Reviewerly/exHarmony huggingface dataset:
A model-based simulator for smart homes: Enabling reproducibility and standardization

Scientific contributions in the area of smart environments cover different tasks of ambient intelligence including action and activity recognition, anomaly detection, and automated enactment. Algorithms solving these tasks need to be validated against sensor logs of smart environments. In order to acquire these datasets, expensive facilities are needed, containing sensors, actuators and an acquisition infrastructure. Even though several freely accessible datasets are available, each of them features a very specific set of sensors, which can limit the introduction of novel approaches that could benefit of particular types of sensors and deployment layouts. Additionally, acquiring a dataset requires a considerable human effort for labeling purposes, thus further limiting the creation of new and general ones. In this paper, we propose a model-based simulator capable to generate synthetic datasets that emulate the characteristics of the vast majority of real datasets while granting trustworthy evaluation results. The datasets are generated using the eXtensible Event Stream – XES international standard commonly used for representing event logs. Finally, the datasets produced by the simulator are validated against two real scenario’s logs from the literature.
Memory Usage Prediction of HPC Workloads Using Feature Engineering and Machine Learning

In High Performance Computing (HPC) systems, numerous applications of varying scale and domain are scheduled to run concurrently, and share the available CPU and memory capacities among themselves. Applications whose run-time memory usage are not known a priori, are commonly allocated with significantly higher amounts of memory than actually needed, which leads to poor resource utilization and performance degradation of the overall system. In this paper, we disseminate our experience of performing user analysis and prediction over a large-scale resource utilization dataset to tightly estimate the memory requirements of a wide variety of applications in the Titan supercomputer system. By coupling our engineered features with random forest and XGBoost supervised machine learning techniques, our models respectively predict the correct class of memory usage in 89% and 90% of the validation data. Furthermore, more than 98% of users have 95% or better average prediction accuracy within one class tolerance range of the actual memory usage.
Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation

The traditional on-site operation of power stations includes inspection and maintenance. However, it heavily relies on experience for maintenance. Most on-site operation and maintenance data are text records. On the one hand, the data processing is tedious for experience to affect the safe on-site operation. On the other hand, we usually cannot give full consideration to the value of maintenance experience, so that the corresponding efficiency is very low. Therefore, this paper proposes a wearable device based remote and intelligent patrol inspection system that uses the cloud video transmission mode of both public and private clouds to realize the video connection between the power stations and the remote diagnosis center and uses the wearable devices for real experience. In this way, the authors can simulate real operation guidance and safety supervision, etc. so as to realize the remote management patrol operations, improve the fault detection efficiency, and improve equipment reliability.
A BRDL-Based Framework for Motivators and Emotions

Motivation and emotion are essential for finalising human reasoning and behaviour toward the accomplishment of life objectives. In this paper we extend our modelling and analysis framework, based on the Behaviour and Reasoning Description Language (BRDL), to include motivation and emotion. We use labelled transition systems to model both external environment and internal human physiology. Their composition with the BRDL model of human cognition supports the description of the way motivation drives need satisfaction and generates emotional responses. When the external environment is a computer/physical system, our approach provides a realistic model of human-computer interaction.
A Depth-Bounded Semantics for Becoming Informed

Abstract We present the three-valued modal logic DBBL-BI $$_{n}$$ to formally express information transmission among ordered agents bounded by limited access to repositories and where secrecy is admissible, viz. agents are not forced to transmit every data they possess to everyone else. The language, along with standard formulae for information holding at reachable states, includes formulae for agent and group information transmission, as well as assertion of trustworthy information. The description of information accessibility and transmission among agents is represented by formulae that hold in virtue of two distinct kinds of relations. We illustrate the application of the formal system with some intuitive examples.
Video Cryptosystem Using Chaotic Systems

In recent years, due to the rapid technological evolution of the communication networks (internet, social networks, Cloud, mobile communication, IoT, etc.), information exchanged have become the digital privacy of users. The use of the word privacy explains the most valuable data (or information) for people. In this context, video data represents the most exchanged information between users given the ease offered by Information and Communication Technologies (ICT) namely Internet networks and mobile technologies (3G, 4G and 5G). Moreover, the interception of data communicated by a malicious third party constitutes a serious threat to the privacy of users by having access to confidential data. Therefore, information security for video data becomes a major challenge in current and future communications systems. However, a video stream presents a very large amount of data compared to the image or others types of data, in addition to a number of images per second having be respected according to the used standard. Consequently, securing video transmission using the compression is a challenge to satisfy the constraints required in real-time transmission. This chapter highlights the advantage of chaos-based cryptography in the field of securing the video stream through the proposal of a chaotic cryptosystem for video encryption transmitted in real time. The presented proposal combines data compression and encryption in a single process while using a robust chaotic synchronization technique. Finally, a thorough security analysis is performed to validate the presented video cryptosystem.
Linear Cryptanalysis of Reduced-Round Simeck Using Super Rounds

The Simeck family of lightweight block ciphers was proposed by Yang et al. in 2015, which combines the design features of the NSA-designed block ciphers Simon and Speck. Previously, we proposed the use of linear cryptanalysis using super-rounds to increase the efficiency of implementing Matsui’s second algorithm and achieved good results on all variants of Simon. The improved linear attacks result from the observation that, after four rounds of encryption, one bit of the left half of the state of the cipher depends on only 17 key bits (19 key bits for the larger variants of the cipher). We were able to follow a similar approach, in all variants of Simeck, with an improvement in Simeck 32 and Simeck 48 by relaxing the previous constraint of a single active bit, using multiple active bits instead. In this paper we present improved linear attacks against all variants of Simeck: attacks on 19-rounds of Simeck 32/64, 28-rounds of Simeck 48/96, and 34-rounds of Simeck 64/128, often with the direct recovery of the full master key without repeating the attack over multiple rounds. We also verified the results of linear cryptanalysis on 8, 10, and 12 rounds for Simeck 32/64.
An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method

Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers who intend to inflict significant infections on a power system. Simply put, an effort to phony up the consumption data of mining devices results in the furtherance of messing up the optimal energy management within the power system. Hence, this paper introduces a new cyber-attack named miner-misuse for power systems equipped by transaction tech. To overwhelm this dispute, this article also addresses an online coefficient anomaly detection approach with reliance on the reinforcement learning (RL) concept for the power system. On account of not being sufficiently aware of the system, we fulfilled the Observable Markov Decision Process (OMDP) idea in the RL mechanism in order to barricade the miner attack. The proposed method would be enhanced in an optimal and punctual way if the setting parameters were properly established in the learning procedure. So to speak, a hybrid mechanism of the optimization approach and learning structure will not only guarantee catching in the best and most far-sighted solution but also become the high converging time. To this end, this paper proposes an Intelligent Priority Selection (IPS) algorithm merging with the suggested RL method to become more punctual and optimum in the way of detecting miner attacks. Additionally, to conjure up the proposed detection approach’s effectiveness, mathematical modeling of the energy consumption of the mining devices based on the hashing rate within BFTS is provided. The uncertain fluctuation related to the needed energy of miners makes energy management unpredictable and needs to be dealt with. Hence, the unscented transformation (UT) method can obtain a high chance of precisely modeling the uncertain parameters within the system. All in all, the F-score value and successful probability of attack inferred from results revealed that the proposed anomaly detection method has the ability to identify the miner attacks as real-time-short as possible compared to other approaches.
Identification and Correction of Grammatical Errors in Ukrainian Texts Based on Machine Learning Technology

