Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomy of an Adaptive Scaffold for Analysts

Adaptive user interfaces offer the potential to improve the learnability of software tools and analytic methodologies by tailoring the operation and experience to a user‟s needs. Scaffolding is an instructional strategy that can be applied by adaptive interfaces to achieve this. Scaffolding theory suggests that the level of guidance should be adjusted to optimize learning and performance levels. This paper explores the use of adaptive techniques to scaffold user interaction and presents a taxonomy of techniques for adaptive scaffolds within complex software systems. The techniques identified in the proposed taxonomy can help software scaffolds select appropriate adaptations in response to the user‟s learning and operating needs. A scaffold called nAble was implemented to explore the application of adaptive techniques from the taxonomy to support an analysis methodology called the Analysis of Competing Hypotheses (ACH).
A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

While demands on video traffic above mobile webs have been souring, the wireless link capacity cannot retain up alongside the traffic demand. The gap amid the traffic demand and the link capacity, alongside alongside time-varying link conditions, aftermath in poor ability quality of video streaming above mobile webs such as long buffering period and intermittent disruptions. Leveraging the cloud computing knowledge, we counsel a new mobile video streaming framework, dubbed AMESCloud, that has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient communal video sharing). AMoV and ESoV craft a confidential agent to furnish video streaming services effectually for every single mobile user. For a given user, AMoV lets her confidential agent adaptively adjust her streaming flow alongside a scalable video coding method established on the feedback of link quality. Likewise, ESoV monitors the communal web contact amid mobile users, and their confidential agents endeavor to prefetch video content in advance. We apply a prototype of the AMES-Cloud framework to clarify its performance. It is shown that the private.
Review

Causal mediation analysis is the study of mechanisms—variables measured between a treatment and an outcome that partially explain their causal relationship. The past decade has seen an explosion of research in causal mediation analysis, resulting in both conceptual and methodological advancements. However, many of these methods have been out of reach for applied quantitative researchers, due to their complexity and the difficulty of implementing them in standard statistical software distributions. The mediation package in R provides a set of simple commands that execute some of the newer causal mediation methods. This article will summarize some of the recent advances in mediation analysis, critically review the mediation package, and demonstrate, by example, some of its capabilities.
Learning by Generating Mental Imagery from Limited Labeled Dataset

Most real-world scenarios such as surveillance and life-long learning on mobile robotic platforms are characterized by a scarcity of labeled training datasets for deep learning. Training deeper networks on limited labeled datasets is non-trivial. Weaker supervision has the potential to alleviate this problem in part. We propose deep convolutional generative adversarial networks (DCGAN) that learn to produce a “canonical image” from input images using an internal representation of the expected distributions of the input data. This canonical image is what the DCGAN “imagines” that the input image category might look like under ideal conditions, i.e. iconic representation. A DCGAN learns this association by training an encoder to capture salient features from the original image and a decoder to convert salient features into its associated canonical image representation. Our new approach, which we refer to as a Mental Image DCGAN (MIDCGAN), learns features that are useful for classification, and that this in turn has the benefit of helping single or few shot recognition from limited labeled datasets. We demonstrate our approach on object instance recognition and handwritten digit recognition tasks.
Exchanging Process Speci(cid:222)cations for Identifying Cooperative Information Systems

I. Abstract In cooperative information systems, private and public elements of business processes are separated from each other. Hereby, public elements are published for (cid:147)nding and working with cooperation partners; private elements are covering strategic knowledge. This architecture requires several types of integration which must be enabled by an appropriate exchange format for the underlying graph representation. The paper demonstrates at the example of GXL the de(cid:147)nition of such a language
Easy Identification from Better Constraints: Multi-shot Person Re-identification from Reference Constraints

Multi-shot person re-identification (MsP-RID) utilizes multiple images from the same person to facilitate identification. Considering the fact that motion information may not be discriminative nor reliable enough for MsP-RID, this paper is focused on handling the large variations in the visual appearances through learning discriminative visual metrics for identification. Existing metric learning-based methods usually exploit pair-wise or triple-wise similarity constraints, that generally demands intensive optimization in metric learning, or leads to degraded performances by using sub-optimal solutions. In addition, as the training data are significantly imbalanced, the learning can be largely dominated by the negative pairs and thus produces unstable and non-discriminative results. In this paper, we propose a novel type of similarity constraint. It assigns the sample points to a set of reference points to produce a linear number of reference constraints. Several optimal transport-based schemes for reference constraint generation are proposed and studied. Based on those constraints, by utilizing a typical regressive metric learning model, the closed-form solution of the learned metric can be easily obtained. Extensive experiments and comparative studies on several public MsP-RID benchmarks have validated the effectiveness of our method and its significant superiority over the state-of-the-art MsP-RID methods in terms of both identification accuracy and running speed.
Research on the Integration Method of Dynamic Heterogeneous Spatial Information Plotting Data

According to the dynamic heterogeneous spatial information plotting data, this paper presented a new integration method about dynamic heterogeneous spatial data, studied the spatial information plotting object and established the semantic ontology database. It declared the mapping rules in ontology, solved the semantic differences of spatial information plotting objects and finished the automatic or semi-automatic integration of plotting content to integrated automatic or semi-automatic of the content for different plotting situations(same name of different place, different name of same place etc) and different plotting data types(text, images, sound, video etc). So it has great significance to enhance the efficiency of the plotting data integration.
Face Identification in Large Galleries

Face Recognition is one of the most studied problems in computer vision due to its importance to areas such as forensics, surveillance, neuroscience and psychology. This work comprises a preliminary study of the face identification (one of the tasks comprising face recognition) considering large galleries. We analyze how the face recognition rate behaves under the presence of an increasing number of individuals in the gallery set, referred to as distractors. Nowadays, many realworld applications demand face search and identification at colossal scale. Therefore, we direct our attention towards the 1:N face identification task, where N, the gallery size, is extremely large. The idea behind this work is to study and evaluate the performance of face identification for such large face galleries. Keywords-Distractors; Partial Least Squares; Face Hashing; Scalability; Face Identification;
Electromagnetic Power Harvester Using Wide-Angle and Polarization-Insensitive Metasurfaces

A new wide-angle and polarization-insensitive metasurface (MS) instead of traditional antenna is built as the primary ambient energy harvester in this paper. The MS is a two-dimensional energy harvesting array that is composed of subwavelength electrical small ring resonator that is working at 2.5 GHz (LTE/WiFi). In the case of different polarization and incidence angles, we demonstrate the metasurface can achieve high harvesting efficiency of 90%. The fabricated prototype of 9 × 9 MS energy harvesting array is measured, and the experimental results validate that the proposed MS has a good performance more than 80% of energy harvesting efficiency for arbitrary polarization and wide-angle incident waves. The good agreement of the simulation with the experiment results verifies the practicability and effectiveness of the proposed MS structure, which will provide a new source of supply in wireless sensor networks (WSN).
Formalizing the Temporal Order of Join Points

Crucial to the programming and debugging of complex aspect-oriented programs is the ability to specify and monitor temporal ordering amongst join-points. We present a temporal logic specification of such constraints in AspectJ. This provides a formal semantics for join-point occurrences and is a step towards the formal verification of AOP programs. We illustrate the usefulness of this approach through the formalization and implementation of aspects in AspectJ.
Action Recognition Based on Sub-action Motion History Image and Static History Image

In this paper, we propose a robust and effective framework to largely improve the performance of human action recognition using depth maps. The key contribution is the proposition of the Sub-action Motion History Image (SMHI) and Static History Image (SHI) in a depth sequence. We evenly subdivide the normalized motion energy into a set of segments which corresponding frame indices are used to partition a video into different sub-actions segments. The Local Binary Patterns (LBP) descriptor is then computed from the SMHI and SHI for the representation of an action. We evaluate the proposed framework on MSR Action3D dataset. Experimental results indicate that the proposed approach outperforms the most of the art methods and demonstrate the effectiveness of the proposed approaches.
An HBPL-based Approach to the Creation of Six-word Stories

Six-word stories are a subgenre of microﬁction that presents signiﬁcant challenges to authors. It is difﬁ-cult to express a story with so few words, let alone an impactful or elegant one, but the best examples in the genre transcend mere storytelling to elicit an emotional response from readers. Six-word stories are an interesting and as-yet-unexplored space in the ﬁeld of computational creativity. We present a system description of MICROS, a computationally creative system that generates six-word stories by making use of a hierarchical Bayesian approach. We detail how MICROS generates, evaluates, and reﬁnes stories; present stories generated by MICROS; and report a preliminary evaluation of its results.
SPACEBORNE SAR ANATOMY OF A CITY

