Miracle-dz/UniFAR-FTUs huggingface dataset:
The Code2Text Challenge: Text Generation in Source Libraries

We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction, and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.
A Novel Line Current Control Strategy to Control the Real Power Flow at PCC using H-bridge Inverter

This paper presents the power flow control between the main grid and the microgrid in low-voltage distribution network using H-bridge inverter. Controlling the real power flow in the line using the H-bridge inverter is investigated by implementing the line current control strategy. The feasibility of the proposed H-bridge inverter and its control strategy is validated by varying the line current reference. Hence, the H-bridge inverter will operate in its inductive or capacitive operation mode depending on the reference current value and the output of the PI controller in the control unit. The control strategy of the microgrid distributed generators has also been investigated in which P/V droop characteristics is applied to the power control of each DG in the microgrid. The effectiveness of the proposed approaches is validated through simulations.
Multi-channel Allocation Algorithm Based on AODV Protocol in Wireless Mesh Networks

A Wireless Mesh Networks (WMN) consists of wireless mesh routers and terminals connected by wireless multi-hop communication. It can be divided into three network types, including terminal mesh networks, infrastructure mesh networks, and hybrid mesh networks according to the network topology and the node function. For infrastructure mesh networks, the network throughput will determine the number of terminals which can access the network. How to effectively use multiple orthogonal channels and multiple interfaces to increase the throughput of WMNs and decrease the radio link transmission interference is of great significance. In this paper, firstly, the characteristics of WMN are studied. According to these characteristics in WMNs, the interfaces in mesh router node are classified to two types: one is Data Backhaul Interface (BI), and another is Internal Traffic Transfer Interface (TI). In view of this, we proposed a channel assignment scheme based on group management. Secondly, the routing protocols of WMN are studied, and a new routing protocol based on AODV and multi-channel is proposed to increase the network throughput by taking advantage of multiple orthogonal channels. Finally, simulation experiments by the OPNET are conducted and the results of our proposed algorithm is compared with the standard AODV. The comparison shows that improved AODV based on our proposed algorithm is superior than the standard AODV from the aspect of increasing the network throughput, decreasing media access delay, and keeping higher packet delivery rate.
Trees for Vertex Cuts, Hypergraph Cuts and Minimum Hypergraph Bisection

In the Minimum Hypergraph Bisection problem, the vertex set of a hypergraph has to be partitioned into two parts of equal size so that the number of hyperedges intersecting both parts is minimized. This problem is a natural generalization of the well-studied Minimum Bisection problem in graphs. In this paper we present a sharp distinction between Minimum Bisection in hypergraphs and graphs. Whereas it is well-known that all bi-criteria approximation algorithms for Minimum Bisection in graphs can be extended to hypergraphs with the exact same guarantees, in this paper we prove that this is not the case when considering true (i.e., non bi-criteria) approximation algorithms. Specifically, we show that Minimum Bisection in Hypergraphs admits an $\tilde\mathcalO (\sqrtn )$ approximation algorithm (and highlight several special cases where a better approximation ratio is possible). Additionally, we show that the problem is at least as hard as the Densest k -Subgraph problem. Assuming the Dense vs. Random Conjecture~\citeCDK12, no approximation ratio better than $\bigO(n^1/4-\varepsilon )$ is possible. In particular, Minimum Hypergraph Bisection is much harder to approximate than Minimum Bisection in graphs, for which a logarithmic approximation algorithms exist~\citeRae08. We also consider the problem of constructing trees that are cut sparsifiers for hypergraph and vertex cuts. While similar trees lie at the heart of powerful algorithms for Minimum Bisection in graphs, we prove that this is not the case for hypergraphs. We give upper and lower bounds to the quality of such trees. Our bounds show that this tree cut sparsifying approach cannot improve the general approximation ratio of Minimum Hypergraph Bisection and Minimum Vertex Bisection.
Studying MOOC completion at scale using the MOOC replication framework

Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. In the proof of concept presented here, we use MORF to attempt to replicate 15 previously published findings across 29 iterations of 17 MOOCs. The findings indicate that 12 of the 15 findings replicated significantly across the data sets, and that two findings replicated significantly in the opposite direction. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.
Simplifying very deep convolutional neural network architectures for robust speech recognition

Very deep convolutional neural networks (VDCNNs) have been successfully used in computer vision. More recently VDCNNs have been applied to speech recognition, using architectures adopted from computer vision. In this paper, we experimentally analyse the role of the components in VDCNN architectures for robust speech recognition. We have proposed a number of simplified VDCNN architectures, taking into account the use of fully-connected layers and down-sampling approaches. We have investigated three ways to down-sample feature maps: max-pooling, average-pooling, and convolution with increased stride. Our proposed model consisting solely of convolutional (conv) layers, and without any fully-connected layers, achieves a lower word error rate on Aurora 4 compared to other VDCNN architectures typically used in speech recognition. We have also extended our experiments to the MGB-3 task of multi-genre broadcast recognition using BBC TV recordings. The MGB-3 results indicate that the same architecture achieves the best result among our VDCNNs on this task as well.
The EfProb Library for Probabilistic Calculations

EfProb is an abbreviation of Effectus Probability. It is the name of a library for probability calculations in Python. EfProb offers a uniform language for discrete, continuous and quantum probability. For each of these three cases, the basic ingredients of the language are states, predicates, and channels. Probabilities are typically calculated as validities of predicates in states. States can be updated (conditioned) with predicates. Channels can be used for state transformation and for predicate transformation. This short paper gives an overview of the use of EfProb.
Preallocation and Planning Under Stochastic Resource Constraints

Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.
Using Visual Histories to Reconstruct the Mental Context of Suspended Activities

Resuming suspended activities is fundamental to knowledge work. As activities grow in complexity or fade from memory, they become increasingly difficult to cognitively resume. Motivated by the efficiency of images for cueing autobiographical memory, we conducted 2 studies of how visual histories of computer-mediated activity might aid users in restoring the mental context of suspended activities. In the first exploratory study we demonstrate that users provided a visual history consisting of small thumbnail images of their desktop can reconstruct detailed mental contexts of earlier computer work. In the second observational study we examine methods and cues that web developers, scientific writers, and graphic designers currently use to restore mental context. We find that participants from all three professions engage in situated sensemaking, reconstructing context by generating stories to explain cues in their documents with implicit meaning, such as sudden changes in writing quality or the shape of blocks of code. We identify characteristics of each activity that influence the information, artifacts, and strategies participants used to reconstruct context. We conclude that visual histories can help users reconstruct mental context but that challenges remain in designing lightweight image-based cues that help users reconstruct context for a range of activities.
Challenges in Contact-Support Planning for Acyclic Motion of Humanoids and Androids

In order to enhance whole body motion capabilities of humanoids or androids, we study the whole-body contact planning and motion generation problem which allows them taking supports by contacting any possible location on any of their parts, with any permissible location of their surrounding environment. Here, the environment is seen both as (i) a supporting mean, on which supporting motion contacts can be formed and generated, and (ii) an obstacle to be avoided for the remaining parts of the robots -that are not in contact- during the transition motion between two successive contact configurations. This problem can be seen as the generalization of walking. From our recent developments and experiments of such acyclic motion generation with humanoid HRP-2, we discuss in this paper technical issues that need to be resolved and sophisticated, and what are the possible extensions of this challenging problem.
Identification of genes regulated during neuronal apoptosis using oligonucleotide microarrays

Neuronal apoptosis occurs extensively during the normal development of the mammalian nervous system and ensures that appropriate connections are made between neurons and their target cells. Developing sympathetic neurons and the PC6-3 cell line, a PC 12 sub-clone, are useful in vitro systems for studying neuronal apoptosis. Both sympathetic neurons and neuronally differentiated PC6-3 cells depend on nerve growth factor (NGF) for survival and die by apoptosis after NGF withdrawal in a transcription and translation-dependent manner. The c-Jun protein is known to be required for neuronal cell death following survival factor withdrawal, however the direct targets of this basic/leucine zipper transcription factor are unknown. The aim of this thesis was to identify and study c-Jun target genes involved in neuronal apoptosis. In experiments using Affymetrix GeneChip oligonucleotide microarrays, 78 genes were identified that had an altered pattern of expression after NGF withdrawal from neuronal PC6-3 cells. One of the induced genes, ATF-3, a basic/leucine zipper transcription factor that can dimerise with c-Jun, was confirmed as an up-regulated transcript during PC6-3 cell apoptosis and this occurred after the induction of c-Jun. In sympathetic neurons, atf-3 RNA and protein levels increase between 8 and 16 hours after NGF withdrawal and remain elevated at 24 hours. This protein induction occurs after that of c-Jun and is inhibited by the c-Jun N-terminal kinase inhibitor SP600125 and the mixed-lineage kinase (MLK) inhibitor CEP-11004. This suggests that induction of ATF-3 may require JNK activity in neurons and that the atf-3 gene might be a target of c-Jun. The function of ATF-3 in sympathetic neurons was investigated in microinjection experiments. Injection of expression vectors for c-Jun and ATF-3 decreased the survival of sympathetic neurons in the presence of NGF, as measured at 72 hours after injection. In contrast, overexpression of ATF-3 or the ATF-3 basic/leucine zipper domain in the absence of NGF increased the survival of sympathetic neurons. In addition, when ATF-3 expression was knocked down, using a pSUPER ATF-3 RNAi expression vector, the amount of cell death observed after 48 hours of NGF deprivation in neurons was increased.
Conditional Value at Risk Sensitive Optimization via Subgradient Methods

