Miracle-dz/UniFAR-FTUs huggingface dataset:
ESTIMATED CHARACTER OF MATH ITEMS AND THE INFORMATION FUNCTION WITH THREE PARAMETER LOGISTIC MODEL APPROACHES

This paper aims to reveal: 1) items that fit the three-parameter logistics model; 2) the function of item information on the parameters of discrimination in mathematical tests; 3) item information function at the level of difficulty of the mathematics test; (4) item information function at the chance level of guessing a math test. The research data used participants' responses on the national mathematics final examination. The measurement model uses a three-parameter logistics model. The results show that: 1) there are about 25 out of 35 items that fit the three-parameter logistics model. 2) The function of each information about the smaller 1 except item 15. 3) The highest and lowest discrimination parameter information function values 816,226 and 1,026, respectively. 4) Difficulty level of information parameter function values above 100 by 46%, while below 100 by 54%. 5) The parameter value of the information function of the chance to guess is above 50 by 20%, while under 50 is 80%.
SOCIAL IMPACT AND READINESS OF VILLAGE APPARATUS TECHNOLOGY IN ADOPTING VILLAGE FINANCE SYSTEM

This research was developed based on Unified Acceptance and Use of Technology (UTAUT) and Technology Readiness (TR) model to measure the acceptability and technological readiness of the village financial system (Siskeudes). Siskeudes is a system that can help village officials to obtain funds to make it easier and transparent. Data were collected based on questionnaires distributed to village officials involved in siskeudes, ie finance and village secretaries in kecamataan Susukan Lebak and Suraneggala in Kabupaten Cirebon using Partial Least Square (PLS) method using SmartPLS 3.0. The results of this study indicate that technology readiness and social influence have influence on behavioral intention. Where social influence has the greatest influence compared with technology readiness. This shows that people around users like friends or superiors have a strong influence in making village officials adopt the village financial system. This study shows an in-depth understanding of acceptance siskeudes that can be integrated by two different models of social influence variable on UTAUT model and Technology readiness theory.
Implementation of a Hierarchical Embedded Cyber-Attack Detection System in Unmanned Aerial Systems

With the development of technology revolving around Unmanned Aerial Systems (UAS), UAS are becoming more widely used across a variety of applications. In every scenario, the system in question needs to have very distinct requirements and specifications, many of which revolve around safety and reliability. As such, these systems need to provide mechanisms in order to handle different attacks by adversaries on the outside. Since these systems have the ability to operate in many different flight modes according to their mission, detection and mitigation of the attacks become increasingly difficult over time. Cyber-attacks and their focuses are often evolving, and as a result, existing mitigation solutions slowly become obsolete. Many solutions that exist involve an intrusive solution or embedded software, which provide another attack surface for adversaries to and gain entry. Here, a hierarchical embedded cyber-attack detection system is explored and implemented, providing different methodologies and strategies for handling both cyber-attacks and hardware faults and failures on a hardware and information level. Thesis Supervisor: Robert H. Klenke, Ph.D Title: Professor, Department of Electrical and Computer Engineering
Graph-based Self-Adaptive Conversational Agent

Conversational agents have been widely adopted in dialogue systems for various business purposes. Many existing conversational agents are rule-based and require significant human intervention to adapt the knowledge and conversational flow. In this paper, we propose a graph-based adaptive conversational agent model which is capable of learning knowledge from human beings and adapting the knowledge-base according to human-agent interactions. Studies to evaluate the proposed model are conducted and presented, which compare the responses from the proposed adaptive agent model and a conventional agent
Molecular Insights on the CH4/CO2 Separation in Nanoporous Graphene and Graphene Oxide Separation Platforms: Adsorbents versus Membranes

Molecular dynamics simulations were performed to gain fundamental molecular insights on the concentration-dependent adsorption and gas transport properties of the components in a CH4/CO2 gaseous mixture in single- and double-layered nanoporous graphene (NPG) and graphene oxide (NPGO) separation platforms. While these platforms are promising for a variety of separation applications, much about the relevant gas separation mechanisms in these systems is still unexplored. Based on the gas adsorption results in this work, at least two layers of CO2 are formed on the gas side of both NPG and NPGO, while no adsorption is observed for pure CH4 on the single-layered NPG. In contrast, increasing the CH4 concentration in the CH4/CO2 mixture leads to an enhancement of the CH4 adsorption on both separation platforms. The through-the-pore diffusion coefficients of both CO2 and CH4 increase with an increase in the CH4 concentration for all NPG and NPGO systems. The permeance of CO2 is smaller than that of CH4, suggestin.
Fabrication of folding polymeric thin films with hydrophilic properties

