Miracle-dz/UniFAR-FTUs huggingface dataset:
Impulsivity and Information Disclosure: Implications for Privacy Paradox

Privacy paradox refers to the inconsistency that sometimes exists between individuals’ expressed privacy concern and the willingness to divulge personal information. Several arguments have been proposed to explain the inconsistency. One set of arguments centers around the effects of individual differences in personality characteristics, e.g., the Big Five. In the current article, we examine the role of a personality characteristic, impulsivity, in explaining the relationship between privacy concern and information disclosure. We report the results of a survey-based study that consisted of two hundred and forty-two (242) usable responses from subjects recruited on Amazon Mechanical Turk. The results show that one of the three dimensions of impulsivity, motor impulsivity, directly influences the extent of information disclosure, and, also moderates the relationship between privacy concern and information disclosure. Furthermore, our study shows impulsivity explains more variance in information disclosure than explained by the Big Five factors only.
Construction of the Barrier for Reach-Avoid Differential Games in Three-Dimensional Space with Four Equal-Speed Players

This paper considers a reach-avoid differential game in three-dimensional space with four equal-speed players. A plane divides the game space into a play subspace and a goal subspace. The evader aims at entering the goal subspace while three pursuers cooperate to prevent that by capturing the evader. A complete, closed-form barrier for this differential game is provided, by which the game winner can be perfectly predicted before the game starts. All possible cooperations among three pursuers are considered and thus the guaranteed winning for each team is a prior. Furthermore, an algorithm is designed to compute the barrier for multiple pursuers of any numbers and any initial configurations. More realistically, since the whole achieved developments are analytical, they require a little memory without computational burden and allow for real-time updates, beyond the capacity of traditional Hamilton-Jacobi-Isaacs method.
A Multifunctional Photonic Integrated Circuit for Diverse Microwave Signal Generation, Transmission, and Processing

Microwave photonics (MWP) studies the interaction between microwaves and optical waves for the generation, transmission, and processing of microwave signals (i.e., three key domains), taking advantage of the broad bandwidth and low loss offered by modern photonics. Integrated MWP using photonic integrated circuits (PICs) can reach a compact, reliable, and green implementation. Most PICs, however, are recently developed to perform one or more functions restricted inside a single domain. Herein, as highly desired, a multifunctional PIC is proposed to cover the three key domains. The PIC is fabricated on an InP platform by monolithically integrating four laser diodes and two modulators. Using the multifunctional PIC, seven fundamental functions across microwave signal generation, transmission, and processing are demonstrated experimentally. Outdoor field trials for electromagnetic environment surveillance along in‐service high‐speed railways are also performed. The success of such a PIC marks a key step forward for practical and massive MWP implementations.
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression

Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQˆ3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
Effectiveness of Diabetes Self-management Education Against Diet Behavior in Patients Type 2 Diabetes Mellitus: A Literature Review

BACKGROUND: Diabetes mellitus (DM) is a disease that describes a problem with insulin deficiency and the inability of the pancreas to produce enough insulin. The World Health Organization predicts that the number of people with DM type II in Indonesia will increase to 12 million by 2030. AIM: This study looks at Journals on diabetes selfmanagement education (DSME) on DM patients in improving dietary behavior. METHODS: This study uses an online journal database that provides free articles and journals in PDF such as: ProQuest, EBCSO, PubMed, Elsevier, Scinapse, MDPI, and Google Scholar. Literature was collected from the past 10 years, namely, 2010–2020 using the following set of keywords: “DSME,” “DSME Program Evaluation,” and “Diabetes Self-Care Education. ” RESULTS: DSME intervention can improve the condition of DM patients if it is routinely carried out. In addition, the DSME intervention was able to control hemoglobin A1c levels and control blood sugar. This intervention still needs to be developed, considering that various forms of education in DM patients have been carried out such as using text messages, leaflets, illustrated pictures or leaflets, by telephone, video, handbooks, or direct education. These investigations can be developed into empirical research. CONCLUSIONS: The application of DSME interventions can help improve the conditions experienced by DM type II sufferers if it is routinely carried out.
A variational characterisation of the second eigenvalue of the p ‐Laplacian on quasi open sets

