nils-herrmann/clean_scirepeval huggingface dataset:
A Link Analysis Approach to Recommendation under Sparse Data

Collaborative filtering is one most successful approach to recommendation reported in the literature. It automates the “Word of Mouth” recommendation by suggesting products liked by other consumers who showed similar preference patterns as the target consumer. A serious limitation of the collaborative filtering approach is the sparsity problem, referring to the situation where insufficient historical transactions are available for inferring reliable consumer similarities. In this paper, we represent the consumer transactional data as a graph consisting nodes representing consumers and products and links representing transactions. Under this consumer-product graph, we propose to explore the global graph structure to facilitate collaborative filtering under sparse data. We developed a link analysis recommendation algorithm based on the similar ideas implemented in Web graph analysis algorithms. We tested the proposed algorithm using an online bookstore dataset against standard collaborative filtering algorithms that do not consider transitive associations. The experimental results showed that our proposed algorithm outperformed the benchmark algorithms, especially when insufficient amount of transactional data is available.
A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization

Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximal-gradient methods. The results in this paper have the potential to enhance the applicability of TV for solving very large-scale imaging inverse problems.
Development and Learning Control of a Human Limb With a Rehabilitation Exoskeleton

This paper describes a novel development of a lower limber exoskeleton for physical assistance and rehabilitation. The developed exoskeleton is a motorized leg device having a total of 4 DOF with hip, knee, and ankle actuated in the sagittal plane. The exoskeleton applies forces and learns the impedance parameters of both robot and human. An adaptive control scheme by incorporating learning control approaches into the exoskeleton system is developed to help the leg movement on a desired periodic trajectory and handle periodic uncertainties with known periods. The proposed control approach does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model and an exoskeleton model in the presence of bounded nonlinear disturbances (e.g., spasticity and fatigue). The performance of the controller is demonstrated through closed-loop experiments on human subjects. The experiments illustrate the ability of the exoskeleton to enable the leg shank to track single and multiple period trajectories with different periods and ranges of motion.
Bus detection device for the blind using RFID application

This paper outlines a bus detection mechanism for the blind in travelling from one place to another. In order to get transportation independently, the blind use auditory touched clues like walking stick or white cane. The limitation of the walking stick is that a blind person must come into close proximity with their surroundings to determine the location of an obstacle. For that basis, various devices have been developed such as the Sonicguide, the Mowat sensor, the Laser cane and the Navbelt . However, these device can only assist the blind at a pedestrian crossing. Therefore, the project is aims to develop a bus detection prototype using Radio Frequency Identification (RFID) for the blind. The paper covers brief idea about the blind and RFID system, review relevant papers and summary of current research. The review of RFID system compare between families of auto-ID technology, the basic principle of RFID, the type of RFID tagging and the antenna characteristic. The summary of current research discussed about the development of prototype, the database system, the output mechanism and integration between hardware and software. Database management will provided. The information such as bus route, final destination and bus number are also provided. This paper also describes the future work intended to be done.
HermitCore: A Unikernel for Extreme Scale Computing

We expect that the size and the complexity of future supercomputers will increase on their path to exascale systems and beyond. Therefore, system software has to adapt to the complexity of these systems for a simplification of the development of scalable applications. In this paper, we present a unikernel operating system design for HPC. It extends the multi-kernel approach while providing better programmability and scalability for hierarchical systems, such as HLRS' Hazel Hen, which base on multiple cluster-on-a-chip processors. We prove the scalability of the design via micro benchmarks by taking the example of HermitCore---our prototype implementation of the new design.
A Game Theoretic Framework for Analyzing Re-Identification Risk

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.
Metrics for Polyphonic Sound Event Detection

This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.
Smooth Nonnegative Matrix Factorization for Unsupervised Audiovisual Document Structuring

