Reviewerly/exHarmony huggingface dataset:
Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan

Business location plays an important role in sales. The business location in cities makes the seller easier to distribute activities for people. Distribution activities are closely related to sales activities. If there is a sales transaction, a classification of potential and non-potential customers will be required. One method that can be used for classification is mining data. One of the most frequently used data mining for classification is the Naive Bayes method. The attributes used in the customer classification process are purchase amount, time interval, and location. The result of the classification system is 23 true reactions and 2 false reactions. Based on the results are using the confusion matrix method, it shows that the accuracy value reaches 92%, the precision value reaches 100%, the recall value reaches 91%.Keywords: Trading Business, Customer Classification, Naive Bayes, Confusion Matrix
Gated Dehazing Network via Least Square Adversarial Learning

In a hazy environment, visibility is reduced and objects are difficult to identify. For this reason, many dehazing techniques have been proposed to remove the haze. Especially, in the case of the atmospheric scattering model estimation-based method, there is a problem of distortion when inaccurate models are estimated. We present a novel residual-based dehazing network model to overcome the performance limitation in an atmospheric scattering model-based method. More specifically, the proposed model adopted the gate fusion network that generates the dehazed results using a residual operator. To further reduce the divergence between the clean and dehazed images, the proposed discriminator distinguishes dehazed results and clean images, and then reduces the statistical difference via adversarial learning. To verify each element of the proposed model, we hierarchically performed the haze removal process in an ablation study. Experimental results show that the proposed method outperformed state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), international commission on illumination cie delta e 2000 (CIEDE2000), and mean squared error (MSE). It also gives subjectively high-quality images without color distortion or undesired artifacts for both synthetic and real-world hazy images.
Large scale analysis of generalization error in learning using margin based classification methods

Large-margin classifiers are popular methods for classification. We derive the asymptotic expression for the generalization error of a family of large-margin classifiers in the limit of both sample size $n$ and dimension $p$ going to $\infty$ with fixed ratio $\alpha=n/p$. This family covers a broad range of commonly used classifiers including support vector machine, distance weighted discrimination, and penalized logistic regression. Our result can be used to establish the phase transition boundary for the separability of two classes. We assume that the data are generated from a single multivariate Gaussian distribution with arbitrary covariance structure. We explore two special choices for the covariance matrix: spiked population model and two layer neural networks with random first layer weights. The method we used for deriving the closed-form expression is from statistical physics known as the replica method. Our asymptotic results match simulations already when $n,p$ are of the order of a few hundreds. For two layer neural networks, we reproduce the recently developed `double descent' phenomenology for several classification models. We also discuss some statistical insights that can be drawn from these analysis.
WORD2VEC NOT DEAD: PREDICTING HYPERNYMS OF CO-HYPONYMS IS BETTER THAN READING DEFINITIONS

Expert-built lexical resources are known to provide information of good quality for the cost of low coverage. This property limits their applicability in modern NLP applications. Building descriptions of lexical-semantic relations manually in sufficient volume requires a huge amount of qualified human labour. However, given some initial version of a taxonomy is already built, automatic or semi-automatic taxonomy enrichment systems can greatly reduce the required efforts. We propose and experiment with two approaches to taxonomy enrichment, one utilizing information from word definitions and another from word usages, and also a combination of them. The first method retrieves co-hyponyms for the target word from distributional semantic models (word2vec) or language models (XLM-R), then looks for hypernyms of co-hyponyms in the taxonomy. The second method tries to extract hypernyms directly from Wiktionary definitions. The proposed methods were evaluated on the Dialogue-2020 shared task on taxonomy enrichment. We found that predicting hypernyms of cohyponyms achieves better results in this task. The combination of both methods improves results further and is among 3 best-performing systems for verbs. An important part of the work is detailed qualitative and error analysis of the proposed methods, which provide interesting observations of their behaviour and ideas for the future work.
No reference Image Quality Measure for Hazy Images

Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.
Social Gaze in Minimalist Games

