Reviewerly/exHarmony huggingface dataset:
Resource Oriented Programming: Graphical Pattern for Describing Restful Service Composition

Context.REST resources via the microservice architecture have become essential in the development of modern applications. Despite their importance, no language or tool allowing us to develop resources for increasingly complex applications in a relatively short time.Objective.Our goal is to find a new programming paradigm to increase the productivity of resource or microservice developers.Method.We proposed a language based on the principles of “divide and rule”, code reuse and automatic code generation. The language thus makes it possible to describe resources in the form of other resources, using design patterns.Results.Our main result is the fact of decomposing a complex application into sub-applications in a normalized way. This normalization of the decomposition makes it easy to generate their code. Thus, through our graphical language, developers can focus on the design and not on the implementation of their applications; this drastically reduces application development time.Conclusion.This new programming paradigm also allows us to better manage the role management of complex applications. One of the limitations of our work is that resource composition is only possible if all the resources are on the same machine. In our future research we would like to explore the composition of remote resources as well as the transactions between them.p { margin-bottom: 0.1in; line-height: 115%; background: transparent }
Improving Chatbot Responses with Context and Deep Seq2Seq Reinforcement Learning

Abstract Developing conversational agents that can generate appropriate responses for meaningful and natural conversations between humans and machines is a difficult task in the field of artificial intelligence. One key factor for agents to produce accurate responses is the ability to fully and effectively utilize the context of the current utterance in a conversation. However, many previous studies have neglected the relationship between utterances in a conversation, which limits their effectiveness. This paper aims to address this limitation by introducing a novel method for modeling the contextual information of the current utterance when generating a response. The goal of this research is to improve the accuracy of responses generated by conversational agents by considering the contextual information of the current utterance in a conversation. To achieve this, we propose a novel approach that combines a Deep Seq2Seq model with reinforcement learning. The Deep Seq2Seq model generates responses based on the left context of the current utterance, while reinforcement learning is used to evaluate the entire generated conversation based on the right context of the current utterance. We also utilize a pre-trained word embedding model to help build reward functions for the reinforcement learning component and to represent words in the generated responses. Experimental results show that the proposed model leads to significant improvements in BLEU scores compared to a baseline model.
The Tendency for Occupational Fraud in Malaysian Small Medium Enterprises and The Effect of Internal Control, Ethical Culture and Awareness on Fraud

Occupational fraud requires serious attention due to its detrimental effect towards businesses, nation's economy, and community well-being.Implications led to businesses losing their revenue while affecting global economy negatively and depriving the society of fair share on nation's wealth.Malaysia's businesses have seen cases of occupational fraud among small, medium sized enterprises (SME) representing the significant portion of country's business population of 95%.Such vulnerability among them were due to deficiency in control system.Hence this study aims to assess the influence of internal control, fraud awareness and ethical culture on tendency of occupational fraud within SMEs in Malaysia.Most past studies have focused on public sector and listed companies while limited study was done on SME with inconsistent results.Questionnaire survey was adopted with SME staffs as respondents from 384 SME out of 907,065 establishments.Data collected was analyzed using multiple regression which revealed significant influence by internal control and fraud awareness factors on tendency of occupational fraud among SME staffs, while ethical culture showed insignificant influence.This study has limitation due to limited number of response rate of 33% and sample may not consist of SME whose staffs had committed occupational fraud.The finding provides further evidence on tendency of occupational fraud among SMEs.It will be useful for organizations, especially SMEs in understanding potential areas requiring further attention to remedy occupational fraud.
Digital Intrusiveness of Unmentionable Products: Construct Differential Analysis Framework Approach

