Reviewerly/exHarmony huggingface dataset:
Personal Factors Of Career Orientations Of Humanitarian Students

Career is the most important component of a person’s life path. Building a productive career in a personal and social aspect requires an understanding of what factors are necessary for it and how they influence career choices. Professional training of specialists in the humanities is focused on the values of such personal characteristics as meaningfulness of life, positive self-attitude, and psychological well-being. The problem of the relationship of these personal characteristics with career orientations of humanitarian students is examined in this article. It was revealed that the most significant for the respondents is the orientation towards the stability of work place, followed by personality-centered orientations towards the integration of lifestyles, autonomy and service, then orientations that imply social activity and risk-taking: towards entrepreneurship, management and challenge. According to the results of the correlation analysis, the significance of personality-oriented and socially active career orientations is directly related to the meaningfulness of life. Specification of the severity of which career orientation is facilitated with a greater meaningfulness of life depends on the characteristics of self-attitude, and in the case of an orientation toward autonomy, and the psychological well-being of the individual. The expression of self-interest in self-attitude is of particular interest in light of the problem of the formation of personal characteristics that contribute to the productive career self-determination of humanitarian students. The level of its development is positively related to the severity of orientations towards professional competence and service in future specialists.
Reproducing a Comparison of Hedged and Non-hedged NLG Texts

This paper describes an attempt to reproduce an earlier experiment, previously conducted by the author, that compares hedged and non-hedged NLG texts as part of the ReproGen shared challenge. This reproduction effort was only able to partially replicate results from the original study. The analyisis from this reproduction effort suggests that whilst it is possible to replicate the procedural aspects of a previous study, replicating the results can prove more challenging as differences in participant type can have a potential impact.
K-means clustering algorithm: a brief review

K-means clustering is a very classical clustering algorithm, and it is also one of the representatives of unsupervised learning.It has the advantages of a simple idea, high efficiency, and easy implementation, so it is widely used in many fields.However, K-means clustering also has some limitations, such as the number of clusters, the value of K is challenging to select, the selection of initial class center, the detection of outliers, and so on.This paper introduces the traditional K-means clustering algorithm and its improved method in detail.The advantages and disadvantages of the improved algorithm are analyzed, and the existing problems are pointed out.The development direction and trend of the K-means algorithm have been prospected.
Hate Crimes: Evaluation of Lithuanian Courts’ Decisions in the Light of the Practice of the European Court of Human Rights

Abstract In this article, the authors analyse the practice of the Lithuanian national courts and the European Court of Human Rights in hate crime cases, provide insights into the synergy between the decisions made by these courts, and suggest further improvement actions. This research shows that proving the circumstances surrounding various forms of hatred is quite complex, often lacking a more comprehensive, in-depth definition of the totality of circumstances by taking account of the need for special knowledge, the identification of guilt, and the system and intensity of actions. There is often a divide between criminal liability and the possibility of other countermeasures, especially when examining cases related to hate speech. Court decisions draw attention to the fact that it is necessary to consider the totality of the data collected, not individual data or individual fragments of circumstances. Among other things, the decisions emphasize the ultima ratio principle: whether criminal liability is an adequate measure in cases of hate speech. The topical issues examined in the article draw attention to the collection of significant data and the organization of investigations of these crimes, issues relating to proof and the emerging practice of the European Court of Human Rights and the Supreme Court of the Republic of Lithuania in this category of cases, highlighting the two main problematic aspects: first, the determination of the totality of objective and subjective features and second, the fact of identifying a real threat.
Text Segmentation Using Light Syntax Parsing and Fuzzy Systems

We present our positions and proposition in regards to the process of text segmentation. The method discussed below aims to improve upon the current processes available for text segmentation by introducing the concept of fuzzy boundaries. our method of segmenting text concerns a population of boundaries to be in a fuzzy state whereby the decision of boundary insertion is determined by a fuzzy system with syntactic information serving as the inputs. Furthermore, we aim to build on this method in the future with the goal of presenting a multifaceted segmentation approach that is applicable across various domains that require segmentation: Text summarisation, Rhetorical structure theory, sentence-based segmentation and paragraph-based segmentation.
RETRACTED ARTICLE: Distinguish between the data of the Internet of Things based on abnormal audio detection and the image of athletes’ human actions

This article discusses methods of abnormal audio detection. Extract audio information from audio acquisition hardware, convert analog signals into digital signals, perform pre-focused audio processing on digital signals, and compare and analyze audio noise reduction algorithms, such as wave noise reduction, homomorphic filtering, and Wiener filtering. After noise reduction processing is performed on the audio data, endpoint detection is performed on the continuous noise reduction frequency data, and the actual audio segment is separated from the continuous audio signal. Consistent with this, the blockchain-based IoT data service reputation evaluation model designed and implemented in this paper can support the data service reputation evaluation requirements. Users can upload IoT data sources to the platform or retrieve data from the platform, and research the data based on reputation or data provider analysis. The interaction with the data authority is realized through access control based on permissions. Finally, according to the latest requirements of the national sports team, based on the research foundation of the “athlete military division,” a new visual word model is used to explain the research on the image discrimination of athletes’ movements, and introduces the athletes’ movements, including traditional word models and automatic algorithms, and then introduces the research content and research methods to improve the traditional new visual word model to adapt to the characteristics of athletes. This algorithm minimizes the impact of short recognition rate and slow recognition rate, the latter causing problems such as the background similarity of the athlete’s image and the difference between the athlete’s actions. By understanding the available modules in the athlete’s motion recognition system, the prototype design of the basic functions and the athlete’s video system was completed.
Optimising for Interpretability: Convolutional Dynamic Alignment Networks

We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly, CoDA Nets can be combined with conventional neural network models to yield powerful classifiers that more easily scale to complex datasets such as Imagenet whilst exhibiting an increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interpretable depth</i> , i.e., the output can be explained well in terms of contributions from intermediate layers within the network.
Multiple feature fusion‐based video face tracking for IoT big data

With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real-time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real-time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness compared with extracting a single feature.
Accuracy analysis of lossless and lossy disparity map compression

In this paper, we analysed lossless and lossy compression of disparity (depth) images with low range resolution. For that goal, the well-known publicly available Middlebury dataset is used with stereo image pairs, their disparity ground truths and disparity estimations obtained using state-of-the-art algorithms. We show that the WebP image format is suitable for lossless compression of disparity images, with compression ratios between 14 and 56 and a mean compression ratio of 20. Much higher compression ratios, better than 60, can be achieved using lossy image compression HEIC algorithm, with acceptable reduction of the disparity map accuracy. This high compression ratio is proportional to the transmission time reduction.
Investigating students’ strategies for interpreting quantum states in an upper-division quantum computing course

Significant focus in the PER community has been paid to student reasoning in undergraduate quantum mechanics.However, these same topics have remained largely unexplored in the context of emerging interdisciplinary quantum information science (QIS) courses.We conducted 15 exploratory think-aloud interviews with students in an upper-division quantum computing course at a large R1 university cross-listed in the physics and computer science departments.Focusing on responses to one particular problem, we identify two notably consistent problem-solving strategies across students in the context of a particular interview prompt, which we term Naive Measurement Probabilities (NMP) and Virtual Quantum Computer (VQC), respectively.Operating from a resources framework, we interpret these strategies as choices of coherent (and potentially mutually-generative) sets of resources to employ and available actions to perform.
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions

Recent research has shown great potential for finding interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions provide controllable generation and support a wide range of semantic editing operations such as zoom or rotation. The discovery of such directions is often performed in a supervised or semi-supervised fashion and requires manual annotations, limiting their applications in practice. In comparison, unsupervised discovery enables finding subtle directions a priori hard to recognize. In this work, we propose a contrastive-learning-based approach for discovering semantic directions in the latent space of pretrained GANs in a self-supervised manner. Our approach finds semantically meaningful dimensions compatible with state-of-the-art methods.
What can Neural Referential Form Selectors Learn?

Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) models to find out to what extent the linguistic features influencing the RE form are learned and captured by state-of-the-art RFS models. The results of 8 probing tasks show that all the defined features were learned to some extent. The probing tasks pertaining to referential status and syntactic position exhibited the highest performance. The lowest performance was achieved by the probing models designed to predict discourse structure properties beyond the sentence level.
Exploring Structural Encoding for Data-to-Text Generation

Due to efficient end-to-end training and fluency in generated texts, several encoder-decoder framework-based models are recently proposed for data-to-text generations. Appropriate encoding of input data is a crucial part of such encoder-decoder models. However, only a few research works have concentrated on proper encoding methods. This paper presents a novel encoder-decoder based data-to-text generation model where the proposed encoder carefully encodes input data according to underlying structure of the data. The effectiveness of the proposed encoder is evaluated both extrinsically and intrinsically by shuffling input data without changing meaning of that data. For selecting appropriate content information in encoded data from encoder, the proposed model incorporates attention gates in the decoder. With extensive experiments on WikiBio and E2E dataset, we show that our model outperforms the state-of-the models and several standard baseline systems. Analysis of the model through component ablation tests and human evaluation endorse the proposed model as a well-grounded system.
Steering of Quantum Walks through Coherent Control of High-dimensional Bi-photon Quantum Frequency Combs

Quantum superpositions enable quantum walks (QWs) to potentially speedup certain computational tasks such as database searches, tests of graph isomorphism, ranking nodes in a network, quantum many-body simulations, etc. [1] . To implement QWs, photonic platforms have the advantages of being robust at room temperature and immune to decoherence [2] . However, photonic circuits either require huge overhead or necessitate modifying the physical layout to attain the tunability of the QW. Recently, QWs exhibiting enhanced ballistic transport (bosonic) or strong energy confinement (fermionic) have been demonstrated [3] using high-dimensional bi-photon quantum frequency combs (QFCs) [4] , which do not require any change of the device arrangement.
Every-Efficient Motion Planning for Dual-Armed Robot by PID Gain Optimization with Genetic Algorithm

The purpose of this paper is to propose an energy-efficient pick-and-place motion planning that a robot grabs a workpiece on a belt-conveyor and releases it to specified position for saving energy of a dual-arm robot. The motion is evaluated by the objective function that has the number overshooting, the time of overshooting, the time of termination and the consumption of energy for the total minimization problem. The robot arm is controlled by PID controller, and its optimal PDI gains are obtained by a Genetic Algorithm. We conducted the numerical simulation to verify that the consumption of energy using a dual-arm robot can be decreased compared with that of one arm. The proposed motion planning has been implemented into real world motion planning problem. A dual-armed robot named duAro released by Kawasaki Heavy Industries, is used to carry out the real implementations to test the proposed pick-and-place motion planning. The experimental results show the effectiveness of the dual-armed motions compared with the single-arm motions for energy efficiency.
AN ANALYSIS OF PRONUNCIATION ERRORS OF ENGLISH CONSONANTS SOUNDS PRODUCED BY ENGLISH DEPARTMENT STUDENTS

In communication, they not only can understand what other people say, but they also should be understood by other people when they do communication. This study is aimed to analyze pronunciation errors of English consonants sounds produced by English department students. This research is conducted by using the Descriptive Qualitative Research. The data is in the form of audio recording and the writer makes the transcription of the data. After getting the transcription of the students, the writer analyses the data to identify the error that occur with the correct transcription as the standard of assessment. The next step is the writer describes the error in a descriptive form. The result showed that the student stillmade some mistakes in producing some consonants sounds. From the total number of errors, there are some consonants that the students mostly pronounce them incorrectly as follow: consonant [v] is 21.4%, [θ] is 19.0%, [ð] is 26.2%, [ʃ] is 4.8%, [ʒ] 26.2% and [ŋ] is 2.4%. The errors occurred because those consonants do not exist in Indonesian language, so that they are not familiar with those sounds. Keywords: pronunciation, consonants, errors.
Unified Graph Structured Models for Video Understanding

