Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dataset for Telling the Stories of Social Media Videos

Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.
Predictors of Executive Functions in Preschoolers: Findings From the SPLASHY Study

Executive functions (EFs) have been reported to play a crucial role in children’s development, affecting their academic achievement, health, and quality of life. This study examined individual and interpersonal predictors for EFs in 555 typically developing preschool children aged 2–6 years. Children were recruited from 84 child care centers in the German- and French-speaking parts of Switzerland within the Swiss Preschoolers’ Health Study (SPLASHY). A total of 20 potential predictors were assessed at the first measurement (T1). These included eight demographic/biological predictors, such as socioeconomic status, preterm birth, physical activity, and motor skills; six psychological predictors, such as hyperactivity, visual perception, and emotionality; and six interpersonal predictors, such as parenting style and stress, presence of siblings, and days spent in the child care center. The predictive value of these variables on EFs 1 year later (T2) was assessed using both standard multiple regression analysis and penalized regression to avoid overfitting due to the number of potential predictors. Female sex (β = 0.14), socio-economic status (β = 0.15), fine motor skills (β = 0.17), visual perception at T1 (β = 0.16), and EFs at T1 (β = 0.30) were all associated with EFs at T2, exhibiting small to medium effect sizes. All predictors together accounted for 31% of the variability in EFs. However, none of the interpersonal predictors were significant. Thus, we conclude that most of the factors that can predict EFs in preschool age are individual variables, and these tend to be more difficult to influence than interpersonal factors. In fact, children from families with low socio-economic status may be particularly vulnerable to poor EFs. Furthermore, encouraging fine motor skills early in life may support the development of EFs.
Video on Demand in IEEE 802.11p-based Vehicular Networks: Analysis and Dimensioning

We consider a VoD (Video on-Demand) platform designed for vehicles traveling on a highway or other major roadway. Typically, cars or buses would subscribe to this delivery service so that their passengers get access to a catalog of movies and series stored on a back-end server. Videos are delivered through IEEE 802.11p Road Side Units deployed along the highway. In this paper, we propose a simple analytical and yet accurate solution to estimate (at the speed of a click) two key performance parameters for a VoD platform: (i) the total amount of data downloaded by a vehicle over its journey and (ii) the total "interruption time'', which corresponds to the time a vehicle spends with the playback of its video interrupted because of an empty buffer. After validating its accuracy against a set of simulations run with ns-3, we show an example of application of our analytical solution for the sizing of an IEEE 802.11p-based VoD platform.
Transactional Database Analysis by Discovering Pairwise Interactions Strengths

We present a study that examines the complex interactions that could exist in transactional databases, among the items that constitute the products or purchases present in a market basket of consumers. Using simulated and real databases, we show that it is possible to reconstruct certain characteristics of aggregated purchasing behavior, with only second order interactions using the inverse Ising problem. While Ising's model is well known, the applications in this context are novel and have the potential to reveal useful information to the retail manager.
Revealing Political Sentiment with Twitter: The Case Study of the 2016 Italian Constitutional Referendum

In this work we argue about the use of Twitter as a Palantir for predicting and revealing the political orientation of users in election campaigns. Specifically, our study aims at revealing the political orientation of a Twitter user in the context of the 2016 Italian Constitutional Referendum. After having collected and processed over 1,200,000 tweets, we classified them as YES-oriented, NO-oriented or UNCERTAIN, by exploiting the Naive Bayes Multinomial Text classification algorithm. We found that Twitter is used massively for political deliberation and just by counting the messages related to a political party we can reveal the election result. Moreover, the words used by the YES party and the NO party twits are in line with the language used by politicians in the real word and this strongly supports our analysis. Furthermore, our approach can be applied to different scenarios, in which it is necessary to distinguish between two main classes and another UNCERTAIN class. Finally, the results obtained by our classification methodology are very promising and encourage us to continue investigating this topic, deriving suggestions for further research.
An Integrated Mechatronic Modelling, Simulation, and Optimization Approach for the Customized Design of Active Vibration Damping Solutions

A crucial factor for the effective implementation of active vibration damping is the application of suitable actuators operating at optimal locations, combined with an appropriate control strategy. The actuators have to provide the required characteristics in view of the applied force in the relevant frequency domain. Additional restrictions such as dimensions, energy consumption, or costs of the system have to be accounted for. Hence, the determination of an optimized tailor-made solution for a vibration damping problem is of multi-objective character and cannot be achieved without consideration of the particular application. In this contribution, a holistic approach for the optimal design of vibration damping solutions is presented, taking into account the specific requirements of the considered application and including the mechanical system, the actuators, as well as the control strategy. The approach is realised within the simulation and optimization framework SyMSpace and applied to an existing use case representing a general vibration damping task based on electromagnetic actuators and an adaptive control strategy. Special attention is given to both the modelling and optimization work flow in Sy MSpace as well as the experimental setup of the use case for the assessment of the results.
Preposition Sense Disambiguation and Representation