In marketing research, it is important to measure consumers’ perceptions related to advertisements. Especially when these perceptions have the potential to set negative attitude. With rapid technological advancements, digital media advertising has become an important tool for advertisers to reach customers. Consumers’ perceptions of intrusiveness, irritation, and offensiveness are highly attributed to digital media advertising of unmentionable products. However, it is evident from the marketing literature that terms such as intrusiveness, irritation, and offensiveness are being interchangeably used. This leads to measurement issues of the consumers’ perceptions at the construct level. Therefore, this study considers it important to formally specify the relationships that exist between intrusiveness, irritation, and offensiveness. This formal specification was required to differentiate the concepts of intrusiveness, irritation, and offensiveness so that consumers’ perceptions at the construct level can be appropriately measured. To achieve this sixty definitions and instruments were extracted from existing literature and analyzed through a construct differential analysis framework. As a result of which hybrid dimensions mainly related to intrusiveness emerged. The hybrid dimensions exclusively representing intrusiveness are intrusive media, intrusive cognition, product intrusiveness, individual pre-dispositions, negative experience, social and cultural influences, religious beliefs, and behavioral outcomes. The insights of this study establish a need to develop a digital advertising intrusiveness construct incorporating the hybrid themes that emerged.
ViCTer: A Semi-Supervised Video Character Tracker

Video character tracking problem refers to tracking certain characters of interest in the video and returning the appearing time slots for those characters. Solutions to this problem can be applied in various video-analysis-related areas, such as movie analysis and automatic video clipping. However, there are very few researches investigating this problem and there are no existing relevant benchmark datasets available. In this paper, we design a novel model to solve this problem by combining a semi-supervised face recognition network and a multi-human tracker. For the face recognition network, we propose a semi-supervised learning method to fully leverage the unlabeled images in the video, thus reducing the required number of labeled face images. Triplet loss is also used during the training to better distinguish among inter-class samples. However, a single face recognition network is insufficient for video character tracking since people do not always show their frontal faces, or sometimes their faces are blocked by some obstacles. Therefore, a multi-human tracker is integrated into the model to address those problems. Additionally, we collect a dataset for the video character tracking problem, Character Face in Video, which can support various experiments for evaluating video character tracker performance. Our proposed model can achieve 70% ~ 80% average intersection-over-union tracking accuracy on this dataset.
Hybrid Method of Using Feedback from Users to Improve Music

Automatic music classification relies primarily on traditional aspects of music encapsulated by content-based features. This means cultural aspects of music classification are not incorporated into the classification process. To improve classification, a hybrid method of using feedback from users is proposed to incorporate these cultural aspects. This paper proposes a pipeline of classifying music by genre using content-based features followed by the implementation of Q-learning. The GTZAN dataset is used to model four content-based classifiers, namely: Logistic Regression, Support Vector Machines, Multilayer Perceptron and Random Forests. The highest performing model was the Support Vector Machine (SVM) with and accuracy score of 75.50% based on a 10-fold confusion matrix, in line with other experiments in this field of research. Q-learning is used to further refine the predictions of the SVM by using user input to develop a Q-table with Q-values for each piece of music, associating it with a certain genre based on user feedback. This resulted in an accuracy score of 75.50%, like that of the SVM classification, indicating that Q-learning accuracy cannot overcome the initial classification introduced by the SVM predicted values.
Predicting Car Sale Time with Data Analytics and Machine Learning

There is no doubt that marketing is an important step in Product Lifecycle Management (PLM) and obviously decreasing time-to-market is crucial to reduce storage costs and increase profit. This paper aims to improve marketing strategies in the automotive field for car dealers and car selling supply chain. Due to the cost of new cars and the high risk of car value depreciation it becomes necessary for car dealers to know which type of cars can be sold faster than others, this will allow dealers to adapt their marketing strategies and satisfy the need of their customers. We propose to use data analysis and machine learning algorithms to address this problem and create models to help these companies in their decision-making processes. In our experiments, we used sale data from two big dealers of multi-maker cars. The dataset contains the sale history of around 73200 cars over a period of 8 years. We compared the different machine-learning algorithms and got promising results classifying cars into different predicted sale time ranges.
Concise and interpretable multi-label rule sets

