Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Geospatial Data for Multiple Reference Points

Data clustering plays a significant role in geospatial data management and analytics. In this light, we propose and study a novel geospatial data clustering method for multiple reference points. Given a set <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of geospatial data points, a candidate set <inline-formula> <tex-math notation="LaTeX">$O$ </tex-math></inline-formula> of reference points, and a threshold <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, each data point <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> will be matched to its closest reference point <inline-formula> <tex-math notation="LaTeX">$o$ </tex-math></inline-formula>. The multi-reference clustering (MRC) method finds a subset <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$A\subseteq O \wedge |A|\leq k$ </tex-math></inline-formula>) reference points from <inline-formula> <tex-math notation="LaTeX">$O$ </tex-math></inline-formula> which define the minimum global travel distance (<inline-formula> <tex-math notation="LaTeX">$\sum _{\forall q\in Q, o\in A} d(q,o)$ </tex-math></inline-formula>), hence the data are grouped into <inline-formula> <tex-math notation="LaTeX">$|A|$ </tex-math></inline-formula> clusters. We believe that the MRC method may benefit a lot of applications including geospatial data clustering, data classification, and data analytics in general. The MRC problem is challenging due to its high computation complexity, and there exist <inline-formula> <tex-math notation="LaTeX">$\sum _{i=1}^ {k} C_{|O|}^{i} = \sum _{i=1}^ {k} \frac {|O|! }{i! (|O|-i)! }$ </tex-math></inline-formula> possibilities for subset <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula>. Because the exact solution cannot be computed in real time, we develop a heuristic method to select subset <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$O$ </tex-math></inline-formula> efficiently. The experimental results show that the accuracy of <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> is very close to the optimal solution. In addition, we also develop a set of optimization techniques to further enhance the efficiency. Finally, we conduct extensive experiments to study the efficiency and accuracy of the heuristic method.
. Study of sector-specific innovation efforts: the case from Russian economy

Accelerated introduction of digital technology has recently become one of the key areas in development of Russian economy. The paper presents the approach to innovation intensity assessment by sector and economic activity. The method makes it possible to identify the growth intensity for an output of innovative products. This serves as an indicator of introduction of new technologies and a transition to a more high-tech conversion. The narration assumes that innovations in various sectors are unstable and uneven. There is an observation that there was the highest efficiency increase over the period under review in production and distribution of electrical power, gas, and water, and in other low-tech sectors (primarily, food production). There is a highly intensive character of innovations observed in high-tech and medium-tech sectors. There is another observation that the reasons for the unstable and multidirectional dynamics are as follows: high dependence of efficiency and intensity of innovations on external economic shocks, significant impact made by measures of state support on intensity of innovations, concentration of innovating at large-scale Russian and transnational companies. The results obtained led to the conclusion on a need in more stimuli for national demand from the part of Russian businesses for innovations, including digital technology.
ANALISIS SWOT SEBAGAI STRATEGI MENINGKATKAN KEUNGGULAN PADA UD. KACANG SARI DI DESA TAMBLANG

Penelitian ini bertujuan untuk mengetahui 1) kekuatan dan kelemahan yang dihadapi oleh UD. Kacang sari, 2) ancaman dan peluang yang dihadapi oleh UD. Kacang Sari, dan 3) alternatif strategi apa yang bisa diambil dalam menghadapi persaingan bisnis di UD. Kacang Sari. Penelitian ini menggunakan rancangan peneltitan deskriptif. Data yang dikumpulkan dengan menggunakan metode wawancara dan observasi dianalisis menggunakan analisis SWOT. Hasil penelitian ini menunjukan bahwa kekuatan memiliki nilai total sebesar 1,40 berbanding kelemahan sebesar 0,70. Posisi perusahaan terhadap lingkungan di luar juga memiliki posisi yang kuat dimana peluang perusahaan memiliki nilai total sebesar 2,30 berbanding kelemahan sebesar 0,70. Dilihat dari nilai total dimana kekuatan sebesar 1,40 lebih kecil dari peluang 2,30 berdasarkan kuadran SWOT maka pilihan alternatif strategi yang bisa diambil adalah strategi Stable Growth.
Development of structural model of adaptive training complex in ergatic systems for professional use

The article considers the structural model of the adaptive training complex (ATC), which reflects the interrelations between the hardware, software and mathematical model of ATC and describes the processes in this subject area. The description of the main components of software and hardware complex, their interaction and functioning within the common system are given. Also the article scrutinizers a brief description of mathematical models of personnel activity, a technical system and influences, the interactions of which formalize the regularities of ATC functioning. The studies of main objects of training complexes and connections between them will make it possible to realize practical implementation of ATC in ergatic systems for professional use.
Identificação de Emoções Aplicada ao Reconhecimento Automático de Locutor

—This paper presents an study of automatic speaker recognition systems performance in the presence of emotions. Experiments to identify the emotions were performed using an attribute based on Teager energy operator (TEO) and Gaussian mixture models (GMM) classiﬁer. The results show that the presence of emotional states strongly affects the accuracy rate of speaker identiﬁcation. A solution based on TEO is also proposed to make the speaker identiﬁcation robust to the presence of different emotional states.
Predicting Humor by Learning from Time-Aligned Comments

In this paper, we describe a novel approach for generating unsupervised humor labels using time-aligned user comments, and predicting humor using audio information alone. We collected 241 videos of comedy movies and gameplay videos from one of the largest Chinese video-sharing websites. We generate unsupervised humor labels from laughing comments, and find high agreement between these labels and human annotations. From these unsupervised labels, we build deep learning models using speech and text features, which obtain an AUC of 0.751 in predicting humor on a manually annotated test set. To our knowledge, this is the first study predicting perceived humor in large-scale audio data.
An Encryption and Decryption Algorithm Based on Random Dynamic Hash and Bits Scrambling

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions.
A way around the exploration-exploitation dilemma

For all animals the decision to explore comes with a risk of getting less. For example, a foraging bee might ﬁnd less nectar, or hunting hawk less prey. This loss is often formalized as regret. It’s been math-ematically proven that exploring an uncertain world with a speciﬁc goal always has some regret. This is why exploration-exploitation can be a dilemma. Given this proof we wondered if the common advice to “focus on learning and not the goal” might have mathematical merit. So we re-imagined exploration in the dilemma as an open ended search for any new information. We then developed a new minimal description of information value, which generalizes existing ideas like curiosity, novelty and information gain. We use this description to model the dilemma as a competition between strategies that maximize reward and information independently. Here we prove this competition has a no regret solution. When we study this solution in simulation – using classic bandit tasks – it outperforms standard approaches, especially when rewards are sparse.
Innovation ecosystems in the bioeconomy

“Innovation ecosystems in the bioeconomy” examines the policy aspects of building the industrial and innovation ecosystems and value chains needed to make a bioeconomy viable as a sustainable means of production. While building biorefineries is more like a formulaic exercise in engineering, enabling these ecosystems and their value chains is a much more complex endeavour. Building on qualitative case studies and face-to-face interviews in eleven participating countries, the study reveals the diverse ways countries are seeking to achieve the goals set by national strategies and policies. The current report draws lessons from this diverse set of national case studies to generate both common and country-specific insights that can enable this transition from a fossil-based economy to a more sustainable one. These lessons include the need for systems-based approaches, attention to policy alignment, more focus on demand-side instruments, diversification of products, enabling medium-sized companies and harnessing converging technologies.
Flexible Multimodal Sensors for Electronic Skin: Principle, Materials, Device, Array Architecture, and Data Acquisition Method

Electronic skin (e-skin) is designed to mimic the comprehensive nature of human skin. Various advances in e-skin continue to drive the development of the multimodal tactile sensor technology on flexible and stretchable platforms. e-skin incorporates pressure, temperature, texture, photographic imaging, and other sensors as well as data acquisition and signal processing units formed on a soft substrate for humanoid robots, wearable devices, and health monitoring electronics that are the most critical applications of soft electronics. This artificial skin has developed very rapidly toward becoming real technology. However, the complex nature of e-skin technology presents significant challenges in terms of materials, devices, sophisticated integration methods, and interference-free data acquisition. These challenges range from functional materials, device architecture, pixel design, array structure, and data acquisition method to multimodal sensing performance with negligible interference. In this article, we present recent research trends and approaches in the field of flexible and stretchable multimodal sensors for e-skin focusing on the following aspects: 1) flexible and stretchable platforms; 2) operating principles and materials suitable for pressure, temperature, strain, photograph, and hairy sensor devices; 3) device and integration architectures, including multimodal single cells, three-axis tactile sensors, vertical-stacked sensor arrays, active matrix sensor arrays, and integration electronics; 4) reliable acquisition methods for various texture sensing and machine-learning algorithms for processing tactile sensing data; and 5) future outlook.
Disease correlation network: a computational package for identifying temporal correlations between disease states from Large-Scale longitudinal medical records