We study a first-order primal-dual subgradient method to optimize risk-constrained risk-penalized optimization problems, where risk is modeled via the popular conditional value at risk (CVaR) measure. The algorithm processes independent and identically distributed samples from the underlying uncertainty in an online fashion, and produces an $\eta/\sqrt{K}$-approximately feasible and $\eta/\sqrt{K}$-approximately optimal point within $K$ iterations with constant step-size, where $\eta$ increases with tunable risk-parameters of CVaR. We find optimized step sizes using our bounds and precisely characterize the computational cost of risk aversion as revealed by the growth in $\eta$. Our proposed algorithm makes a simple modification to a typical primal-dual stochastic subgradient algorithm. With this mild change, our analysis surprisingly obviates the need for a priori bounds or complex adaptive bounding schemes for dual variables assumed in many prior works. We also draw interesting parallels in sample complexity with that for chance-constrained programs derived in the literature with a very different solution architecture.
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment, we enforce pixels with similar affinity relations in the input domains to be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach is compared with the state-of-the-art machine learning and deep learning algorithms. Experiments conducted on four real and representative datasets show the effectiveness of our methodology.
Background fields and self-dual Skyrmions

We show that a suitable background field can bring a non-BPS topological soliton into its BPS, self-dual, counterpart. As an example we consider Skyrmions in the minimal Skyrme model. We prove the triviality of the corresponding moduli space. This means that the resulting self-dual Skyrmion does statically interact with the background field. We also show that the originally self-dual Skyrmions (e.g. solutions of the BPS Skyrme model) can preserve the self-duality after a coupling with a background field. In this case, BPS Skyrmions can be effortless moved with respect to the background.
A Human Evaluation of AMR-to-English Generation Systems

Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation. In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems. We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons. We also analyze common errors made by these systems.
New approach for designing cVEP BCI stimuli based on superposition of edge responses

The purpose of this study is to develop a new methodology for designing stimulus sequences for Brain Computer Interfaces that utilize code modulated Visually Evoked Potentials (cVEP BCIs), based on experimental results regarding the behavior and the properties of the actual EEG responses of the visual system to binary-coded visual stimuli, such that training time is reduced and possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 sps, in response to visual stimuli presented on a computer monitor with 60 Hz refresh rate. EEG responses of the visual system to black-to-white and white-to-black transitions of a target area on the monitor are recorded for 500 ms, for 160 trials, and signal-averaged to obtain the onset (positive edge) and offset (negative edge) responses, respectively. It is found that both edge responses are delayed by 50 ms and wane completely within 350 ms. These edge responses are then used to generate (predict) the EEG responses to arbitrary binary stimulus sequences using the superposition principle. It is found that the generated and the measured EEG responses to certain (16) simple short sequences (16.67–350 ms) are highly correlated. These ‘optimal short patterns’ are then randomly combined to design the long (120 bit, 2 sec) ‘Superposition Optimized Pulse (SOP)’ sequences, and their EEG response templates are obtained by superposition of the edge responses. A SOP sequence-based Visual Speller BCI application yielded higher accuracy (95.9%) and Information Transfer Rate (ITR) (57.2 bpm), compared to when superposition principle is applied to conventional m-sequences and randomly generated sequences. Training for the BCI application involves only the acquisition of the edge responses and takes less than 4 min. This is the first study in which the EEG templates for cVEP BCI sequences are obtained by the superposition of edge responses.
Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge

The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.
Unified Dynamic Convolutional Network for Super-Resolution With Variational Degradations

Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on Single Image Super-Resolution (SISR). Despite considering only a single degradation, recent studies also include multiple degrading effects to better reflect real-world cases. However, most of the works assume a fixed combination of degrading effects, or even train an individual network for different combinations. Instead, a more practical approach is to train a single network for wide-ranging and variational degradations. To fulfill this requirement, this paper proposes a unified network to accommodate the variations from inter-image (cross-image variations) and intra-image (spatial variations). Different from the existing works, we incorporate dynamic convolution which is a far more flexible alternative to handle different variations. In SISR with non-blind setting, our Unified Dynamic Convolutional Network for Variational Degradations (UDVD) is evaluated on both synthetic and real images with an extensive set of variations. The qualitative results demonstrate the effectiveness of UDVD over various existing works. Extensive experiments show that our UDVD achieves favorable or comparable performance on both synthetic and real images.