Reviewerly/exHarmony huggingface dataset:
Shortest Length Total Orders Do Not Minimize Irregularity in Vector-Valued Mathematical Morphology

Mathematical morphology is a theory concerned with non-linear operators for image processing and analysis. The underlying framework for mathematical morphology is a partially ordered set with well-defined supremum and infimum operations. Because vectors can be ordered in many different ways, finding appropriate ordering schemes is a major challenge in mathematical morphology for vector-valued images, such as color and hyperspectral images. In this context, the irregularity issue plays a key role in designing effective morphological operators. Briefly, the irregularity follows from a disparity between the ordering scheme and a metric in the value set. Determining an ordering scheme using a metric provide reasonable approaches to vector-valued mathematical morphology. Because total orderings correspond to paths on the value space, one attempt to reduce the irregularity of morphological operators would be defining a total order based on the shortest length path. However, this paper shows that the total ordering associated with the shortest length path does not necessarily imply minimizing the irregularity.
A Deep Understanding of Long Short-Term Memory for Solving Vanishing Error Problem

Long Short-Term Memory (LSTM) is a specific kind of recurrent neural network (RNN) structure that addresses the constraints of conventional RNNs in effectively capturing and learning long-term relationships in sequential input. In this chapter, the authors examine the LSTM cell and its modifications to investigate the LSTM cell's capability for learning. Furthermore, future study prospects for LSTM networks are outlined. LSTM networks have gotten extensive attention in scientific papers, technical websites, and deployment manuals because of their efficacy in a variety of practical situations. Gradient-based learning techniques used in RNNs are too slow because as the error is transmitted back, it disappears, resulting in a much more extended learning period. LSTMs handle the issue with a novel additive gradient design that incorporates direct access towards the forget gate's activations, allowing the network to promote desirable behavior from the error gradient by updating the gates often at each time step of learning.
Sociolinguistics of unhappiness: the display of emotions in a multilingual migrants’ community

This contribution aims at analyzing the communicative expression of emotions among Senegalese migrants settled in the city of Pescara, in Abruzzo, Southern Italy. These individuals ﬁnd their own ‘selves’ at the junction of their subjective well-being and two cultures, creating a sort of intercultural and interlingual personal experience which shapes their relationships with their own community on the one hand and the host community on the other hand. Recent studies (Sevinç 2020) have argued that a better understanding of the concept of subjective ‘emotion’ (contra that of ‘attitude’) and its role in immigrant contexts is key to enlighten the connection between intercultural communication and possible outcomes of language contact settings. This qualitative study speciﬁcally considers the language of ﬁrst-generation migrants that have been living in Pescara for a minimum of eight years, looking at their linguistic choices when it comes to expressing feelings of discontent (unhappiness, sadness, anger, disappointment) with respect to their role in the Italian social environment and their integration in the host community.
Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis

Abstract Sentiment analysis is becoming a very popular research technique. It can effectively identify hidden emotional trends in social networks to understand people’s opinions and feelings. This research therefore focuses on analyzing the sentiments of the public on the social media platform, YouTube, about the Thailand-China high-speed train project and the Laos-China Railway, a mega-project that is important to the country and a huge investment to develop transportation infrastructure. It affects both the economic and social dimensions of Thai people and is also an important route to connect the rail systems of ASEAN countries as part of the Belt and Road Initiative. We gathered public Thai reviews from YouTube using the Data Application Program Interface. This dataset was used to train six sentiment classifiers using machine learning and deep learning algorithms. The performance of all six models by means of precision, recall, F1-score and accuracy are compared to find the most suitable model architecture for sentiment classification. The results show that the transformer model with the WangchanBERTa language model yields best accuracy, 94.57%. We found that the use of a Thai language-specific model that was trained from a large variety of data sources plays a major role in the model performance and significantly increases the accuracy of sentiment prediction. The promising performance of this sentiment classification model also suggests that it can be used as a tool for government agencies to plan, make strategic decisions, and improve communication with the public for better understanding of their projects. Furthermore, the model can be integrated with any online platform to monitor people's sentiments on other public matters. Regular monitoring of public opinions could help the policy makers in designing public policies to address the citizens’ problems and concerns as well as planning development strategies for the country.
Pelatihan 3D Modelling Menggunakan Blender Pada SMA Methodist-8 Medan

Training in the field of 3D Modeling using Blender has been completed for students at Methodist-8 Medan High School (SMA). The aim of this training is to provide basic understanding and skills in creating and designing animated 3D objects that can be expressed in real visual form, both in terms of shape, texture and size of the object using the Blender design tool. This training method includes several practical stages focusing on the introduction of Blender 3D, basic concepts of 3D animation, design, 3D measurements, framework and modeling, texturing and visualization. The students were given a step-by-step 3D Modeling learning guide and were guided directly on how to create and design animated 3D objects until completion by forming group teams to hone their skills in improving their creativity and understanding of the art of design that is responsive and has aesthetic value. The results of this training showed a 90% increase in students' understanding in learning the basic concepts of 3D animation with practical design tools using Blender. They were able to produce simple 3D modeling designs with good animated 3D objects, appropriate textures so that the resulting objects looked real, then implemented the results of their designs in the form of animating character visualizations and 100% response from the students requested that this training be continued in the activity session next period of community service.
Art X Instagram Filters: A New Aesthetic Experience for Seeing Artworks in the Contemporary Era

In this contemporary era, technology has become quite important in the field of art. Recent innovations include the phenomenon of combining Art X Technology with Instagram filters, in which artists create 2D artworks and filters through Instagram that can be used by the audience to see 2D artworks that are transformed into real or moving
MS-RAFT+: High Resolution Multi-Scale RAFT

Abstract Hierarchical concepts have proven useful in many classical and learning-based optical flow methods regarding both accuracy and robustness. In this paper we show that such concepts are still useful in the context of recent neural networks that follow RAFT’s paradigm refraining from hierarchical strategies by relying on recurrent updates based on a single-scale all-pairs transform. To this end, we introduce MS-RAFT+: a novel recurrent multi-scale architecture based on RAFT that unifies several successful hierarchical concepts. It employs a coarse-to-fine estimation to enable the use of finer resolutions by useful initializations from coarser scales. Moreover, it relies on RAFT’s correlation pyramid that allows to consider non-local cost information during the matching process. Furthermore, it makes use of advanced multi-scale features that incorporate high-level information from coarser scales. And finally, our method is trained subject to a sample-wise robust multi-scale multi-iteration loss that closely supervises each iteration on each scale, while allowing to discard particularly difficult samples. In combination with an appropriate mixed-dataset training strategy, our method performs favorably. It not only yields highly accurate results on the four major benchmarks (KITTI 2015, MPI Sintel, Middlebury and VIPER), it also allows to achieve these results with a single model and a single parameter setting. Our trained model and code are available at https://github.com/cv-stuttgart/MS_RAFT_plus .
ARTIFICIAL NEURAL NETWORK APPLICATION IN MODELING MORTALITY OF COVID-19 PATIENTS IN INDONESIA

The Indonesian government and public healthcare system have been under massive pressure due to increased infections and mortality rates among Covid-19 patients. An appropriate model is needed to model the mortality of Covid-19 patients in Indonesia to help the Indonesian government develop the right policy for dealing with the Covid-19 pandemic. Artificial neural networks are increasingly popular in various research fields. Artificial neural networks can detect specific patterns in mortality modeling. In this study, we use artificial neural networks to model the mortality rate of Covid-19 patients in Indonesia. We try combinations of activation functions, learning rates, and hidden layers for the best predictions. We compare the prediction accuracy of artificial neural networks with that of the Holt-Winters method. The results showed that the best model of artificial neural networks produced an RMSE of 3.0530. In contrast, the Holt-Winters method produced an RMSE of 664.9022. Therefore, the artificial neural networks performed better than the Holt-Winters method in analyzing mortality data of Covid-19 patients in Indonesia.
CLUSTERING LARGE APPLICATION USING METAHEURISTICS (CLAM) FOR GROUPING DISTRICTS BASED ON PRIMARY SCHOOL DATA ON THE ISLAND OF SUMATRA

K-medoids is one of the partitioning methods with the medoid as its center cluster, where medoid is the most centrally located object in a cluster, which is robust to outliers. The k-medoids algorithm used in this study is Clustering Large Application Using Metaheuristics (CLAM), where CLAM is a development of the Clustering Large Application based on Randomized Search (CLARANS) algorithm in improving the quality of cluster analysis by using hybrid metaheuristics between Tabu Search (TS) and Variable Neighborhood Search (VNS). In the case study, the best cluster analysis method for classifying sub-districts on the island of Sumatra based on elementary school availability and elementary school process standards is the CLAM method with k=6, num local = 2, max neighbor = 154, tls = 50 and set radius = 100-10:5. It can be seen that based on the overall average silhouette width value, the CLAM method is better than the CLARANS method.
Interview with Sandra Uhling: “Against Homogenization and on Becoming Aware and More Sensitive Towards Human Diversity”

In this interview, Beate Ochsner and Markus Spöhrer conducted an interview with accessibility expert and human rights defender Sandra Uhling, who is the founder of GameAccessibility.de—a project that evaluates, discusses, and recommends barrier-free or ‘accidentally accessible’ mainstream games as well as accessibility guidelines and design choices. In this interview, Sandra Uhling discusses the co-relation of accessibility and usability—a pair of terms that is usually considered to be related to separate aspects of game design. Usability suggests individual configurability and adaptability that is supposed to accommodate larger groups of users with different individual demands, goals, concerns, or practices. Another important aspect of this interview is the demand for both public and economic attention to human diversity and an acceptance of such instead of attempting to homogenize a social group—namely gamers—that has never been homogeneous and never will be. Being enabled or disabled to play games, they conclude, is an individual and highly dynamic process and not necessarily a bodily deficit on one side or a technological incompatibility on the other side.
A BACKTRACKING APPROACH FOR SOLVING PATH PUZZLES

We study algorithmic aspects of the Path puzzle--a logic puzzle created in 2013 and confirmed NP-complete (Non-deterministic Polynomial-time-complete) in 2020. We propose a polynomial time algorithm for verifying an arbitrary Path puzzle solution and a backtracking-based method for finding a solution to an arbitrary Path puzzle instance.To our knowledge, our study is the first rigorous investigation of an imperative algorithmic approach for solving Path puzzles. We prove that the asymptotic running time of our proposed method in solving an arbitrary Path puzzle instance of size $m \times n$ is $O(3^{mn})$. Despite this exponential upper bound, experimental results imply that a C++ implementation of our algorithm can quickly solve $6 \times 6$ Path puzzle instances in less than 30 milliseconds with an average of 3.02 milliseconds for 26 test cases. We finally prove that an $m \times n$ Path puzzle instance without row and column constraints is polynomially solvable in $O(\max\{m,n\})$ time.
Eksplorasi Eksplorasi Bahasa pada Anak Afasia Wernicke-Motorik Transkortikal Usia 7 Tahun_Vrestanti Novalia Santosa

Language disorders of aphasia have not been widely studied. Moreover, regarding the language comprehension of the sufferer. Public understanding of aphasia is still lacking. People underestimate aphasic. This condition encourages research on language comprehension in autistic children aged 7 years with transcortical wernicke-motoric aphasia. Based on this background, the problems of this study are (1) how is the language comprehension in autistic children aged 7 years with transcortical wernicke-motoric aphasia? The theory that becomes the research reference is the aphasia theory of Arifuddin, Lumbantobing, and Fuller with the qualitative-descriptive research method of the type of case study. The results showed that the comprehension of aphasia language was studied from the aspect of the language unit form, consisting of a) phonas (including articulatory phonetics); b) words (nouns; verbs; adjectives; adverbs; pronouns: first person, possess pronouns, and indicative pronouns; numeralia: cardinal numbers; and interrogative words; c) sentences with a pattern: (1) S + P + O, (2) S + P, (3) P + S, (4) K (time) + P, (5) P (+ particle-lah) + S + P + O, (6) K (time) + S + P, (7) K (place) + S.
Application of Metaheuristic Algorithms and Their Combinations to Travelling Salesman Problem

This paper presents four classical metaheuristic algorithms: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Tabu Search (TS) for solving the Travelling Salesman Problem (TSP). In addition, this paper introduces two novel hybrid approaches, (SAACO) based on Ant Colony Optimization and Simulated Annealing, and (TSACO) based on Ant Colony Optimization and Tabu Search. To compare the efficiency of the considered algorithms and to verify the effectiveness of the novel hybrid algorithms, this paper uses ten well-known benchmark instances from TSPLIB; the instances are of variable difficulty and size, ranging from 70 to 783 nodes with different node topologies. Assessment criteria involve computational time, fitness value, convergence speed, and robustness of algorithms. The experimental results show that hybrid algorithms overcome the limitations of individual algorithms, namely the slow convergence and local optima. Moreover, when applied solely, hybrid algorithms achieve better fitness values than GA, ACO, and TS in most simulations.
A Simple Theory of Overt and Covert Dogwhistles

Politicians select their words meticulously, never losing sight of their ultimate communicative goal. Sometimes, their objective may be that of not being fully understood by a large portion of the audience. They can achieve this by means of dogwhistles; linguistic expressions that, in addition to their literal meaning, convey a concealed message to a specific sub-group of the audience. This paper focuses on the distinction between overt and covert dogwhistles introduced by J. Saul (2018). I argue that, even if the distinction successfully captures a genuine divide within the category of dogwhistles, the account proposed by Saul to explain the distinction is unsatisfactory. In response to this state of affairs, I illustrate how the distinction between overt and covert dogwhistle can be refined and illuminated by incorporating it into the 'Simple Theory' of dogwhistles advanced by J. Khoo (2017).
A Generic Construction of Tightly Secure Password-Based Authenticated Key Exchange

We propose a generic construction of password-based authenticated key exchange (PAKE) from key encapsulation mechanisms (KEM). Assuming that the KEM is oneway secure against plaintext-checkable attacks (OW-PCA), we prove that our PAKE protocol is tightly secure in the Bellare-Pointcheval-Rogaway model (EUROCRYPT 2000). Our tight security proofs require ideal ciphers and random oracles. The OW-PCA security is relatively weak and can be implemented tightly with the Diffie-Hellman assumption, which generalizes the work of Liu et al. (PKC 2023), and “almost” tightly with lattice-based assumptions, which tightens the security loss of the work of Beguinet et al. (ACNS 2023) and allows more efficient practical implementation with Kyber. Beyond these, it opens an opportunity of constructing tight PAKE based on various assumptions.
Interval Piece-Wise Transfer Function for One Class of Dynamical Systems

The paper deals with the design of theoretical backgrounds to study and design nonlinear systems dynamics. These backgrounds are based on the use of transfer functions apparatus but contrary to the conventional approach we offer to take into account system nonlinearities and uncertainties by using interval methods. Our approach allows us to consider the changing of dynamic system parameters during its operations as well as the non-accurate measurement of system outputs. We use interval methods to define minimal and maximal combinations of parameters. These combinations define the top and bottom trajectories of system motions in system phase space. We consider these trajectories as the top and bottom boundaries for a domain in the system phase space and think that the real system motion is always between these boundaries. It is necessary to say that the considered dynamical system can be nonlinear and its nonlinearities can be non-stationary ones. Our approach makes it possible to study and use such systems by defining a domain where nonlinear functions are defined and/or where they can change their parameters. In the general case, the above-mentioned domain can have nonlinear boundaries. We use piece-wise linear approximation of these boundaries to simplify their description and define interval piece-wise linear differential equations which define all possible system dynamics in the selected intervals of system parameters. We apply the Laplace-Carson transformation to define system dynamics in the operator form and use operator equations to find system interval transfer functions.
Computer vision syndrome and homoeopathy

In the twenty-first century personal computers are one of the commonest office tools, in almost all institutions, for a wide variety of vocational and non- vocational purposes. The introduction of computers and visual display terminals has brought a phenomenal change in our lives and soon has become an integral part of our daily life. With the continuous improvement in computer-related devices and an enhanced audio-visual experience, even the elderly people are using it with great interest to stay active in this ignorant society. The children and students of any age have very gradually switched on to computer-based learning thinking that it's a better option than classroom teachings. This paradigm change very slowly has penetrated in our youth as well as most of the business-related activities are based on the use of computers by the employees whose job demands long continued hours of sitting in front of computers.
On Black-Box Knowledge-Sound Commit-And-Prove SNARKs

Gentry and Wichs proved that adaptively sound SNARGs for hard languages need non-falsifiable assumptions. Lipmaa and Pavlyk claimed Gentry-Wichs is tight by constructing a non-adaptively sound zk-SNARG FANA for $$\textsf{NP}$$ from falsifiable assumptions. We show that FANA is flawed. We define and construct a fully algebraic F-position-binding vector commitment scheme $$\textrm{VCF}$$ . We construct a concretely efficient commit-and-prove zk-SNARK Punic, a version of FANA with an additional $$\textrm{VCF}$$ commitment to the witness. Punic satisfies semi-adaptive black-box $$G$$ -knowledge-soundness, a new natural knowledge-soundness notion for commit-and-prove SNARKs. We use a new proof technique to achieve global consistency using a functional somewhere-extractable commitment scheme to extract vector commitment’s local proofs.
A Chinese Dialogue Corpus Annotated with Dialogue Act

This chapter will introduce a Chinese dialogue corpus with annotated dialogue acts and users’ intent, which contains 5026 multi-turn and multiplayer dialogue messages with an average of 13 utterances per round. This dataset will provide a unique resource of Chinese dialogue corpora as well as a baseline for research on Chinese dialogue act classification and dialogue intent prediction. We proposed a detailed annotation scheme and annotation specification using three dimensions and manually annotated the dataset. We also implemented the conditional random field (CRF) model and the recurrent neural network (RNN) model to predict dialogue acts and dialogue intent. The experimental results of the baseline methods will be presented in Section “Experimental Results,” followed by the conclusion of this chapter.
Does Gender Diversity Drive Financial Performance? Empirical Evidence in Indonesia

Previous references provide evidence that Gender Diversity has a positive impact on financial performance. Gender Diversity has the potential to stimulate competitive advantages in terms of innovation and improved problem-solving abilities. The purpose of this study is to explore the relationship be
Semantic Annotation and Mandarin VerbNet

This chapter will examine the challenging issues in the semantic annotation of verbal information in Mandarin Chinese. It will also probe into the unique array of semantic properties encoded in the Mandarin verbal lexicon and propose a frame-based constructional approach that is aligned with linguistic premises in functional theories, including frame semantics, construction grammar, and cognitive grammar. Given that semantic processing pertains to cognitive mechanisms in general, the semantic transfer and profile of base schemas in event chains should be considered for verbal categorization and representation. The proposed approach has been adopted in the development of Mandarin VerbNet, which was designed to provide the lexical semantic information of the major classes of Mandarin verbs in an attempt to offer a linguistically valid and application useful representation of the Mandarin verbal lexicon.