Miracle-dz/UniFAR-FTUs huggingface dataset:
Foster Strategy Alignment in the Digital Age A methodic approach for guiding SME to a Digital Transformation

Goal of this paper is to demonstrate the need for a structured framework and method to support and lead SME into the Digital Age. A framework including all relevant business areas of an enterprise where technology is seen as an enabler serves as a starting point for strategic discussion and possible reorientation of the company. Hence, the framework-based method named Transformation Compass combines the aspects Customer Centricity, Operational Excellence, Business Models, and Organizational Excellence. Since SME in particular face many challenges transforming their business, a specific approach that takes into account the challenges and opportunities of an SME has a high potential for success.
Preservice Teachers’ Concerns about the Classroom-based Internship Experience

In Abu Dhabi, Emirati preservice teachers attending a teacher-training college are allocated to government elementary schools for a twelve-week internship during the fourth and final year. The preservice teachers are non-native English speakers responsible for teaching in the English language to students who are also non-native English speakers. Data from a questionnaire, interview, and samples of participants‟ reflective writing from their internship portfolios were used to discuss the concerns preservice teachers had about teaching during the final internships and the importance for future practice. To analyze the data, a mixed methods approach was used. Results indicated participants‟ concerns about teaching were distributed amongst four themes: (1) classroom management, (2) communication, (3) instruction, and (4) evaluation. Implications for teacher education programs are discussed as a way to address the concerns and challenges prospective teachers encounter.
Heterogeneous Intra-Network Social Meta Path

Given a screenshot of the online social networks, the problem of inferring the missing links or the links to be formed in the networks in the future is called the link prediction problem. Link prediction problem has concrete applications in the real world, and many concrete services can be cast to the link prediction problem. For instance, the friend recommendations problem [15] in online social networks can be modeled as the friendship link prediction problem among users. Users’ trajectory prediction problem [4] can be formulated as the prediction task of potential check-in links between users and offline POIs (points of interest). The user identifier resolution problem [19, 26] across networks (i.e., the network alignment problem introduced in the previous chapters) can be modeled as the anchor link prediction problem of user accounts across different online social networks. In this chapter, we will take the friendship link as an example to introduce the general link prediction problem in online social networks. Formally, given the training set Ttrain involving links belong to different classes (Y = {+1,−1}, where +1 denotes the positive class and −1 denotes the negative class; sometimes we also use 0 to denote the negative class) and the test set Ttest (with unknown labels for the links), the link prediction problem aims at building a mapping f : Ttrain ∪ Ttest → Y to project these links to their potential labels in Y . Depending on the scenarios where we study the link prediction problem, the existing link prediction works can be divided into several different categories. Traditional link prediction problems are mainly focused on inferring the links in one single homogeneous network, like inferring the friendship links [62] among users in online social networks or co-author links [39] in bibliographic networks. As the network structures are becoming more and more complicated, many complex network structures can be modeled as the heterogeneous networks involving different types of nodes and complex connections among them. The heterogeneity of the networks leads to many new link prediction problems, like predicting the links between nodes belonging to different categories [58] and the concurrent inference of multiple types of links in the heterogeneous networks [54, 58]. In recent years, many online social networks have appeared, and lots of new research opportunities exist for researchers and practitioners to study the link prediction problems from the cross-network perspective [57, 58, 61]. Meanwhile, depending on the learning settings used in the link prediction problem formulation and models, the existing link prediction works can be categorized into different groups according to the supervision information involved in the model building. For some of the link prediction models, they
Temporal Contrastive Pretraining for Video Action Recognition

In this paper, we propose a self-supervised method for video representation learning based on Contrastive Predictive Coding (CPC) [27]. Previously, CPC has been used to learn representations for different signals (audio, text or image). It benefits from the use of an autoregressive modeling and contrastive estimation to learn long-term relations inside raw signal while remaining robust to local noise. Our self-supervised task consists in predicting the latent representation of future segments of the video. As opposed to generative models, predicting directly in the feature space is easier and avoid incertitude problems for long-term predictions. Today, using CPC to learn representations for videos remains challenging due to the structure and the high dimensionality of the signal. We demonstrate experimentally that the representations learned by the network are useful for action recognition. We test it with different input types such as optical flows, image differences and raw images on different datasets (UCF-101 and HMDB51). It gives consistent results across the modalities. At last, we notice the utility of our pre-training method by achieving competitive results for action recognition using few labeled data.
Incremental Based Top-k Similarity Search Framework for Interactive-Data-Analysis Sessions

