nils-herrmann/clean_scirepeval huggingface dataset:
Collaborative Deep Ranking: A Hybrid Pair-Wise Recommendation Algorithm with Implicit Feedback

Collaborative Filtering with Implicit Feedbacks (e.g., browsing or clicking records), named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing works of CF-IF can be mainly classified into two categories, i.e., point-wise regression based and pairwise ranking based, where the latter one relaxes assumption and usually obtains better performance in empirical studies. In real applications, implicit feedback is often very sparse, causing CF-IF based methods to degrade significantly in recommendation performance. In this case, side information (e.g., item content) is usually introduced and utilized to address the data sparsity problem. Nevertheless, the latent feature representation learned from side information by topic model may not be very effective when the data is too sparse. To address this problem, we propose collaborative deep ranking (CDR), a hybrid pair-wise approach with implicit feedback, which leverages deep feature representation of item content into Bayesian framework of pair-wise ranking model in this paper. The experimental analysis on a real-world dataset shows CDR outperforms three state-of-art methods in terms of recall metric under different sparsity level.
Changing Focus on Interoperability in Information Systems:From System, Syntax, Structure to Semantics

Interoperability has been a basic requirement for the modern information systems environment for over two decades. How have key requirements for interoperability changed over that time? How can we understand the full scope of interoperability issues? What has shaped research on information system interoperability? What key progress has been made? This chapter provides some of the answers to these questions. In particular, it looks at different levels of information system interoperability, while reviewing the changing focus of interoperability research themes, past achievements and new challenges in the emerging global information infrastructure (GII). It divides the research into three generations, and discusses some of achievements of the past. Finally, as we move from managing data to information, and in future knowledge, the need for achieving semantic interoperability is discussed and key components of solutions are introduced. Data and information interoperability has gained increasing attention for several reasons, including: • excellent progress in interconnection afforded by the Internet, Web and distributed computing infrastructures, leading to easy access to a large number of independently created and managed information sources of broad variety;
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data

Market analysis is a representative data analysis process with many applications. In such an analysis, critical numerical measures, such as profit and sales, fluctuate over time and form time-series data. Moreover, the time series data correspond to market segments, which are described by a set of attributes, such as age, gender, education, income level, and product-category, that form a multi-dimensional structure. To better understand market dynamics and predict future trends, it is crucial to study the dynamics of time-series in multi-dimensional market segments. This is a topic that has been largely ignored in time series and data cube research. In this study, we examine the issues of anomaly detection in multi-dimensional time-series data. We propose timeseries data cube to capture the multi-dimensional space formed by the attribute structure. This facilitates the detection of anomalies based on expected values derived from higher level, “more general” time-series. Anomaly detection in a time-series data cube poses computational challenges, especially for high-dimensional, large data sets. To this end, we also propose an efficient search algorithm to iteratively select subspaces in the original high-dimensional space and detect anomalies within each one. Our experiments with both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed solution.
VoCo: text-based insertion and replacement in audio narration

Editing audio narration using conventional software typically involves many painstaking low-level manipulations. Some state of the art systems allow the editor to work in a text transcript of the narration, and perform select, cut, copy and paste operations directly in the transcript; these operations are then automatically applied to the waveform in a straightforward manner. However, an obvious gap in the text-based interface is the ability to type new words not appearing in the transcript, for example inserting a new word for emphasis or replacing a misspoken word. While high-quality voice synthesizers exist today, the challenge is to synthesize the new word in a voice that matches the rest of the narration. This paper presents a system that can synthesize a new word or short phrase such that it blends seamlessly in the context of the existing narration. Our approach is to use a text to speech synthesizer to say the word in a generic voice, and then use voice conversion to convert it into a voice that matches the narration. Offering a range of degrees of control to the editor, our interface supports fully automatic synthesis, selection among a candidate set of alternative pronunciations, fine control over edit placements and pitch profiles, and even guidance by the editors own voice. The paper presents studies showing that the output of our method is preferred over baseline methods and often indistinguishable from the original voice.
Real-Time Heuristic Search

