vehicle (UAV) over cellular networks would open the barriers of remote navigation and far-flung flying by combining the benefits of UAVs and the ubiquitous availability of cellular networks. In this letter, we provide an initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel. In particular, we model the statistical behavior of the path-loss from a cellular base station toward a flying UAV. Where we report the value of the path-loss as a function of the depression angle and the terrestrial coverage beneath the UAV. The provided model is derived based on extensive experimental data measurements conducted in a typical suburban environment for both terrestrial (by drive test) and aerial coverage (using a UAV). The model provides simple and accurate prediction of CtU path-loss that can be useful for both researchers and network operators alike.
Applying the Cognitive-Affective Processing Systems Approach to Conceptualizing Rejection Sensitivity.

The Cognitive-Affective Processing Systems or CAPS theory (Mischel & Shoda, 1995) was proposed to account for the processes that explain why and how people's behavior varies stably across situations. Research on Rejection Sensitivity is reviewed as a programmatic attempt to illustrate how personality dispositions can be studied within the CAPS framework. This research reveals an if … then … (e.g., if situation X, he does A, but if situation Y, he does B) pattern of rejection sensitivity such that high rejection sensitive people's goal to prevent rejection can lead to accommodating behavior; yet, the failure to achieve this goal can lead to aggression, reactivity, and lack of self-concept clarity. These situation-behavior relations or personality signatures reflect a stable activation network of distinctive personality processing dynamics. These dynamics link fears and expectations of rejection, perceptions/attributions of rejection, and affective/behavioral overreactions to perceived rejection. Self-regulatory and attentional mechanisms may interact with these dynamics as buffers against high rejection sensitivity, illustrating how multiple processes within a CAPS network play out in behavior.
From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC) – an initiative to collect unique, longitudinal smartphone data set for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; discuss the key design and implementation aspects introduced in order to generate privacypreserving and scientifically relevant mobile data resources for wider use by the research community; and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.
Bio-inspired knee joint mechanism for a hydraulic quadruped robot

Over the last few decades, legged robots are becoming a promising solution for rough terrain navigation, however, existing legged machines often lack versatility to perform a wide range of different gaits. To build a highly dynamic and versatile legged robot, it is essential to have lightweight legs with optimized design and suitable actuators for the desired robot performance and tasks. The design goals are to achieve (1) a wide range of motion for bigger foot workspace which will increase rough terrain walking performance by increasing the number of reachable footholds for each step, (2) optimized joint torque curve since torque output is related to joint angle if linear actuators like pistons are used. In this paper, we focus on the knee joint and propose the adaptation and optimization of the so-called isogram mechanism. It exhibits a changeable instantaneous center of rotation (CICR), similar to a human knee joint. We will show how an optimization of design parameters lead to a knee joint design that satisfies the above-mentioned goals. The main contributions of this paper are the kinematic and torque analysis of the isogram mechanism that is actuated by a linear actuator; the optimization of the mechanism's design parameters; a comparison between the proposed knee joint with the hinge-type knee joint of the quadruped robot HyQ; and experimental results of a proof-of-concept prototype leg featuring the proposed mechanism.
Farasa: A Fast and Furious Segmenter for Arabic

In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is based on SVM-rank using linear kernels. We measure the performance of the segmenter in terms of accuracy and efficiency, in two NLP tasks, namely Machine Translation (MT) and Information Retrieval (IR). Farasa outperforms or is at par with the stateof-the-art Arabic segmenters (Stanford and MADAMIRA), while being more than one order of magnitude faster.
Cooperative Sensing among Cognitive Radios

Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.
The Impact of Comments and Recommendation System on Online Shopper Buying Behaviour

The author aims at studying the influence of comments and recommendation on online shopping behaviours. With 285 study subjects, the author used experimental research design to study comments and recommendation system on online shopping experience, online shopping satisfaction, online shopping intention and items chosen. The data is analyzed by SPSS 15.0 and LISREL 8.8. The results show that comments and recommendation influence online shopping experience, online shopping satisfaction and online shopping intention directly. Online shopping experience and online shopping satisfaction influence online shopping intention significantly. There is remarkable positive relation between online shopping intention and items chosen.
Reading Between the Lines: Content-Agnostic Detection of Spear-Phishing Emails

Spear-phishing is an effective attack vector for infiltrating companies and organisations. Based on the multitude of personal information available online, an attacker can craft seemingly legit emails and trick his victims into opening malicious attachments and links. Although anti-spoofing techniques exist, their adoption is still limited and alternative protection approaches are needed. In this paper, we show that a sender leaves content-agnostic traits in the structure of an email. Based on these traits, we develop a method capable of learning profiles for a large set of senders and identifying spoofed emails as deviations thereof. We evaluate our approach on over 700,000 emails from 16,000 senders and demonstrate that it can discriminate thousands of senders, identifying spoofed emails with 90% detection rate and less than 1 false positive in 10,000 emails. Moreover, we show that individual traits are hard to guess and spoofing only succeeds if entire emails of the sender are available to the attacker.
Privacy-preserving personal health record using multi-authority attribute-based encryption with revocation