Interactive Data Analysis (IDA) is a core knowledge-discovery process, in which data scientists explore datasets by issuing a sequence of data analysis actions (e.g. filter, aggregation, visualization), referred to as a session. Since IDA is a challenging task, special recommendation systems were devised in previous work, aimed to assist users in choosing the next analysis action to perform at each point in the session. Such systems often record previous IDA sessions and utilize them to generate next-action recommendations. To do so, a compound, dedicated session-similarity measure is employed to find the top-k sessions most similar to the session of the current user. Clearly, the efficiency of the top-k similarity search is critical to retain interactive response times. However, optimizing this search is challenging due to the non-metric nature of the session similarity measure. To address this problem we exploit a key property of IDA, which is that the user session progresses incrementally , with the top-k similarity search performed, by the recommender system, at each step . We devise efficient top-k algorithms that harness the incremental nature of the problem to speed up the similarity search, employing a novel, effective filter-and-refine method. Our experiments demonstrate the efficiency of our solution, obtaining a running-time speedup of over 180X compared to a sequential similarity search.
A new way of accelerating web by compressing data with back reference-prefer geflochtener

: This research focused on the synthesis of an iterative approach to improve speed of the web and also learning the new methodology to compress the large data with enhanced backward reference preference. In addition, observations on the outcomes obtained from experimentation, group-benchmarks compressions, and time splays for transmissions, the proposed system have been analysed. This resulted in improving the compression of textual data in the Web pages and with this it also gains an interest in hardening the cryptanalysis of the data by maximum reducing the redundancies. This removes unnecessary redundancies with 70% efficiency and compress pages with the 23.75-35% compression ratio.
IN FIELD TIME IN MOTION COMPARISONS OF CONVENTIONAL, JOHN DEERE 7760 AND CASE 625 MODULE EXPRESS COTTON PICKERS

This paper should be viewed as a summary of preliminary results for the purpose of establishing machinery parameters for an economic evaluation that will be conducted at a later time for three different harvesting systems. At no time were all three harvesters operated in the same field. Six “example fields” were chosen based on logistics and necessities of data management for the study presented in this paper. Field performance data from the actual harvester is included along with “predicted” calculations of what the other harvesters might be expected to achieve in this field for the same actual turning time row length and field geometry that was harvested. Down time was eliminated from the analysis; therefore, the actual field efficiencies and capacities reported will be lower than in actual harvesting operations. A more complete annual report for this project will be available at a later date from Cotton Incorporated. A half a million data points from three different harvester systems were collected over 28 days from a total of 14 different farm operations and 29 fields. Package and load weights were determined to be 3.74 bales/round module, 6.5 bales/half module and 16 bales/conventional module when adjusting to 480 lb net weight/bale for the John Deere 7760, Case IH 625 ME and conventional pickers, respectively. Truck loads were found to be 14.96 bales, 13 bales and 16 bales per load for the John Deere 7760, Case IH 625 ME and conventional pickers, respectively. When speeds were held constant at 4 mph and compared (without down time other than unloading and turning), the Case IH 625 ME achieved 7.5 ac/hr at 81% time on the row harvesting, compared to the JD 7760 which achieved 8.2 ac/hr at 89% efficiency when averaged over all six fields. The conventional picker attained 7.5 ac/hr at 81% field efficiency averaged over all six fields. Based on these average field capacity values, the JD 7760’s ability to unload without needing time to get to a position to unload or stopping contributed to about a 9.4% increase in field capacity above the Case IH 625 ME and conventional machines when all were operated at a constant speed of 4 mph. When the John Deere 7760 was compared to the Case IH 625 ME for first gear picking, the added speed increase and the advantage of the unloading system amounted to 12.5% to 16.5% increase in field capacity (5% due to the speed increase) depending on the field characteristics and yield. Speed on the row had the second greatest effect on field capacity in comparison to the unloading system. Decreasing row length from 2538 ft to 1274 ft decreased field efficiency by approximately 3.2% (conventional), 4.2% (JD 7760) and 3.2% (Case IH 625 ME) depending on speed and unloading system and was 83.1% vs. 79.9%; 91.5% vs. 87.3%; 83% vs. 79.8% for the Case IH 625 ME, JD 7760, and conventional harvesters and longer rows vs. shorter rows respectively (first gear operation).
Shear jamming and shear melting in mechanically trained frictionless particles

