Miracle-dz/UniFAR-FTUs huggingface dataset:
Entity Resolution in the Presence of Constraints

In this project, I present a declarative framework for entity resolution in the presence of constraints. Constraints occur naturally in various data cleaning domains and can improve the quality of entity resolution. For example, there is a table containing titles which look similar but actually represent di erent publications. So if two publications' titles are in that table, then they are two di erent publications. The main feature of my work is that I incorporate weights into the framework. For pairwise matching, I use rule based matching combined with weighted score to determine whether a pair is a positive pair or negative pair. The rules are based on a declarative Datalog-style language with precise semantics. The weights of rules are measured through experiments on training dataset. For con icts solving, I also use weights to revise the clustering result. My framework mainly focuses on single entity reference relation. However, I also use other relations to illustrate how collective entity resolution works in my framework. The relationships between di erent entity reference relations are monodirectional, and which entity reference relation a ecting other relations is also determined by weights . My experiments are based on the Cora dataset. From the evaluation result, my framework has a high precision and recall (>0.97) for the clustering result of the entity reference relation. I also infer that quality of one entity reference relation will a ect quality of the others.
A Signature-based Intrusion Detection System for the Internet of Things

Internet of Things (IoT) is envisioned as a transformative approach with a wide range of applications in various sectors such as home automation, industrial control, and agriculture. It promises innovative business models and improved user experience. However, as evidenced by recent attacks such as the Mirai botnet, IoT networks and systems remain very vulnerable and require stronger protection mechanisms. Furthermore, due to processing, memory, and power constraints of typical IoT devices, traditional Internet security mechanisms are not always feasible or appropriate. In this work, we are concerned with designing an Intrusion Detection System (IDS) for protecting IoT networks from external threats as well as internal compromised devices. Our proposed design adopts a signature-based intrusion detection approach and involves both certralised and distributed IDS modules. Using the Cooja simulator, we have implemented a Denial of Service (DoS) attack scenario on IoT devices. This scenario exploits the RPL protocol, which is widely used for routing in low-power networks, including IoT networks. In particular, we have implemented two variants of DoS attacks, namely “Hello” flooding and version number modification. As shown by simulation results, these attacks may impact the reachability of certain IoT devices and their power consumption.
Persistence of suicides in G 20 countries : SPSM approach to three generations of unit root tests

Suicides represent an encompassing measure of psychological well-being, emotional stability as well as life satisfaction and have been recently identified by the World Health Organization (WHO) as a major global health concern. The G20 countries represent the powerhouse of global economic governance and hence possess the ability to influence the direction of global suicide rates. In applying the sequential panel selection method (SPSM) to three generations of unit root testing procedures, the study investigates whether G20 countries should be concerned with possible persistence within suicide rates. The results obtained from all three generation of tests provide rigid evidence of persistence within the suicides for most member states of the G20 countries hence supporting the current strategic agenda pushed by the WHO in reducing suicides to a target rate of 10 percent. In addition, we further propose that such strategies should emulate from within G20 countries and spread globally thereafter.
A simple model for a minimal environment: the two-atom Tavis–Cummings model revisited

ABSTRACT Individual quantum systems may be interacting with surrounding environments having a small number of degrees of freedom. Here we discuss a simple toy model: a system constituted by a two-level atom (atom 1) interacting with a single mode cavity field which is (weakly) coupled to a small environment (atom 2). We investigate the influence of the minimal environment on the dynamics of the linear entropy and the atomic dipole squeezing of atom 1, as well as the entanglement between atom 1 and the field. We also obtain the full analytical solution of the two-atom Tavis–Cummings model for both arbitrary coupling strengths and frequency detunings, necessary to analyse the influence of the field-environment detuning on the evolution of the system’s quantum properties. For complementarity, we discuss the role of the degree of mixedness of the environment by analysing the time-averaged linear entropy of atom 1.
The Effect of Open Innovation on Technology Value and Technology Transfer: A Comparative Analysis of the Automotive, Robotics, and Aviation Industries of Korea

With the advent of the Fourth Industrial Revolution, the Korean industrial environment has been rapidly changing. As a result, several industries are facing different innovation conditions, including: the automotive industry, which is a traditional manufacturing industry in Korea; the aerospace industry, which has been vertically integrated, and has been led by a single large company—Korea Aerospace Industries—since Korea went through an economic crisis in the 1990s; and the robotics industry, which is currently emerging. This study used parameters related to patents originating from the automobile, robotics, and aerospace fields. The patents subjected to analysis were those registered in Korea from 2001 to 2014, and included those that were applied for, jointly applied for, or registered by Korean companies. This study analyzed the differences caused by the effect of open innovation in the three industries. According to our results, first, the three industries experienced different effects of open innovation. Second, when large companies controlled a given industry, open innovation either worked a little or did not work at all. According to this research result, the Korean robotics industry, which has a weak dominance of large companies, experienced a strong effect of open innovation, in that joint patent applications led to economic gains from the patents and an increase in technology transfer and technology value. The aerospace industry of Korea, which has the highest dominance of large companies, experienced a slight effect of open innovation, as the effect of joint patent application only improved the technology value. Finally, the automotive industry of Korea had an intermediate level of open innovation, between the aerospace and robotics sectors.
Savings Accounts to Borrow Less Experimental Evidence from Chile 1

Poverty is often characterized not only by low and unstable income, but also by heavy debt burdens. In a randomized field experiment with over 3,500 low-income micro-entrepreneurs in Chile, we find that providing access to free savings accounts decreases participants’ short-term debt. In addition, participants who experience an economic shock have less need to reduce consumption, and subjective well-being improves significantly. Precautionary savings and credit therefore act as substitutes in providing self-insurance, and participants prefer borrowing less when a free formal savings account is available. Take-up patterns suggest that requests by others for participants to share their resources may be a key obstacle to saving
Student’s Opinions on Online Educational Games for Learning Programming Introductory

Use of educational games is an approach that has potential to change the existing educational method. This is due to games popularity among younger generation as well as engagement and fun features of games compared to conventional learning method. In addition, games are among the most widespread media amongst younger generation or so-called “digital natives” apart from movie, music and internet technology. Game play activities is an important issue to be thoroughly understood due to the facts that many of them are addicted to game play activity. In contrast, conventional learning approaches are not interesting enough to the younger generation. Thus, integration of games technology into education is potentially believed to increase student interest and motivation to learn. This study developed and evaluates an online educational game for learning Programming Introductory course at a university in Malaysia. A total of 180 undergraduate students from computer and engineering background participate in the study. Findings shows that about 80% of students have positive attitude towards the games with around 84% of them find that the games is a fun way to learn, at the same time, an average of 80% agreed that the game provide them with opportunity to learn. Furthermore, about 75% of the students agreed that the game make them able to do self-assessment for Programming course. It was interesting to find that almost 85% of the student said that they will want to use educational games as their future learning approach. Despite many more evidence will be needed especially in Malaysia context, this study is important to rationalize that games can be one of the new learning approaches in the future.
Misspecified Bayesian Cramér-Rao Bound for Sparse Bayesian

We consider a misspecified Bayesian Cramér-Raobound (MBCRB), justified in a scenario where the assumed data model is different from the true generative model. As an example of this scenario, we study a popular sparse Bayesian learning (SBL) algorithm where the assumed data model, different from the true model, is constructed so as to facilitate a computationally feasible inference of a sparse signal within the Bayesian framework. Formulating the SBL as a Bayesian inference with a misspecified data model, we derive a lower bound on the mean square error (MSE) corresponding to the estimated sparse signal. The simulation study validates the derived bound and shows that the SBL performance approaches the MBCRB at very high signal-to-noise ratios.
A Cryptographic Look at Multi-party Channels

Cryptographic channels aim to enable authenticated and confidential communication over the Internet. The general understanding seems to be that providing security in the sense of authenticated encryption for every (unidirectional) point-to-point link suffices to achieve this goal. As recently shown (in FSE17/ToSC17), however, the security properties of the unidirectional links do not extend, in general, to the bidirectional channel as a whole. Intuitively, the reason for this is that the increased interaction in bidirectional communication can be exploited by an adversary. The same applies, a fortiori, in a multi-party setting where several users operate concurrently and the communication develops in more directions. In the cryptographic literature, however, the targeted goals for group communication in terms of channel security are still unexplored. Applying the methodology of provable security, we fill this gap by defining exact (game-based) authenticity and confidentiality goals for broadcast communication, and showing how to achieve them. Importantly, our security notions also account for the causal dependencies between exchanged messages, thus naturally extending the bidirectional case where causal relationships are automatically captured by preserving the sending order. On the constructive side we propose a modular and yet efficient protocol that, assuming only point-to-point links between users, leverages (non-cryptographic) broadcast and standard cryptographic primitives to a full-fledged broadcast channel that provably meets the security notions we put forth.
Distillation of Greenberger–Horne–Zeilinger States by Combinatorial Methods

We prove a lower bound on the rate of Greenberger–Horne–Zeilinger states distillable from pure multipartite states by local operations and classical communication (LOCC). Our proof is based on a modification of a combinatorial argument used in the fast matrix multiplication algorithm of Coppersmith and Winograd. Previous use of methods from algebraic complexity in quantum information theory concerned transformations with stochastic LOCC (SLOCC), resulting in an asymptotically vanishing success probability. In contrast, our new protocol works with an asymptotically vanishing error.
Reciprocity versus Reelection : Theory and Experiment ∗

We study how reelection concerns affect reciprocity by elected leaders to the voters who elected them. If showing kindness to past voters reduces the chances of reelection, will an elected leader reduce or eliminate such intrinsic reciprocity? We present a theoretical model of signaling congruence with voter preferences to gain reelection, where we show that candidates may limit intrinsic reciprocity to signal type and selfish candidates may mimic reciprocal behavior for instrumental purposes. We then present an experiment that tests these ideas in the laboratory and finds support for the model. Both candidates and voters behave as the signaling model predicts. A key finding is that the desire to be reelected may limit intrinsic reciprocity of an elected leader to reciprocate to the voters who put her in offi ce, but doesn’t eliminate it entirely. JEL classification: D72, E62, D78
Regional-based mitigation to reduce wildlife-vehicle collisions

Vehicular collisions with large ungulates pose serious challenges for managing and conserving large ungulates throughout the world. Despite the global frequency, mitigation efforts are mostly limited to localized hotspots and not effective on broad scales. Our goal was to determine whether dynamic, regional attributes could inform broader focus for mitigation efforts. We applied a spatiotemporal dynamic model to examine the regional influences on white-tailed deer (Odocoileus virginianus)–vehicle collisions (DVCs) throughout theMidwest United States from traffic, abundance of deer, and composition and configuration of the landscape during 2000–2011. The regions included eco-zones representing landscape dominated by shelter-forage habitats with ubiquitous and abundant distribution of deer (i.e., forest-agriculture matrix), landscape dominated by agriculture with sparse refugia (i.e., agriculture), and landscape dominated by forests with seasonal migration for deer (i.e., northern forest). We found little fluctuation in the factors affecting collisions through time but substantial differences among regions. In the forest-agriculture matrix eco-zone, fragmentation of the landscape was the most important predictor of collisions. In the agriculture eco-zone, traffic and abundance of deer best predicted collisions. In the northern forest eco-zone, the predictors of collisions were variable and likely related to winter severity and deer migration. This research provides new justification for broadening the focus of current mitigation measures to regional extents. In regions dominated by forest and agriculture, new policies that reduce habitat fragmentation should be the primary focus for reducing collisions. Reducing abundance of ungulates will have the most direct effect in regions dominated by agriculture. Finally, a variety of seasonal and local mitigation measures will be most effective in northern forests where large ungulates migrate. 2018 The Wildlife Society.
Phytoplankton succession and nitrogen utilization during the development of an upwelling bloom

Diatoms typically constitute the majority of large phytoplankton in coastal systems, where they dominate upwelling conditions. The exact mechanism for diatom success has never been explicitly demonstrated, and we hypothesize that it is attributable to their early, rapid response to newly upwelled nitrate. This strategy allows diatoms to grow quickly and consume a disproportion- ate fraction of the available nutrients. To test this hypothesis and investigate differential growth of phytoplankton during the early stages of upwelling, an upwelling event — and the subsequent phyto- plankton bloom — was simulated in a mesocosm experiment in Monterey Bay. Nitrate, ammonium and inorganic carbon uptake by 3 size fractions (0.7-5 µm, 5-20 µm, >20 µm) was measured daily through stable isotope tracer incubations. The particulate nitrogen and carbon biomass was initially dominated by the smallest phytoplankton, but the largest fraction increased most rapidly to dominate the biomass and nitrogen and carbon assimilation after a few days. All size fractions achieved similar maximum specific nitrate uptake rates (VNO3), but this occurred most rapidly and was maintained longest by the largest fraction. This initial acceleration of VNO3 appears to be the mechanism by which diatoms exploit upwelling conditions. All size fractions assimilated carbon and nitrogen in a nearly constant ratio of ~6.3 as the assemblage returned to balanced growth. Pigment measurements and light microscopy independently documented changing phytoplankton abundance, with diatoms demonstrating a characteristic pattern of succession. Changes in diatom diversity and assemblage composition occurred early, providing evidence of a faster response than observable changes in nitrate or biomass implied.
Crystal Structures and Electrode Performance of Alpha-NaFeO2 for Rechargeable Sodium Batteries

Single phase, well-crystallized O3-type NaFeO2 (alpha NaFeO2) is prepared by a solid-state method. Electrode performance of O3-type NaFeO2 is examined as positive electrode materials for rechargeable sodium batteries. O3type NaFeO2 can deliver 80–100mAhg−1 of reversible capacity with a nearly flat voltage profile at approximately 3.3V vs. Na metal. The electrode performance is significantly deteriorated by oxidation beyond x > 0.5 in Na1−xFeO2. X-ray diffraction study reveals that loss of electrode reversibility originates from irreversible structural change, possibly accompanied by iron ion migration in layered host structures. The sodium ion insertion into the host structures would be disturbed by the irreversible structural change when charged beyond x > 0.5 in Na1−xFeO2. Acceptable cyclability is, therefore, achieved for O3-type NaFeO2 as the positive electrode materials in the limited composition of x = 0–0.45 in Na1−xFeO2. © The Electrochemical Society of Japan, All rights reserved.
Expansion of Mechanized Agriculture and Land-Cover Change in Southern Rondonia, Brazil

region-wide soybean production and deforestation rates to suggest that soybean farmers are the next destroyers or "relentless foes" of the Amazon (Branford and Freris 2000; Fearnside 2001; Rohter 2003). It is argued that soybean cultivation will spread into fron tier forests, resulting in deforestation and consolidation of landholdings. In turn, this consolidation is feared to create a rural exodus of peasants, leading to even further ad vancement of forest destruction (Fearnside 2001; Bickel and Dros 2003; WWF et al. 2003). Agro-industrialists, however, describe mechanized farming as a uwin-win" sce nario for development and conservation, arguing that it increases land-use intensification which reduces the need for clearing new land while allowing for economic development (EMBRAPA 2002; Mueller and Bustamante 2002). No published empirical study has used spatially explicit data, human or physical, to measure or explain the mechanisms of forest change due to mechanized cultivation. Our current project engages this debate by seeking answers to three main ques tions: (1) To what extent are forests converted for mechanized annual cropping? ; (2) What are the key human variables that explain land change patterns? (3) What is the relationship between land-use intensification and forest conversion? Our study area is a modern agricultural frontier in southern Rondonia, an area at the humid Amazon forest savanna transition that is poised to become another major soybean-producing region.
Deep Learning for Classification of Peak Emotions within Virtual Reality Systems

Research has demonstrated well-being benefits from positive, ‘peak’ emotions such as awe and wonder, prompting the HCI community to utilize affective computing and AI modelling for elicitation and measurement of those target emotional states. The immersive nature of virtual reality (VR) content and systems can lead to feelings of awe and wonder, especially with a responsive, personalized environment based on biosignals. However, an accurate model is required to differentiate between emotional states that have similar biosignal input, such as awe and fear. Deep learning may provide a solution since the subtleties of these emotional states and affect may be recognized, with biosignal data viewed in a time series so that researchers and designers can understand which features of the system may have influenced target emotions. The proposed deep learning fusion system in this paper will use data collected from a corpus, created through collection of physiological biosignals and ranked qualitative data, and will classify these multimodal signals into target outputs of affect. This model will be real-time for the evaluation of VR system features which influence awe/wonder, using a bio-responsive environment. Since biosignal data will be collected through wireless, wearable sensor technology, and modelled through the same computer powering the VR system, it can be used in field research and studios.
Rapid deployment of curved surfaces via programmable auxetics

Deployable structures are physical mechanisms that can easily transition between two or more geometric configurations; such structures enable industrial, scientific, and consumer applications at a wide variety of scales. This paper develops novel deployable structures that can approximate a large class of doubly-curved surfaces and are easily actuated from a flat initial state via inflation or gravitational loading. The structures are based on two-dimensional rigid mechanical linkages that implicitly encode the curvature of the target shape via a user-programmable pattern that permits locally isotropic scaling under load. We explicitly characterize the shapes that can be realized by such structures---in particular, we show that they can approximate target surfaces of positive mean curvature and bounded scale distortion relative to a given reference domain. Based on this observation, we develop efficient computational design algorithms for approximating a given input geometry. The resulting designs can be rapidly manufactured via digital fabrication technologies such as laser cutting, CNC milling, or 3D printing. We validate our approach through a series of physical prototypes and present several application case studies, ranging from surgical implants to large-scale deployable architecture.