Personal health record (PHR) service is an emerging model for health information exchange. In PHR systems, patient’s health records and information are maintained by the patient himself through the Web. In reality, PHRs are often outsourced to be stored at the third parties like cloud service providers. However, there have been serious privacy concerns about cloud service as it may expose user’s sensitive data like PHRs to those cloud service providers or unauthorized users. Using attribute-based encryption (ABE) to encrypt patient’s PHRs in cloud environment, secure and flexible access control can be achieved. Yet, problems like scalability in key management, fine-grained access control, and efficient user revocation remain to be addressed. In this paper, we propose a privacy-preserving PHR, which supports fine-grained access control and efficient revocation. To be specific, our scheme achieves the goals (1) scalable and fine-grained access control for PHRs by using multi-authority ABE scheme, and (2) efficient on-demand user/attribute revocation and dynamic policy update. In our scheme, we consider the situation that multiple data owners exist, and patient’s PHRs are encrypted and stored in semi-trust servers. The access structure in our scheme is expressive access tree structure, and the security of our scheme can be reduced to the standard decisional bilinear Diffie–Hellman assumption.
Head-worn displays: a review

Head-worn display design is inherently an interdisciplinary subject fusing optical engineering, optical materials, optical coatings, electronics, manufacturing techniques, user interface design, computer science, human perception, and physiology for assessing these displays. This paper summarizes the state-of-the-art in head-worn display design (HWD) and development. This review is focused on the optical engineering aspects, divided into different sections to explore principles and applications. Building on the guiding fundamentals of optical design and engineering, the principles section includes a summary of microdisplay or laser sources, the Lagrange invariant for understanding the trade-offs in optical design of HWDs, modes of image presentation (i.e., monocular, biocular, and stereo) and operational modes such as optical and video see-through. A brief summary of the human visual system pertinent to the design of HWDs is provided. Two optical design forms, namely, pupil forming and non-pupil forming are discussed. We summarize the results from previous design work using aspheric, diffractive, or holographic elements to achieve compact and lightweight systems. The applications section is organized in terms of field of view requirements and presents a reasonable collection of past designs
The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research

The widespread use of 'statistical significance' as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p-values into 'significant' and 'nonsignificant' contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p-values at face value, but mistrust results with larger p-values. In either case, p-values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance (p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be 'conflicting', meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p-hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p-values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p-values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that 'there is no effect'. Information on possible true effect sizes that are compatible with the data must be obtained from the point estimate, e.g., from a sample average, and from the interval estimate, such as a confidence interval. We review how confusion about interpretation of larger p-values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, for example that decision rules should rather be more stringent, that sample sizes could decrease, or that p-values should better be completely abandoned. We conclude that whatever method of statistical inference we use, dichotomous threshold thinking must give way to non-automated informed judgment.
A 28GHz Bulk-CMOS dual-polarization phased-array transceiver with 24 channels for 5G user and basestation equipment

Developing next-generation cellular technology (5G) in the mm-wave bands will require low-cost phased-array transceivers . Even with the benefit of beamforming, due to space constraints in the mobile form-factor, increasing TX output power while maintaining acceptable PA PAE, LNA NF, and overall transceiver power consumption is important to maximizing link budget allowable path loss and minimizing handset case temperature. Further, the phased-array transceiver will need to be able to support dual-polarization communication. An IF interface to the analog baseband is desired for low power consumption in the handset or user equipment (UE) active antenna and to enable use of arrays of transceivers for customer premises equipment (CPE) or basestation (BS) antenna arrays with a low-loss IF power-combining/splitting network implemented on an antenna backplane carrying multiple tiled antenna modules.
The One-Piece Orbitozygomatic Approach: The MacCarty Burr Hole and the Inferior Orbital Fissure as Keys to Technique and Application

Objective. Use of the MacCarty keyhole burr hole and the inferior orbital fissure provides simplicity and safety to perform the one-piece frontotemporal orbitozygomatic (FTOZ1) approach.  Methods. We performed the FTOZ1 approach with its three subtypes (i.e., total, temporal, and frontal) in cadaveric head specimens in the Goodyear Laboratory and subsequently in surgical cases.  Results. The orbitozygomatic osteotomy, when added to a frontotemporal craniotomy, comprises the frontotemporal orbitozygomatic (FTOZ) approach, provides an expanded exposure to the anterior and middle cranial fossae, and enables the surgeon to create a window to the posterior cranial fossa. The MacCarty burr hole is used to facilitate orbital cuts, and the anterolateral portion of the inferior orbital fissure connects the orbital cuts to the zygomatic cuts. This allows the FTOZ1 craniotomy flap to be “out-fractured” with ease. The three types of FTOZ1 approach, i.e., the total, the temporal, and the frontal, are described step by step.   Understanding the MacCarty keyhole burr hole and the microsurgical anatomy of the inferior orbital fissure is essential to performing the FTOZ1 approach. The three types of FTOZ1 approach enable the surgeon to tailor the approach according to the surgical exposure needed for each lesion.
An Expression Deformation Approach to Non-rigid 3D Face Recognition

The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively.