Reviewerly/exHarmony huggingface dataset:
Comparing Multi-Agent Path Finding Algorithms in a Real Industrial Scenario

There is an increasing trend for automating warehouses and factories leveraging on teams of autonomous agents. The orchestration problem for a fleet of autonomous robotic cooperating agents has been tackled in the literature as Multi-Agent Path Finding (MAPF), for which several algorithms have been proposed. However, these algorithms have been only applied to synthetic randomly generated scenarios. The application in real scenarios demands scalability (being able to deal with realistic size warehouses) and efficiency (being able to quickly adapt to changes in the problems, e.g., new orders or change in their priorities). In this work we perform an analysis of the MAPF literature, we selected the most effective algorithms, we implemented them and we carried out an experimental analysis on a real scalable warehouse of a large distribution company to evaluate their applicability in such scenarios. The results show that a) no algorithm prevails on the others; b) there are difficult (realistic) cases out of the scope of all the algorithms.
Why Can Neural Networks Recognize Us by Our Finger Movements?

Neurobehavioral evidence suggests that human movement may be characterized by relatively stable individual differences (i.e. individual motor signatures or IMS). While most research has focused on the macroscopic level, all attempts to extract IMS have overlooked the fact that functionally relevant discontinuities are clearly visible when zooming into the microstructure of movements. These recurrent (2–3 Hz) speed breaks (sub-movements) reflect an intermittent motor control policy that might provide a far more robust way to identify IMSs. In this study, we show that individuals can be recognized from motion capture data using a neural network. In particular, we trained a classifier (a convolutional neural network) on a data set composed of time series recording the positions of index finger movements of 60 individuals; in tests, the neural network achieves an accuracy of 80%. We also investigated how different pre-processing techniques affect the accuracy in order to assess which motion features more strongly characterize each individual and, in particular, whether the presence of submovements in the data can improve the classifier’s performance.
Quantum Circuit Compilation for the Graph Coloring Problem

In this work we investigate the performance of greedy randomised search (GRS) techniques to the problem of compiling quantum circuits that solve instances of the Graph Coloring problem. Quantum computing uses quantum gates that manipulate multi-valued bits (qubits). A quantum circuit is composed of a number of qubits and a series of quantum gates that operate on those qubits, and whose execution realises a specific quantum algorithm. Current quantum computing technologies limit the qubit interaction distance allowing the execution of gates between adjacent qubits only. This has opened the way to the exploration of possible techniques aimed at guaranteeing nearest-neighbor (NN) compliance in any quantum circuit through the addition of a number of so-called swap gates between adjacent qubits. In addition, technological limitations (decoherence effect) impose that the overall duration (i.e., depth) of the quantum circuit realization be minimized. One core contribution of the paper is the application of an upgraded version of the greedy randomized search (GRS) technique originally introduced in the literature that synthesises NN-compliant quantum circuits realizations, starting from a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class tailored for the Graph Coloring problem. We propose a comparison between the presented method and the SABRE compiler, one of the best-performing compilation procedures present in Qiskit, an open-source SDK for quantum development, both from the CPU efficiency and from the solution quality standpoint.
DelBERTo: A Deep Lightweight Transformer for Sentiment Analysis

This article introduces DelBERTo, a resource-efficient Transformer architecture for Natural Language Processing (NLP). Transformers replace convolutions and recurrence with the self-attention mechanism and represent the state-of-the-art in NLP. However, self-attention’s complexity grows quadratically with the size of the input, which limits their applications. DelBERTo relies on adaptive input and on a deep yet lightweight Transformer architecture to reduce the number of learnable parameters, and relies on adaptive softmax to improve pre-training speed and memory footprint. We evaluate the proposed architecture in a sentiment analysis task and compare it against AlBERTo, a BERT model representing the state-of-the-art in sentiment analysis over Italian tweets. DelBERTo has only one-seventh of AlBERTo’s learnable parameters, is faster, and requires less memory. Despite this, our experiments show that DelBERTo is competitive with AlBERTo over the three SENTIPOLC sub-tasks proposed at EVALITA 2016: subjectivity classification, polarity classification, and irony detection.
Multi-agent Cooperative Argumentation in Arg2P

This work focuses on cooperative argumentation and conversation in multi-agent systems by introducing an extension of the Arg2P technology that enables parallelisation and distribution of the argumentation process. The computational model and the implementation underpinning the Arg2P technology are presented and discussed.
Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions Using Knowledge Graph Embedding

In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users and robots. With the existing HRI pipelines, users can teach the robots some concepts to increase their knowledge base. Unfortunately, the data coming from the users are usually not enough dense for building a consistent representation. Furthermore, the existing approaches are not able to incrementally build up their knowledge base, which is very important when robots have to deal with dynamic contexts. To this end, we propose an architecture to gather data from users and environments in long-runs of continual learning. We adopt Knowledge Graph Embedding techniques to generalize the acquired information with the goal of incrementally extending the robot’s inner representation of the environment. We evaluate the performance of the overall continual learning architecture by measuring the capabilities of the robot of learning entities and relations coming from unknown contexts through a series of incremental learning sessions.
CORRELATION BETWEEN THE STUDENTS VOCABULARY MASTERY AND THEIR ABILITY IN TRANSLATING ENGLISH TEXT INTO INDONESIAN AT MAS AL-BAROKAH KOLESE

The formulation of the problem in this study is whether there is a correlation between students' vocabulary mastery and their ability to translate English texts into Indonesian at MAS Al-Barokah Kolese students. The purpose of this study was to determine whether there was a correlation between students' vocabulary mastery and their ability to translate English texts into Indonesian at MAS Al-Barokah Kolese students. Data collection in this study was carried out using a research instrument in the form of a test to students to determine the correlation between students' vocabulary mastery and their ability to translate English text into Indonesian. This research is a quantitative research. The subjects of this study were 27 grade 10 students of MAS Al-Barokah Kolese. Data were analyzed using Statistical Product and Service Solution (SPSS) version 22.0. Based on the results of research and data analysis, it was concluded that there was a correlation between students' vocabulary mastery and their ability to translate English texts into Indonesian from grade 10 students at MAS Al-Barokah Kolese. This conclusion is based on the results of statistical analysis which found a significance level of 0.996 with an r table value of 0.381. The rxy value is greater than the r table value (0.996&gt; 0.381) and the significance value is less than (0.5) or in other words the Null hypothesis (HO) is rejected and the alternative hypothesis (H1) is accepted.
Using Layer-Wise Training for Road Semantic Segmentation in Autonomous Cars

A recently developed application of computer vision is pathfinding in self-driving cars. Semantic scene understanding and semantic segmentation, as subfields of computer vision, are widely used in autonomous driving. Semantic segmentation for pathfinding uses deep learning methods and various large sample datasets to train a proper model. Due to the importance of this task, accurate and robust models should be trained to perform properly in different lighting and weather conditions and in the presence of noisy input data. In this paper, we propose a novel learning method for semantic segmentation called layer-wise training and evaluate it on a light efficient structure called an efficient neural network (ENet). The results of the proposed learning method are compared with the classic learning approaches, including mIoU performance, network robustness to noise, and the possibility of reducing the size of the structure on two RGB image datasets on the road (CamVid) and off-road (Freiburg Forest) paths. Using this method partially eliminates the need for Transfer Learning. It also improves network performance when input is noisy.
Ensemble Alignment Subspace Adaptation Method for Cross-Scene Classification

An ensemble alignment subspace adaptation method is proposed in this letter for the cross-scene classification. It can settle the problem of both foreign objects in the same spectrum and different spectrums. The algorithm combines the idea of ensemble learning with the domain adaptive (DA) algorithm. Considering the sample imbalance problem of the original data (OD), the source data (SD) is obtained by multiple random sampling of OD according to certain rules and used as input. Then, geometric alignment and statistical alignment of SD and target data (TD) are performed to build a communal subspace, followed by the classification of TD. The classification labels are finally ensembled by counting the multiple classification results with retaining valid information. This technique can reduce the uncertainty and randomness of generating subspace projections. The experimental results on two real datasets show that the proposed algorithm has a terrific accuracy improvement compared with the traditional machine learning and DA methods.
A Multifaceted Approach to Oral Assessment Based on the Conformer Architecture

Automatic speaking assessment methods are essential for helping non-native learners to learn native pronunciation.The automated speaking assessment method consists of mispronunciation detection and pronunciation quality assessment.In the past, researchers have usually focused their research on only one specific aspect of the speaking assessment task.Research on multifaceted speaking tasks has been rare, and model building has often led to reduced performance due to the omission of local feature details.In this paper, we propose a multi-width band (MB) method and apply it to the Conformer model.This method can effectively increase the ability of the model to obtain local feature information at different scales.At the same time, we used a multi-task learning approach to train a multifaceted speaking assessment model based on GOP features.We conducted experiments on a self-built monosyllabic Mandarin mispronunciation detection dataset (PSC-MonoSyllable) and an English open-source pronunciation quality assessment dataset (SpeechOcean762), respectively.The experimental results show that the method's mispronunciation detection metrics in terms of phonemes, tones, and words on the PSC-MonoSyllable dataset (F1 scores) reached 70.18%, 80.06%, and 79.82%, respectively.The results of the method on the SpeechOcean 762 dataset for the pronunciation quality assessment task also showed a certain degree of improvement in all aspects of the phoneme-and grapheme-level correlation metrics compared with the baseline model.
Unsupervised Online Hashing with Multi-Bit Quantization

Online hashing methods aim to update hash functions with newly arriving data streams, which can process large-scale data online. To this end, most existing methods update projection functions online and adopt a single-bit quantization strategy that quantizes each projected component with one bit. However, single-bit quantization results in large information loss in the quantization process and thus cannot preserve the similarity information of original data well. In this paper, we propose a novel unsupervised online hashing method with multi-bit quantization towards solving this problem, which consists of online data sketching and online quantizer learning. By maintaining a small-size data sketch to preserve the streaming data information, an orthogonal transformation is learned from the data sketch to make the components of the streaming data independent. Then, an optimal quantizer is learned to adaptively quantize each component with multiple bits by modeling the data distribution. Therefore, our method can quantize each component with multiple bits rather than one bit to better preserve the data similarity online. The experiments show that our method can achieve better search accuracy than the relevant online methods for approximate nearest neighbor search.
An Overlap-Integral Approach to Quantum Optical Simulation

A technique for the simulation of multimode quantum optical interferometry and protocols in quantum communications is introduced. This technique is very efficient at simulating in the single-photon-counting regime. This works by treating the photons in the system as members of a multiphoton pulse and reducing the computation of measurable quantities to overlap integrals that may be precomputed and combined in a recursive algorithm. The simulation of a Mach-Zehnder interferometer and the Hong-Ou-Mandel effect are demonstrated using this technique. The results of these simulations perfectly agree with the theoretical results. Additionally, since the effects of the components in the system can be integrated into the quantum operators involved, the technique is agnostic to the components introduced into the system.
OMIRD: Orientated Modality Independent Region Descriptor for Optical-to-SAR Image Matching

Image matching between optical and SAR images remains challenging due to their different imaging principles. Despite recent developments in image matching methods for optical-to-SAR images, few have seen practical application success due to the significant geometric and radiometric differences between the two modalities. In this letter, we propose a new method called the Orientated Modality Independent Region Descriptor (OMIRD) to overcome these challenges. Our template-based approach effectively extracts consistent structural and textural features from both optical and SAR images. Additionally, we introduce a principal orientation selection method, using the Ratio of Exponentially Weighted Averages (ROEWA) and logarithm Histogram of Gradient (HOG) to identify the most meaningful feature orientations and further simplify the OMIRD descriptor. The experimental results indicate that the proposed OMIRD exhibits superior matching accuracy and robustness under various geometric distortions and non-linear grayscale variations.
An Enhance Relative Total Variation With BF Model for Edge-Preserving Image Smoothing

In image processing, edge-preserving image smoothing methods that maintain weak structure while smoothing multiscale textures with strong gradients remain challenging. In this paper, a new global optimization method named Enhance Relative Total Variation Embedded with Bilateral Filtering is proposed. The texture and structure are intuitively considered separately from the model generalization. First, the weak structures are separated from the over-penalized texture and structure items by embedding bilateral filtering. Then, the in-set shrinking edges/structures are gradually recovered by constructing a contrast stretching function. In comparison to current state-of-the-art methods, experimental results demonstrate that the method is effective in maintaining weak structures and suppressing multiscale textures.
Factors predicting human performance in error annotation for non-native speech corpus

With low inter-annotator agreement and large time consumption, phonetic annotation of non-native speech is complicated. However, having a large-scale non-native speech corpus with error annotation is important for second language phonology and computer-assisted pronunciation training research. To achieve large-scale annotation, we must clarify the causes of difficulty in error annotation. This study identified the factors that can predict annotation performance of non-native Mandarin speech by considering two perspectives.1) We used a decomposed method (that involved dividing a traditional error annotation task into two different subtasks, namely the error location task and error description task) to determine whether human performance, from the cognitive viewpoint, can be predicted using the decomposed annotation method (Experiment 1). 2) We determined whether the features of annotation task materials (e.g., error severity) can be employed to predict annotation performance (Experiment 2). The results of Experiment 1 revealed that the decomposed method leads to a shorter annotation time and a higher consistent hit rate than does the traditional method. The regressions of Experiment 2 revealed that the features of error severity and sentence length accounted for 96% of the hit rate and the features of sentence length and number of errors accounted for 95.5% of the annotation time. These results suggest that the performance of manual annotation can be predicted through both cognitive factors and material features.
Spectral pathways for effective delineation of mafic-ultramafic rocks by using PRISMA hyperspectral data