Prepositions are highly polysemous, and their variegated senses encode significant semantic information. In this paper we match each preposition’s left- and right context, and their interplay to the geometry of the word vectors to the left and right of the preposition. Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense disambiguation (PSD) algorithm, which is comparable to and better than state-of-the-art on two benchmark datasets. Our reliance on no linguistic tool allows us to scale the PSD algorithm to a large corpus and learn sense-specific preposition representations. The crucial abstraction of preposition senses as word representations permits their use in downstream applications–phrasal verb paraphrasing and preposition selection–with new state-of-the-art results.
Towards quantifying the development value of code contributions

Quantifying the value of developers’ code contributions to a software project requires more than simply counting lines of code or commits. We define the development value of code as a combination of its structural value (the effect of code reuse) and its non-structural value (the impact on development). We propose techniques to automatically calculate both components of development value and combine them using Learning to Rank. Our preliminary empirical study shows that our analysis yields richer results than those obtained by human assessment or simple counting methods and demonstrates the potential of our approach.
Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition

This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection

We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.
Trojan Horses in Amazon's Castle: Understanding the Incentivized Online Reviews

During the past few years, sellers have increasingly offered discounted or free products to selected reviewers of e-commerce platforms in exchange for their reviews. Such incentivized (and often very positive) reviews can improve the rating of a product which in turn sways other users' opinions about the product. Despite their importance, the prevalence, characteristics, and the influence of incentivized reviews in a major e-commerce platform have not been systematically and quantitatively studied. This paper examines the problem of detecting and characterizing incentivized reviews in two primary categories of Amazon products. We describe a new method to identify Explicitly Incentivized Reviews (EIRs) and then collect a few datasets to capture an extensive collection of EIRs along with their associated products and reviewers. We show that the key features of EIRs and normal reviews exhibit different characteristics. Furthermore, we illustrate how the prevalence of EIRs has evolved and been affected by Amazon's ban. Our examination of the temporal pattern of submitted reviews for sample products reveals promotional campaigns by the corresponding sellers and their effectiveness in attracting other users. Finally, we demonstrate that a classifier that is trained by EIRs (without explicit keywords) and normal reviews can accurately detect other EIRs as well as implicitly incentivized reviews. Overall, this analysis sheds an insightful light on the impact of EIRs on Amazon products and users.
How should compilers explain problems to developers?

Compilers primarily give feedback about problems to developers through the use of error messages. Unfortunately, developers routinely find these messages to be confusing and unhelpful. In this paper, we postulate that because error messages present poor explanations, theories of explanation---such as Toulmin's model of argument---can be applied to improve their quality. To understand how compilers should present explanations to developers, we conducted a comparative evaluation with 68 professional software developers and an empirical study of compiler error messages found in Stack Overflow questions across seven different programming languages. Our findings suggest that, given a pair of error messages, developers significantly prefer the error message that employs proper argument structure over a deficient argument structure when neither offers a resolution---but will accept a deficient argument structure if it provides a resolution to the problem. Human-authored explanations on Stack Overflow converge to one of the three argument structures: those that provide a resolution to the error, simple arguments, and extended arguments that provide additional evidence for the problem. Finally, we contribute three practical design principles to inform the design and evaluation of compiler error messages.
Parallel Multigrid for Nonlinear Cloth Simulation

Accurate high‐resolution simulation of cloth is a highly desired computational tool in graphics applications. As single‐resolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi‐resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high‐resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton's method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi‐resolution collision handling for real‐time simulation in the future.
Fixing Translation Divergences in Parallel Corpora for Neural MT

Corpus-based approaches to machine translation rely on the availability of clean parallel corpora. Such resources are scarce, and because of the automatic processes involved in their preparation, they are often noisy. This paper describes an unsupervised method for detecting translation divergences in parallel sentences. We rely on a neural network that computes cross-lingual sentence similarity scores, which are then used to effectively filter out divergent translations. Furthermore, similarity scores predicted by the network are used to identify and fix some partial divergences, yielding additional parallel segments. We evaluate these methods for English-French and English-German machine translation tasks, and show that using filtered/corrected corpora actually improves MT performance.
Range Optimistic Concurrency Control for a Composite OLTP and Bulk Processing Workload

This work addresses the need for efficient key-range validation for a composite OLTP and bulk processing workload characterized by modern enterprise applications. In-memory database system (IMDB), mostly adopting the optimistic concurrency control (OCC) mechanism, performs well if the contention of conventional OLTP workloads is low and each transaction only contains point read/write query with primary key. In this work we present the performance problem of IMDBs under mixed OLTP and bulk processing workloads. The reason is that existing OCC protocols take expensive cost to generate a serializable schedule if the OLTP workload contains bulk processing operations with key-range scan. To this end, we develop an efficient and scalable range optimistic concurrency control (ROCC) which uses logical ranges to track the potential conflicting transactions and to reduce the number of transactions to be validated. At the read phase, a transaction keeps a set of predicates to remember the version and precise scope of scanned ranges, which eliminates the cost of maintaining scanned records. Before entering the validation phase, if the transaction intends to update records in the logical range, it needs to register to the corresponding lock-free list implemented by a circular array. Finally, ROCC filters out unrelated transactions and validates the bulk operation at range level. Experimental results show that ROCC has good performance and scalability under heterogeneous workloads mixed with point access and bulk processing.
Addressing Troublesome Words in Neural Machine Translation