The ensemble of st able rada r targets (the so-called Permanent Scatterers -PS-, [1], [2]) in urban area can be interpreted as the radar skeleton of a city. By means of the PS technique, developed at POLIMI, the skeleton radar reflectors are exploit ed for overcoming the difficulties related to the conventional S AR interferometry approach (phase decorrelation and atmospheric effects), achieving millimetre accuracy in monitoring possible relative displacements. Even if the PS technique is an operational tool since 2000, the physical nature of the targets is still a subject of investigation. In this work we present the last results of this research program obtained by using 120 ERS and Envisat repeated acquisitions on the area of Milano (Italy). First, the PS’s posi tion and their elevation with respect to the ground are estimated with high precision. Then the scattering pattern of targets as a function of their geometrical characteristics (dimensions and orientation) is analysed. The variation of the Radar Cross Section as a function of the acquisition geometry (normal baseline and Doppler centroid) is linked to the extension of the scatterer in range and azimuth. Temperature dependent amplitude variations have been exploited for individuating retro-reflecting periodic structures (Bragg scattering). Exploiting the auto-interferogram generated from an Envisat Alternating Polarization acquisition, odd and even bounces of radar echoes are identified thus separating dihedrals from spec ular or trihedral reflectors [8]. Finally, by means of the collected radar measurements, 5 principa l PS typologies have been identified: ground retro-reflectors (gratings), elevated retro-reflectors (roofs), dihedrals, poles and trihedrals.
Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres

Data assimilation (DA) methods for convective‐scale numerical weather prediction at operational centres are surveyed. The operational methods include variational methods (3D‐Var and 4D‐Var), ensemble methods (LETKF) and hybrids between variational and ensemble methods (3DEnVar and 4DEnVar). At several operational centres, other assimilation algorithms, like latent heat nudging, are additionally applied to improve the model initial state, with emphasis on convective scales. It is demonstrated that the quality of forecasts based on initial data from convective‐scale DA is significantly better than the quality of forecasts from simple downscaling of larger‐scale initial data. However, the duration of positive impact depends on the weather situation, the size of the computational domain and the data that are assimilated. Furthermore it is shown that more advanced methods applied at convective scales provide improvements over simpler methods. This motivates continued research and development in convective‐scale DA.
Dissecting Company Names using Sequence Labeling

Understanding the inherent structure of company names by identifying their constituent parts yields valuable insights that can be leveraged by other tasks, such as named entity recognition, data cleansing, or deduplication. Unfortunately, segmenting company names poses a hard problem due to their high structural heterogeneity. Besides obvious elements, such as the core name or legal form, company names often contain additional elements, such as personal and location names, abbreviations, and other unexpected elements. While others have addressed the segmentation of person names, we are the first to address the segmentation of the more complex company names. We present a solution to the problem of automatically labeling the constituent name parts and their semantic role within German company names. To this end we propose and evaluate a collection of novel features used with a conditional random field classifier. In identifying the constituent parts of company names we achieve an accuracy of 84%, while classifying the colloquial names resulted in an F1 measure of 88%.
Diffusion of User Tracking Data in the Online Advertising Ecosystem

Abstract Advertising and Analytics (A&A) companies have started collaborating more closely with one another due to the shift in the online advertising industry towards Real Time Bidding (RTB). One natural way to understand how user tracking data moves through this interconnected advertising ecosystem is by modeling it as a graph. In this paper, we introduce a novel graph representation, called an Inclusion graph, to model the impact of RTB on the diffusion of user tracking data in the advertising ecosystem. Through simulations on the Inclusion graph, we provide upper and lower estimates on the tracking information observed by A&A companies. We find that 52 A&A companies observe at least 91% of an average user’s browsing history under reasonable assumptions about information sharing within RTB auctions. We also evaluate the effectiveness of blocking strategies (e.g., AdBlock Plus), and find that major A&A companies still observe 40–90% of user impressions, depending on the blocking strategy.
Low-frequency eddy modulations in the Hawaiian Lee Countercurrent: Observations and connection to the Pacific Decadal Oscillation

[1] Interannual-to-decadal time scale eddy variability in the Hawaiian Lee Countercurrent (HLCC) band is investigated using the available sea surface height, sea surface temperature, and surface wind stress data sets. In the HLCC band of 17°N–21.7°N and 170E°–160°W, the prevailing interannual eddy kinetic energy (EKE) signals show enhanced eddy activities in 1993–1998 and 2002–2006, and subpar eddy activities in 1999–2001 and 2007–2009. These interannual EKE signals exhibit little connection to the zonal HLCC velocity changes generated by the dipolar wind stress curl forcing in the immediate lee of the island of Hawaii. Instead, they are highly correlated to the time series of the Pacific Decadal Oscillation (PDO) index. Through a budget analysis for the meridional temperature gradient along the HLCC, we find that during the positive phase of the PDO index, the surface heat flux forcing induces cold (warm) sea surface temperature (SST) anomalies to the north (south) of the HLCC, intensifying the vertical shear between the surface, eastward-flowing HLCC and the subsurface, westward-flowing North Equatorial Current (NEC). This increased vertical shear enhances the baroclinic instability of the HLCC-NEC system and leads to a higher regional EKE level. The opposite processes occur when the PDO switches to a negative phase with the resulting lowered EKE level along the HLCC band. Compared to the surface heat flux forcing, the Ekman flux convergence forcing is found to play a minor role in modifying the meridional SST changes along the HLCC band.
Unlock with Your Heart

In this paper, we propose to use the vibration of the chest in response to the heartbeat as a biometric feature to authenticate the user on mobile devices. We use the built-in accelerometer to capture the heartbeat signals on commercial mobile phones. The user only needs to press the phone on his/her chest, and the system can identify the user within a few heartbeats. To reliably extract heartbeat features, we design a two-step alignment scheme that can handle the natural variability in human heart rates. We further use an adaptive template selection scheme to authenticate the user under different body postures and body states. Based on heartbeat signals collected on twenty users, the experimental results show that our method can achieve an authentication accuracy of 96.49% and the heartbeat features are stable over a period of three months.
Exploiting Best Practice of Deep CNNs Features for National Costume Image Retrieval

Convolutional neural networks (CNNs) have recently achieved remarkable success with superior performances in computer vision applications. In most CNN-based image retrieval methods, deep CNNs features are verified as discriminative descriptors for effective image representation. This paper exploits the best practice for CNNs application to national costume image retrieval. Several important aspects that affect the discriminative ability of deep CNNs features are investigated thoroughly, including layers selection, aggregation and weighting methods. Firstly, an effective weighting method for sum-pooling features aggregation is given, which is more suitable for national costume image than some typical aggregation methods such as SPoC and SCDA. Secondly, in view of the complementary strengths, compact multi-layer CNN features combined with low dimensions are proposed and proven to be effective for national costume expression. Finally, a re-ranking strategy of diffusion process is applied to further enhance the performance for national costume images retrieval. The experimental results show that the proposed method outperforms the existing methods remarkably, which will provide some new research ideas and technical references for researchers in the field of national costume image retrieval.
Mapping Salt-affected Soils Using Remote Sensing Indicators-A Simple Approach With the Use of GIS IDRISI -

This research deals with the problem of monitoring saline soils of Faisalabad, Pakistan. The analysis is based on remote sensing data of LISS-II sensor of IRS-1B satellite using GIS-IDRISI functions. We have examined how different remote sensing indicators work for salt-affected soil classification in the study area. The study has suggested some new but simple and practicle approaches for the problem. We have analyzed the effectiveness of several indicators for the presence of salts in the area: salinity indices, normalized differential salinity index, and ratio of the signals received by the sensor in the 3rd spectral band to others. As salt-affected soils are characterized also by stressed vegetation, we therefore have analyzed vegetation indices concurrently. The probability of correct classification of the satellite image was observed strongly dependent on the season for all indicators analyzed. The best results can be achieved for the dry season (March-April) but not in humid or high temperate periods which may create confusion some times with other classes, specially settlements areas. The most difficult part in the classification processes was to distinguish between soil-affected areas and populated areas, which has muddy roofs similar to dry barren soils along with patchy saline areas within the village. We have come-up with two original schemes of classification through the analysis of the available data for this specific area. The first one uses COMPOSITE and STRETCH modules to produce two new images and then analyze their ratio. The COMPOSITE module takes data received in 1st, 2nd and 4 channels and STRETCH function produces histogram equalization contrast stretch of the 4 channel data. The second scheme uses ISOCLUST function of GIS IDRISI that performs classification based on specifically created images (through salinity indices and PCA analysis) instead of common practice of using just satellite measurements. Both schemes are shown to be able to perform good classification of the study area.
Differential Geometry: An Introduction to the Theory of Curves

Differential geometry is a discipline of mathematics that uses the techniques of calculus and linear algebra to study problems in geometry. The theory of plane, curves and surfaces in the Euclidean space formed the basis for development of differential geometry during the 18th and the 19th century. The core idea of both differential geometry and modern geometrical dynamics lies under the concept of manifold. A manifold is an abstract mathematical space, which locally resembles the spaces described by Euclidean geometry, but which globally may have a more complicated structure. The purpose of this paper is to give an elaborate introduction to the theory of curves, and those are, in general, curved. Differential geometry of curves is the branch of geometry that deals with smooth curves in the plane and in the Euclidean space by applying the concept of differential and integral calculus. The curves are represented in parametrized form and then their geometric properties and various quantities associated with them, such as curvature and arc length expressed via derivatives and integrals using the idea of vector calculus.