Social gaze-based interactions in games form a timely field of research as they can enrich communicative acts within the game world. Apart from portraying gaze via realistic-looking characters, we deem that these aspects could also be applied to non-anthropomorphic characters found in so-called minimalist games. These games use simple mechanics and an abstract art style to convey deep and meaningful experiences. The paper examines concepts of social gaze in minimalist games via a comparative study that focuses on the players' perceived quality of social interaction towards abstract non-player characters. By creating a game made up of three levels, it is aimed to uncover differences between the inclusion and exclusion of gaze-aware characters. The results show that the integration of gaze-based social interactions led to a positive social experience among players. Social gaze in this context is not superfluous but adds a crucial element in the interpretation of game objects: Players in the gaze condition tended to see the abstract entities as characters instead of lifeless game objects.
‘Free In, Free Out’: Exploring Dutch Firewall Protections for Irregular Migrant Victims of Crime

Abstract Real and perceived risks of deportation may compromise the effective right of irregular migrants to report to the police if they have been a victim of crime. Some localities have therefore introduced so-called ‘firewall protection’, providing a clear separation between the provision of public services and immigration enforcement. This article explores one such policy in the Netherlands: ‘free in, free out’. While the policy began as a local pilot project, in 2015 it was introduced at the national-level alongside implementation of EU Victim’s Rights Directive, and currently represents the only national-level example of ‘firewall protection’ for victims of crime in Europe. This article is based on a socio-legal study that included interviews with informants from governmental and non-governmental organisations. It documents the legal and social reasons for instituting the policy, while critically assessing the challenges in implementation. Finally, it discusses the lessons and opportunities for expanding firewall protection more broadly in a European context.
A hybrid CNN-LiGRU acoustic modeling using raw waveform sincnet for Hindi ASR

Deep Neural Network (DNN) is currently playing the most vital role in Automatic Speech Recognition (ASR). Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are the advanced versions of DNN. CNN and RNN are right to deal with spatial and temporal properties of the speech signal, respectively, and both properties have a higher impact on accuracy. In today’s scenario, many acoustic modeling techniques often switches due to the battle of CNNs and RNNs. In the last few years, CNN, with raw speech signal, shows their superiority over precomputed acoustic features. Recently, a novel first convolution layer named SincNet was proposed to produce the interpretable filters with better accuracy. In this work, we proposed a hybrid SincNet-CNN-RNN architecture with low computation cost and high accuracy. Different configurations of the hybrid model were extensively examined to achieve this goal. All experiments were performed on the Hindi speech dataset.
Pedagogical Competence of the High School Teacher

The need conditions the relevance of the article for the formation of higher education teachers’ pedagogical competence as the optimal system and purposeful work in this direction has not yet been formed. The goal of the article lies in the analysis of the study of the theoretical aspects of higher education teachers’ pedagogical competence and identifying the main types of professional competence. The leading approach to the study of this problem for the authors was their understanding of the concept of "competence", the distinction between the concepts of “qualification” and “competence”, as well as an understanding of the essence of higher school teacher’s pedagogical competence. The article presents a scheme of competencies of a higher school teacher, a distinction between key, subject and professional competencies. Taking into account the results of this study, it is possible to identify several scientific problems and promising areas that require further consideration: in particular, the development of new training programs for teachers of higher education institutions.
Information-disturbance trade-off in generalized entanglement swapping

We study information-disturbance trade-off in generalized entanglement swapping protocols wherein starting from Bell pairs $\left(1,2\right)$ and $\left(3,4\right)$, one performs an arbitrary joint measurement on $\left(2,3\right)$, so that $\left(1,4\right)$ now becomes correlated. We obtain trade-off inequalities between information gain in correlations of $\left(1,4\right)$ and residual information in correlations of $\left(1,2\right)$ and $\left(3,4\right)$, respectively, and we argue that information contained in correlations (information) is conserved if each inequality is an equality. We show that information is conserved for a maximally entangled measurement but is not conserved for any other complete orthogonal measurement and Bell measurement mixed with white noise. However, rather surprisingly, we find that information is conserved for rank-2 Bell diagonal measurements, although such measurements do not conserve entanglement. We also show that a separable measurement on $\left(2,3\right)$ can conserve information, even if, as in our example, the post-measurement states of all three pairs $\left(1,2\right), \left(3,4\right)$, and $\left(1,4\right)$ become separable. This implies that correlations from an entangled pair can be transferred to separable pairs in nontrivial ways so that no information is lost in the process.
Re-solve it: simulating the acquisition of core semantic competences from small data

Many tasks are considered to be 'solved' in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition.I illustrate this by coming back to the notion of semantic competence, which includes basic linguistic skills encompassing both referential phenomena and generic knowledge, in particular a) the ability to denote, b) the mastery of the lexicon, or c) the ability to model one's language use on others.Even though each of those faculties has been extensively tested individually, there is still no computational model that would account for their joint acquisition under the conditions experienced by a human.In this paper, I focus on one particular aspect of this problem: the amount of linguistic data available to the child or machine.I show that given the first competence mentioned above (a denotation function), the other two can in fact be learned from very limited data (2.8M token), reaching state-of-theart performance.I argue that both the nature of the data and the way it is presented to the system matter to acquisition.
Sit-to-Stand Analysis in the Wild Using Silhouettes for Longitudinal Health Monitoring

We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery.
Sudden death of entanglement in fermionic systems under collective decoherence

We analyze the dynamics of entanglement due to decoherence in a system of two identical fermions with spin 3/2 interacting with a global bosonic environment. We make use of an appropriate measure of the so-called fermionic entanglement to quantify the fermionic correlations, and compare the dynamical effects due to decoherence with those that arise in a pair of distinguishable qubits immersed in the same environment. According to the system's initial state, three types of qualitatively different dynamics are identified: i) invariant regime, corresponding to initial states that belong to a decoherence free subspace (DFS), which maintain invariant their entanglement and coherence throughout the evolution; ii) exponential decay, corresponding to initial states orthogonal to the DFS, and evolve towards states whose entanglement and coherence decrease exponentially; iii) entanglement sudden death, corresponding to initial states that have some overlap with the DFS and exhibit a richer dynamics leading, in particular, to the sudden death of the fermionic entanglement, while the coherence decays exponentially. Our analysis offers insights into the dynamics of entanglement in open systems of identical particles and into the existence of decoherence free subspaces and entanglement sudden death in indistinguishable-fermion systems.
Data-to-Text Generation with Style Imitation

Recent neural approaches to data-to-text generation have mostly focused on improving content fidelity while lacking explicit control over writing styles (e.g., word choices, sentence structures).More traditional systems use templates to determine the realization of text.Yet manual or automatic construction of highquality templates is difficult, and a template acting as hard constraints could harm content fidelity when it does not match the record perfectly.We study a new way of stylistic control by using existing sentences as "soft" templates.That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.The problem is challenging due to the lack of parallel data.We develop a neural approach that includes a hybrid attention-copy mechanism, learns with weak supervisions, and is enhanced with a new content coverage constraint.We conduct experiments in restaurants and sports domains.Results show our approach achieves stronger performance than a range of comparison methods.Our approach balances well between content fidelity and style control given exemplars that match the records to varying degrees.
Neurosymbolic Transformers for Multi-Agent Communication

We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication. We quantify the amount of communication as the maximum degree of the communication graph; this metric captures settings where agents have limited bandwidth. Minimizing communication is challenging due to the combinatorial nature of both the decision space and the objective; for instance, we cannot solve this problem by training neural networks using gradient descent. We propose a novel algorithm that synthesizes a control policy that combines a programmatic communication policy used to generate the communication graph with a transformer policy network used to choose actions. Our algorithm first trains the transformer policy, which implicitly generates a soft communication graph; then, it synthesizes a programmatic communication policy that hardens this graph, forming a neurosymbolic transformer. Our experiments demonstrate how our approach can synthesize policies that generate low-degree communication graphs while maintaining near-optimal performance.
Virtual medical education in Peru during COVID-19