Abstract Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of simple “if-then” rules, and thus, it offers better interpretability compared to black-box models. Notably, our method is able to find a small set of relevant patterns that lead to accurate multi-label classification, while existing rule-based classifiers are myopic and wasteful in searching rules, requiring a large number of rules to achieve high accuracy. In particular, we formulate the problem of choosing multi-label rules to maximize a target function, which considers not only discrimination ability with respect to labels, but also diversity. Accounting for diversity helps to avoid redundancy, and thus, to control the number of rules in the solution set. To tackle the said maximization problem we propose a 2-approximation algorithm, which circumvents the exponential-size search space of rules using a novel technique to sample highly discriminative and diverse rules. In addition to our theoretical analysis, we provide a thorough experimental evaluation, which indicates that our approach offers a trade-off between predictive performance and interpretability that is unmatched in previous work.
Constrained Clustering: General Pairwise and Cardinality Constraints

We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (cannot-link constraints). Cardinality constraints encourage cluster sizes to satisfy a user-specified distribution. Most existing constrained clustering models can only utilize one category of constraints at a time. We enforce the above two categories into a unified clustering model starting with the integer program formulation of the standard K-means. As the two categories provide different useful information, utilizing both allow for better clustering performance. However, the optimization is difficult due to the binary and quadratic constraints in the unified formulation. To solve this, we utilize two techniques: equivalently replacing the binary constraints by the intersection of two continuous constraints; the other is transforming the quadratic constraints into bi-linear constraints by introducing extra variables. We derive an equivalent continuous reformulation with simple constraints, which can be efficiently solved by Alternating Direction Method of Multipliers. Extensive experiments on both synthetic and real data demonstrate when: (1) utilizing a single category of constraint, the proposed model is superior to or competitive with SOTA constrained clustering models, and (2) utilizing both categories of constraints jointly, the proposed model shows better performance than the case of the single category. The experiments show that the proposed method exploits the constraints to achieve perfect clustering performance with improved clustering to 2%-5% in classical clustering metrics, e.g. Adjusted Random, Mirkin's, and Huber's, indices outerperfomring other methods.
Using Gradual Extrapolation for Automating Latent Feature Detection

Rapid advancement in ML and AI has brought the latent vulnerability to the forefront and questioned the reliability of AI technologies. This study validates the reliability of the latent features which remain hidden during validation and compromise the accuracy and precision of detection. Latent features are semantically relevant aggregates of observed features. The discriminating features that should be considered by the deep neural network are identified. Predominant techniques like object detection and saliency map with the Gradual Extrapolated GradCAM function are used to identify the most crucial feature of the object. This area significantly contributes to the identification of the object. Simultaneously, learning of partial correlations is avoided. Region of Interest is also used to identify a classified object within an image and determine the classification-important pixels concerning all pixels in the ROI with higher value. It is specified that the Deep Neural Network could learn about the entire image rather than confining it to a specific region. The proposition of the said evaluation metric, the effectiveness of the method, and its shortcomings are the parts of this study.
STRATEGI MENSTIMULASI PERKEMBANGAN BAHASA ANAK MELALUI METODE BERCERITA

Language development is the child's ability to process vocabulary, give opinions, and socialise with others. Language development factors are very important for early childhood because using good and correct language can make it easier for children to communicate with others. The purpose of this study was to determine the extent of children's language development through the storytelling method. The problem that occurs is the lack of children's confidence to communicate and interact with the school environment, as well as a lack of understanding related to knowledge in the field of stimulating children. This research uses a qualitative approach where the results are collected through observation, interviews, observations, and documentation. Where the observation step carried out in this study went directly to the field to make direct observations, observing the process of children's language development by telling stories. The results of the research on children's language development at Paud Kumara Cita developed well by using the storytelling method even though there was one child who still could not communicate well.
The Use of Storybird Platform to Arise Students’ Interest in Writing Recount Text

AbstractInterest led to students’ behavior during the learning process. This research was aimed to describe students’ interest in writing recount text through the implementation of Storybird platform as a media in the classroom. The subjects were 35 students VIII A class of SMPN 20 Pontianak. This research was a classroom action research used the observation checklist, field notes, interview and writing test as the instruments in collecting data. It conducted in two cycles. The finding showed that using Storybird platform to teach writing recount text encouraged the students’ interest as same as writing skills. The students were more active, enthusiastic, focus, motivated and confident in their writing class. They enjoyed writing recount text and followed the learning activities. Based on the indicators of interest, the improvement of students’ interest was around 83% to 100% in the last cycle. Moreover it also increased the students’ ability of writing aspects. Based on the quantitative data, the students’ score in cycle 1 was 72.18 and increased into 80, 03 in the test of Cycle II.
Students’ Perception toward the Use of Moodle-Reader for Improving Reading Comprehension

This research aimed to investigate the students’ perception toward the use of Moodle-reader for improving reading comprehension. This study was descriptive research with a qualitative approach that used questionnaires and interviews. The respondents were 20 participants who were part of the member of Academic Resources Center (ARC) as students in the English Education Student Program at Universitas Tanjungpura. The researcher found that the perceptions of each student about using Moodle-reader for improvement could be the same and also different. It depends on the student’s perspective or thoughts, feelings, and beliefs. Overall, the students responded positively about the use of Moodle-reader for improving reading comprehension. The researcher concluded that the students agreed that Moodle-reader is suitable for independent learning and improving reading comprehension. The researcher found that the five aspects (ease of access to Moodle-reader, perceived usefulness in Moodle-reader, students’ motivation using Moodle-reader, students’ satisfaction, and the features in Moodle reader) can influence students reading comprehension. Moodle-reader provided varieties of books based on students’ levels. Students could be enjoying the books. It makes students motivated to read many books. Therefore, Moodle-reader could positively impact students and improve their reading ability, especially in reading comprehension.Keywords: Students’ perception, Moodle-reader, Reading comprehension
Self-testing composite measurements and bound entangled state in a unified framework

Within the quantum networks scenario, we introduce a single scheme allowing to certify three different types of composite projective measurements acting on a three-qubit Hilbert space: one constructed from genuinely entangled GHZ-like states, one constructed from fully product vectors that exhibit the phenomenon of nonlocality without entanglement (NLWE), and a hybrid measurement obtained from an unextendible product basis (UPB). Noticeably, we certify a basis exhibiting NLWE in the smallest dimension capable of supporting this phenomenon. On the other hand, the possibility of certification of a measurement obtained from a UPB has an interesting implication that one can also self-test a bound entangled state in the considered quantum network. Such a possibility does not seem to exist in the standard Bell scenario.
Are Labels Needed for Incremental Instance Learning?

In this paper, we learn to classify visual object instances, incrementally and via self-supervision (self-incremental). Our learner observes a single instance at a time, which is then discarded from the dataset. Incremental instance learning is challenging, since longer learning sessions exacerbate forgetfulness, and labeling instances is cumbersome. We overcome these challenges via three contributions: i. We propose VINIL, a self-incremental learner that can learn object instances sequentially, ii. We equip VINIL with self-supervision to by-pass the need for instance labelling, iii. We compare VINIL to label-supervised variants on two large-scale benchmarks, and show that VINIL significantly improves accuracy while reducing forgetfulness.
Secure synchronization of artificial neural networks used to correct errors in quantum cryptography

Quantum cryptography can provide a very high level of data security. However, a big challenge of this technique is errors in quantum channels. Therefore, error correction methods must be applied in real implementations. An example is error correction based on artificial neural networks. This paper considers the practical aspects of this recently proposed method and analyzes elements which influence security and efficiency. The synchronization process based on mutual learning processes is analyzed in detail. The results allowed us to determine the impact of various parameters. Additionally, the paper describes the recommended number of iterations for different structures of artificial neural networks and various error rates. All this aims to support users in choosing a suitable configuration of neural networks used to correct errors in a secure and efficient way.
On granularity of prosodic representations in expressive text-to-speech

In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training. Same text may correspond to various acoustic realizations, which is known as a one-to-many mapping problem in text-to-speech. Utterance, word, or phoneme-level representations are extracted from target signal in an auto-encoding setup, to complement phonetic input and simplify that mapping. This paper compares prosodic embeddings at different levels of granularity and examines their prediction from text. We show that utterance-level embeddings have insufficient capacity and phoneme-level tend to introduce instabilities when predicted from text. Word-level representations impose balance between capacity and predictability. As a result, we close the gap in naturalness by 90% between synthetic speech and recordings on LibriTTS dataset, without sacrificing intelligibility.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation.