Miracle-dz/UniFAR-FTUs huggingface dataset:
Avoided motifs: short amino acid strings missing from protein datasets

Abstract According to the amino acid composition of natural proteins, it could be expected that all possible sequences of three or four amino acids will occur at least once in large protein datasets purely by chance. However, in some species or cellular context, specific short amino acid motifs are missing due to unknown reasons. We describe these as Avoided Motifs, short amino acid combinations missing from biological sequences. Here we identify 209 human and 154 bacterial Avoided Motifs of length four amino acids, and discuss their possible functionality according to their presence in other species. Furthermore, we determine two Avoided Motifs of length three amino acids in human proteins specifically located in the cytoplasm, and two more in secreted proteins. Our results support the hypothesis that the characterization of Avoided Motifs in particular contexts can provide us with information about functional motifs, pointing to a new approach in the use of molecular sequences for the discovery of protein function.
Textual Approach for Designing Database Conceptual Models: A Focus Group

Different approaches to software development are based on at least three database models: conceptual, logical, and physical, requiring distinct abstraction levels to represent complementary concepts. The selection of a conceptual database modeling approach, among other things, depends on the problem domain, knowledge, and preferences of the developer. This paper presents a new domain-specific textual language devoted to the conceptual relational database modeling, i.e., entity-relationship modeling. Furthermore, we present a preliminary evaluation conducted to analyze our proposed DSL, comparing two grammar versions in a focus group research method.
On Explanation of Propositional Logic-based Argumentation System

: We present a characterization about argumentation and proof in logic. Indeed, we show that proof for a claim α from a set of premises Φ can be deemed as a structured form of an argument for that claim. Due to the expressivity of classical propositional logic (PL), this work considers that the knowledge-base is represented in PL, in which the semantics and proof systems for individual arguments are studied and utilized. We show that natural deduction (ND) can be used as a basis of proof for an argument and also for modeling counterarguments in the form of canonical undercut. We reveal that ND does not merely enable for the construction of arguments but also paves the way naturally for a human-understandable form of argumentative reasoning. Finally, we demonstrate that our approach gives the feasibility of developing explainable artiﬁcial intelligence systems that can offer human-friendly explanations to the users.
Smart Broker Agent Learning How to Reach Appropriate Goal by Making Appropriate Compromises

In this paper a new Smart Broker Learning Agent (SBLA) has been proposed, which trains to find the most acceptable solution to a given problem, according to the individual requirements and emotions of a particular user. For this purpose, a new structure of the agent has been proposed and reinforcement-learning algorithm has been used. When the scenarios and criteria under consideration are complex, and when mixed emotions arise, it may be necessary to compromise on certain criteria in order to achieve the goal. Then knowledge of the preferences and emotions of the particular user is needed. In these cases, the SBLA does not allow compromises that are unacceptable to this user. The structure and the way of acting of the agent have been considered. The knowledge that the SBLA must have and the process of its formation have been described. The scenarios for solving a specific task and the conducted experiments have been presented. Some contributions, arising from the use of the proposed agent’s architecture have been discussed, such as: the opportunity for the agent to explain decisions; to offer the most appropriate solution for each specific user; to avoid unacceptable compromises, to have empathy, and the greater approval of the offered solutions.
A Comparison of Few-shot Classification of Human Movement Trajectories

: In the active research area of human action recognition, a lot of different approaches to classify behavior have been proposed and evaluated. However, evaluations on movement recognition with a limited number of training examples, also known as Few-shot classiﬁcation, are rare. In many applications, the generation of labeled training data is expensive. Manual efforts can be reduced if algorithms are used which give reliable results on small datasets. In this paper, three recognition methods are compared on gesture and stick-throwing movements of different complexity performed individually without detailed instructions in experiments in which the number of the examples used for training is limited. Movements were recorded with marker-based motion capture systems. Three classiﬁcation algorithms, the Hidden Markov Model, Long Short-Term Memory network and k-Nearest Neighbor, are compared on their performance in recognition of these arm movements. The methods are evaluated regarding accuracy with limited training data, computation time and generalization to different subjects. The best results regarding training with a small number of examples and generalization are achieved with LSTM classiﬁcation. The shortest calculation times are observed with k-NN classiﬁcation, which shows also very good classiﬁcation accuracies on data of low complexity.
Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features

Today project managers estimate time and other project relevant key performance indicators by using project management tools e.g. milestone trend analysis. We believe that predicting the project’s progress with traditional methods will soon reach its limitations due to the increasing complexity in vehicle development. Machine learning methods provide one possible solution. The vision is to predict the progress of development projects in the early stages of the project. In order to make this vision come true, we need to define measurable input features for machine learning models. In this paper, we focus on representing an approach to identify parameters that exert influence on the progress of development projects.
Construct-Extract: An Effective Model for Building Bilingual Corpus to Improve English-Myanmar Machine Translation

When dealing with low resource languages such as Myanmar, using additional pseudo parallel data for training machine translation systems is often an effective approach. As a pseudo parallel corpus is generated by back-translating target monolingual texts into the source language, it potentially contains a lot of noise including translation errors and weakly paired sentences and is thus required cleaning. In this paper, we propose a noisy parallel-sentences filtering system called Construct-Extract based on cosine similarity and Siamese BERT-Networks based cross-lingual sentence embeddings. The proposed system filters out noisy sentences by extracting high score sentence pairs from the constructed pseudo parallel data to finally obtain better synthetic parallel data. As part of the proposed system, we also introduce an unsupervised Myanmar sub-word segmenter to improve the quality of current English-Myanmar translation models that are potential to be used as backward systems for back-translation and often suffer from Myanmar word segmentation errors. Experiments show that the proposed Myanmar word segmentation could help the backward system to construct more accurate back-translated pseudo parallel data and using our extracted pseudo parallel corpus led to improve the performance of English-Myanmar translation systems in the two directions.
Information-theoretic Cost of Decision-making in Joint Action

We investigate the information processing cost relative to utility, associated with joint action in dyadic decision-making. Our approach, built on the Relevant Information formalism, combines Shannon’s Information Theory and Markov Decision Processes for modelling dyadic interaction, where two agents with independent controllers move an object together with fully redundant control in a grid world. Results show that increasing collaboration relaxes the pressure on required information intake and vice versa, antagonistic behavior takes a higher toll on information bandwidth. In this trade-off the particular embodiment of the environment plays a key role, demonstrated in simulations with informationally parsimonious optimal controllers.
Junction Temperature Measurement in Optically-Controlled Power Mosfet

The temperature-dependent optical properties of silicon carbide (SiC), such as refractive index and reflectivity, have been used for a direct monitoring of the junction temperature of a power MOSFET. In particular, the optical response of a 4H-SiC MOSFET-integrated Fabry-Perot cavity to temperature changes has been investigated through parametric optical simulations at the wavelength of 450 nm. The reflected optical power exhibited oscillatory patterns caused by the multiple beam interference for which the MOSFET epilayer, between the gate-oxide and the doped 4H-SiC substrate, acts as a Fabry-Perot etalon. These results were used to calculate the refractive index change and, therefore, the optical phase shift of ∆φ= π/2 corresponding to a temperature variation that can be considered as a warning for the device “health”. In practical applications, the periodic monitoring of the optic spectrum at the interferometric structure output gives an essential information about the device operating temperature condition that, for high power operations, may lead to device damages or system failure.
Mixed Deep Reinforcement Learning-behavior Tree for Intelligent Agents Design

: Intelligent agent design has increasingly enjoyed the great advancements in real-world applications but most agents are also required to possess the capacities of learning and adapt to complicated environments. In this work, we investigate a general and extendable model of mixed behavior tree (MDRL-BT) upon the option framework where the hierarchical architecture simultaneously involves different deep reinforcement learning nodes and normal BT nodes. The emphasis of this improved model lies in the combination of neural network learning and restrictive behavior framework without conﬂicts. Moreover, the collaborative nature of two aspects can bring the beneﬁts of expected intelligence, scalable behaviors and ﬂexible strategies for agents. Afterwards, we enable the execution of the model and search for the general construction pattern by focusing on popular deep RL algorithms, PPO and SAC. Experimental performances in both Unity 2D and 3D environments demonstrate the feasibility and practicality of MDRL-BT by comparison with the-state-of-art models. Furthermore, we embed the curiosity mechanism into the MDRL-BT to facilitate the extensions.
Automatic Definition of MOISE Organizations for Adaptive Workflows

The enactment of dynamic workflows may take advantage of the multi-agent system paradigm. The approach presented in this paper allows exploiting a high-level BPMN process definition to generate an agent organisation that can enact the workflow using different strategies. These are implemented as organisational schemes representing alternative goal decomposition trees. The availability of several equivalent solutions enables the optimisation and adaptation features of the approach. The mapping of the initial workflow to organisations starts with the automatic generation of goals from the BPMN, and it exploits a metamodeling approach to generate MOISE organisation definition.
On-demand Robotic Fleet Routing in Capacitated Networks with Time-varying Transportation Demand

In large-scale automated mobility-on-demand systems, the fleet manager is able to assign routes to individual automated vehicles in a way that minimizes formation of congestion. We formalize the problem of on-demand fleet routing in capacitated networks with time-varying demand. We demonstrate the limits of application of the steady-state flows approach in systems with time-varying demand and formulate a linear program to compute congestion-free routes for the vehicles in capacitated networks under time-varying demand. We evaluate the proposed approach in the simulation of a simplified, but characteristic illustrative example. The experiment reveals that the proposed routing approach can route 42% more traffic in congestion-free regime than the steady-state flow approach through the same network.
A Unified Chaotic System with Various Coexisting Attractors

This article presents a unified four-dimensional autonomous chaotic system with various coexisting attractors. The dynamic behaviors of the system are determined by its special nonlinearities with multiple zeros. Two cases of nonlinearities with sine function of the system are discussed. The symmetrical coexisting attractors, asymmetrical coexisting attractors and infinitely many coexisting attractors in the system are numerically demonstrated. This shows that such a system has an ability to produce abundant coexisting attractors, depending on the number of equilibrium points determined by nonlinearities.
Towards an Ontology for Enterprise Level Information Security Policy Analysis

Securing the information and ICT assets in an enterprise is a vital as well as a challenging task because of the increase in cyber-attacks. Information Security policies are designed for an enterprise to prevent security breaches. An enterprise needs to adhere to and abide by the policies for its disciplined functioning. Analysis of the policies is necessary to find their applicability, conflict detection, revision and compliance checking for the enterprise. To analyze the policies, it is necessary to decompose them into its constituent parts. This decomposition is facilitated by ontologies. An in-depth analysis of the policy decomposition show that the published information security ontologies are grossly inadequate for any policy analysis application. In this paper we present an approach for development of an ontology specifically for information security policy analysis. The structure of the ontology and its implementation are presented and the importance of this ontology in information security policy analysis is established.
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better Performance

: Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the optimal action values by using a target that maximizes over uncertain action-value estimates. Although the overestimation bias of Q-learning is generally considered harmful, a recent study suggests that it could be either harmful or helpful depending on the reinforcement learning problem. In this paper, we propose a new Q-learning variant, called Variation-resistant Q-learning, to control and utilize estimation bias for better performance. Firstly, we present the tabular version of the algorithm and mathematically prove its convergence. Secondly, we combine the algorithm with function approximation. Finally, we present empirical results from three different experiments, in which we compared the performance of Variation-resistant Q-learning, Q-learning, and Double Q-learning. The empirical results show that Variation-resistant Q-learning can control and utilize estimation bias for better performance in the experimental tasks.
A Best-first Backward-chaining Search Strategy based on Learned Predicate Representations

Inference methods for first-order logic are widely used in knowledge base engines. These methods are powerful but slow in general. Neural networks make it possible to rapidly approximate the truth values of ground atoms. A hybrid neural-symbolic inference method is proposed in this paper. It is a best-first search strategy for backward chaining. The strategy is based on neural approximations of the truth values of literals. This method is precise and the results are explainable. It speeds up inference by reducing backtracking.
Towards Collaborative Cyber Threat Intelligence for Security Management

Managing access to resources is one of the security mechanisms used for protecting the organization’s assets from unauthorized usage, and thus potential data leaks. Thus, keeping access control policies up to date is a crucial task for any organization. However, the access control policy update process usually requires direct interaction of security specialists, which have knowledge and experience in counteracting abuse of privileges. Therefore, in this paper, we consider access control policies update using collaborative knowledge in the latest cyber activities. We describe the correlation between security policies and security reports using ontology for cybersecurity. Finally, we present a framework that enables access control policies update within the Cloud infrastructure offered by Amazon using Cyber Threat Intelligence.
A Method for Run-Time Prediction of On-Chip Thermal Conditions in Dynamically Reconfigurable SOPCs

Autonomous mobile systems nowadays deploy FPGA-based System on Programmable Chips (SoPCs) for supporting their dynamic multitask multimodal workloads. For such field-deployed systems, activation times, execution periods of tasks, and variations in environmental conditions are usually difficult to predict. 'ese dynamic variations result in a new challenge of dynamic thermal cycling stress on the SoPC die, which can result in transient and even permanent hardware faults in the computing system. 'is paper proposes the approach of run-time structural adaptation (RTSA) to mitigate dynamic thermal cycling stress on the SoPC dies. RTSA assumes the tasks to have multiple implementation variants, called Application Specific Processing (ASP) circuit variants, which vary in hardware resources, operating frequency, and power consumption. Dynamically reconfiguring appropriate ASP circuit variants of tasks allow systems to maintain their die temperature in the desired range while taking into account variations in power budget and modes of operation. 'is means the essence of RTSA is a decision-making mechanism which can select at run-time, a suitable system configuration (set of ASP circuit variants of active tasks), whenever needed, to meet the die temperature constraints. To do so, run-time die temperature prediction for potential system configurations using an estimation model is required. 'is paper presents a generic method to derive an analytical model for any SoPC that can estimate the die temperature in real time and thus support the decision-making mechanism. To develop this method, the thermal behavior of SoPC die under different task scenarios is studied and relation of die temperature to frequency, resource utilization, and power consumption is analyzed. An RTSA-enabled experimental platform is set up on Xilinx Zynq XC7Z020 SoPC for this purpose. Experimental results also demonstrate that the proposedmethod can be used to derive a model in run-time, thus enabling systems to self-derive and dynamically update the model in run-time.
Symbolic Translation of Time Series using Piecewise N-gram Similarity Voting

This paper studies a way to discriminate user behaviour from their viewed pages in a web-application. This technique is on similarity measure selection and time sequence splitting techniques. Using temporal splitting techniques, the proposed similarity measures greatly improve the result accuracy. We applied these ones on several datasets from the well known UCR Archive and our research is focused on a private dataset (ORIENTOI) and a public one called UCR-CBF. Some of the proposed temporal tricks appear to make similarity measures efficient with noises. They make them possible to deal with repeating terms, which is a drawback for most of the similarity measures. Thus the similarity measures are shown to reach the state of the art on UCR datasets. We also evaluated the proposed technique on our private (ORIENTOI) dataset with success. We finally discuss about the weakness of our method and the ways to improve it.
CyExec*: Automatic Generation of Randomized Cyber Range Scenarios

With the development of information technology, the need for information security education is increasing, and the effectiveness of cyber range exercises is attracting attention. The cyber range is a system to learn knowledge and skills by experiencing an incident scenario reproduced in a virtual environment. Many scenarios are required to train a security expert through various incident experiences. However, scenario development requires highly specialized expertise. Thus, in practice, only a limited number of scenarios are worn out around. Identical scenarios may decrease the educational effect since the other teams’ actions or write-ups on the internet will hint the students. We propose CyExec*, a cyber range system that automatically generates multiple scenarios based on DAG(Directed Acyclic Graph)-based scenario randomization. Multiple scenarios with the same learning objectives can enhance teaching effectiveness and prevent cheating. We developed the DAGbased scenario randomization technique on a Docker-based cyber range system called CyExec. By taking full advantage of Docker’s system/network configuration power, we can randomize complex scenarios across multiple networks. Comparison with the VM-based scenario generators, CyExec* outperforms, especially in storage usage. Further, CyExec∗ only consumes 1/3 memories, 1/4 CPU loads, and 1/10 storage usages. Thus, Cyexec∗ can operate approximately 3-times more complex scenarios than VM-based systems.
Empirical Evaluation on Utilizing CNN-features for Seismic Patch Classification

This paper empirically evaluates two kinds of features, which are extracted, respectively, with traditional statistical methods and convolutional neural networks (CNNs), in order to improve the performance of seismic patch image classification. In the latter case, feature vectors, named “CNN-features”, were extracted from one trained CNN model, and were then used to learn existing classifiers, such as support vector machines. In this case, to learn the CNN model, a technique of transfer learning using synthetic seismic patch data in the source domain, and real-world patch data in the target domain, was applied. The experimental results show that CNN-features lead to some improvements in the classification performance. By analyzing the data complexity measures, the CNN-features are found to have the strongest discriminant capabilities. Furthermore, the transfer learning technique alleviates the problems of long processing times and the lack of learning data.
Comparative Evaluation of Road Traffic Simulators based on Modeler's Specifications: An Application to Intermodal Mobility Behaviors

: Today, large cities and peri-urban areas experience problems in the mobility of their population. Faced with this problem, decision-makers must have reliable tools to help them to build and evaluate their policies of mobility. Computer simulations especially trafﬁc simulation tools are, therefore, the solution to better understand (study) the problem and test different resolution scenarios. Unfortunately, there are numerous simulation tools and the choice can be very difﬁcult for trafﬁc modelers. In this paper, we present, based on a generic method, a comparison of the most popular trafﬁc simulation tools in two steps: 1) a comparison part using a weighted system of evaluation criteria to automatically select the candidate simulators. 2) a deeper study of the candidate simulators according to a simulation scenario corresponding to the study case. Finally, this paper presents an application of this method for the selection of a simulator for the study of intermodal mobility behaviors where MATSIM and SUMO were studied in deeper.