Miracle-dz/UniFAR-FTUs huggingface dataset:
Stretch-VST: Getting Flexible With Visual Stories

In visual storytelling, a short story is generated based on a given image sequence. Despite years of work, most visual storytelling models remain limited in terms of the generated stories’ fixed length: most models produce stories with exactly five sentences because five-sentence stories dominate the training data. The fix-length stories carry limited details and provide ambiguous textual information to the readers. Therefore, we propose to “stretch” the stories, which create the potential to present in-depth visual details. This paper presents Stretch-VST, a visual storytelling framework that enables the generation of prolonged stories by adding appropriate knowledge, which is selected by the proposed scoring function. We propose a length-controlled Transformer to generate long stories. This model introduces novel positional encoding methods to maintain story quality with lengthy inputs. Experiments confirm that long stories are generated without deteriorating the quality. The human evaluation further shows that Stretch-VST can provide better focus and detail when stories are prolonged compared to state of the art. We create a webpage to demonstrate our prolonged capability.
Salix purpurea and Eleocharis obtusa Rhizospheres Harbor a Diverse Rhizospheric Bacterial Community Characterized by Hydrocarbons Degradation Potentials and Plant Growth-Promoting Properties

Phytoremediation, a method of phytomanagement using the plant holobiont to clean up polluted soils, is particularly effective for degrading organic pollutants. However, the respective contributions of host plants and their associated microbiota within the holobiont to the efficiency of phytoremediation is poorly understood. The identification of plant-associated bacteria capable of efficiently utilizing these compounds as a carbon source while stimulating plant-growth is a keystone for phytomanagement engineering. In this study, we sampled the rhizosphere and the surrounding bulk soil of Salix purpurea and Eleocharis obusta from the site of a former petrochemical plant in Varennes, QC, Canada. Our objectives were to: (i) isolate and identify indigenous bacteria inhabiting these biotopes; (ii) assess the ability of isolated bacteria to utilize alkanes and polycyclic aromatic hydrocarbons (PAHS) as the sole carbon source, and (iii) determine the plant growth-promoting (PGP) potential of the isolates using five key traits. A total of 438 morphologically different bacterial isolates were obtained, purified, preserved and identified through PCR and 16S rRNA gene sequencing. Identified isolates represent 62 genera. Approximately, 32% of bacterial isolates were able to utilize all five different hydrocarbons compounds. Additionally, 5% of tested isolates belonging to genera Pseudomonas, Acinetobacter, Serratia, Klebsiella, Microbacterium, Bacillus and Stenotrophomonas possessed all five of the tested PGP functional traits. This culture collection of diverse, petroleum-hydrocarbon degrading bacteria, with multiple PGP traits, represents a valuable resource for future use in environmental bio- and phyto-technology applications.
Statistial Methods for Native Language Identiﬁcation

Native Language Identiﬁcation is the task of predicting the native language of a speaker, given audio of their speech. We employ traditional (e.g., SVMs) and neural (e.g., Feed Forward Nets, RNNs, GRUs) methods of Machine Learning on speech data procured from the TOEFL exam administered by the ETS. Through utilizing both i-vector representations of speech audio, as well as textual speech transcripts, we obtain results that rival cutting-edge accuracy with our best performing one, a linear SVM at 83% on a dataset using both speech transcripts and I-vectors.
EEG Thinking1 Datasets: Think-Count-Recall (TCR) and Read-Write-Type (RWT)

EEG-based Brain-Computer Interfaces (BCI) have been widely used in clinical 1 and non-clinical research. In this paper, we present a framework to collect a 2 large amount of EEG data with easy-to-use experiment setup, using non-invasive, 3 wireless, and affordable hardware. Interpretable feedback generated by benchmark 4 machine learning algorithms have been provided to the researchers and end-users. 5 Two existing datasets are used as case studies for the framework: Read-Write-Type 6 (RWT) and Think-Count-Recall (TCR). The goal is to inspire new machine learning 7 approaches for decoding behavior from large-scale EEG data. The framework 8 of experimental design, data collection, data analysis, feedback generation, and 9 community building could pave the way towards a future when everyone can easily 10 use BCI systems every day, similar to smartphones nowadays. 11
Adversarially Trained Neural Policies in the Fourier Domain

Reinforcement learning policies based on deep neural networks are vulnerable to imperceptible adversarial perturbations to their inputs, in much the same way as neural network image classiﬁers. Recent work has proposed several methods for adversarial training for deep reinforcement learning agents to improve robustness to adversarial perturbations. In this paper, we study the effects of adversarial training on the neural policy learned by the agent. In particular, we compare the Fourier spectrum of minimal perturbations computed for both adversarially trained and vanilla trained neural policies. Via experiments in the OpenAI Atari environments we show that minimal perturbations computed for adversarially trained policies are more focused on lower frequencies in the Fourier domain, indicating a higher sensitivity of these policies to low frequency perturbations. We believe our results can be an initial step towards understanding the relationship between adversarial training and different notions of robustness for neural policies.
Poverty And Vulnerability To Poverty: Conceptual Overview, Measurements And Causes

Poverty and vulnerability to poverty are two concepts measuring the same economic reality at different time horizons. While the first is measured ex-post, the second relates to an ex-ante measurement. We first attempt to identify the different approaches of poverty analysis. From the work of Ruggeri Laderchi et al. (2003) on the multidimensional approach to poverty, Carr (2008) traces an intermediate path between the absolutes of universalizing approaches and the tendencies to atomize basic approaches to the understanding of poverty / poverties. He thus assigns a role to local knowledge, external expertise and the practitioner's development perspective to build the basic critical approach to development. We fund that economic literature measures poverty according to subjective and objective point of view. Three subjective approaches are identified namely the relative deprivation approach, the consensus approach and the capability approach which understands poverty and well-being in terms of human capacities or opportunities to achieve positive freedoms. The objective measure of poverty that overcomes the limitations of all the previous measurements (counting ratio and intensity of poverty) is that based on the pioneering work of Sen (1976) and developed by Foster, Greer, and Thorbecke (1984). Vulnerability to poverty is the likelihood that a non-poor (or poor) household will fall (or will remain) in poverty in the near future. It is mainly the presence of risk in the daily life of these households and their low capacity to cope with these risks and the shocks they could generate that explain this state of vulnerability. The economic literature identifies four distinct approaches to analyzing household vulnerability to poverty namely the income or consumption approach, the asset approach, the livelihood approach and the capability approach. We identify three main instruments to assess vulnerability namely the vulnerability as expected poverty (VEP), the vulnerability as low expected utility (VEU) and the vulnerability as uncovered exposure to risks (VER). The message here is that identifying those who are poor now is an inadequate indicator of those who should be poor in the future. Households change their poverty status from one year to an other due to risks they face in their daily lives. Thus, implementing sustainable poverty alleviation strategies requires taking into account the poor and the vulnerable to poverty.
PASS: An ImageNet replacement for self-supervised pretraining without humans

Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical shortcomings, such as containing personal information taken without consent, unclear license usage, biases, and, in some cases, even problematic image content. On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining. We thus propose an unlabelled dataset PASS: Pictures without humAns for Self-Supervision. PASS only contains images with CC-BY license and complete attribution metadata, addressing the copyright issue. Most importantly, it contains no images of people at all, and also avoids other types of images that are problematic for data protection or ethics. We show that PASS can be used for pretraining with methods such as MoCo-v2, SwAV and DINO. In the transfer learning setting, it yields similar downstream performances to ImageNet pretraining even on tasks that involve humans, such as human pose estimation. PASS does not make existing datasets obsolete, as for instance it is insufficient for benchmarking. However, it shows that model pretraining is often possible while using safer data, and it also provides the basis for a more robust evaluation of pretraining methods.
TRANSCRIPTIONAL REGULATION OF SOX5 AND WWOX BY FANCD2

Fanconi anemia (FA) is a rare genetic disease characterized by an increased risk for bone marrow failure, leukemia, and premature cancers (Alter et al., 2018). The FA pathway is involved in the repair of DNA damage such as stalled replication forks and DNA interstrand crosslinks (ICL) (Feng et al., 2019; Schlacher et al., 2012). It has been previously seen that under conditions of replication stress, FANCD2, a key protein in the FA pathway, binds to and traverses large actively transcribed genes (Okamoto et al., 2018). Using U2OS 3xFLAG cells we seek to answer the question of why FANCD2 is binding to these genes and if by doing so is it acting as a regulator of transcription? To answer this question, we used a candidate gene approach and chose WWOX and SOX5 and observed if under conditions of aphidicolin (APH), there were changes in their protein and transcript levels. We also observed if the absence of FANCD2 would affect their protein and transcript levels. In this study, it was found that under conditions of replication stress there was little change in both the protein and transcript levels of SOX5 and WWOX. Interestingly, the analysis of an RNA sequencing experiment brought to light the potential for FANCD2 to be involved in neuronal development as it was found that there were significant differences in normalized read counts in a subset of neuronal genes in the absence of FANCD2.
Cairo - a Turing-complete STARK-friendly CPU architecture