In this article, we prove a minimax characterisation of the second eigenvalue of the p ‐Laplacian operator on p ‐quasi open sets, using a construction based on minimizing movements. This leads also to an existence theorem for spectral functionals depending on the first two eigenvalues of the p ‐Laplacian.
Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment

3D scan registration is a classical, yet a highly useful problem in the context of 3D sensors such as Kinect and Velodyne. While there are several existing methods, the techniques are usually incremental where adjacent scans are registered first to obtain the initial poses, followed by motion averaging and bundle-adjustment refinement. In this paper, we take a different approach and develop minimal solvers for jointly computing the initial poses of cameras in small loops such as 3-, 4-, and 5-cycles. Note that the classical registration of 2 scans can be done using a minimum of 3 point matches to compute 6 degrees of relative motion. On the other hand, to jointly compute the 3D registrations in n-cycles, we take 2 point matches between the first n-1 consecutive pairs (i.e., Scan 1 & Scan 2, . , and Scan n-1 & Scan n) and 1 or 2 point matches between Scan 1 and Scan n. Overall, we use 5, 7, and 10 point matches for 3-, 4-, and 5-cycles, and recover 12, 18, and 24 degrees of transformation variables, respectively. Using simulations and real-data we show that the 3D registration using mini n-cycles are computationally efficient, and can provide alternate and better initial poses compared to standard pairwise methods.
A Frame-Theoretic Scheme for Robust Millimeter Wave Channel Estimation

We propose a new scheme for the robust estimation of the millimeter wave (mmWave) channel. Our approach is based on a sparse formulation of the channel estimation problem coupled with a frame theoretic representation of the sensing dictionary. To clarify, under this approach, the combined effect of transmit precoders and receive beamformers is modeled by a single frame, whose design is optimized to improve the accuracy of the sparse reconstruction problem to which the channel estimation problem is ultimately reduced. The optimized sensing dictionary frame is then decomposed via a Kronecker decomposition back into the precoding and beamforming vectors used by the transmitter and receiver. Simulation results illustrate the significant gain in estimation accuracy obtained over state of the art alternatives. As a bonus, the work offers new insights onto the sparse mmWave-multiple-input multiple-output (MIMO) channel estimation problem by casting the trade-off between correlation and variation range in terms of frame coherence and tightness.
A Reduction for the Distinct Distances Problem in ${\mathbb R}^d$

We introduce a reduction from the distinct distances problem in ${\mathbb R}^d$ to an incidence problem with $(d-1)$-flats in ${\mathbb R}^{2d-1}$. Deriving the conjectured bound for this incidence problem (the bound predicted by the polynomial partitioning technique) would lead to a tight bound for the distinct distances problem in ${\mathbb R}^d$. The reduction provides a large amount of information about the $(d-1)$-flats, and a framework for deriving more restrictions that these satisfy. Our reduction is based on introducing a Lie group that is a double cover of the special Euclidean group. This group can be seen as a variant of the Spin group, and a large part of our analysis involves studying its properties.
Resource theories of quantum channels and the universal role of resource erasure

We initiate the systematic study of resource theories of quantum channels, i.e. of the dynamics that quantum systems undergo by completely positive maps, in abstracto: Resources are in principle all maps from one quantum system to another, but some maps are deemed free. The free maps are supposed to satisfy certain axioms, among them closure under tensor products, under composition and freeness of the identity map (the latter two say that the free maps form a monoid). The free maps act on the resources simply by tensor product and composition. This generalizes the much-studied resource theories of quantum states, and abolishes the distinction between resources (states) and the free maps, which act on the former, leaving only maps, divided into resource-full and resource-free ones. We discuss the axiomatic framework of quantifying channel resources, and show two general methods of constructing resource monotones of channels. Furthermore, we show that under mild regularity conditions, each resource theory of quantum channels has a distinguished monotone, the robustness (and its smoothed version), generalizing the analogous concept in resource theories of states. We give an operational interpretation of the log-robustness as the amount of heat dissipation (randomness) required for resource erasure by random reversible free maps, valid in broad classes of resource theories of quantum channels. Technically, this is based on an abstract version of the recent convex-split lemma, extended from its original domain of quantum states to ordered vector spaces with sufficiently well-behaved base norms (which includes the case of quantum channels with diamond norm or variants thereof). Finally, we remark on several key issues concerning the asymptotic theory.
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples

This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78% F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59% on Subtask B (i.e. out-of-domain), even without using any additional external data.
Cross-Site Scripting (XSS) Detection Integrating Evidences in Multiple Stages

As Cross-Site Scripting (XSS) remains one of the top web security risks, people keep exploring ways to detect such attacks efficiently. So far, existing solutions only focus on the payload in a web request or a response, a single stage of a web transaction. This work proposes a new approach that integrates evidences from both a web request and its response in order to better characterize XSS attacks and separate them from normal web transactions. We first collect complete payloads of XSS and normal web transactions from two databases and extract features from them using the Word2vec technique. Next, we train two Gaussian mixture models (GMM) with these features, one for XSS transaction and one for normal web transactions. These two models can generate two probability scores for a new web transaction, which indicate how similar this web transaction is to XSS and normal traffics respectively. Finally, we put together these two GMM models in classification by combining these two probabilities to further improve detection accuracy.
Nonzero-sum stochastic games with impulse controls

We consider a general class of nonzero-sum $N$-player stochastic games with impulse controls, where players control the underlying dynamics with discrete interventions. We adopt a verification approach and provide sufficient conditions for the Nash equilibria (NEs) of the game. We then consider the limit situation of $N \to \infty$, that is, a suitable mean-field game (MFG) with impulse controls. We show that under appropriate technical conditions, the MFG is an $\epsilon$-NE approximation to the $N$-player game, with $\epsilon=\frac{1}{\sqrt{N}}$. As an example, we analyze in details a class of stochastic games which extends the classical cash management problem to the game setting. In particular, we characterize the NEs for its two-player case and compare the results to the single-player case, showing the impact of competition on the player's optimal strategy, with sensitivity analysis of the model parameters.
Data-Driven Economic NMPC Using Reinforcement Learning

Reinforcement learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, nonlinear model predictive control (NMPC) and economic NMPC (ENMPC) are standard tools for the closed-loop optimal control of complex systems with constraints and limitations, and benefit from a rich theory to assess their closed-loop behavior. Unfortunately, the performance of (E)NMPC hinges on the quality of the model underlying the control scheme. In this paper, we show that an (E)NMPC scheme can be tuned to deliver the optimal policy of the real system even when using a wrong model. This result also holds for real systems having stochastic dynamics. This entails that ENMPC can be used as a new type of function approximator within RL. Furthermore, we investigate our results in the context of ENMPC and formally connect them to the concept of dissipativity, which is central for the ENMPC stability. Finally, we detail how these results can be used to deploy classic RL tools for tuning (E)NMPC schemes. We apply these tools on both, a classical linear MPC setting and a standard nonlinear example, from the ENMPC literature.
Disentangling Language and Knowledge in Task-Oriented Dialogs

The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such changes. We propose an encoder-decoder architecture (BoSsNet) with a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled learning of the response’s language model and its knowledge incorporation. Consequently, the KB can be modified with new knowledge without a drop in interpretability. We find that BoSsNeT outperforms state-of-the-art models, with considerable improvements (>10%) on bAbI OOV test sets and other human-human datasets. We also systematically modify existing datasets to measure disentanglement and show BoSsNeT to be robust to KB modifications.
Collective Classification for Social Media Credibility Estimation

We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media.
Semantic Attribute Matching Networks

We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations. SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences. To learn the networks using weak supervisions in the form of image pairs, we present a semantic attribute matching loss based on the matching similarity between an attribute transferred source feature and a warped target feature. With SAM-Net, the state-of-the-art performance is attained on several benchmarks for semantic matching and attribute transfer.
A Social Network Analysis (SNA) Study On Data Breach Concerns Over Social Media

In the current era of digital devices, the concerns over data privacy and security breaches are rampant. Understanding these concerns by analyzing the messages posted on the social media from linguistic perspective has been a challenge that is increasing in complexity as the number of social media sites increase and the volume of data increases. We investigate the diffusion characteristics of the information attributed to data breach messages, first based on the literary aspects of the message and second, we build a social network of the users who are directly involved in spreading the messages. We found that the messages that involve the technicalities, threat and severity related security characteristics spread fast. Contrary to conventional news channels related posts on social media that capture wide attention, breach information diffusion follows a different pattern. The messages are widely shared across the tech-savvy groups and people involved in security-related studies. Analyzing the messages in both linguistic and visual perspective through social networks, researchers can extract grounded insights into these research questions.
Training of Ecological Detection Dogs for Wolf Scat (Canis lupus)

Detection dogs for wolf scat can assist in conservation-monitoring of wolves. In this pilot project, the first encounters of experienced detection dogs with wolf scat were documented. It was tested how a dog, trained with only a few scat samples (minimally four, maximally six), could generalise towards new unknown wolf scats and discriminate them from distracting odours. Four dogs all showed a behavioural expression of aversion upon first presentation with wolf scat during their initial training to fixate on wolf scat. The dog that showed the least aversion to wolf scats was trained during two weeks. His ability to discriminate wolf scat from other odours and to generalise to new wolf samples was evaluated. He was presented a line-up with new wolf scat, dog scat and other distracting odours in eight boxes, seven trials and random rotations of odours. The test was conducted blindly without the dog handler knowing if or where a positive sample was present. The dog showed partial generalisation of wolf scat odour from only a few known training samples to new wolf samples. The test was evaluated for methodological improvements.
Metrics and Stabilization in One Parameter Persistence

We propose a new way of thinking about one parameter persistence. We believe topological persistence is fundamentally not about decomposition theorems but a central role is played by a choice of metrics. Choosing a pseudometric between persistent vector spaces leads to stabilization of discrete invariants. We develop theory behind this stabilization and stable rank invariant. We give evidence of the usefulness of this approach in concrete data analysis.
CRISPR/Cas9 assisted gene targeting efficiently inhibits bovine herpesvirus-1 replication

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/ CRISPR associated protein (cas) are now being accepted as a highly specific method of gene editing. Among many other applications, CRISPR/cas has an immense potential to be used as antivirals. In this study, we successfully demonstrated CRISPR/Cas9 mediated inhibition of Bovine Herpes virus -1 (BHV-1) replication. BHV-1 causes economically important diseases in bovines with establishment of latency. Six essential genes and one non-essential gene of BHV-1 were targeted to assess the impact on virus replication. Inhibition of UL52, circ, and UL27 genes showed promising results, whereas the other three genes US6, UL18, and UL34 resulted in lower level of inhibition. Non-specific gene editing in host and virus was in-silico evaluated and was demonstrated by inhibition of virus induced apoptosis. Successful editing of one viral non-essential gene without any alterations in virus replication demonstrated the potential of CRISPR/Cas9 in replicating viral genome. Complete abrogation of virus replication was observed transiently (~24 hours post-transfection/hpt) when transfected with short lived in-vitro transcribed sgRNAs. Whereas, constant expression with plasmid based delivery may result in off-target activity which can be ruled out with short lived in-vitro transcribed sgRNA.
Buyers of Lemons: Addressing Buyers' Needs in the Market for Lemons with Blockchain Technology

The automotive market is in the top three markets with the least trust from consumers. In particular, in the second-hand car market, consumers suffer from such problems as the car being in worse condition than initially indicated, accident damage that is not disclosed, fraud, etc. Akerlof, described the market for used cars as an example of the problem of information asymmetries and resulting quality uncertainty. In order to cope with quality uncertainties, used car buyers actively engage themselves in information seeking. Blockchain technology promises to automatize the tracking of cars through their lifecycles and provide reliable information at any point in time it is needed. In our study, we investigate the problems car buyers face during information seeking and propose requirements for the design of a blockchain-based system to address these.