Polydimethylsiloxane (PDMS) sheets were used as a mold for the generation of low cost polymeric thin films. Poly(ethylene glycol) diacrylate average Mn 575, Poly(ethylene glycol) diacrylate average Mn 250 and Phenylbis (2, 4, 6-trimethylbenzoyl) phosphine oxide were used to create hydrophilic and hydrophobic thin films. The thickness of the films was highly dependent on the amount of the polymeric mixture and the pressure between the PDMS sheets. The hydrophobicity of the films was measured by observing the contact angle of a water drop on the film, as well as the degrees of folding when the film absorbed humidity. The films produced with Poly(ethylene glycol) diacrylate average Mn 575 were found to be hydrophobic, while the films produced with Poly(ethylene glycol) diacrylate average Mn 250 were found to be hydrophilic. Hydrophobic films fold when exposed to a drop of water on their surface. The time and the angle of folding were directly dependent on the thickness of the film. A relation between the relative mass of the thin film and the angle of folding was found.
Analyzing the Nuances of Transformers' Polynomial Simplification Abilities

Symbolic Mathematical tasks such as integration often require multiple well-defined steps and understanding of sub-tasks to reach a solution. To understand Transformers' abilities in such tasks in a fine-grained manner, we deviate from traditional end-to-end settings, and explore a step-wise polynomial simplification task. Polynomials can be written in a simple normal form as a sum of monomials which are ordered in a lexicographic order. For a polynomial which is not necessarily in this normal form, a sequence of simplification steps is applied to reach the fully simplified (i.e., in the normal form) polynomial. We propose a synthetic Polynomial dataset generation algorithm that generates polynomials with unique proof steps. Through varying coefficient configurations, input representation, proof granularity, and extensive hyper-parameter tuning, we observe that Transformers consistently struggle with numeric multiplication. We explore two ways to mitigate this: Curriculum Learning and a Symbolic Calculator approach (where the numeric operations are offloaded to a calculator). Both approaches provide significant gains over the vanilla Transformers-based baseline.
A Comprehensive Attempt to Research Statement Generation

For a researcher, writing a good research statement is crucial but costs a lot of time and effort. To help researchers, in this paper, we propose the research statement generation (RSG) task which aims to summarize one's research achievements and help prepare a formal research statement. For this task, we conduct a comprehensive attempt including corpus construction, method design, and performance evaluation. First, we construct an RSG dataset with 62 research statements and the corresponding 1,203 publications. Due to the limitation of our resources, we propose a practical RSG method which identifies a researcher's research directions by topic modeling and clustering techniques and extracts salient sentences by a neural text summarizer. Finally, experiments show that our method outperforms all the baselines with better content coverage and coherence.
Hardware-Friendly Synaptic Orders and Timescales in Liquid State Machines for Speech Classification

Liquid State Machines are brain inspired spiking neural networks (SNNs) with random reservoir connectivity and bio-mimetic neuronal and synaptic models. Reservoir computing networks are proposed as an alternative to deep neural networks to solve temporal classification problems. Previous studies suggest 2nd order (double exponential) synaptic waveform to be crucial for achieving high accuracy for TI-46 spoken digits recognition. The proposal of long-time range (ms) bio-mimetic synaptic waveforms is a challenge to compact and power efficient neuromorphic hardware. In this work, we analyze the role of synaptic orders namely: $\delta$ (high output for single time step), 0th (rectangular with a finite pulse width), 1st (exponential fall) and 2nd order (exponential rise and fall) and synaptic timescales on the reservoir output response and on the TI-46 spoken digits classification accuracy under a more comprehensive parameter sweep. We find the optimal operating point to be correlated to an optimal range of spiking activity in the reservoir. Further, the proposed 0th order synapses perform at par with the biologically plausible 2nd order synapses. This is substantial relaxation for circuit designers as synapses are the most abundant components in an in-memory implementation for SNNs. The circuit benefits for both analog and mixed-signal realizations of 0th order synapse are highlighted demonstrating 2–3 orders of savings in area and power consumptions by eliminating Op-Amps and Digital to Analog Converter circuits. This has major implications on a complete neural network implementation with focus on peripheral limitations and algorithmic simplifications to overcome them.
TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter

The problem of restoring images corrupted by Poisson noise is common in many application fields and, because of its intrinsic ill posedness, it requires regularization techniques for its solution. The effectiveness of such techniques depends on the value of the regularization parameter balancing data fidelity and regularity of the solution. Here we consider the Total Generalized Variation regularization introduced in [SIAM J. Imag. Sci, 3(3), 492–526, 2010], which has demonstrated its ability of preserving sharp features as well as smooth transition variations, and introduce an automatic strategy for defining the value of the regularization parameter. We solve the corresponding optimization problem by using a 3-block version of ADMM. Preliminary numerical experiments support the proposed approach.
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

Recommender systems have achieved great success in modeling user’s preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users’ interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user’s preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.
LaSS: Running Latency Sensitive Serverless Computations at the Edge

Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the constrained nature of the edge and the latency sensitive nature of workloads result in many challenges for serverless platforms. In this paper, we present LaSS, a platform that uses model-driven approaches for running latency-sensitive serverless computations on edge resources. LaSS uses principled queuing-based methods to determine an appropriate allocation for each hosted function and auto-scales the allocated resources in response to workload dynamics. LaSS uses a fair-share allocation approach to guarantee a minimum of allocated resources to each function in the presence of overload. In addition, it utilizes resource reclamation methods based on container deflation and termination to reassign resources from over-provisioned functions to under-provisioned ones. We implement a prototype of our approach on an OpenWhisk serverless edge cluster and conduct a detailed experimental evaluation. Our results show that LaSS can accurately predict the resources needed for serverless functions in the presence of highly dynamic workloads, and reprovision container capacity within hundreds of milliseconds while maintaining fair share allocation guarantees.
RECKONition: a NLP-based system for Industrial Accidents at Work Prevention (Discussion Paper)

Extracting patterns and useful information from Natural Language datasets is a challenging task, especially when dealing with data written in a language different from English, like Italian. Machine and Deep Learning, together with Natural Language Processing (NLP) techniques have widely spread and improved lately, providing a plethora of useful methods to address both Supervised and Unsupervised problems on textual information. We propose RECKONition, a NLP-based system for Industrial Accidents at Work Prevention. RECKONition, which is meant to provide Natural Language Understanding, Clustering and Inference, is the result of a joint partnership with the Italian National Institute for Insurance against Accidents at Work (INAIL). The obtained results showed the ability to process textual data written in Italian describing industrial accidents dynamics and consequences.
Freeze-in sterile neutrino dark matter in a class of U$(1)^\prime$ models with inverse seesaw

We study a class of general U$(1)^\prime$ models to explain the observed dark matter relic abundance and light neutrino masses. The model contains three right handed neutrinos and three gauge singlet Majorana fermions to generate the light neutrino mass via the inverse seesaw mechanism. We assign one pair of degenerate sterile neutrinos to be the dark matter candidate whose relic density is generated by the freeze-in mechanism. We consider different regimes of the masses of the dark matter particle and the ${Z^\prime}$ gauge boson. The production of the dark matter can occur at different reheating temperatures in various scenarios depending on the masses of the ${Z^\prime}$ boson and the dark matter candidate. We also note that if the mass of the sterile neutrino dark matter is $\gtrsim 1 \rm{MeV}$ and if the $Z^\prime$ is heavier than the dark matter, the decay of the dark matter candidate into positrons can explain the long standing puzzle of the galactic $511\rm{keV}$ line in the Milky Way center observed by the INTEGRAL satellite. We constrain the model parameters from the dark matter analysis, vacuum stability and the collider searches of heavy ${Z^\prime}$ at the LHC. For the case with light $Z^\prime$, we also compare how far the parameter space allowed from dark matter relic density can be probed by the future lifetime frontier experiments SHiP and FASERs in the special case of $U(1)_{B-L}$ model.
Bayesian Deep Networks for Supervised Single-View Depth Learning

—Uncertainty quantiﬁcation is a key aspect in robotic perception, as overconﬁdent or point estimators can lead to collisions and damages to the environment and the robot. In this paper, we evaluate scalable approaches to uncertainty quantiﬁcation in single-view supervised depth learning, specifically MC dropout and deep ensembles. For MC dropout, in particular, we explore the effect of the dropout at different levels in the architecture. We demonstrate that adding dropout in the encoder leads to better results than adding it in the decoder, the latest being the usual approach in the literature for similar problems. We also propose the use of depth uncertainty in the application of pseudo-RGBD ICP and demonstrate its potential for improving the accuracy in such a task.
Uncertainty-aware deep learning for robot touch: Application to Bayesian tactile servo control

This work investigates uncertainty-aware deep learning (DL) in tactile robotics based on a general framework introduced recently for robot vision. For a test scenario, we consider optical tactile sensing in combination with DL to estimate the edge pose as a feedback signal to servo around various 2D test objects. We demonstrate that uncertainty-aware DL can improve the pose estimation over deterministic DL methods. The system estimates the uncertainty associated with each prediction, which is used along with temporal coherency to improve the predictions via a Kalman filter, and hence improve the tactile servo control. The robot is able to robustly follow all of the presented contour shapes to reduce not only the error by a factor of two but also smooth the trajectory from the undesired noisy behaviour caused by previous deterministic networks. In our view, as the field of tactile robotics matures in its use of DL, the estimation of uncertainty will become a key component in the control of physically interactive tasks in complex environments.
Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments: Extended Version

This study uses eight years of undergraduate course enrollment data from a major university to form networks of courses based on student co-enrollments. The networks are analyzed to identify "hub" courses often taken with many other courses. Two notions of hubs are considered: one based on raw popularity and another on proportional likelihoods of co-enrollment with other courses. Network metrics are calculated to describe the course networks. Academic departments and high-level academic categories (e.g., humanities), are studied for their influence over course groupings. The identification of hub courses has practical applications, since it can help better predict the impact of changes in course offerings and in course popularity, and in the case of interdisciplinary hub courses, can be used to increase or decrease interest and enrollments in specific academic departments and areas.
Learning Actor-centered Representations for Action Localization in Streaming Videos using Predictive Learning

Event perception tasks such as recognizing and localizing actions in streaming videos are essential for tackling visual understanding tasks. Progress has primarily been driven by the use of large-scale, annotated training data in a supervised manner. In this work, we tackle the problem of learning actor-centered representations through the notion of continual hierarchical predictive learning to localize actions in streaming videos without any training annotations. Inspired by cognitive theories of event perception, we pro-pose a novel, self-supervised framework driven by the notion of hierarchical predictive learning to construct actor-centered features by attention-based contextualization. Extensive experiments on three benchmark datasets show that the approach can learn robust representations for localizing actions using only one epoch of training i.e., we train the model continually in streaming fashion - one frame at a time, with a single pass through training videos. We show that the proposed approach outperforms unsupervised and weakly supervised baselines while offering competitive performance to fully supervised approaches. Finally, we show that the proposed model can generalize to out-of-domain data without signiﬁcant loss in performance without any ﬁnetuning for both the recognition and localization tasks.
Uranus’s Hidden Narrow Rings

In addition to its suite of narrow dense rings, Uranus is surrounded by an extremely complex system of dusty rings that were most clearly seen by the Voyager spacecraft after it flew past the planet. A new analysis of the highest-resolution images of these dusty rings reveals that a number of them are less than 20 km wide. The extreme narrowness of these rings, along with the fact that most of them do not appear to fall close to known satellite resonances, should provide new insights into the forces responsible for sculpting the Uranian ring system.
Surface theory of a second-order topological insulator beyond the Dirac approximation

We study the surface states and chiral hinge states of a 3D second-order topological insulator in the presence of an external magnetic gauge field. Surfaces pierced by flux host Landau levels, while surfaces parallel to the applied field are not significantly affected. The chiral hinge modes mediate spectral flow between neighbouring surfaces. As the magnetic field strength is increased, the surface Landau quantization deviates from that of a massive Dirac cone. Quantitatively, the $n = 0$ Landau level falls inside the surface Dirac gap, and not at the gap edge. The $n \ne 0$ levels exhibit a further, qualitative discrepancy: while the massive Dirac cone is expected to produce pairs of levels ($\pm n$) which are symmetric around zero energy, the $n$ and $-n$ levels become asymmetric in our lattice model -- one of the pair may even be absent from the spectrum, or hybridized with the continuum. In order to resolve the issue, we extend the standard 2D massive Dirac surface theory, by including additional Hamiltonian terms at $\mathcal{O} (k^2)$. While these terms do not break particle-hole symmetry in the absence of magnetic field, they lead to the aforementioned Landau level asymmetry once the magnetic field is applied. We argue that similar $\mathcal{O}(k^2)$ correction terms are generically expected in lattice models containing gapped Dirac fermions, using the BHZ model of a 2D topological insulator as an example.