Proof systems allow one party to prove to another party that a certain statement is true. Most existing practical proof systems require that the statement will be represented in terms of polynomial equations over a finite field. This makes the process of representing a statement that one wishes to prove or verify rather complicated, as this process requires a new set of equations for each statement. Various approaches to deal with this problem have been proposed, see for example [1]. We present Cairo, a practically-efficient Turing-complete STARK-friendly CPU architecture. We describe a single set of polynomial equations for the statement that the execution of a program on this architecture is valid. Given a statement one wishes to prove, Cairo allows writing a program that describes that statement, instead of writing a set of polynomial equations.
Dimensioning of a traction motor for the FST using a Vanadium-Cobalt-Iron alloy

Hybrid and fully electric vehicles have pursued a recent growth and, along with this, new electric machines technologies are being developed focusing on increasing efficiency and specific power through the application of optimisation techniques and new materials. In this work, a high specific-power spoke-type Interior Permanent Magnet Synchronous Machine (IPMSM) for the competition’s Formula Student Técnico (FST) electric car has been optimised, demanding high-performance requirements with a large ratio of power/volume. Two different magnetic materials were tested for the motor’s magnetic core: the typical Silicon-Iron alloy (FeSi) and a Vanadium-Cobalt-Iron alloy (VCoFe) alloy. This project attempts to define in which conditions the VCoFe alloy, holding a remarkable high saturation point of 2.2-2.4 T, provides advantages in this application, over the FeSi. Multi-objective optimisation was developed to identify the best parameters and machine designs, using VCoFe and FeSi, regarding specific power and efficiency, while fulfilling the relevant applicable constraints. The optimisation was done using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm coupled with a hybrid analytical/finite element method to provide sufficiently accurate electromagnetic and thermal results within a feasible time. Results demonstrate VCoFe as a beneficial core material, providing higher specific power than FeSi. Outperforming FeSi in an estimated increase of up to 3% in efficiency for the same torque, or up to 70% torque increase for the same efficiency level. For the FST competition car, with a required 20 Nm of nominal torque, there is an efficiency increase of 1.7% and a reduction of 11% in core volume for the motor.
Toward robots that learn to summarize their actions in natural language: a set of tasks

: Robots should be able to report in natural language what they have done. They should provide concise summaries, respond to questions about them, and be able to learn from the natural language responses they receive to their summaries. We propose that developing the capabilities for robots to summarize their actions is a new and necessary challenge which should be taken up by the robotic learning community. We propose an initial framework for robot action summarization , presented as a set of tasks which can serve as a target for research and a measure of progress.
Applying a Recurrent Neural Network using Connectionist Temporal Classiﬁcation to Automatic Recognition of Lyrics in Singing

We apply techniques originally developed for Automatic Speech Recognition (ASR) towards the more challenging task of Automatic Recognition of Lyrics in Singing (ARLS). Our system is based on a deep bidirectional recurrent neural network architecture and the Connectionist Temporal Classiﬁcation objective function. Due to a lack of suitable song datasets for training data, we train the system on speech utterances transformed to more closely resemble singing. Testing the system on monophonic singing (i.e., isolated vo-cals) shows that it successfully under-stands words from the training set and can apply them during testing, but it requires a signiﬁcant amount of training data and time in order to achieve superior performance.
Efficient Multi-level Clustering for Very Large Wireless Sensor Networks with Gateways Support and Meta-heuristic Integration

Node clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper we present a novel hybrid multi-level clustering scheme that combines a traditional, appropriately modified, gradient-based clustering protocol with an evolutionary optimization method that is mainly based on the Gravitational Search Algorithm (GSA). The proposed scheme aims at improved performance over large in size networks, where classical schemes in most cases lead to non-efficient solutions. It first creates suitably balanced multi-hop clusters, in which the sensors energy gets larger as coming closer to the cluster head (CH). This scheme is then extended to generate a hierarchy of cluster-heads with the same characteristics; note that the energy savings increase with the number of levels in the hierarchy. In the last level of the proposed scheme a suitable protocol based on the GSA runs to associate sets of top-level cluster-heads to specific gateway nodes for the eventual relaying of data to the base station (BS). The fitness function was appropriately chosen considering both the distance from the cluster heads to the gateway nodes and the remaining energy of the gateway nodes, and it was further optimized in order to gain more accurate results for large instances. Extended experimental measurements demonstrate the efficiency and scalability of the presented approach over very large WSNs, as well as its superiority over other known clustering approaches of the literature, with the same objectives. General Terms Networks, Sensors, Algorithms
Probabilistic Abstract Intepretation: From Trace Semantics to DTMC’s via Linear Regression