Medical education aims at excellence in the training of health professionals. Thus, virtual education arises from the difficulty of access of many students to educational centers. Medical education is no stranger to this transition, so it is important for both teachers and students to adopt these tools to achieve a quality teaching-learning process. There is evidence on the effectiveness and acceptance of virtual learning within the medical community, where students describe their satisfaction with this modality of education as a good option for better training. However, some educational institutions are not prepared for this change and many of them do not have virtual platforms or do not have the capacity to impart knowledge properly in a virtual way, which exposes a poor reality in this digital age. Virtual education has the challenge of training and adapting its teachers to these new teaching methods in front of its students who have already been born in a digital world. However, not all students have access at home to these technologies or connectivity necessary for virtual education, in many cases due to lack of resources for the purchase of computers or the internet, added to the limitations of those living in rural areas.
Stratification of Neolithic Ceramic Complexes in the Konda River Basin

Purpose. Over the past 40 years, dozens of pit-houses, semi-dugouts, above-ground huts, hearth-like structures, and burial grounds have been investigated on the territory of the Konda River basin. Classification of cultural artifacts is based on stylistic attributes and distinctions of ceramics, while chronology rests on conventional 14C dating. At the present stage, one of the most important tasks is to develop the chronology and periodization of the Neolithic sites located in the Konda River basin. The starting point in the solution of this problem should be the multilayer stratified habitation sites. This paper presents the results of the investigation of Mulymya-3 settlement, which is one of such sites. Results. In 2019, early Neolithic stratified complexes were studied in Mulymya-3 settlement. The excavations of the early buildings revealed that among the predominant artifacts of flat-bottomed dishes, there also were layers of Shoushma and Sumpanya type pottery. The stroke-ornamented ware, i. e. flat-bottomed ceramic dishes with collars and rims, were classified as “Mulymya type pottery”. 14C dating of the soot from a flat-bottomed vessel suggested that the site was more or less continuously occupied between 6 690–6 500 cal. BC. Conclusion. The artifacts from Mulymya-3 settlement made it possible to shift the lower boundary of the Neolithic age in the Konda River basin to earlier dates. A typical feature of the earliest period (6 600 / 6 500 – 3 600 / 3 400 cal. BC) is Mulymya, Shoushma, and Umytinsky type pottery characterized by original traditions of ceramic production. The appearance of pottery in the second third of the 7th millennium BC did not affect the traditional economy of taiga societies. The stone-working technique was based on fracturing technologies (percussion flaking, pressure flaking, chipping, splintering), abrasive processing, and battering. In the 6th millennium BC, the people belonging to Shoushma and Umytinsky cultures started contacting with each other, which can be proved by dishes of both types found in the excavated pit-dwelling in similar stratigraphic conditions. Another proof is the appearance of mixed-looking pottery, in particular dishes of the Sumpanya type. From the end of the 6th millennium, the Shoushma pottery traditions were gradually degrading. From that time and until the turn of the 5th–4th millennia BC the Konda River basin was inhabited by the Umytinsky population. It is probable that those were the people who continued the tradition of making flat-bottomed dishes (Satyginsky / Boborykinsky type pottery).
On-Chip Multiphoton Entangled States by Path Identity

Multiphoton entanglement, as a quantum resource, plays an essential role in linear optical quantum information processing. Krenn et al. (Phys. Rev. Lett. 118, 080401 2017) proposed an innovative scheme that generating entanglement by path identity, in which two-photon interference (called Hong-Ou-Mandel effect) is not necessary in experiment. However, the experiments in this scheme have strict requirements in stability and scalability, which is difficult to be realized in bulk optics. To solve this problem, in this paper we first propose an on-chip scheme to generate multi-photon polarization entangled states, including Greenberger-Horne-Zeilinger (GHZ) states and W states. Moreover, we also present a class of generalized graphs for W states (odd-number-photon) by path identity in theory. The on-chip scheme can be implemented in existing integrated optical technology which is meaningful for multi-party entanglement distribution in quantum communication networks.