Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategy Maps: Converting Intangible Assets into Tangible Outcomes

Strategy Maps: Converting Intangible Assets into Tangible Outcomes Robert S. Kaplan and David P. Norton Harvard Business School Press (2004) 454 pages, hardcover, $23.95 As global competition continues to change the strategic playing field, firms are increasingly looking for an advantage. Peter Drucker suggested that knowledge and knowledge workers are important for companies' success, yet now it is thought that there are many differing types of "intangible assets" and resources that are important for a competitive advantage. These intangible assets are considered far more valuable than tangible assets (cash, buildings, etc.) and are important to the sustainability of successful strategies as they are inimitable. This book continues the authors' research in improving performance measurement systems so executives can develop and mobilize the intangible assets of the firm for value creation. This book is another valuable tool that should be on every executive's bookcase. The authors followed over 300 organizations for more than 12 years that were utilizing the Balance Scorecard method and explored the executive's motives and actions of applying the system to implement new strategies. Intangible market based assets such as customers, alliances, and networks are driving the current marketplace so executives are faced with the dual challenge of mobilizing human capital and informational resources to transform their organization to new strategies. The authors succinctly summarize the entire contribution of the book as: Breakthrough results = {Strategy maps} + {Balanced Scorecard} + {Strategy-Focused Organization}. In essence, firm success can be developed through the proposed detailed framework that describe measures and aligns three major intangible assets (human capital, information capital, organization capital) to strategic processes and objectives. The book utilizes a case study format that gives the executive examples of how successful firms (ex. Saatchi & Saatchi, Ingersoll-Rand, Bank of Tokyo-Mitsubishi, T. Rowe Price, Thornton oil, etc.) apply the various concepts detailed by the authors. This assists the reader in not only application but also in the confidence of comparing worldbest practices to the firm's critical internal processes. As no two organizations think about strategy in the same way and that the failure rate of failed strategies is about 70 percent, not only does the development of frameworks and maps assist the reader, but the case study examples were highly beneficial for applying to one's own firm. …
Relating Reputation and Money in Online Markets

Reputation in online economic systems is typically quantified using counters that specify positive and negative feedback from past transactions and/or some form of transaction network analysis that aims to quantify the likelihood that a network user will commit a fraudulent transaction. These approaches can be deceiving to honest users from numerous perspectives. We take a radically different approach with the goal of guaranteeing to a buyer that a fraudulent seller cannot disappear from the system with profit following a set of fabricated transactions that total a certain monetary limit. Even in the case of stolen identity, such an adversary cannot produce illegal profit unless a buyer decides to pay over the suggested limit.
Interpreting and Acting on Mobile Awareness Cues

Mobile awareness systems provide user-controlled and automatic, sensor-derived cues of other users’ situations and in that way attempt to facilitate group practices and provide opportunities for social interaction. We are interested in investigating how users interpret these cues as a situation, action, or intention of a remote person and then act on them in everyday social interactions. Three field trials utilizing A–B intervention research methodology were conducted with three types of teenager groups (N = 15, total days = 243). Each trial had a slightly different variation of ContextContacts—a smartphone-based multicue mobile awareness system. We report on several analyses on how the cues were accessed, viewed, monitored, inferred, and acted on. HUMAN–COMPUTER INTERACTION, 2007, Volume 22, pp. 97–135 Copyright © 2007, Lawrence Erlbaum Associates, Inc. Antti Oulasvirta is a cognitive scientist with an interest in the psychology of mobile interaction; he is a researcher at the Helsinki Institute for Information Technology HIIT. Renaud Petit is a computer scientist with an interest in software for context-aware mobile services; he is a researcher in the From Data to Knowledge group at the University of Helsinki and at the Helsinki Institute for Information Technology HIIT. Mika Raento is a computer scientist with an interest in context awareness, particularly privacy issues; he is a researcher in the From Data to Knowledge group at the University of Helsinki and at the Helsinki Institute for Information Technology HIIT. Sauli Tiitta is a cognitive scientist with an interest in field trial methodology; he is a researcher at the Helsinki Institute for Information Technology HIIT. D ow nl oa de d by [ T he U C I rv in e L ib ra ri es ] at 1 6: 15 1 1 Ju ne 2 01 3 98 OULASVIRTA ET AL.
AIS Electronic