A machine learning model for correcting errors in Ukrainian texts has been developed. It was established that the neural network has the ability to correct simple sentences written in Ukrainian; however, the development of a full-fledged system requires the use of spell-checking using dictionaries and the checking of rules, both simple and those based on the result of parsing dependencies or other features. In order to save computing resources, a pre-trained BERT (Bidirectional Encoder Representations from Transformer) type neural network was used. Such neural networks have half as many parameters as other pre-trained models and show satisfactory results in correcting grammatical and stylistic errors. Among the ready-made neural network models, the pre-trained neural network model mT5 (a multilingual variant of T5 or Text-to-Text Transfer Transformer) showed the best performance according to the BLEU (bilingual evaluation understudy) and METEOR (metric for evaluation of translation with explicit ordering) metrics.
Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.
A forensic method for investigating manipulated video recordings

Forensic image analysis can be used to resolve difficult incidents, such as the increasing number of child sexual abuse cases. Although the United Nations and European Union countries are trying to fight these crimes, criminals are developing new methods to circumvent the measures taken. Sharing videos that do not contain any criminal elements and inserting child sexual abuse videos between frames is a new method that has been seen recently. This article proposes a new method to analyse the videos prepared this way. The proposed method is particularly advantageous for detecting and analysing videos that have been manipulated and embedded in different content and can be applied as a new technique.
A Bayesian method for the automatic extraction of meaningful clinical sequences from large clinical databases

Clinical event recognition can have several applications, such as the examination of clinical stories that can be associated with negative hospital outcomes, or its use in clinical education to assist medical students recognize frequent clinical events.The purpose of this study is to develop a non-annotated Bayes-based algorithm to extract useful clinical events from medical data.We used subsets of MIMIC and CMS LDS datasets that include respiratory diagnoses to calculate two-itemset rules(one item in antecedent and one in consequent) which were used as building blocks for the construction of clinical event sequence order. The main condition for the event sequence is a sequential increase in the conditional probability of two-itemset rules having positive certainty factor, when they are studied together.A clinical event in our framework is defined to be a collection of several blocks of events that meet the aforementioned condition, when considered together. The correctness of our clinical sequences has been validated by two physicians.Our results showed that medical experts scored the rules of this algorithm better than random Apriori rules. A GUI was designed that can be used to examine the association of each clinical event with the clinical outcomes of the length of stay, inpatient mortality, and hospital charges.The present work provides a new approach on how we can improve extraction of clinical event sequences automatically, without user annotation. Our algorithm can successfully find, in several cases, blocks of rules which can tell correct clinical event stories.
Neural Logic Circuits: An evolutionary neural architecture that can learn and generalize

We introduce Neural Logic Circuits (NLC), an evolutionary, weightless, and learnable neural architecture loosely inspired by the neuroplasticity of the brain. This new paradigm achieves learning by evolution of its architecture through reorganization of augmenting synaptic connections and generation of artificial neurons functioning as logic gates. These neural units mimic biological nerve cells stimulated by binary input signals and emit excitatory or inhibitory pulses, thus executing the “all-or-none” character of their natural counterparts. Unlike Artificial Neural Networks (ANN), our model achieves generalization ability without intensive weight training and dedicates computational resources solely to building network architecture with optimal connectivity. We evaluated our model on well-known binary classification datasets using advanced performance metrics and compared results with modern and competitive machine learning algorithms. Extensive experimental data reveal remarkable superiority of our initial model, called NLCv1, on all test instances, achieving outstanding results for implementation of this new paradigm.
Counterfactual Scenario-relevant Knowledge-enriched Multi-modal Emotion Reasoning

Multi-modal video emotion reasoning (MERV) has recently attracted increasing attention due to its potential application in human-computer interaction. This task needs to not only recognize utterance-level emotions for conspicuous speakers, but also perceive the emotions of non-speakers in videos. Existing methods focus on modeling multi-modal multi-level contexts to capture emotion-relevant clues from the complex scenarios in videos. However, the context information is far from enough to infer the emotion labels of non-speakers due to the large gap between the scenario situation and emotions labels. Inspired by the observation that humans can find solutions to complex problems with the leverage of experience and knowledge, we propose SK-MER , a Scenario-relevant Knowledge-enhanced Multi-modal Emotion Reasoning framework for MERV task, which can leverage external knowledge to enhance the video scenario understanding and emotion reasoning. Specifically, we use scenario concepts extracted from videos to build knowledge subgraphs from external knowledge bases. The knowledge subgraphs are then utilized to obtain scenario-relevant knowledge representations through dynamic knowledge graph attention. Next, we incorporate the knowledge representations into context modeling to enhance emotion reasoning with external scenario-relevant knowledge. In addition, we propose a counterfactual knowledge representation learning approach to obtain more effective scenario-relevant knowledge representations. Extensive experimental results on MEmoR dataset show that the proposed SK-MER framework achieves new state-of-the-art results.
A visual measurement method of the three-dimensional coordinates of the center of forgings with asymmetric flash for the intelligent hammer die forging

Hammer die forging is a widely used mechanical manufacturing technology. Although it is easy to realize the automation of cutting and heating raw material, trimming the forging flash, and shaping the forgings in the processing of hammer die forging, it is difficult to achieve the automation of the forging process of hammer die forging, which is critical to the automated hammer die forging. It is because of the malposition phenomenon in which the forgings are often extracted out of the lower die cavity. Manual judgement of the phenomenon and manual clamping of the malpositioned forging are required to reset the forging. To achieve the automated forging process, it is necessary to identify the three-dimensional coordinates of the forging centre. However, the asymmetric flash of the forging reduces the localization accuracy. Therefore, a bidirectional correction and binocular vision localization (BCBVL) algorithm is proposed in this paper. However, identifying whether the malposition phenomenon occurs by the BCBLV method is time-consuming and can affect the execution of the subsequent processes such as the trimming and shaping processes. Hence, two methods based on the forging angle and the forging nonoverlapping area are proposed to obtain the fast judgement of the malpositioned forging. Experiments are conducted using a test bench composed of a binocular camera, an industrial manipulator, a 3D-printed die cavity, and a scraper forging of the coal mine scraper conveyor. The results indicate that the average localization error of BCBVL method is 7.31 mm in the left camera coordinate system and is 8.89 mm in the manipulator coordinate system. The two proposed fast judgement methods of the malpositioned forging take 0.32 s and 0.88 s, respectively. The study provides coordinates for the manipulator to grasp malpositioned forgings to achieve the automated forging process of hammer die forging.
Correcting Coherent Errors by Random Operation on Actual Quantum Hardware

Characterizing and mitigating errors in current noisy intermediate-scale devices is important to improve performance of next generations of quantum hardware. In order to investigate the importance of the different noise mechanisms affecting quantum computation, we perform full quantum process tomography of single qubits in a real quantum processor in which echo experiments are implemented. Besides error sources already included in standard models, the obtained results show the dominant role of coherent errors, which we practically correct by inserting random single-qubit unitaries in the quantum circuit, significantly increasing the circuit length over which quantum computations on actual quantum hardware produce reliable results.
SMLSOM: The shrinking maximum likelihood self-organizing map

Determining the number of clusters in a dataset is a fundamental issue in data clustering. Many methods have been proposed to solve the problem of selecting the number of clusters, considering it to be a problem with regard to model selection. This paper proposes an efficient algorithm that automatically selects a suitable number of clusters based on a probability distribution model framework. The algorithm includes the following two components. First, a generalization of Kohonen's self-organizing map (SOM) is introduced. In Kohonen's SOM, clusters are modeled as mean vectors. In the generalized SOM, each cluster is modeled as a probabilistic distribution and constructed by samples classified based on the likelihood. Second, the dynamically updating method of the SOM structure is introduced. In Kohonen's SOM, each cluster is tied to a node of a fixed two-dimensional lattice space and learned using neighborhood relations between nodes based on Euclidean distance. The extended SOM defines a graph with clusters as vertices and neighborhood relations as links and updates the graph structure by cutting weakly connected and unnecessary vertex deletions. The weakness of a link is measured using the Kullback–Leibler divergence, and the redundancy of a vertex is measured using the minimum description length. Those extensions make it efficient to determine the appropriate number of clusters. Compared with existing methods, the proposed method is computationally efficient and can accurately select the number of clusters.