nils-herrmann/clean_scirepeval huggingface dataset:
Animal-inspired design and aerodynamic stabilization of a hexapedal millirobot

The VelociRoACH is a 10 cm long, 30 gram hexapedal millirobot capable of running at 2.7 m/s, making it the fastest legged robot built to date, relative to scale. We present the design by dynamic similarity technique and the locomotion adaptations which have allowed for this highly dynamic performance. In addition, we demonstrate that rotational dynamics become critical for stability as the scale of a robotic system is reduced. We present a new method of experimental dynamic tuning for legged millirobots, aimed at finding stable limit cycles with minimal rotational energy. By implementing an aerodynamic rotational damper, we further reduced the rotational energy in the system, and demonstrated that stable limit cycles with lower rotational energy are more robust to disturbances. This method increased the stability of the system without detracting from forward speed.
Learning from Time-Changing Data with Adaptive Windowing

We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and
Maglev: A Fast and Reliable Software Network Load Balancer

Maglev is Google’s network load balancer. It is a large distributed software system that runs on commodity Linux servers. Unlike traditional hardware network load balancers, it does not require a specialized physical rack deployment, and its capacity can be easily adjusted by adding or removing servers. Network routers distribute packets evenly to the Maglev machines via Equal Cost Multipath (ECMP); each Maglev machine then matches the packets to their corresponding services and spreads them evenly to the service endpoints. To accommodate high and ever-increasing traffic, Maglev is specifically optimized for packet processing performance. A single Maglev machine is able to saturate a 10Gbps link with small packets. Maglev is also equipped with consistent hashing and connection tracking features, to minimize the negative impact of unexpected faults and failures on connection-oriented protocols. Maglev has been serving Google’s traffic since 2008. It has sustained the rapid global growth of Google services, and it also provides network load balancing for Google Cloud Platform.
Approaching the Limit of Predictability in Human Mobility

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
A large-scale car dataset for fine-grained categorization and verification

This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html.
Exponentiated Exponential Family : An Alternative to Gamma and Weibull Distributions

In this article we study some properties of a new family of distributions, namely Exponentiated Exponential distribution, discussed in Gupta, Gupta, and Gupta (1998). The Exponentiated Exponential family has two parameters (scale and shape) similar to a Weibull or a gamma family. It is observed that many properties of this new family are quite similar to those of a Weibull or a gamma family, therefore this distribution can be used as a possible alternative to a Weibull or a gamma distribution. We present two real life data sets, where it is observed that in one data set exponentiated exponential distribution has a better fit compared to Weibull or gamma distribution and in the other data set Weibull has a better fit than exponentiated exponential or gamma distribution. Some numerical experiments are performed to see how the maximum likelihood estimators and their asymptotic results work for finite sample sizes.
A New High-Efficiency Single-Phase Transformerless PV Inverter Topology

There is a strong trend in the photovoltaic inverter technology to use transformerless topologies in order to acquire higher efficiencies combining with very low ground leakage current. In this paper, a new topology, based on the H-bridge with a new ac bypass circuit consisting of a diode rectifier and a switch with clamping to the dc midpoint, is proposed. The topology is simulated and experimentally validated, and a comparison with other existing topologies is performed. High conversion efficiency and low leakage current are demonstrated.
Multiple rank multi-linear SVM for matrix data classification

Matrices, or more generally, multi-way arrays (tensors) are common forms of data that are encountered in a wide range of real applications. How to classify this kind of data is an important research topic for both pattern recognition and machine learning. In this paper, by analyzing the relationship between two famous traditional classification approaches, i.e., SVM and STM, a novel tensor-based method, i.e., Multiple Rank Multi-Linear SVM (MRMLSVM), is proposed. Different from traditional vector-based and tensor based methods, multiple-rank left and right projecting vectors are employed to construct decision boundary and establish margin function. We reveal that the rank of transformation can be regarded as a tradeoff parameter to balance the capacity of learning and generalization in essence. We also proposed an effective approach to solve the proposed non-convex optimization problem. The convergence behavior, initialization, computational complexity and parameter determination problems are analyzed. Compared with vector-based classification methods, MRMLSVM achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRMLSVM performs better for matrix data classification. Promising experimental results on various kinds of data sets are provided to show the effectiveness of our method.
Does Inclusion of CIO in Top Management Team Impact Firm Performance? Evidence from a Long-Term Event Analysis

Inclusion of chief information officers (CIOs) in top management teams (TMTs) increases the functional and knowledge diversity of the teams, and elevates the role of IT in strategic business decisions. Considering that firms are increasingly relying on IT for operational efficiency and competitive advantage, TMTs with IT diversity can be expected to have superior performance compared to those without. While many studies have argued conceptually the importance of CIOs membership in TMTs, very few studies examined the performance effects of this membership. The goal of this study is to investigate this relationship. Moreover, we consider contingency factors that may have an effect on the focal relationship. Using firm-level secondary data and long-term event study methodology, we show that inclusion of CIO in TMT has a significant positive effect on firm performance, and this positive effect is larger for firms in dynamic environments and during the more recent years.
Software engineering for big data projects: Domains, methodologies and gaps

Big data has become the new buzzword in the information and communication technology industry. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them. However, developing and maintaining stable and scalable big data applications is still a distant milestone.  To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects.  A literature survey was performed. A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review.  The search results helped in identifying data rich application projects that have the potential to utilize big data successfully. The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention.
The eyeBook – Using Eye Tracking to Enhance the Reading Experience

A rapid development of eye tracking technology has been observed in recent years. Today’s eye trackers can determine the current focus point of the eye precisely while being relatively unobtrusive in their application. Also, a variety of research and commercial groups has been working on this technology, and there is a growing interest for such devices on the market. Eye tracking has great potential and it can be assumed that it will advance further and might become a widespread technology used at a large number of personal or office computer workplaces. Approaches using simple webcams for eye tracking already exist, for example webcams integrated into laptop computers by default. Thus, they allow for new kinds of applications using eye gaze data. However, not only eye tracking technology is advancing rapidly to an easily usable state. Additionally, during the past 100 years researchers gathered a considerable amount of knowledge on eye movements, why and how they occur, and what they might mean. So, today, we have the technology and knowledge for tracking and analyzing eye movements, making an excellent starting point for sophisticated interactive gaze-based applications. Naive approaches where gaze data is directly employed for interacting with the system, e. g., pressing buttons on the screen with the “blink of an eye” generally have serious problems. Because the eyes are organs used for perceiving the world and not for manipulating the world, it is hard and against human nature to control eye movements deliberately. However, a highly promising approach is just to observe eye movements of the user during his or her daily work in front of the computer, to infer user intentions based on eye movement behavior, and to provide assistance where helpful. Gaze can be seen as a proxy for the user’s attention, and eye movements are known to be usually tightly coupled with cognitive processes in the brain, so that a great deal about those processes can be observed by eye tracking. For example, by interpreting eye movements, reading behavior of the user can be detected, which most likely entails cognitive processes of understanding with regard to the currently read text. In this paper we are focusing particularly on reading behavior since reading is probably the most common activity of knowledge workers sitting in front of a computer screen. We present an algorithm for online reading detection based on eye tracking data and introduce an application for assisted and augmented reading called the eyeBook. The idea behind the eyeBook is to create an interactive and entertaining reading experience. The system observes, which text parts are currently being read by the user on the screen and generates appropriate effects such as playing sounds, presenting
Sense-Aaware Semantic Analysis: A Multi-Prototype Word Representation Model Using Wikipedia

Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The “sense-specific” prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.
Y-MAC: An Energy-Efficient Multi-channel MAC Protocol for Dense Wireless Sensor Networks

As the use of wireless sensor networks (WSNs) becomes widespread, node density tends to increase. This poses a new challenge for Medium Access Control (MAC) protocol design. Although traditional MAC protocols achieve low-power operation, they use only a single channel which limits their performance. Several multi-channel MAC protocols for WSNs have been recently proposed. One of the key observations is that these protocols are less energy efficient than single-channel MAC protocols under light traffic conditions. In this paper, we propose an energy efficient multi-channel MAC protocol, Y-MAC, for WSNs. Our goal is to achieve both high performance and energy efficiency under diverse traffic conditions. In contrast to most of previous multi-channel MAC protocols for WSNs, we implemented Y-MAC on a real sensor node platform and conducted extensive experiments to evaluate its performance. Experimental results show that Y-MAC is energy efficient and maintains high performance under high-traffic conditions.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech without transcripts from thousands of speakers, to generate a fixed-dimensional embedding vector from only seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2 that generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder network that converts the mel spectrogram into time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the multispeaker TTS task, and is able to synthesize natural speech from speakers unseen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.
RefSeer: A citation recommendation system

Citations are important in academic dissemination. To help researchers check the completeness of citations while authoring a paper, we introduce a citation recommendation system called RefSeer. Researchers can use it to find related works to cited while authoring papers. It can also be used by reviewers to check the completeness of a paper's references. RefSeer presents both topic based global recommendation and also citation-context based local recommendation. By evaluating the quality of recommendation, we show that such recommendation system can recommend citations with good precision and recall. We also show that our recommendation system is very efficient and scalable.
Artificial vision for the blind by connecting a television camera to the visual cortex.

Blindness is more feared by the public than any ailment with the exception of cancer and AIDS. We report the development of the first visual prosthesis providing useful "artificial vision" to a blind volunteer by connecting a digital video camera, computer, and associated electronics to the visual cortex of his brain. This device has been the objective of a development effort begun by our group in 1968 and represents realization of the prediction of an artificial vision system made by Benjamin Franklin in his report on the "kite and key" experiment, with which he discovered electricity in 1751.
Voting for candidates: adapting data fusion techniques for an expert search task

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.
Custom soft robotic gripper sensor skins for haptic object visualization

Robots are becoming increasingly prevalent in our society in forms where they are assisting or interacting with humans in a variety of environments, and thus they must have the ability to sense and detect objects by touch. An ongoing challenge for soft robots has been incorporating flexible sensors that can recognize complex motions and close the loop for tactile sensing. We present sensor skins that enable haptic object visualization when integrated on a soft robotic gripper that can twist an object. First, we investigate how the design of the actuator modules impact bend angle and motion. Each soft finger is molded using a silicone elastomer, and consists of three pneumatic chambers which can be inflated independently to achieve a range of complex motions. Three fingers are combined to form a soft robotic gripper. Then, we manufacture and attach modular, flexible sensory skins on each finger to measure deformation and contact. These sensor measurements are used in conjunction with an analytical model to construct 2D and 3D tactile object models. Our results are a step towards soft robot grippers capable of a complex range of motions and proprioception, which will help future robots better understand the environments with which they interact, and has the potential to increase physical safety in human-robot interaction. Please see the accompanying video for additional details.
Complete chloroplast DNA sequences of Zanduri wheat (Triticum spp.)

Georgia plays an important role in wheat formation. In the past, the Zanduri population of Georgia was a set of diploid—Triticum monococcum var. hornemanii (2n = 14) (Gvatsa Zanduri), tetraploid Triticum timopheevii (2n = 28) (Chelta Zanduri) and hexaploid Triticum zhukovskyi Men. et Er. (2n = 42). It is a Zanduri puzzle that wild T. araraticum was not found in Georgia, though cultivated T. timopheevii was only detected here. Next-generation sequencing technologies, which have been developed in recent years, enable the determination of complete nucleotide sequences of both chloroplast and mitochondrial DNA of many higher plants, including wheat. The genetic structure of Zanduri wheat is more accurately inferred by the complete sequences of chloroplast DNA. In the present investigation, the complete sequences of three Zanduri wheats (T. timopheevii, T. zhukovskyi, and T. monococcum var. hornemanii) and wild T. araraticum are presented. Sequencing of chloroplast DNA was performed on an Illumina MiSeq platform. Chloroplast DNA molecules were assembled using the SOAPdenovo computer program. In comparison to T. araraticum, there are 12 SNPs, a 25 bp inversion in the ccsA-ndhD intergenic sequence, and a 38-bp inversion in the intergenic sequence rbcL-rpl23 pseudogene identified in T. timopheevii and T. zhukovskyi. In addition, a 24 bp repeat of trnG-trnI intergenic sequence is present as a double copy in T. araraticum, whereas in T. timopheevii and T. zhukovskyi, it is present as a triple copy. Unlike T. araraticum, T. timopheevii and T. zhukovskyi have a 6 bp repeat in the gene ndhH, which results in a dipeptide duplication in the corresponding protein. Gvatsa Zanduri (T. monococcum var. hornemanii) chloroplast DNA slightly differs from other einkorn chloroplast DNA. In comparison to T. monococcum, four SNPs can be identified in T. monococcum (Gvatsa Zanduri), two in gene matK and one in gene ndhD. The sequenced chloroplast DNA molecules were compared to other Triticum and Aegilops species, and a phylogenetic tree was constructed. T. araraticum, T. timopheevii and T. zhukovskyi chloroplast DNA showed the closest phylogenetic relationship with the chloroplast DNA of Ae. speltoides. The most significant difference was in the 114-bp deletion within the gene ndhH in the Timopheevi species.