This paper introduces a new paradigm for unsupervised audiovisual document structuring. In this paradigm, a novel Nonnegative Matrix Factorization (NMF) algorithm is applied on histograms of counts (relating to a bag of features representation of the content) to jointly discover latent structuring patterns and their activations in time. Our NMF variant employs the Kullback-Leibler divergence as a cost function and imposes a temporal smoothness constraint to the activations. It is solved by a majorization-minimization technique. The approach proposed is meant to be generic and is particularly well suited to applications where the structuring patterns may overlap in time. As such, it is evaluated on two person-oriented video structuring tasks (one using the visual modality and the second the audio). This is done using a challenging database of political debate videos. Our results outperform reference results obtained by a method using Hidden Markov Models. Further, we show the potential that our general approach has for audio speaker diarization.
Using "Annotator Rationales" to Improve Machine Learning for Text Categorization

We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: annotations with “rationales.” When annotating an example, the human teacher will also highlight evidence supporting this annotation—thereby teaching the machine learner why the example belongs to the category. We provide some rationale-annotated data and present a learning method that exploits the rationales during training to boost performance significantly on a sample task, namely sentiment classification of movie reviews. We hypothesize that in some situations, providing rationales is a more fruitful use of an annotator’s time than annotating more examples.
Linear and Kernel Classification: When to Use Which?

Kernel methods are known to be a state-of-the-art classification technique. Nevertheless, the training and prediction cost is expensive for large data. On the other hand, linear classifiers can easily scale up, but are inferior to kernel classifiers in terms of predictability. Recent research has shown that for some data sets (e.g., document data), linear is as good as kernel classifiers. In such cases, the training of a kernel classifier is a waste of both time and memory. In this work, we investigate the important issue of efficiently and automatically deciding whether kernel classifiers perform strictly better than linear for a given data set. Our proposed method is based on cheaply constructing a classifier that exhibits nonlinearity and can be automatically trained. Then we make a decision by comparing the performance of our constructed classifier with the linear classifier. We propose two methods: the first one trains the degree2 feature expansion by a linear-classification method, while the second dissects the feature space into several regions and trains a linear classifier for each region. The design considerations of our methods are very different from past works for speeding up the kernel training. They still aim at obtaining accuracy close to the kernel classifier, but ours would like to give a quick and accurate decision without worrying about accuracy. Empirically our methods can efficiently make correct indications for a wide variety of data sets. Our proposed process can thus be a useful component for automatic ma-
The price of innovation: new estimates of drug development costs.

The research and development costs of 68 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per new drug is 403 million US dollars (2000 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 11% yields a total pre-approval cost estimate of 802 million US dollars (2000 dollars). When compared to the results of an earlier study with a similar methodology, total capitalized costs were shown to have increased at an annual rate of 7.4% above general price inflation.
CrsRecs: A personalized course recommendation system for college students

Every college student has different needs when it comes to learning. It can be difficult to decide which course is best to take on the road to graduation, and which professor will best suit the student's learning style. CrsRecs, our proposed course/professor recommendation system, makes that process much easier. Using topic analysis, tag analysis, sentiment analysis, predicted course/professor ratings, and survey data revealing student priorities with respect to classes (i.e., easy A, quality of the class, etc.), CrsRecs ranks potential courses in order of perceived preference for the student based on a hybrid technique combining the analysis results of a course. Empirical studies conducted to evaluate the performance of CrsRecs have revealed that CrsRecs not only suggests relevant courses to users by considering all the features of a course, but also outperforms existing state-of-the-art course recommendation approaches.
Personality-based Adaptation for Teamwork in Game Agents

This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted to-
Activity Recognition in Smart Homes Using Clustering Based Classification