Accurate video understanding involves reasoning about the relationships between actors, objects and their environment, often over long temporal intervals. In this paper, we propose a message passing graph neural network that explicitly models these spatio-temporal relations and can use explicit representations of objects, when supervision is available, and implicit representations otherwise. Our formulation generalises previous structured models for video understanding, and allows us to study how different design choices in graph structure and representation affect the model's performance. We demonstrate our method on two different tasks requiring relational reasoning in videos -- spatio-temporal action detection on AVA and UCF101-24, and video scene graph classification on the recent Action Genome dataset -- and achieve state-of-the-art results on all three datasets. Furthermore, we show quantitatively and qualitatively how our method is able to more effectively model relationships between relevant entities in the scene.
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery

Access to high resolution satellite imagery has dramatically increased in recent years as several new constellations have entered service. High revisit frequencies as well as improved resolution has widened the use cases of satellite imagery to areas such as humanitarian relief and even Search and Rescue (SaR). We propose a novel remote sensing object detection dataset for deep learning assisted SaR. This dataset contains only small objects that have been identified as potential targets as part of a live SaR response. We evaluate the application of popular object detection models to this dataset as a baseline to inform further research. We also propose a novel object detection metric, specifically designed to be used in a deep learning assisted SaR setting.
A Tool to Explore the Population of a CIDOC-CRM Ontology

This paper presents a visualising tool to explore the population of an Ontology, obtained through the processes of automatic migration and text information extraction. It was developed in the context of EPISA project, a R&D project that aims to represent the Portuguese National Archives records information in CIDOC-CRM, an ontology developed for museums. The tool allows the migration process developers to visualise the instances and their properties, and to debug the migration process and the migration representation model, or to explore the Archives by final users. It uses modeling and reasoners OWL-API with SPARQL-DL queries to obtain the exploration results.
Classification of Multiple Myeloma Cancer Cells Using Convolutional Neural Networks and Transfer Learning

An image processing based computer aided diagnostic tool for Multiple Myeloma cancer diagnosis was developed. Multiple Myeloma involves white blood cells in the bone marrow becoming cancerous, which can severely damage the bones and kidneys and cause death. We used 1451 images for our study from a recently publicly released dataset (SegPC-2021 Challenge) of stained microscope images which contained normal and cancerous cells. For the task of classifying images containing cancerous cells or not, we obtained a good classification accuracy of 93.58%, which was further improved using Transfer Learning and Fine Tuning with different pre-trained Convolutional Neural Networks models, namely VGG (Visual Geometry Group), VGG-16, VGG-19, MobileNetV1, and MobileNetV2 that gave an accuracy of 94.00%, 95.50%, 99.90%, and 97% respectively.
TRUST-TECH-Based Systematic Search for Multiple Local Optima in Deep Neural Nets

Training deep neural networks (DNNs) rested heavily on efficient local solvers. Due to their local property, local solvers are sensitive to initialization and hyperparameters. In this article, a systematical method for finding multiple high-quality local optimal DNNs, based on the transformation under stability-retaining equilibria characterization (TRUST-TECH) method, is introduced. Our goal is to systematically search for multiple local optimal parameters for large models, such as DNNs, trained on large datasets. To achieve this, a dynamic searching path (DSP) method is proposed to provide improved search guidance used in TRUST-TECH. By integrating the DSP method with the TRUST-TECH (DSP-TT) method, multiple optimal training solutions with higher quality than randomly initialized ones can be obtained. To take advantage of these optimal solutions, a DSP-TT ensemble method is further developed. Experiments on various test cases show that the proposed DSP-TT method achieves considerable improvement over other ensemble methods developed for deep architectures. The DSP-TT ensemble method also shows diversity advantages over other ensemble methods.