In order to perform probabilistic program analysis we need to consider probabilistic languages or languages with a probabilistic semantics, as well as a corresponding framework for the analysis which is able to accomodate probabilistic properties and properties of probabilistic computations. To this purpose we investigate in this paper the relationship between three diﬀerent types of probabilisitic semantics for a core imperative language, namely Kozen’s Fixpoint Semantics (KFS), our Linear Operator Semantics (LOS) as well as probabilistic versions of Maximal Trace Semantics (MTS). We also discuss the relationship between Probabilistic Abstract Interpretation (PAI) and statistical or linear regression analysis. While classical Abstract Interpretation, based on Galois connection, allows only for worst-case analyses, the use of the Moore-Penrose pseudo inverse in PAI opens the possibility of exploiting statistical and noisy observations in order to analyse and identify various system properties.
Observation of the Evolution of Hide and Seek AI

The purpose of this project is to observe the evolution of two artificial agents, a ‘Seeker’ and a ‘Hider’, as they play a simplified version of the game Hide and Seek. These agents will improve through machine learning, and will only be given an understanding of the rules of the game and the ability to navigate through the grid-like space where the game shall be played; they will not be taught or given any strategies, and will be made to learn from a clean slate. Of particular interest is observing the particular playstyle of hider and seeker intelligences as new elements are introduced into the game, such as obstacles, doors, among other environmental influences. Through this observation, I hope to identify not only key strategies in the game of hide and seek, but to achieve a greater understanding of the evolution of machine learning AI searching and hiding patterns, which are relevant to several fields such as networking, artificial intelligence, and cyber security.
Innovations and Applications of Quantum Computing

Moore’s law will cease to be relevant once transistors are the size of electrons. Once this occurs, we will no longer be able to use silicon for technological advancement. However, breakthroughs will continue in the form of a sixth paradigm: a 3D molecular computing model such as quantum comput- ing. Some of the promising areas of quantum computing researched in this study are (i) algorithmic advancements - such as Shor’s, Grover’s, error correction, fault tolerance and other new algorithms; (ii) hardware - such as the quantum system architecture, circuit models, entanglement, superposition and no-cloning qubits; (iii) market segments - such as finance, healthcare, and chemistry; (iv) the supremacy established over classical computing; and (v) areas of applicability. Significant progress has been made in these areas in recent decades, and they have garnered immense interest, attention and the funding required to make good progress.
CNF Contents-17-5

Methods: We evaluated sensory characteristics of a local food-based enteral nutrition product consisting of color, odor, flavor, and texture using triangle test. The sensory evaluation involved 30 semi-trained sensory panelists. Physicochemical properties were also assessed through standardized laboratory analyses. The ingredients were arrowroot starch, rice flour, palm sugar, cork fish (Channa striata), tempeh powder, milk, and yellow pumpkins. The flavors were original, ginger and cinnamon. Panelists were asked to assess the color, odor, flavor and texture, and provide a score for each variable according to a 5-point Likert scale from strongly do not like to strongly like. The physicochemical properties, i.e. , the water and ash contents, water activity, antioxidant activity, heavy metal content, and viscosity, were analyzed.
LiveSchema: A Gateway to Relational Data Analysis and Transformation

One of the major barriers to the training of statistical models on knowledge representations is the diﬃculty that scientists have in ﬁnding the best input data to be used for addressing their prediction goal. In addition to this, a key challenge is to determine how to manipulate these relational data , which are often in the form of particular triples (i.e., subject , predicate , object ), to enable the learning process. This paper describes the LiveSchema initiative, namely a gateway that leverages the gold-mine of relational data collected by many existing ontology catalogs . By implementing a continuously updating aggregation facility, LiveSchema aims at providing a family of services that can be used to easily access, accurately analyze, transform and re-use data in a machine learning scenario.
Analyzing Vulnerabilities in the Chromium Browser

Web browsers are some of the most widely used software in the world. Security vulnerabilities in browsers open risks for billions of users around the world; quickly discovering these vulnerabilities and uncovering their source is not only a crucial, but also a time sensitive issue if developers are to effectively combat those who would exploit them. In this paper, we present a visualization tool for a high-level overview of bug statistics in the Chromium browser which may help developers to understand past causes of vulnerabilities, and so may aid in building secure software in the future. We also present our attempt to build a predictive model for commits to the Chromium repository with the ability to classify insecure code before it is pushed to the codebase. This attempt failed due to the very limited availability of data linking vulnerabilities to their occurrences in source code; for future studies to find more success, collection and maintenance of such a dataset is required.
Coherent and cat states of open and closed strings

The covariant quantization and light cone quantization formalisms are followed to construct the coherent states of both open and closed bosonic strings. We make a systematic and straightforward use of the original definition of coherent states of harmonic oscillators to establish the coherent and their corresponding cat states. We analyze the statistics of these states by explicitly calculating the Mandel parameter and obtained interesting results about the nature of distribution of the states. A tachyonic state with imaginary mass and positive norm is obtained.