One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chinese-to-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.
Estimation Error Analysis of Stator Flux Observer for DTC-Based PMSM Drives

Direct torque control can be applied to any AC motor drive and requires an estimation of stator flux linkage. The stator flux observer was developed for this solution. However, there are few reports about the way of the observer design. This paper analyzes the influence of the armature resistance error in the stator flux observer and proposes the way of the design for PMSM drives.
Investigating the influence of seasonality and structural breakpoint on telecommunication network traffic prediction

Mobile network traffic forecasting enhances the adequate use of available resources and promote bandwidth allocation to mobile service users. This study is proposing a combination of first order difference and periodogram to determine the existence of seasonal pattern in 3G downlink traffic. In addition, the study seeks to identify the existence of structural break and also the exact day the structural break occur in the 3G downlink traffic data using Perron methodology. The study have established that there is no presence of seasonality pattern in the 3G downlink traffic data used for analysis. The results also indicated that, even though there is existence of breaks on day 622 at intercept level, on day 599 at trend level and on day 396 for both intercept and trend level there is no influence on the prediction of 3G downlink traffic. The study also proved that ARIMA (0,1,2) can be used to predict 3G downlink traffic with no influence of structural break and seasonality.
Evaluation of Political Party Cohesion Using Exponential Random Graph Modeling

The United States is becoming increasingly politically divided. In addition to polarization between the two-major political parties, there is also divisiveness in intra-party dynamics. In this paper, we attempt to understand these intraparty divisions by using an exponential random graph model (ERGM) to compute a political cohesion metric to quantify the strength within the party at a given point in time. The analysis is applied to the 105th through 113th congressional sessions of the House of Representatives. We find that the Republican party not only generally exhibits stronger intra-party cohesion, but when voting patterns are broken out by topic, the party has a higher and more consistent cohesion factor compared to the Democratic Party.
Measurements of Reflection and Penetration Loss of Construction Materials at 28 GHz and 38 GHz

This paper presents measurements of penetration losses and reflection coefficients for common construction materials at 28 GHz and 38 GHz. Using a signal generator, a CW at 0 dBm was transmitted by 20 dBi horn antenna and received by a similar antenna. The materials tested were thin and thick polystyrene, thin and thick pressed wood, thin wood, window glass, a wooden door and a brick wall. As expected, polystyrene have neglected penetration loss, independently of its thickness and frequency. Brick wall presents high penetration loss, 58 dB at 28 GHz and 63 dB at 38 GHz. A strong relation between thickness and penetration loss was observed in pressed wood. Reflection coefficients were measured in an indoor meeting room. Incidence angle was varying from 15 degrees to 45 degrees with steps of 5 degrees. Reflection coefficients of polystyrene are close to 0. Glass window presented the largest reflection coefficient, of 0.65 at 38 GHz at incidence angle of 45 degrees.
Discovering Expert Drivers from Trajectories

Discovering expert drivers is highly important for a broad range of location based services, but this issue is largely untouched in previous trajectory mining and search studies. In this paper, we study the problem of trajectory data driven expert driver discovery. It aims to find out top-k expert drivers about a region of interest, based on the understanding of their historical trajectories. To this end, we first investigate the construction of reference system, which collectively describes exemplar routes among important junctions, so that the driving behaviors embedded in each trajectory can be evaluated. To discover expert drivers accurately, a novel tf-idf concept based measure is proposed afterwards, such that the rationality of their trajectories are not only precisely evaluated by the match to reference system, but also properly aggregated for modelling expert drivers. Extensive experimental evaluation using real trajectory datasets demonstrates the effectiveness and efficiency of our proposed solutions.
Measuring the Information-Foraging Behaviors of Social Bots Through Word Usage

Automated social bots are reported to account for a large sum of activity on social media sites such as Twitter. In this short paper, we study the information-foraging behaviors of social media users including bots. We present here a preliminary investigation which compares the behaviors of a set of suspected bots with non-automated accounts. To do so, we measure the distance between word distributions on a daily basis. We posit that this methodology provides a quantitative measure of behavior, which allows for more rigorous descriptions of bot behaviors that move beyond the assumption of bots as a monolithic category.
A Class Φ2 Resonant Buck Converter with Ripple Injection Burst Control Method

This paper presents the design and implementation of a DC 120-170V input and DC 48V output class Φ2 resonant buck converter with ripple injection burst control method. For the converter operated at switching frequency of 13.56 MHz, low loss magnetic material is used as resonant inductors instead of air core inductors. By eliminating radiated leakage magnetic flux, small size and high power density can be achieved. Furthermore, this paper proposes a novel ripple injection burst control method to control the class Φ2 resonant buck converter. This method can avoid the effect of output capacitance. As a result of experiment, efficiency of 88.4% is shown at DC 140V input and 144W output, and very good output voltage regulation and dynamic response can be achieved.