Over the last decades researchers seem to have big concerns on the complexity of business-IT alignment with the government organizational strategy goals. The Saudi Arabian (SA) government is one of the world’s governments that has launched the national government strategy for 2030 to all public sectors to enhance performance. This research proposal is aimed at investigating the adoption performance of alignment strategy incorporated by the SA government in order to benefit of IT infrastructure and achieve high performance, quality of service (QoS) and return of investment (ROI). This study will use mixed method design combines both qualitative and quantitative methods to understand and analyses the maturity level of alignment between IT strategy and business strategy.
Expert Systems With Applications

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classiﬁcation research focuses on one-technique-ﬁts-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which ﬁrst classiﬁes sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we ﬁnd that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to ﬁrst apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classiﬁcation. Our approach has been evaluated on four sentiment classiﬁcation datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classiﬁcation can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
MxTasks: How to Make Efficient Synchronization and Prefetching Easy

The hardware environment has changed rapidly in recent years: Many cores, multiple sockets, and large amounts of main memory have become a commodity. To benefit from these highly parallel systems, the software has to be adapted. Sophisticated latch-free data structures and algorithms are often meant to address the situation. But they are cumbersome to develop and may still not provide the desired scalability. As a remedy, we present MxTasking, a task-based framework that assists the design of latch-free and parallel data structures. MxTasking eases the information exchange between applications and the operating system, resulting in novel opportunities to manage resources in a truly hardware- and application-conscious way.
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

ABSTRACT We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-grained details about a disaster such as affected individuals, damaged infrastructure and disrupted services; (2) distinguish impact areas and time periods, and relative prominence of each category of disaster-related information across space and time. We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disaster-relatedness. We employ LSTM (Long Short-Term Memory) networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies. Second, we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation (LDA), and identify latent categories of tweets such as affected individuals and disrupted services. Third, we employ spatially-adaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category, respectively. Using Hurricane Irma as a case study, we analyze over 500 million keyword-based and geo-located collection of tweets before, during and after the disaster. Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.
Photon statistics of resonantly driven spectrally diffusive quantum emitters

In the solid state, a large variety of single-photon emitters present high quality photophysical properties together with a potential for integration. However, in many cases, the host matrix induces fluctuations of the emission wavelength in time, limiting the potential applications based on indistinguishable photons. A deep understanding of the underlying spectral diffusion processes is therefore of high importance for improving the stability of the light emission. Here, we theoretically investigate the photon statistics of an emitter driven by a resonant laser, and subject to either of two qualitatively different stationary spectral diffusion processes - a continuous diffusion process and a process based on discrete spectral jumps, both of which being known to model the spectral diffusion of various solid-state emitters. We show that the statistics of light emission carries several experimentally accessible signatures that allow to discriminate between the two classes of models, both at short times in the intensity correlation function, and at long times in the fluctuations of the integrated intensity. These results establish that resonant excitation combined with photon statistics offers a rich access to the spectral diffusion processes, yielding information that goes beyond the bare characterization of the inhomogeneous shape and noise correlation time. Incidentally, our findings shed a new light on recent experimental results of spectral diffusion of B centers in hexagonal boron nitride, providing more insight in their spectral diffusion mechanisms.
Aligning Knowledge Graph with Visual Perception for Object-goal Navigation

Object-goal navigation is a challenging task that requires guiding an agent to specific objects based on first-person visual observations. The ability of agent to comprehend its surroundings plays a crucial role in achieving successful object finding. However, existing knowledge-graph-based navigators often rely on discrete categorical one-hot vectors and vote counting strategy to construct graph representation of the scenes, which results in misalignment with visual images. To provide more accurate and coherent scene descriptions and address this misalignment issue, we propose the Aligning Knowledge Graph with Visual Perception (AKGVP) method for object-goal navigation. Technically, our approach introduces continuous modeling of the hierarchical scene architecture and leverages visual-language pre-training to align natural language description with visual perception. The integration of a continuous knowledge graph architecture and multimodal feature alignment empowers the navigator with a remarkable zero-shot navigation capability. We extensively evaluate our method using the AI2-THOR simulator and conduct a series of experiments to demonstrate the effectiveness and efficiency of our navigator. Code available: https://github.com/nuoxu/AKGVP.
Coastal Hazard Assessments for Sandy Coasts: Appraisal of Five Methodologies

ABSTRACT Narra, P.; Coelho, C. ; Sancho, F. ; Escudero, M. , and Silva, R. , 2019. Coastal hazard assessments for sandy coasts: Appraisal of five methodologies. Journal of Coastal Research, 35(3), 574–589. Coconut Creek (Florida), ISSN 0749-0208. The acceptable development of coastal areas requires thorough and comprehensive management processes. In this context, the evaluation and identification of areas endangered by coastal hazards is fundamental for an appropriate implementation of coastal zone management plans, in which coastal hazard assessment methodologies become a vital tool for the identification and classification of the littoral lands. This work presents an overview of five coastal hazard assessment methods that are currently available. The objective is to provide coastal managers an understanding of the most adequate approaches to apply in specific areas, taking into consideration the basis of each method and the user purpose. Furthermore, this work also aims to be a guide for the application of the featured methodologies. The work focuses on coastal erosion; therefore, all methods discussed (coastal erosion risk assessment, coastal vulnerability index, Smartline, Coastal Hazard Wheel, and the RISC-KIT Coastal Hazard Assessment module) include coastal erosion in their assessments. A total of nine coastal hazard assessments were performed in three different study areas (Aveiro, Portugal; Macaneta, Mozambique; Quintana Roo, Mexico). The procedure for each appraisal and the data collected for each study area are presented here, as well as the results obtained. Each methodology has an ideal set of conditions for its application, varying in the ease of application and the data requirements. Despite that, most methods agree in the identification of the most endangered areas in each study site.
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

Existing approaches towards anomaly detection (AD) often rely on a substantial amount of anomaly-free data to train representation and density models. However, large anomaly-free datasets may not always be available before the inference stage; in which case an anomaly detection model must be trained with only a handful of normal samples, a.k. a. few-shot anomaly detection (FSAD). In this paper, we propose a novel methodology to address the challenge of FSAD which incorporates two important techniques. Firstly, we employ a model pre-trained on a large source dataset to initialize model weights. Secondly, to ameliorate the covariate shift between source and target domains, we adopt contrastive training to fine-tune on the few-shot target domain data. To learn suitable representations for the downstream AD task, we additionally incorporate cross-instance positive pairs to encourage a tight cluster of the normal samples, and negative pairs for better separation between normal and synthesized negative samples. We evaluate few-shot anomaly detection on 3 controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness of the proposed method.
Strong divisibility sequences and sieve methods

We investigate strong divisibility sequences and produce lower and upper bounds for the density of integers in the sequence which only have (somewhat) large prime factors. We focus on the special cases of Fibonacci numbers and elliptic divisibility sequences, discussing the limitations of our methods. At the end of the paper there is an appendix by Sandro Bettin on divisor closed sets, that we use to study the density of prime terms that appear in strong divisibility sequences.
Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks

Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nodes and edges. This paper investigates the application of curriculum learning techniques to improve the performance and robustness of Heterogeneous Graph Neural Networks (GNNs). To better classify the quality of the data, we design a loss-aware training schedule, named LTS that measures the quality of every nodes of the data and incorporate the training dataset into the model in a progressive manner that increases difficulty step by step. LTS can be seamlessly integrated into various frameworks, effectively reducing bias and variance, mitigating the impact of noisy data, and enhancing overall accuracy. Our findings demonstrate the efficacy of curriculum learning in enhancing HGNNs capabilities for analyzing complex graph-structured data. The code is public at https: //github.com/LARS-research/CLGNN/.
RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods.
Error estimation for finite element method on meshes that contain thin elements

In an error estimation of finite element solutions to the Poisson equation, we usually impose the shape regularity assumption on the meshes to be used. In this paper, we show that even if the shape regularity condition is violated, the standard error estimation can be obtained if"bad"elements (elements that violate the shape regularity or maximum angle condition) are covered virtually by"good"simplices. A numerical experiment confirms the theoretical result.
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming

Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can"dream better"in a more structured and strategic way. In this paper, inspired by the observation from cognitive science suggesting that humans use a spatial divide-and-conquer strategy in planning, we propose a new MBRL agent, called Dr. Strategy, which is equipped with a novel Dreaming Strategy. The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming. This is achieved by learning a set of latent landmarks and then utilizing these to learn a landmark-conditioned highway policy. With the highway policy, the agent can first learn in the dream to move to a landmark, and from there it tackles the exploration and achievement task in a more focused way. In experiments, we show that the proposed model outperforms prior pixel-based MBRL methods in various visually complex and partially observable navigation tasks. The source code will be available at https://github.com/ahn-ml/drstrategy
Edit and Alphabet-Ordering Sensitivity of Lex-parse

We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and lower bounds for both operations. For both lower bounds, we use the family of Fibonacci words. For the bounds on edit operations, our analysis makes heavy use of properties of the Lyndon factorization of Fibonacci words to characterize the structure of lex-parse.
Thermodynamic Trade-Off Relations in Trace-Preserving Completely Positive Maps and Its Observation on a Quantum Computer

Thermodynamic trade-off relations reveal the costs inherent in quantum information processing. Using the framework of trace-preserving completely positive maps, we derive a generalized quantum thermodynamic uncertainty relation applicable to arbitrary observables. Exploiting this relation, we establish multiple trade-offs that connect thermodynamic costs with the precision, evolution of observables, and quantum time correlations. We experimentally demonstrate the trade-off relations using superconducting qubits on a quantum computer to verify the theory. The empirical results not only show remarkable agreement with the theoretical predictions but also reveal that the precision of an observable and the quantum time correlator are tightly constrained by the thermodynamic cost. Our findings highlight the relevance of the thermodynamic trade-off relations in current quantum technologies.
DeepEMC-T2 Mapping: Deep Learning-Enabled T2 Mapping Based on Echo Modulation Curve Modeling

Purpose: Echo modulation curve (EMC) modeling can provide accurate and reproducible quantification of T2 relaxation times. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes without a dictionary. Methods: DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and Proton Density (PD) maps directly from multi-echo spin-echo (MESE) images. The modified U-Net employs several new features to improve the accuracy of T2/PD estimation. MESE datasets from 68 subjects were used for training and evaluation of the DeepEMC-T2 mapping technique. Multiple experiments were conducted to evaluate the impact of the proposed new features on DeepEMC-T2 mapping. Results: DeepEMC-T2 mapping achieved T2 estimation errors ranging from 3%-12% in different T2 ranges and 0.8%-1.7% for PD estimation with 10/7/5/3 echoes, which yielded more accurate parameter estimation than standard EMC-T2 mapping. The new features proposed in DeepEMC-T2 mapping enabled improved parameter estimation. The use of a larger echo spacing with fewer echoes can maintain the accuracy of T2 and PD estimations while reducing the number of 180-degree refocusing pulses. Conclusions: DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without a time-consuming dictionary-matching step and requires fewer echoes. This allows for increased volumetric coverage and/or decreased SAR by reducing the number of 180-degree refocusing pulses.