Abstract Objective To provide an open-source software package for determining temporal correlations between disease states using longitudinal electronic medical records (EMR). Materials and Methods We have developed an R-based package, Disease Correlation Network (DCN), which builds retrospective matched cohorts from longitudinal medical records to assess for significant temporal correlations between diseases using two independent methodologies: Cox proportional hazards regression and random forest survival analysis. This optimizable package has the potential to control for relevant confounding factors such as age, gender, and other demographic and medical characteristics. Output is presented as a DCN which may be analyzed using a JavaScript-based interactive visualization tool for users to explore statistically significant correlations between disease states of interest using graph-theory-based network topology. Results We have applied this package to a longitudinal dataset at Loyola University Chicago Medical Center with 654 084 distinct initial diagnoses of 51 conditions in 175 539 patients. Over 90% of disease correlations identified are supported by literature review. DCN is available for download at https://github.com/qunfengdong/DCN. Conclusions DCN allows screening of EMR data to identify potential relationships between chronic disease states. This data may then be used to formulate novel research hypotheses for further characterization of these relationships.
Consumers' Responses to Facebook Advertising across PCs and Mobile Phones

ABSTRACT Social-networking sites, such as Facebook, are an important and fast-growing advertising channel. Because access to social-networking sites may happen through different devices, it is important to identify whether the drivers and mitigating factors affecting responses to social-networking-site advertising differ between consumers who access Facebook primarily on PCs versus on mobile phones. The authors tested a model anchored in social-exchange theory and drawing on uses and gratifications theory, using data from a survey of Facebook users in France. Results show that the trade-off between the intrinsic and social value Facebook advertisements bring and their perceived intrusiveness and privacy invasiveness drives consumers' approach and avoidance of Facebook advertising. Trust in Facebook also emerged as a key driver of advertisement acceptance.
A Performance Model For Gpu Architectures: Analysis And Design Of Fundamental Algorithms

Over the past decade, “many-core” architectures have become a crucial resources for solving computationally challenging problems. These systems rely on hundreds or thousands of simple compute cores to achieve high computational throughput. However, their divergence from traditional CPUs makes existing algorithms and models inapplicable. Thus, developers rely on a set of heuristics and hardware-dependent rules of thumb to develop algorithms for many-core systems, such as GPUs. This dissertation attempts to remedy this by presenting the Synchronous Parallel Throughput (SPT) model, a general performance model that aims to capture the factors that most impact algorithm performance on many-core architectures. The model focuses on two factors that often create performance bottlenecks: memory latency and synchronization overhead. We instantiate the SPT model on three separate modern GPU platforms using a series of microbenchmarks that measure hardware parameters such as memory access latency and peak bandwidth. We further show how multiplicity affects performance by hiding latencies and increasing overall throughput. We consider three fundamental problems as case studies in this dissertation: general matrix-matrix multiplication, searching, and sorting. Using our SPT model, we analyze a state-of-the-art library implementation of a matrix multiplication algorithm and show that our model can generate runtime estimates with an average error of 5% across our GPU platforms. We then consider the problem of batched predecessor search in the context of two levels of the GPU memory hierarchy. In slow, global memory, we demonstrate the accuracy of the SPT model, while in fast, shared memory, we determine that the memory access patterns create a performance bottleneck that degrades performance. We develop a new searching algorithm that improves the access pattern and increases performance by up to 293% on our GPUs. Finally, we look at comparison-based sorting on GPUs by analyzing two state-of-the-art algorithms and, using our SPT model, determine that they each suffer from bottlenecks that stifle performance. With these bottlenecks in mind, we develop GPU-MMS, our GPU-efficient multiway mergesort algorithm, and demonstrate that it outperforms highly optimized library implementations of existing algorithms by an average of 21% when sorting random inputs and up to 67% on worst-case input permutations. These case studies demonstrate both the accuracy and applicability of the SPT model for analyzing and developing GPU-efficient algorithms.
Quality Evaluation of Maize Based Complimentary Food Supplemented with Garden Peas

The quality of complimentary food made from blends of maize ogi flour and garden pea flour was studied. Five blend ratios and codes of 100:0 (Sample A), 90:10 (Sample B), 80:20 (Sample C). 70:30 (Sample D) and 60:40 (Sample E) were developed for Maize ogi flour and garden pea flour respectively. Proximate compositions, functional and sensory properties of the samples were analyzed using standard methods. The result for the functional properties of the formulated complimentary food samples showed that the bulk density, water absorption capacity, oil absorption capacity, foaming capacity, swelling index and least gelation concentration ranged between 0.65 g/mL-0.75 g/mL, 1.73 g/g-1.87 g/g, 1.61 g/g-1.78 g/g, 5.97%-8.91%, 1.51 v/v-1.96 v/v and 6.00%-14.00% respectively. The proximate composition result revealed that the moisture, protein, fat, fibre, ash and carbohydrate contents were 7.34%-7.96%, 6.24%-12.04%, 1.11%-10.16%, 2.13%-4.27%, 0.66%-1.44% and 64.75%-81.90% accordingly. The sensory evaluation carried out on the formulated samples by the panelists showed that the sample incorporated with 10% garden pea flour was the most preferred in terms of appearance, aroma, texture, taste and overall acceptability. The study has indeed shown that it is possible to produce a maize based complimentary food supplemented with garden pea flour with up to 40% supplementation level. This will not only improve the macro- and micro-nutrient content, but it will also enhance the functional quality attributes as well. The formulated food product also has low moisture content, which will reduce drastically the proliferation of microorganisms which will ultimately guarantee the shelf life stability of the food. However, further studies should be carried out in exploring processing methods that produces Pea protein hydrolysate from Garden pea grains which can improve the protein content of the maize based complimentary food and help in meeting the protein demands of infants.
Working Around the Military Revisited: Spouse Employment in the 2000 Census Data

Abstract : Previous studies have shown that military wives women married to U.S. military service members are more likely to be unemployed than their civilian counterparts (Grossman, 1981; Hayghe, 1974; Schwartz, Wood, and Griffith, 1991; Payne Warner, and Little, 1992; Wardynski, 2000; Hosek et al., 2002; and Harrell et al., 2004). Those who are employed earn less on average than do civilian wives. These studies, however, in so far as they are based on large, representative samples, rely on information that is now somewhat dated, and they have little to say about military husbands. The purpose of the current study is to remedy these deficiencies by repeating earlier analyses of military wives using data from the 2000 census and by extending those analyses to military husbands. Specifically, we seek to determine the following: Background characteristics of military and civilian spouses that are potentially related to employment and earnings (e.g., race/ethnicity, education, mobility, and location). Employment and earnings status of military and civilian spouses, in general and for each service. Trends in all of these variables since 1990. The impact of individual and contextual characteristics of military and civilian spouses on employment disparities. "
Evaluating the Impact of Name Resolution Dependence on the DNS

In the process of resolving domain names to IP addresses, there exist complex dependence relationships between domains and name servers. This paper studies the impact of the resolution dependence on the DNS through constructing a domain name resolution network based on large-scale actual data. The core nodes of the resolution network are mined from different perspectives by means of four methods. Then, both core attacks and random attacks on the network are simulated for further vulnerability analysis. The experimental results show that when the top 1% of the core nodes in the network are attacked, 46.19% of the domain names become unresolved, and the load of the residual network increases by nearly 195%, while only 0.01% of domain names fail to be resolved and the load increases with 18% in the same attack scale of the random mode. For these key nodes, we need to take effective security measures to prevent them from being attacked. The simulation experiment also proves that the resolution network is a scale-free network, which exhibits robustness against random failure and vulnerability against intentional attacks. These findings provide new references for the configuration of the DNS.
The Role of Graphics in Video Lectures

Abstract With the increase in online course use (Allen & Seaman, 2017), there is an increasing need to determine the most effective (i.e., the most conducive for learning) way to present lectures online (e.g., video lectures). Lecture graphics that are interesting but extraneous to the content (e.g., a celebrity), have been shown to impair comprehension of the material, likely resulting from an increase in cognitive load. In this study, the use of graphics on the slides of an online psychology lecture was manipulated to determine the extent to which images can improve (or impair) comprehension as well as the effect it may have on intentional and unintentional mind-wandering. Across our two experiments, we demonstrate no differences across conditions (i.e., unnecessary graphics, relevant graphics, no graphics) in overall comprehension and limited differences in mind wandering behavior.
Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning

We present our work on the problem of Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs in many languages when more than one grammatical form of a word fits syntactically and semantically in a given context. In second language (L2) education - in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL) systems, which generate exercises automatically - this implies that multiple alternative answers are possible. We treat the problem as a grammaticality judgement task. We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a “simulated learner corpus”: a dataset with correct text, and with artificial errors generated automatically. While MA occurs commonly in many languages, this paper focuses on learning Russian. We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by the users of a running language learning system.