Reviewerly/exHarmony huggingface dataset:
Specific Emitter Identification Based on Temporal Convolutional Network Sequence Processing

Specific emitter identification (SEI) is a method to identify radiation sources using radio frequency fingerprints (RFF) during signal transmission. Since transient features are difficult to capture accurately, we often analyze steady-state fingerprints extracted from signals. In this letter, temporal convolutional networks (TCN) are utilized in the SEI field. It performs as well as recurrent neural network (RNN) on traditional sequential tasks, and it retains the excellent classification capabilities of convolutional neural network (CNN), so it can be used to process time series like signals. We can better extract and analyze fingerprints from the signal using the TCN modules instead of the original convolutional layers. Experimental results show that TCNs provide better recognition performance than methods that exclusively use traditional neural networks to process feature sequences.
Flexible Carbon Neutralization Strategy: Customized Dynamic Server Management for Energy Efficiency Optimization

In recent years, large data centers have increased significantly as data usage has increased because of digital innovations. However, data centers are 24-hour operation facilities that consume large amounts of power, thus causing environmental problems. Recently, research has been conducted using deep learning methods from various perspectives to predict traffic and reduce power consumption in data centers and servers. However, the traffic processed by servers is highly variable, which is a factor that makes server management difficult. Thus, the traffic processed by servers is irregular, and more research is required on dynamic server management. This study proposes Customized Dynamic Server Management (CDSM) based on Long-Term Short Memory (LSTM), a neural network that is effective in predicting time-series data, to address the aforementioned problem. The proposed method can more effectively save the power used by servers, thereby managing servers more reliably and efficiently than before in the current operating environment. To validate the proposed model, we collected the traffic data at six Wikipedia data centers. We then analyzed the relationship between each traffic data using statistical analysis and conducted experiments. Furthermore, we calculated the server power consumption based on the actual power consumption according to the CPU usage of different servers provided by SPECpower, a benchmark for evaluating server power efficiency. Additionally, we calculated the amount of computation required for the program and deep learning model of the proposed. Based on this, practical results were derived considering the trade-off between the server’s power saving and computation performance. Based on these results, the performance of the method proposed in this study was validated in a statistically significant manner, which showed that server management can be performed reliably and efficiently.
Development of Testability Prediction Models Considering Complexity Diversity for C Programs

Testability prediction can help developers identify software components that require significant effort to ensure software quality, plan test activities, and recognize the need for refactoring to reduce the test effort. Previous studies have predicted code coverage as a measure of testability based on software metrics. However, these studies have primarily used object-oriented software with simple code structures. Industrial software developed using C is often more complex than the object-oriented software used in these studies. Models trained primarily on low-complexity training data may have insufficient training for the testability of high-complexity software. In this study, we developed a testability prediction model for C programs by considering the complexity diversity. We analyzed the impact of the complexity of the training/test data on the testability prediction model for C programs. The results showed that the model with the best performance achieves an MAE of 7.436 and an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.813. Moreover, the results demonstrated that as the complexity diversity of the training data decreased, MAE increased from 5.203 to 6.361, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> decreased from 0.809 to 0.725. Furthermore, the performance of the model trained with low complexity-diversity deteriorated as the complexity level of the test data increased, with MAE increasing from 3.498 to 6.631, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> decreasing from 0.841 to 0.687. Additionally, in the correlation analysis between the model performance and the difference in the complexity of the training and test data, a strong correlation was observed, with MAE of 0.898 and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of -0.848.
Ellipsoidal State Estimation for UAV Real Time Target Tracking

Due to the equivalent relation between UAV target tracking in practice and state estimation in academy, this paper concentrates on state estimation and its application in UAV target tracking. To relax the strict assumption on white noise for classical Kalman filter, we consider the more general bounded noise, being included in an ellipsoid. Then for better understanding our proposed ellipsoidal state estimation, three continuous processes are shown sequently, i.e. ellipsoidal approximation of arithmetic sum, ellipsoidal approximations of intersections between ellipsoid and strip, real time recursive form for generating a sequence of ellipsoids, including each state estimation at each time instant. To combine the theoretic result and engineering application, the detailed simulation example is given to prove the efficiency of our real time ellipsoidal state estimation algorithm.
Enabling Large Batch Size Training for DNN Models Beyond the Memory Limit While Maintaining Performance

Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory to accommodate both the model and a large data batch size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity because the batch size can only fit into the remaining memory after the model is uploaded. Moreover, the data item size is also an important factor because if each data item size is larger then the batch size that can fit into the remaining memory becomes smaller. This paper proposes a method called Micro-Batch Processing (MBP) to address this problem. This method helps deep learning models to train by providing a batch processing method that splits a batch into a size that can fit in the remaining memory and processes them sequentially. After processing the small batches individually, a loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using larger batch sizes that exceed the memory capacity of a system without increasing the memory size or using multiple devices (GPUs).
A 3.8-<i>μ</i>W 10-Keyword Noise-Robust Keyword Spotting Processor Using Symmetric Compressed Ternary-Weight Neural Networks

A ternary-weight neural network (TWN) inspired keyword spotting (KWS) processor is proposed to support complicated and variable application scenarios. To achieve high-precision recognition of 10 keywords under 5dB Clean wide range of background noises, a convolution neural network consists of 4 convolution layers and 4 fully connected layers, with modified sparsity-controllable Truncated Gaussian Approximation based ternary-weight training is used. End to end optimization composed of three techniques are utilized: 1) the stage-by-stage bit-width selection algorithm to optimize the hardware overhead of FFT; 2) the lossy compressed TWN with symmetric kernel training (SKT) and dedicated internal data reuse computation flow; 3) the error inter-compensation approximate addition tree to reduce the computation overhead with marginal accuracy loss. Fabricated in an industrial 22-nm CMOS process, the processor realizes up to 10 keywords real-time recognition under 11 background noise types, with the accuracy of 90.6%@clean and 85.4%@5dB. It consumes an average power of 3.8 μW at 250KHz and the normalized energy efficiency is 2.79× higher than state-of-the-art.
Distributed Multirobot Path Planning Based on MRDWA-MADDPG

Multirobot path planning in complex environments is a challenging research area. This article proposes a path planning method for multirobot systems based on distributed multiagent deep reinforcement learning. We propose a multirobot dynamic window approach (MRDWA) in which a central controller facilitates sensor information sharing among robots, enabling locally optimal path planning considering the behavior of other robots. We incorporate the output velocity information into the observation function to form an efficient and low-dimensional state representation. Additionally, we employ the multiagent deep deterministic policy gradient (MADDPG) reinforcement learning algorithm to directly map part of the observation information to motion commands for multiple robots, enabling effective obstacle avoidance strategies. An improved action module is developed by using velocity and angular velocity increments and an action selector to refine the output. Furthermore, we introduce a multirobot reward module utilizing heuristic functions to guide the robots to quickly and efficiently identify feasible paths. We also propose a multirobot dynamic constraint reward function to optimize the multirobot trajectories. The MRDWA–MADDPG algorithm is validated through simulations and real-world experiments, demonstrating its effectiveness in diverse complex multirobot path planning scenarios. Our method outperforms conventional algorithms in terms of success rate, arrival time, and overall decision making in complex scenarios. Moreover, our method has a faster computation speed and a shorter training time, produces smoother trajectories, and is easier to deploy on real robots than other learning-based methods.
Assessment of Virtual Reality Motion Sickness Severity Based on EEG via LSTM/BiLSTM

Virtual reality motion sickness (VRMS), which is mainly caused by visual-vestibular-somatic conflict, is a risk to the health and security of users who have fun in virtual reality (VR). To evaluate the VRMS in VR experience, this article introduces regression models based on recurrent neural network to predict the user’s VRMS severity from their EEG data. The EEG before, during, and after a VRMS exposure task is collected and divided into five rhythms as the inputs. The simulator sickness questionnaire (SSQ) is performed after each task to label the EEG data as the VRMS severity. In the proposed four regression models, four different approaches used to extract electrode-frequency features are combined with a three-layer long short-term memory (LSTM) or bi-directional long short-term memory (BiLSTM) network used to study the temporal features. Besides, the different numbers of hidden units in LSTM and BiLSTM network are compared to choose an optimal one. The results suggest the fusion of all electrodes and rhythms information using convolutional neural network before inputting the LSTM/BiLSTM network provides the best regression performance, in which the mean MSE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}$ </tex-math></inline-formula> -score are 53.6159 and 0.8683, respectively. The work introduced in this article provides a method to assess the performance of VR productions and is an objective and direct guideline to overcome VRMS and optimize VR systems.
Knowledge Graph Enabled Open-Domain Conversational Question Answering

With the advent of natural language enabled applications, there has been a growing appetite for conversational question answering systems. This demand is being largely satisfied with the help of such powerful language models as Open AI’s GPT models, Google’s BERT, and BigScience’s BLOOM. However, the astounding amount of training data and computing resources required to create such models is a huge challenge. Furthermore, for such systems, catering to multiple application domains typically requires the acquisition of even more training data. We discuss an alternative approach to the problem of open-domain conversational question answering by utilizing knowledge graphs to capture relevant information from a body of text in any domain. We achieve this by allowing the relations of the knowledge graphs to be drawn directly from the body of text being processed, rather than from a fixed ontology. By connecting this process with SPARQL queries generated from natural language questions, we demonstrate the foundations of an open-domain question answering system that requires no training and can switch domains flexibly and seamlessly.
An Orthographic Similarity Measure for Graph-Based Text Representations

Computing the orthographic similarity between words, sentences, paragraphs and texts has become a basic functionality of many text mining and flexible querying systems and the resulting similarity scores are often used to discover similar text documents. However, when dealing with a corpus that is inherently known for its orthographic inconsistencies and intricate interconnected nature on multiple levels (words, verses and full texts), as is the case with Byzantine book epigrams, this task becomes complex. In this paper, we propose a technique that tackles these two challenges by representing text in a graph and by computing a similarity score between multiple levels of the text, modelled as subgraphs, in a hierarchical manner. The similarity between all words is computed first, followed by the calculation of the similarity between all verses (resp. full texts) by using the formerly determined similarity scores between the words (resp. verses). The resulting similarities, on each level, allow for a deeper insight into the interconnected nature in (parts of) text collections, indicating how and to what degree the texts are related to each other.
Automatic Generation of Coherent Natural Language Texts

Text generation models are widespread nowadays, there are more and more use cases for them. The majority of the most prominent and efficient, in terms of output quality, ones require vast computational resources for training and inference, which becomes an obstacle for their practical usage. However, even for big and powerful architectures, the texts they generate often lack coherence, logical links can be broken, which makes texts less readable and less useful. This paper presents a method for increasing coherence of text generated by neural network models and we emphasize the importance of languages’ nature analysis prior to building new generation methods. We have analysed the existing decoding methods and have built the mechanism for maximizing coherence of the output sequences into some of them.
JaKtA: BDI Agent-Oriented Programming in Pure Kotlin

Multi-paradigm languages are becoming more and more popular, as they allow developers to choose the most suitable paradigm for each task. Most commonly, we observe the combination of object-oriented (OOP) and functional programming (FP), however, in principle, other paradigms could be hybridised. In this paper, we present JaKtA, an internal DSL adding support for the definition of belief-desire-intention (BDI) agents in Kotlin. We believe is a first step to investigate the blending of Agent-Oriented Programming (AOP) with other popular paradigms and we discuss the opportunity and value of doing so with an internal DSLs. Finally, through JaKtA, we show how this can already lead to compactly and expressively create BDI agents that smoothly interoperate with the host language, its libraries and tooling.
Behavioral QLTL

This paper introduces Behavioral QLTL, a "behavioral" variant of Linear Temporal Logic (ltl) with second-order quantifiers. Behavioral qltl is characterized by the fact that the functions that assign the truth value of the quantified propositions along the trace can only depend on the past. In other words, such functions must be "processes" [1]. This gives the logic a strategic flavor that we usually associate with planning. Indeed we show that temporally extended planning in nondeterministic domains and ltl synthesis are expressed in Behavioral qltl through formulas with a simple quantification alternation. While as this alternation increases, we get to forms of planning/synthesis in which contingent and conformant planning aspects get mixed. We study this logic from the computational point of view and compare it to the original qltl (with non-behavioral semantics) and simpler forms of behavioral semantics.
Interlingual Semantic Validation

The validation of machine translation systems has depended on metrics that compare both form and content of the translated text, where direct human participation was almost indispensable. Within the paradigm of “language pairs” and direct translation, whether rule or statistics-based, the process of evaluating MT systems rely on assessing grammatical correctness and similarity to reference translations. As a counterpart, interlingua-based systems pivot on an intermediate representation of the contents to be translated. In this article, we propose a model for validating the output of automatically translated text in a interlingua-based system, based on the automatization of the semantic validation of source, target and interlingual representation.
Value-Awareness Engineering: Towards Learning Context-Based Value Taxonomies