The Spectral Pathway Analysis (SPA) by using hyperspectral remote sensing data has a significant role in the demarcation of litho boundaries. The SPA is a 5 step continuous process 1) The reflectance spectral characterization through a controlled environment to the direct field sample along with satellite data acquisition 2) The generation of ICA and MNF 3) The endmember spectral extraction through the sub-pixel-based classification 4) The extraction of mineral characters from spectral absorption behavior 5) Mapping of litho units based on the integrative approaches by the data from the field, geology, and geochemical characters. In the present study, mafic and ultramafic complexes of the Bhavani shear zone which have a greater significance in the economically viable mineral aspects are taken into the consideration of lithological delineation by using PRISMA hyperspectral data. As part of SPA, ICA and MNF were generated using the PRISMA data and validated with geology, which shows a tremendous result in demarcating the lithological boundaries. Subpixel classification (PPI, SAM, LSU, MF, MTMF, TCIMF, and MITTCIMF) was performed and evaluated using ancillary data from field observation, XRF, and laboratory-based ASD spectra.
BP19: Tek Boyutlu İkili Modele Dayalı Doğru Bir Sesli Şiddet Tespit Modeli

Audio violence detection (AVD) is a hot-topic research area for sound forensics but there are limited AVD researches in the literature. Our primary objective is to contribute to sound forensics. Therefore, we collected a new audio dataset and proposed a binary pattern-based classification algorithm. &#x0D; Materials and method: In the first stage, a new AVD dataset was collected. This dataset contains 301 sounds with two classes and these classes are violence and nonviolence. We have used this dataset as a test-bed. A feature engineering model has been presented in this research. One-dimensional binary pattern (BP) has been considered to extract features. Moreover, we have applied tunable q-factor wavelet transform (TQWT) to generate features at both frequency and space domains. In the feature selection phase, we have applied to iterative neighborhood component analysis (INCA) and the selected features have been classified by deploying the optimized support vector machine (SVM) classifier. &#x0D; Results: Our model achieved 97.01% classification accuracy on the used dataset with 10-fold cross-validation.&#x0D; Conclusions: The calculated results clearly demonstrated that feature engineering is the success solution for violence detection using audios.&#x0D; .
LSTC: When label-specific features meet third-order label correlations

Multi-label learning (MLL) copes with applications such as text classification and image recognition where each instance is associated with multiple labels. Currently, some label-specific feature conversion approaches such as LIFT serve each individual label well, however do not take advantage of label correlation. Some label correlation exploitation approaches such as GLOCAL have independent model prediction and label correlation stages, thus limiting their learning ability. This paper introduces LSTC, a new algorithm that generates label-specific features and exploits third-order label correlation. On the one hand, for each label, we choose the same number of positive and negative representative instances inspired by density peak. These instances are used to generate a new data matrix with label-specific features. On the other hand, we train a paired output prediction network for each label based on these matrices of its own and that of two auxiliary labels. In this way, third-order label correlations are implicitly exploited. Specifically, the two auxiliary labels are the most similar and least similar, respectively, to avoid getting stuck in local optima. Experiments are conducted on seventeen benchmark datasets in comparison with ten popular algorithms. Results on eight measures demonstrate the superiority of LSTC on data from various domains except the text-domain. The source code is available at github.com/fansmale/lstc.
Dual-image reversible data hiding method using maximum embedding ability of each pixel

Compared to conventional reversible data hiding (RDH) methods, dual-image RDH methods have greater embedding capacity, better stego image quality and higher security. Among existing dual-image RDH methods, three methods of Lu et al., Yao et al. and Kim et al. are remarkable. These three methods have much higher embedding capacities than other methods, while still maintaining stego image quality. However, these three methods all have a limitation that in order to avoid underflow/overflow, they have to ignore pixels around two pixels with values 0 and 255. In many cases, the number of such pixels is not small and this limits the embedding capacity of all three methods above. In this paper, the maximum number of bits that can be embedded in each pixel is evaluated correctly. Embedding a number of bits less than or equal to the maximum number at each pixel will not cause the underflow/overflow. Therefore the proposed method can embed data bits in all pixels of an original image. As a result, the proposed method has not only a larger embedding capacity, but also better stego image quality than mentioned above methods. With an embedding ratio of 3.45 bits per a pixel, the proposed method has a greater embedding capacity than existing dual image reversible data hiding methods while maintaining good image quality.