Activity recognition in smart homes plays an important role in healthcare by maintaining the well being of elderly and patients through remote monitoring and assisted technologies. In this paper, we propose a two level classification approach for activity recognition by utilizing the information obtained from the sensors deployed in a smart home. In order to separates the similar activities from the non similar activities, we group the homogeneous activities using the Lloyd's clustering algorithm. For the classification of non-separated activities within each cluster, we apply a computationally less expensive learning algorithm Evidence Theoretic K-Nearest Neighbor, which performs better in uncertain conditions and noisy data. The approach enables us to achieve improved recognition accuracy particularly for overlapping activities. A comparison of the proposed approach with the existing activity recognition approaches is presented on two publicly available smart home datasets. The proposed approach demonstrates better recognition rate compared to the existing methods.
Video In Sentences Out

We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture.
StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to “respond.” In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm BiPageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full STALEMATEBREAKER model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.
Performance analysis of the Janus WebRTC gateway

This paper takes an in-depth look at the performance of the Janus WebRTC gateway. Janus is a modular, open-source gateway allowing WebRTC clients to seamlessly interact with legacy real-time communication technologies, both standard and proprietary, and with each other. This is achieved by attaching technology-specific plugins on top of a barebones core implementing all of the functions and protocols mandated by the RTCWEB/WebRTC specification suites. The paper focuses on assessing the scalability of the Janus architecture, by selecting three representative use cases, followed by a detailed analysis of a real-world scenario associated with multi-point audio conferencing.
Design a Circular Microstrip Patch Antenna for Dual Band

This paper presents a circular patch microstrip antenna operate in dual band (1.66 GHz and 2.777GHz). The proposed Circular patch antenna will be in light weight, flexible, slim and compact unit compare with current antenna used in dual band . The paper also presents the detail steps of designing the circular patch microstrip antenna and the simulated result. IE3D software is used to compute the gain, power, radiation pattern, and S11 of the antenna. KeywordsCircular patch microstrip antenna, VSWR, Return loss, Bandwidth.
Model Shrinking for Embedded Keyword Spotting

In this paper we present two approaches to improve computational efficiency of a keyword spotting system running on a resource constrained device. This embedded keyword spotting system detects a pre-specified keyword in real time at low cost of CPU and memory. Our system is a two stage cascade. The first stage extracts keyword hypotheses from input audio streams. After the first stage is triggered, hand-crafted features are extracted from the keyword hypothesis and fed to a support vector machine (SVM) classifier on the second stage. This paper focuses on improving the computational efficiency of the second stage SVM classifier. More specifically, select a subset of feature dimensions and merge the SVM classifier to a smaller size, while maintaining the keyword spotting performance. Experimental results indicate that we can remove more than 36% of the non-discriminative SVM features, and reduce the number of support vectors by more than 60% without significant performance degradation. This results in more than 15% relative reduction in CPU utilization.
Compact four-port MIMO antenna system at 3.5 GHz

This work proposed a compact multiple-input multiple-output (MIMO) antenna system having four antennas each fed by coplanar waveguide (CPW) operating for 3.5GHz band (3400–3600) MHz. The proposed MIMO antenna system is realized with two groups of printed Inverted L-monopole of size 5×4 mm2 placed orthogonally to each other to attain the diversity capability of polarization and pattern at the edge of non-ground portion of size 15 × 15 mm2 of mobile handset. The proposed MIMO antenna system consists of four monopole inverted-L shape radiator each with parasitic shorted stripe extending from the ground plane having same dimensions. The parasitic inverted-L shorted striped served as tuning element by reducing the resonating frequency of the monopole radiator and improving the impedance matching. The mutual coupling between the closely packed antennas is addressed by the neutralization line by feeding anti-phase current to the neighbor antenna. The simulation result showed that that the impedance bandwidth at S11 ≤ −10 dB is 200 MHz enough for 5G network communication channel. The diversity parameters of interest are envelop correlation coefficient and mean effective gain are also calculated.
Stanley: The robot that won the DARPA Grand Challenge

Sebastian Thrun1, Mike Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel Hoffmann1, Kenny Lau1, Celia Oakley1, Mark Palatucci1, Vaughan Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott Ettinger3, Adrian Kaehler3, Ara Nefian3, and Pamela Mahoney4