We apply the two-pluyer game assumprio~ls of 1i111ited search horizon and cornn~itnrent to nroves i constant time, to .single-agent heuristic search problems. We present a varicrtion of nrinimcr lookuhead search, and an analog to ulphu-betu pruning rlrot signijicantly improves the efficiency c. the algorithm. Paradoxically. the search horizon reachuble with this algorithm increases wir. increusing branching facior. hl addition. we present a new algorithm, called Real-Time-A ', fo interleaving planning and execution. We prove that the ulgorithm makes locally optimal decision and is guaranteed to find a solution. We also present a learning version of this algorithm thrr improves its performance over successive problen~ solving trials by learning more accurate heuristi values, and prove that the learned values converge to their exact values along every optimal path These algorithms ef/ectively solve significanrly larger problems rhan have previously beerr solvabk using heuristic evaluation functions.
Classification of global catastrophic risks connected with artificial intelligence

A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at various levels of AI development, namely, (1) before it starts self-improvement, (2) during its takeoff, when it uses various instruments to escape its initial confinement, or (3) after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level.
FEEDBACK RESEARCH REVISITED

In a previous examination of feedback research (Mory, 1996), the use of feedback in the facilitation of learning was examined extensively according to various historical and paradigmatic views of the past feedback literature. Most of the research presented in that volume in the area of feedback was completed with specific assumptions as to what purpose feedback serves. This still holds true, and even more so, because our theories and paradigms have expanded, and the field of instructional design has undergone and will continue to undergo rapid changes in technologies that will afford new advances to take place in both the delivery and the context of using feedback in instruction. It is not surprising that feedback may have various functions according to the particular learning environment in which it is examined and the particular learning paradigm under which it is viewed. In fact, feedback is incorporated in many paradigms of learning, from the early views of behaviorism (Skinner, 1958), to cognitivism (Gagné, 1985; Kulhavy & Wager 1993) through more recent models of constructivism (Jonassen, 1991, 1999; Mayer, 1999; Willis, 2000), settings such as open learning environments (Hannafin, Land, & Oliver, 1999), and views that support multiple approaches to understanding (Gardner, 1999), to name just a few. While feedback has been an essential element of theories of learning and instruction in the past (Bangert-Drowns, Kulik, Kulik, & Morgan, 1991), it still pervades the literature and instructional models as an important aspect of instruction (Collis, De Boer, & Slotman, 2001; Dick, Carey, & Carey, 2001).
Continuous Authentication Using Eye Movement Response of Implicit Visual Stimuli

Smart head-worn or head-mounted devices, including smart glasses and Virtual Reality (VR) headsets, are gaining popularity. Online shopping and in-app purchase from such headsets are presenting new e-commerce opportunities to the app developers. For convenience, users of these headsets may store account login, bank account and credit card details in order to perform quick in-app purchases. If the device is unattended, then an attacker, which can include insiders, can make use of the stored account and banking details to perform their own in-app purchases at the expense of the legitimate owner. To better protect the legitimate users of VR headsets (or head mounted displays in general) from such threats, in this paper, we propose to use eye movement to continuously authenticate the current wearer of the VR headset. We built a prototype device which allows us to apply visual stimuli to the wearer and to video the eye movements of the wearer at the same time. We use implicit visual stimuli (the contents of existing apps) which evoke eye movements from the headset wearer but without distracting them from their normal activities. This is so that we can continuously authenticate the wearer without them being aware of the authentication running in the background. We evaluated our proposed system experimentally with 30 subjects. Our results showed that the achievable authentication accuracy for implicit visual stimuli is comparable to that of using explicit visual stimuli. We also tested the time stability of our proposed method by collecting eye movement data on two different days that are two weeks apart. Our authentication method achieved an Equal Error Rate of 6.9% (resp. 9.7%) if data collected from the same day (resp. two weeks apart) were used for testing. In addition, we considered active impersonation attacks where attackers trying to imitate legitimate users' eye movements. We found that for a simple (resp. complex) eye tracking scene, a successful attack could be realised after on average 5.67 (13.50) attempts and our proposed authentication algorithm gave a false acceptance rate of 14.17% (3.61%). These results show that active impersonating attacks can be prevented using complex scenes and an appropriate limit on the number of authentication attempts. Lastly, we carried out a survey to study the user acceptability to our proposed implicit stimuli. We found that on a 5-point Likert scale, at least 60% of the respondents either agreed or strongly agreed that our proposed implicit stimuli were non-intrusive.
Learning multiple-question decision trees for cold-start recommendation