We investigate criticality near the jamming transition in both quiescent systems and those under shear by considering the effect of mechanical training on the jamming transition and nonlinear rheology. We simulate frictionless soft particles undergoing athermal quasi-static shear using initial configurations trained with athermal quasi-static cyclic volume deformations. The jamming transition density of the initial configuration $\varphi_{\rm J0}$ is systematically altered by tuning the ``depth'' of mechanical training. We exert a steady shear on these configurations and observe either shear jamming (gain of stiffness due to shear) or shear melting (loss of stiffness due to shear), depending on the depth of training and proximity to the jamming transition density. We also observe that the characteristic strains, at which shear jamming or melting occur, diverge at a unique density $\varphi_{\rm JS}$. This is due to the shift of the jamming transition density from $\varphi_{\rm J0}$ to $\varphi_{\rm JS}$ under shear, associated with loss of memory of the initial configuration. Finally, we thoroughly investigate nonlinear rheology near the jamming transition density, and contrary to previous works, we find a nonlinear ``softening'' takes place below as well as above the jamming transition density.
FacebookVideoLive18: A Live Video Streaming Dataset for Streams Metadata and Online Viewers Locations

With the advancement in personal smart devices and pervasive network connectivity, users are no longer passive content consumers, but also contributors in producing new contents. This expansion in live services requires a detailed analysis of broadcasters' and viewers' behavior to maximize users' Quality of Experience (QoE). In this paper, we present a dataset gathered from one of the popular live streaming platforms: Facebook. In this dataset, we stored more than 1,500,000 live stream records collected in June and July 2018. These data include public live videos from all over the world. However, Facebook live API does not offer the possibility to collect online videos with their fine grained data. The API allows to get the general data of a stream, only if we know its ID (identifier). Therefore, using the live map website provided by Facebook and showing the locations of online streams and locations of viewers, we extracted video IDs and different coordinates along with general metadata. Then, having these IDs and using the API, we can collect the fine grained metadata of public videos that might be useful for the research community. We also present several preliminary analyses to describe and identify the patterns of the streams and viewers. Such fine grained details will enable the multimedia community to recreate realworld scenarios particularly for resource allocation, caching, computation, and transcoding in edge networks. Existing datasets do not provide the locations of the viewers, which limits the efforts made to allocate the multimedia resources as close as possible to viewers and to offer better QoE.
The Internet of Things as a Deep Neural Network

An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processing, inference, and analysis. When the measurements are high-dimensional (e.g., videos or time-series data), IoT networks with limited bandwidth and low-power devices may not be able to support such frequent transmissions with high data rates. To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN). We propose a new framework where the data to be transmitted from nodes are the intermediate outputs of a layer of the DNN. We show how to learn the model parameters of the DNN and study the trade-off between the communication rate and the inference accuracy. The experimental results show that we can save approximately 96 percent transmissions with only a degradation of 2.5 percent in inference accuracy, which shows the potentiality to enable many new IoT data analysis applications that generate a large amount of measurements.
ISOLATION AND PROPERTIES OF CASEIN KINASES FROM HUMAN PLATELETS

Two cyclic AMP-independent casein kinases were partially puriﬁed from human platelets by chromatography on DEAE-sephacel and phosphocellulose, and desig-nated CK-I and CK-II. These kinases differ as follows: 1) the molecular weight of CK-I is about 48,000 and that of CK-II is about 130,000; 2) optimum pH and Mg“, Km for ATP and casein are similar but not identical in these enzymes; 3) heparin can inhibit CK-II completely, but it cannot inhibit CK-I; 4) polylysine stimulates CK-II, but it cannot stimulate CK-I; 5) NaCl (200 mM) stimulates the activity of CK-II about 400 %, but CK-I is stimulated by NaCl only 20%; 6) although CK-I can phosphorylate only seryl residue of casein, CK-II can phosphorylate both seryl and threonyl residue in about equal amounts. These results show that CK-I and CK-II have quite different physical and kinetic properties, and the physiological role of these kinases in human platelets seems to be different.
Experimental control of remote spatial indistinguishability of photons to realize entanglement and teleportation

Remote spatial indistinguishability of identical subsystems as a direct controllable quantum resource at distant sites has not been yet experimentally proven. We design a setup capable to tune the spatial indistinguishability of two photons by independently adjusting their spatial distribution in two distant regions, which leads to polarization entanglement starting from uncorrelated photons. The amount of entanglement uniquely depends on the degree of remote spatial indistinguishability, quantified by an entropic measure $\mathcal{I}$, which enables teleportation with fidelities above the classical threshold. This experiment realizes a basic nonlocal entangling gate by the inherent nature of identical quantum subsystems.
The Ramsey property and higher dimensional mad families.

Suppose every set has the Ramsey property and Ramsey-co-null uniformization, as well as the Principle of Dependent Choice hold. Then there is no infinite $\mathcal I$-mad family, for any ideal $\mathcal I$ in smallest class of ideals containing the Frechet ideal and closed under taking Fubini sums. In fact, we show a local form of this theorem which in turn has many consequences, improving and unifying the proofs of several results which were already known for classical mad families. These results were previously announced in Proceedings of the National Academy of Sciences of the U.S.A. We show as a contrasting result that there is a co-analytic infinite mad family in the Laver extension of $L$.
Synergic Adversarial Label Learning with DR and AMD for Retinal Image Grading

The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD and DR share some common features like hemorrhagic points and exudation but most classification algorithms only train those disease models independently. Inspired by knowledge distillation where additional monitoring signals from various sources is beneficial to train a robust model with much fewer data. We propose a method called synergic adversarial label learning (SALL) which leverages relevant retinal disease labels in both semantic and feature space as additional signals and train the model in a collaborative manner. Our experiments on DR and AMD fundus image classification task demonstrate that the proposed method can significantly improve the accuracy of the model for grading diseases. In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
Gadget3 on GPUs with OpenACC

We present preliminary results of a GPU porting of all main Gadget3 modules (gravity computation, SPH density computation, SPH hydrodynamic force, and thermal conduction) using OpenACC directives. Here we assign one GPU to each MPI rank and exploit both the host and accellerator capabilities by overlapping computations on the CPUs and GPUs: while GPUs asynchronously compute interactions between particles within their MPI ranks, CPUs perform tree-walks and MPI communications of neighbouring particles. We profile various portions of the code to understand the origin of our speedup, where we find that a peak speedup is not achieved because of time-steps with few active particles. We run a hydrodynamic cosmological simulation from the Magneticum project, with $2\cdot10^{7}$ particles, where we find a final total speedup of $\approx 2. $ We also present the results of an encouraging scaling test of a preliminary gravity-only OpenACC porting, run in the context of the EuroHack17 event, where the prototype of the porting proved to keep a constant speedup up to $1024$ GPUs.
Experimental Investigation of Frequency Domain Channel Extrapolation in Massive MIMO Systems for Zero-Feedback FDD

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.
Controlled Lagrangians and Stabilization of Euler-Poincaré Mechanical Systems with Broken Symmetry I: Kinetic Shaping

We extend the method of controlled Lagrangians with kinetic shaping to those mechanical systems on semidirect product Lie groups with broken symmetry, more specifically to the Euler--Poincar\'e equations with advected parameters. We find a matching condition for the controlled Lagrangian for such systems whose configuration manifold is a general semidirect product Lie group $\mathsf{G} \ltimes V$. Our motivating examples are a bottom-heavy underwater vehicle and a top spinning on a movable base. Their configuration space is the special Euclidean group $\mathsf{SE}(3) = \mathsf{SO}(3) \ltimes \mathbb{R}^{3}$, where the $\mathsf{SE}(3)$-symmetry is broken by the gravity. The controls resulting from the matching condition stabilize unstable equilibria of these examples. Furthermore, the matching helps us find additional dissipative controls that asymptotically stabilize those unstable equilibria.