The emerging field of value awareness engineering claims that software agents and systems should be value-aware, i.e. they should be able to explicitly reason about the value-alignment of their actions. Existing approaches characterize values in various ways, from which we defend the recently introduced context-based value taxonomies, which allow a very rich context-dependent value representation while providing alignment explainability. We propose further work in the area, that would strive mainly on the feasibility for a system to learn value taxonomies from streams of value-aware preferences (using CEP rule learning), so the result is human-readable and representative of a complex value system.
Using a BDI Agent to Represent a Human on the Factory Floor of the ARIAC 2023 Industrial Automation Competition

The "Agile Robotics for Industrial Automation Competition" (ARIAC) is an international robotic competition carried out in a simulated factory floor using ROS 2 (Robot Operating System)/Gazebo. Competitors control one gantry robot, four AGVs, and many other elements/devices, overcoming a range of agility challenges in this simulated environment, and are provided with a scoring system to evaluate their performance during the tasks. This paper describes one of the agility challenges in ARIAC 2023, which pertains to a simulated human operator on the factory floor. In undertaking manufacturing tasks, competitors must avoid close proximity between the gantry robot and the human not to get penalized. The human operator is implemented as a Belief-Desire-Intention (BDI) agent in Jason. It is provided with a range of different potential types of behaviour in what concerns with how such human reacts when in proximity to the gantry robot. Three different personalities are presented, ranging from a minimally intrusive up to a very intrusive one. A preliminary analysis was conducted to evaluate the impact of using the developed Jason agent in the ARIAC 2023 competition.
Lorenzen-Style Strategies as Proof-Search Strategies

Dialogical logic, originated in the work of Lorenzen and his student Lorenz, is an approach to logic in which the validity of a certain formula is defined as the existence of a winning strategy for a particular kind of turn-based two-players games. This paper studies the relationship between winning strategies for Lorenzen-style dialogical games and sequent calculus derivations. We define three different classes of dialogical logic games for the implicational fragment of intuitionistic logic, showing that winning strategies for such games naturally correspond to classes of derivations defined by uniformly restraining the rules of the sequent calculus.
Optimal Rescue Sequences in Disastrous Incidents

With embedded technology becoming able to pursue autonomous decision-making in complex environments, reliable multi-agent robotic systems drawing optimal decisions in autonomous search and rescue (SAR) missions, without human involvement, become feasible. The objective of this study is to design a control algorithm and provide simulation for an autonomous robotic system in the event of a hazardous incident where the environment is highly stochastic and supply injured people/patients with help. Therefore, time management plays the key role in the number of patients saved as their health condition follows a probabilistic degrading figure. We have modeled robot and patients utilizing an interactive continuous Markov chain, followed by a Genetic-based algorithm for the robot to plan a path with the close to the theoretically possible maximum expected number of the patient rescued.
Decidability Borders of Verification of Communicating Datalog Agents

We present our recent results in charting the decidability boundary of formal verification of Communicating Datalog Agents, a multi-agent system grounded in logic programming.
A Step Forward to Widespread BDI AOP: JaKtA

The Agent-Oriented Programming (AOP) is a paradigm introduced thirty years ago [6] to model autonomous entities with a mental state: agents. Agents are a form of abstraction used to model complex systems: they are designed to perform tasks and interact with each other.
Agent Behavior Composition in Stochastic Settings

Behavior composition problem is particularly relevant for multi-agent systems and aims at building a complex target behavior using several agent behaviors. In this work, we develop a framework that models the agent behaviors in stochastic settings both when the target is represented as a Finite State Machine and when it is represented as an ltl $$_f$$ formula.
Capacity ATL: Reasoning About Agent Profiles and Applications to Cybersecurity

Cybersecurity is a context in which at least two agents, namely a defender and an attacker, interact to achieve conflicting objectives. As such, its analysis with game theory is natural. Most game theoretic approaches for cybersecurity rely on analytical games described by a reward function depending on agent actions, and the goal is often to find equilibriums (e.g, Nash equilibrium). However, these techniques imply a new analysis for each particular system or network. Contrarily, defining Multi-Agent System (MAS) formalisms adapted to describe multi-step attacks can help generically design defense systems. Moreover, model checking defender strategic abilities in the MAS offers guarantees on active cyber defenses leveraged by the security team, including honeypots (i.e, deception mechanisms) and Moving Target Defenses (i.e, system reconfiguration). The existing formalisms do not capture all the aspects of active defenses, so we developed Capacity Alternating-time Temporal Logic to reason about strategic abilities under imperfect information of the agents' capacities. During my thesis, we plan to explore further the use of MAS verification for active cybersecurity.