For cold-start recommendation, it is important to rapidly profile new users and generate a good initial set of recommendations through an interview process --- users should be queried adaptively in a sequential fashion, and multiple items should be offered for opinion solicitation at each trial. In this work, we propose a novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split. The splits, represented as sparse weight vectors, are learned through an L_1-constrained optimization framework. The users are directed to child nodes according to the inner product of their responses and the corresponding weight vector. More importantly, to account for the variety of responses coming to a node, a linear regressor is learned within each node using all the previously obtained answers as input to predict item ratings. A user study, preliminary but first in its kind in cold-start recommendation, is conducted to explore the efficient number and format of questions being asked in a recommendation survey to minimize user cognitive efforts. Quantitative experimental validations also show that the proposed algorithm outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts.
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories

In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or corrupted entries. In this paper, we develop a robust subspace separation scheme that deals with these practical issues in a unified mathematical framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively on the Hopkins155 motion segmentation database and other motion sequences with outliers and missing data. We compare the performance of our methods to state-of-the-art motion segmentation methods based on expectation-maximization and spectral clustering. For data without outliers or missing information, the results of our methods are on par with the state-of-the-art results and, in many cases, exceed them. In addition, our methods give surprisingly good performance in the presence of the three types of pathological trajectories mentioned above. All code and results are publicly available at
The 2009 Simulated Car Racing Championship

In this paper, we overview the 2009 Simulated Car Racing Championship-an event comprising three competitions held in association with the 2009 . First, we describe the competition regulations and the software framework. Then, the five best teams describe the methods of computational intelligence they used to develop their drivers and the lessons they learned from the participation in the championship. The organizers provide short summaries of the other competitors. Finally, we summarize the championship results, followed by a discussion about what the organizers learned about 1) the development of high-performing car racing controllers and 2) the organization of scientific competitions.
Joint Word Segmentation and POS Tagging Using a Single Perceptron

For ChinesePOS tagging, word segmentation is a preliminary step. To avoid error propagation and improve segmentation by utilizing POS information, segmentation and tagging can be performed simultaneously. A challenge for this joint approach is the large combined search space, which makes efficient decoding very hard. Recent research has explored the integration of segmentation and POS tagging, by decoding under restricted versions of the full combined search space. In this paper, we propose a joint segmentation and POStagging model that does not impose any hard constraints on the interaction between word and POS information. Fast decoding is achieved by using a novel multiple-beam search algorithm. The system uses a discriminative statistical model, trained using the generalized perceptron algorithm. The joint model gives an error reduction in segmentation accuracy of 14.6% and an error reduction in tagging accuracy of12.2%, compared to the traditional pipeline approach.
On-chip Delay Line for Extraction of Decorrelated Phase Noise in FMCW Radar Transceiver MMICs

Estimation of phase noise (PN) has become economically feasible for integration in many of today's semiconductors. Several contributions propose concepts that obtain a good estimate of the PN's power spectrum without even requiring a reference oscillator. Differently, in this work we aim to estimate the time domain representation of the residual PN in the intermediate frequency domain of a frequency modulated continuous wave (FMCW) radar transceiver. For that, an artificial on-chip target is utilized, which is to be incorporated into an existing monolithic microwave integrated circuit (MMIC). The estimated decorrelated phase noise is required for cancelation of short-range leakage originating from an unwanted signal reflection superimposing the overall channel response of the radar. We determine the minimum required delay such that the residual PN of the on-chip target exceeds the intrinsic noise. Further, three different realizations of the delay line in the MMIC are compared. We verify our analytical derivations with a full FMCW radar system simulation.
Towards QoS-Oriented SLA Guarantees for Online Cloud Services

