nils-herrmann/clean_scirepeval huggingface dataset:
Proofs of retrievability with public verifiability and constant communication cost in cloud

For data storage outsourcing services, it is important to allow data owners to efficiently and securely verify that the storage server stores their data correctly. To address this issue, several proof-of-retrievability (POR) schemes have been proposed wherein a storage server must prove to a verifier that all of a client's data are stored correctly. While existing POR schemes offer decent solutions addressing various practical issues, they either have a non-trivial (linear or quadratic) communication complexity, or only support private verification, i.e., only the data owner can verify the remotely stored data. It remains open to design a POR scheme that achieves both public verifiability and constant communication cost simultaneously.
 In this paper, we solve this open problem and propose the first POR scheme with public verifiability and constant communication cost: in our proposed scheme, the message exchanged between the prover and verifier is composed of a constant number of group elements; different from existing private POR constructions, our scheme allows public verification and releases the data owners from the burden of staying online. We achieved these by tailoring and uniquely combining techniques such as constant size polynomial commitment and homomorphic linear authenticators. Thorough analysis shows that our proposed scheme is efficient and practical. We prove the security of our scheme based on the Computational Diffie-Hellman Problem, the Strong Diffie-Hellman assumption and the Bilinear Strong Diffie-Hellman assumption.
Comparing Potential and Actual Innovators: An Empirical Study of Mobile Data Services Innovation

Firms are increasingly opening up their innovation efforts to allow users to tap into the benefits they can offer, such as mobile data service (MDS) innovation on iOS and Google Android platforms. For this purpose, platforms typically provide toolkits to facilitate user participation, aiming to create an ecosystem for sustainable innovation. However, with the barriers to user innovation and attrition of existing innovators, it could be challenging for firms to attract and sustain users’ MDS innovation. With the possible benefits from user innovation, and considering the challenges faced, firms need to understand how to influence potential user innovators to take part and to encourage extant user innovators to innovate again. However, there is a lack of comprehensive research and understanding of what drives users’ intentions to innovate services and the differences in the antecedents of such intention between potential and actual user innovators. Further, although prior studies have suggested that toolkits can support user innovation, little research has theorized and empirically tested their influence. Motivated thus, this study proposes a model based on (1) user innovation theory to explain the antecedents (including toolkit support) of user MDS innovation intention and (2) construal level theory to explain the differential effects of the antecedents for actual and potential user innovators. We tested the model through survey data from potential and actual MDS user innovators on Google Android and iOS platforms. We find that trend leadership and anticipated extrinsic reward influence both potential and actual user innovators’ intentions to innovate. However, anticipated recognition and toolkit support affect only actual user innovators, while anticipated enjoyment affects only potential user innovators. Interestingly, toolkit support strengthens the influence of anticipated enjoyment for actual user innovators but weakens its influence for potential user innovators. Further, potential user innovators value anticipated extrinsic rewards less than actual innovators do. The implications for research and practice are discussed.
Isomorphism of graphs of bounded valence can be tested in polynomial time

Suppose we are given a set of generators for a group G of permutations of a colored set A. The color automorphism problem for G involves finding generators for the subgroup of G which stabilizes the color classes. Testing isomorphism of graphs of valence ≤ t is polynomial-time reducible to the color automorphism problem for groups with small simple sections. The algorithm for the latter problem involves several divide-and-conquer tricks. The problem is solved sequentially on the G-orbits. An orbit is broken into a minimal set of blocks permuted by G. The hypothesis on G guarantees the existence of a 'large' subgroup P which acts as a p-group on the blocks. A similar process is repeated for each coset of P on G. Some results on primitive permutation groups are used to show that the algorithm runs in polynomial time.
Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis

Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.
An empirical study of cognition and theatrical improvisation

This paper presents preliminary findings from our empirical study of the cognition employed by performers in improvisational theatre. Our study has been conducted in a laboratory setting with local improvisers. Participants performed predesigned improv "games", which were videotaped and shown to each individual participant for a retrospective protocol collection. The participants were then shown the video again as a group to elicit data on group dynamics, misunderstandings, etc. This paper presents our initial findings that we have built based on our initial analysis of the data and highlights details of interest.
Intent Recognition for Human-Robot Interaction

Effective human-robot cooperation requires robotic devices that understand human goals and intentions. We frame the problem of intent recognition as one of tracking and predicting human actions within the context of plan task sequences. A hybrid mode estimation approach, which estimates both discrete operating modes and continuous state, is used to accomplish this tracking based on possibly noisy sensor input. The operating modes correspond to plan tasks, hence, the ability to estimate and predict these provides a prediction of human actions and associated needs in the plan context. The discrete and continuous estimates interact in that the discrete mode selects continous dynamic models used in the continuous estimation, and the continuous state is used to evaluate guard conditions for mode transitions. Two applications: active prosthetic devices, and cooperative assembly, are described.
DESIGN OF MICROSTRIP HAIRPIN BAND PASS FILTER USING DEFECTED GROUND STRUCTURE AND OPEN

A dumbbell shape defected ground structure is proposed to suppress the second and third harmonics of five pole microstrip hairpin band pass filter. Two pairs of dumbbell shaped DGS cells are used at the input and output feed lines of band pass filter. The DGS is operated as a low pass filter to suppress second and third order harmonics. DGS cells are etched under feed lines for improving stop band rejection without affecting the centre frequency and insertion loss of basic filter. For effective suppression of third order harmonic open stubs are added to dumbbell DGS module under both feed lines. Open stubs are used at the input and output port to improve the out-of-band suppression. A five pole chebyshev microstrip band pass filter is designed with 700 MHz bandwidth at the centre frequency of 2.5 GHz. Band pass filter is simulated using ADS 2009 software. The harmonics are suppressed by 23 dB and 48 dB at second and third harmonics respectively. Extra stop band rejection to the second harmonic frequency is achieved by using open stubs at the input and output feed line. The return loss of the proposed filter is -32 dB.
1 Experiments with Unit Selection Speech Databases for Indian Languages

This paper presents a brief overview of unit selection speech synthesis and discuss the issues relevant to the development of voices for Indian languages. We discuss a few perceptual experiments conducted on Hindi and Telugu voices. 1 Role of Language Technologies Most of the Information in digital world is accessible to a few who can read or understand a particular language. Language technologies can provide solutions in the form of natural interfaces so that digital content can reach to the masses and facilitate the exchange of information across different people speaking different languages. These technologies play a crucial role in multi-lingual societies such as India which has about 1652 dialects/native languages. While Hindi written in Devanagari script, is the official language, the other 17 languages recognized by the constitution of India are: 1) Assamese 2) Tamil 3) Malayalam 4) Gujarati 5) Telugu 6) Oriya 7) Urdu 8) Bengali 9) Sanskrit 10) Kashmiri 11) Sindhi 12) Punjabi 13) Konkani 14) Marathi 15) Manipuri 16) Kannada and 17) Nepali. Seamless integration of speech recognition, machine translation and speech synthesis systems could facilitate the exchange of information between two people speaking two different languages. Our overall goal is to develop speech recognition and speech synthesis systems for most of these languages. In this paper we discuss the issues related to the development of speech synthesis systems for Indian languages using unit selection techniques. This work is done within the FestVox voice building framework , which offers general tools for building unit selection synthesizers in new languages. FestVox offers a language independent method for building synthetic voices, offering mechanisms to abstractly describe phonetic and syllabic structure in the language. It is that flexibility in the language building process that we exploited to build voices for Indian languages. Voices generated by this system may be run in the Festival Speech Synthesis System . 2 Speech Synthesis Systems The objective of a text to speech system is to convert an arbitrary given text into a corresponding spoken waveform. Text processing and speech generation are two main components of a text to speech system. The objective of the text processing component is to process the given input text and produce appropriate sequence of phonemic units. These phonemic units are realized by the speech generation component either by synthesis from parameters or by selection of a unit from a large speech corpus. For natural sounding speech synthesis, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. 3 Text Processing Front End Before we discuss the issues related to text processing, let us briefly understand the nature of the text for which the synthesis systems are built.
Emergent: a novel data-set for stance classification

We present Emergent, a novel data-set derived from a digital journalism project for rumour debunking. The data-set contains 300 rumoured claims and 2,595 associated news articles, collected and labelled by journalists with an estimation of their veracity (true, false or unverified). Each associated article is summarized into a headline and labelled to indicate whether its stance is for, against, or observing the claim, where observing indicates that the article merely repeats the claim. Thus, Emergent provides a real-world data source for a variety of natural language processing tasks in the context of fact-checking. Further to presenting the dataset, we address the task of determining the article headline stance with respect to the claim. For this purpose we use a logistic regression classifier and develop features that examine the headline and its agreement with the claim. The accuracy achieved was 73% which is 26% higher than the one achieved by the Excitement Open Platform (Magnini et al., 2014).
Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.
Generating artificial errors for grammatical error correction

This paper explores the generation of artificial errors for correcting grammatical mistakes made by learners of English as a second language. Artificial errors are injected into a set of error-free sentences in a probabilistic manner using statistics from a corpus. Unlike previous approaches, we use linguistic information to derive error generation probabilities and build corpora to correct several error types, including open-class errors. In addition, we also analyse the variables involved in the selection of candidate sentences. Experiments using the NUCLE corpus from the CoNLL 2013 shared task reveal that: 1) training on artificially created errors improves precision at the expense of recall and 2) different types of linguistic information are better suited for correcting different error types.
Unified Closed Form Inverse Kinematics for the KUKA youBot

Mobile manipulators are of high interest to industry because of the increased flexibility and effectiveness they offer. The combination and coordination of the mobility provided by a mobile platform and of the manipulation capabilities provided by a robot arm leads to complex analytical problems for research. These problems can be studied very well on the KUKA youBot, a mobile manipulator designed for education and research applications. Issues still open in research include solving the inverse kinematics problem for the unified kinematics of the mobile manipulator, including handling the kinematic redundancy introduced by the holonomic platform of the KUKA youBot. As the KUKA youBot arm has only 5 degrees of freedom, a unified platform and manipulator system is needed to compensate for the missing degree of freedom. We present the KUKA youBot as an 8 degree of freedom serial kinematic chain, suggest appropriate redundancy parameters, and solve the inverse kinematics for the 8 degrees of freedom. This enables us to perform manipulation tasks more efficiently. We discuss implementation issues, present example applications and some preliminary experimental evaluation along with discussion about redundancies.
Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being.

Five studies tested two general hypotheses: Individuals differ in their use of emotion regulation strategies such as reappraisal and suppression, and these individual differences have implications for affect, well-being, and social relationships. Study 1 presents new measures of the habitual use of reappraisal and suppression. Study 2 examines convergent and discriminant validity. Study 3 shows that reappraisers experience and express greater positive emotion and lesser negative emotion, whereas suppressors experience and express lesser positive emotion, yet experience greater negative emotion. Study 4 indicates that using reappraisal is associated with better interpersonal functioning, whereas using suppression is associated with worse interpersonal functioning. Study 5 shows that using reappraisal is related positively to well-being, whereas using suppression is related negatively.
Analysis and Design of UWB Vivaldi Antenna

A type of antipodal Vivaldi antenna is designed in this paper, which can be used for pulse radiation in UWB communication. Vivaldi antenna is a special tapered slot antenna with planar structure and is easy to be integrated with transmitting elements and receiving elements to form a compact structure. The antenna designed has good performance in the band 3.1GHz to 10.6GHz, which is assigned to Ultra Wide Band (UWB) wireless communication by FCC. The antenna proposed can radiate or receive UWB pulses with little distortion and a good directivity and a high efficiency.
Solving TSP with Shuffled Frog-Leaping Algorithm

Shuffled frog-leaping algorithm (SFLA) is a new memetic meta-heuristic algorithm with efficient mathematical function and global search capability. Traveling salesman problem (TSP) is a complex combinatorial optimization problem, which is typically used as benchmark for testing the effectiveness as well as the efficiency of a newly proposed optimization algorithm. When applying the shuffled frog-leaping algorithm in TSP, memeplex and submemeplex are built and the evolution of the algorithm, especially the local exploration in submemeplex is carefully adapted based on the prototype SFLA. Experimental results show that the shuffled frog leaping algorithm is efficient for small-scale TSP. Particularly for TSP with 51 cities, the algorithm manages to find six tours which are shorter than the optimal tour provided by TSPLIB. The shortest tour length is 428.87 instead of 429.98 which can be found cited elsewhere.
Detecting symbols on road surface for mapping and localization using OCR

In this paper, we present a system to detect symbols on roads (e.g. arrows, speed limits, bus lanes and other pictograms) with a common monoscopic or stereoscopic camera system. No manual labeling of images is necessary since the exact definitions of the symbols in the legal instructions for road paintings are used. With those vector graphics an Optical Character Recognition (OCR) System is trained. If only a monoscopic camera is used, the vanishing point is estimated and an inverse perspective transformation is applied to obtain a distortion free top-view. In case of the stereoscopic camera setup, the 3D reconstruction is projected to a ground plane. TESSERACT, a common OCR system is used to classify the symbols. If odometry or position information is available, a spatial filtering and mapping is possible. The obtained information can be used on one side to improve localization, on the other side to provide further information for planning or generation of planning maps.
Dual Sentiment Analysis: Considering Two Sides of One Review

Bag-of-words (BOW) is now the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. We propose a model called dual sentiment analysis (DSA), to address this problem for sentiment classification. We first propose a novel data expansion technique by creating a sentiment-reversed review for each training and test review. On this basis, we propose a dual training algorithm to make use of original and reversed training reviews in pairs for learning a sentiment classifier, and a dual prediction algorithm to classify the test reviews by considering two sides of one review. We also extend the DSA framework from polarity (positive-negative) classification to 3-class (positive-negative-neutral) classification, by taking the neutral reviews into consideration. Finally, we develop a corpus-based method to construct a pseudo-antonym dictionary, which removes DSA's dependency on an external antonym dictionary for review reversion. We conduct a wide range of experiments including two tasks, nine datasets, two antonym dictionaries, three classification algorithms, and two types of features. The results demonstrate the effectiveness of DSA in supervised sentiment classification.
Auditory displays in human-machine interfaces

Auditory displays are described for several application domains: transportation, industrial processes, health care, operation theaters, and service sectors. Several types of auditory displays are compared, such as warning, state, and intent displays. Also, the importance for blind people in a visualized world is considered with suitable approaches. The service robot domain has been chosen as an example for the future use of auditory displays within multimedia process supervision and control applications in industrial, transportation, and medical systems. The design of directional sounds and of additional sounds for robot states, as well as the design of more complicated robot sound tracks, are explained. Basic musical elements and robot movement sounds have been combined. Two exploratory experimental studies, one on the understandability of the directional sounds and the robot state sounds as well as another on the auditory perception of intended robot trajectories in a simulated supermarket scenario, are described. Subjective evaluations of sound characteristics such as urgency, expressiveness, and annoyance have been carried out by nonmusicians and musicians. These experimental results are briefly compared with time-frequency analyses.