Cloud Computing provides a convenient means of remote on-demand and pay-per-use access to computing resources. However, its ad hoc management of quality-of-service and SLA poses significant challenges to the performance, dependability and costs of online cloud services. The paper precisely addresses this issue and makes a threefold contribution. First, it introduces a new cloud model, the SLAaaS (SLA aware Service) model. SLAaaS enables a systematic integration of QoS levels and SLA into the cloud. It is orthogonal to other cloud models such as SaaS or PaaS, and may apply to any of them. Second, the paper introduces CSLA, a novel language to describe QoS-oriented SLA associated with cloud services. Third, the paper presents a control theoretic approach to provide performance, dependability and cost guarantees for online cloud services, with time-varying workloads. The proposed approach is validated through case studies and extensive experiments with online services hosted in clouds such as Amazon EC2. The case studies illustrate SLA guarantees for various services such as a MapReduce service, a cluster-based multi-tier e-commerce service, and a low-level locking service.
Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures

Emotion and attention heighten sensitivity to visual cues. How neural activation patterns associated with emotion change as a function of the availability of attentional resources is unknown. We used positron emission tomography (PET) and 15O-water to measure brain activity in male volunteers while they viewed emotional picture sets that could be classified according to valence or arousal. Subjects simultaneously performed a distraction task that manipulated the availability of attentional resources. Twelve scan conditions were generated in a 3 x 2 x 2 factorial design involving three levels of valence (pleasant, unpleasant and neutral), two levels of arousal and two levels of attention (low and high distraction). Extrastriate visual cortical and anterior temporal areas were independently activated by emotional valence, arousal and attention. Common areas of activation derived from a conjunction analysis of these separate activations revealed extensive areas of activation in extrastriate visual cortex with a focus in right BA18 (12, -88, -2) (Z=5.73, P < 0.001 corrected) and right anterior temporal cortex BA38 (42, 14, -30) (Z=4.03, P < 0.05 corrected). These findings support an hypothesis that emotion and attention modulate both early and late stages of visual processing.
Computational Entropy and Information Leakage

We investigate how information leakage reduces computational entropy of a random variable X. Recall that HILL and metric computational entropy are parameterized by quality (how distinguishable is X from a variable Z that has true entropy) and quantity (how much true entropy is there in Z). We prove an intuitively natural result: conditioning on an event of probability p reduces the quality of metric entropy by a factor of p and the quantity of metric entropy by log2 1/p (note that this means that the reduction in quantity and quality is the same, because the quantity of entropy is measured on logarithmic scale). Our result improves previous bounds of Dziembowski and Pietrzak (FOCS 2008), where the loss in the quantity of entropy was related to its original quality. The use of metric entropy simplifies the analogous the result of Reingold et. al. (FOCS 2008) for HILL entropy. Further, we simplify dealing with information leakage by investigating conditional metric entropy. We show that, conditioned on leakage of λ bits, metric entropy gets reduced by a factor 2 in quality and λ in quantity. Our formulation allow us to formulate a “chain rule” for leakage on computational entropy. We show that conditioning on λ bits of leakage reduces conditional metric entropy by λ bits. This is the same loss as leaking from unconditional metric entropy. This result makes it easy to measure entropy even after several rounds of information leakage.
Gamifying learning experiences: Practical implications and outcomes

Gamification is the use of game design elements and game mechanics in non-game contexts. This idea has been used successfully in many web based businesses to increase user engagement. Some researchers suggest that it could also be used in web based education as a tool to increase student motivation and engagement. In an attempt to verify those theories, we have designed and built a gamification plugin for a well-known e-learning platform. We have made an experiment using this plugin in a university course, collecting quantitative and qualitative data in the process. Our findings suggest that some common beliefs about the benefits obtained when using games in education can be challenged. Students who completed the gamified experience got better scores in practical assignments and in overall score, but our findings also suggest that these students performed poorly on written assignments and participated less on class activities, although their initial motivation was higher.
Introduction to arules — Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.