Reviewerly/exHarmony huggingface dataset:
Mixed Embedding of XLM for Unsupervised Cantonese-Chinese Neural Machine Translation (Student Abstract)

Unsupervised Neural Machines Translation is the most ideal method to apply to Cantonese and Chinese translation because parallel data is scarce in this language pair. In this paper, we proposed a method that combined a modified cross-lingual language model and performed layer to layer attention on unsupervised neural machine translation. In our experiments, we observed that our proposed method does improve the Cantonese to Chinese and Chinese to Cantonese translation by 1.088 and 0.394 BLEU scores. We finally developed a web service based on our ideal approach to provide Cantonese to Chinese Translation and vice versa.
Expressivity of Planning with Horn Description Logic Ontologies

State constraints in AI Planning globally restrict the legal environment states. Standard planning languages make closed-domain and closed-world assumptions. Here we address open-world state constraints formalized by planning over a description logic (DL) ontology. Previously, this combination of DL and planning has been investigated for the light-weight DL DL-Lite. Here we propose a novel compilation scheme into standard PDDL with derived predicates, which applies to more expressive DLs and is based on the rewritability of DL queries into Datalog with stratified negation. We also provide a new rewritability result for the DL Horn-ALCHOIQ, which allows us to apply our compilation scheme to quite expressive ontologies. In contrast, we show that in the slight extension Horn-SROIQ no such compilation is possible unless the weak exponential hierarchy collapses. Finally, we show that our approach can outperform previous work on existing benchmarks for planning with DL ontologies, and is feasible on new benchmarks taking advantage of more expressive ontologies.
New Results in Bounded-Suboptimal Search

In bounded-suboptimal heuristic search, one attempts to find a solution that costs no more than a prespecified factor of optimal as quickly as possible. This is an important setting, as it admits faster-than-optimal solving while retaining some control over solution cost. In this paper, we investigate several new algorithms for bounded-suboptimal search, including novel variants of EES and DPS, the two most prominent previous proposals, and methods inspired by recent work in bounded-cost search that leverages uncertainty estimates of the heuristic. We perform what is, to our knowledge, the most comprehensive empirical comparison of bounded-suboptimal search algorithms to date, including both search and planning benchmarks, and we find that one of the new algorithms, a simple alternating queue scheme, significantly outperforms previous work.
NEUROCRYPT: Coercion-Resistant Implicit Memory Authentication (Student Abstract)

Overcoming the threat of coercion attacks in a cryptographic system has been a top priority for system designers since the birth of cyber-security. One way to overcome such a threat is to leverage implicit memory to construct a defense against rubber-hose attacks where the users themselves do not possess conscious knowledge of the trained password. We propose NeuroCrypt, a coercion-resistant authentication system that uses an improved version of the Serial Interception Sequence Learning task, employing additional auditory and haptic modalities backed by concepts borrowed from cognitive psychology. We carefully modify the visual stimuli as well as add auditory and haptic stimuli to improve the implicit learning process, resulting in faster training and longer retention. Moreover, our improvements guarantee that explicit recognition of the trained passwords remains suppressed.
Unsupervised Item-Related Recommendation Method Combining BERT and Collaborative Filtering

Item-related recommendation is widely used in e-commerce, news, video, and other business scenarios, but there are problems such as sparse data, a large amount of implicit feedback data, limited sample annotation, cold start of items, poor serendipity, insufficient real-time performance, and the recommendation effect needs to be continuously improved. An unsupervised recommendation method is proposed. The method included four recall strategies. The first was to use the search engine and BM25 for real-time text matching recommendation about multi fields, and the second was to combine pre-trained language model BERT and ANN algorithm for real-time semantic matching recommendation about multi fields, and the third was to calculate the similarity by reducing the influence of popular items and active users to optimize the item-based collaborative filtering recommendation algorithm, and the fourth was to introduce the heat index based on Wilson Confidence Intervals to assist the recommendation ranking. Finally, the four recall results were merged and sorted to generate the final recommendation result. Through multiple sets of comparative experiments by the AlB test in the online recommendation system, it is shown that the proposed unsupervised recommendation method is superior to the baseline method in multiple indicators and can effectively improve the recommendation effect and user satisfaction.
Pelatihan Metode Pembelajaran dalam Jaringan di SD Muhammadiyah Sokonandi Yogyakarta (Daring GGZ)

The Covid-19 pandemic is not just about health issues but also about social issues, one of which is the world of education. For that is necessary to use other alternative media in learning using the internet. However, some teachers are not proficient in using and operating online platform applications. Changing the way of learning requires teachers to know information technology. For this reason, training activities were held using the G Form, Google Meet, and Zoom applications to increase knowledge in using these platforms. This activity was carried out at SD Muhammadiyah Sokonandi, involving 18 teachers in classes 1-3. Methods for implementing this activity include planning, implementation, and evaluation. In the planning stage, the activity program is first drawn up. At the implementation stage, the training participants were given a pre-test, practical presentation, and mentoring accompanied by modules and ended with a post-test. In the final stage, data analysis was carried out using Microsoft Excel. The results of this activity indicate an increase in the understanding of class 1-3 teachers by 17,08%, and overall, 100% of teachers stated that this activity was beneficial. These results indicate that this training activity helps increase the knowledge of class 1-3 teachers who participated in the activity.
Resolving Inconsistencies in Simple Temporal Problems: A Parameterized Approach

The simple temporal problem (STP) is one of the most influential reasoning formalisms for representing temporal information in AI. We study the problem of resolving inconsistency of data encoded in the STP. We prove that the problem of identifying a maximally large consistent subset of data is NP-hard. In practical instances, it is reasonable to assume that the amount of erroneous data is small. We therefore parameterize by the number of constraints that need to be removed to achieve consistency. Using tools from parameterized complexity we design fixed-parameter tractable algorithms for two large fragments of the STP. Our main algorithmic results employ reductions to the Directed Subset Feedback Arc Set problem and iterative compression combined with an efficient algorithm for the Edge Multicut problem. We complement our algorithmic results with hardness results that rule out fixed-parameter tractable algorithms for all remaining non-trivial fragments of the STP (under standard complexity-theoretic assumptions). Together, our results give a full classification of the classical and parameterized complexity of the problem.
IMPROVING THE STUDENTS’ SPEAKING ACHIEVEMENT BY APPLYING THE STUDENTS TEAM ACHIEVEMENT DIVISION AT CLASS XI MIA 2 OF SMA NEGERI 6 MEDAN

This study aims to improve the ability to speak English in students in Medan. In this study, research data collection techniques used observation, interviews and documentation methods. The data sources for this study were students at SMA Negeri 6 Medan in class XI MIA2. By applying the Student Team Achievement Division learning model and using dialogue media. the research instrument is test, observation sheet, interview (Classroom Acton Research) . This study uses a qualitative and quantitative design. The research data analysis technique uses three steps, namely 1. Data reduction, 2. Data presentation, 3. Making conclusions. Findings from data analysis, Before applying the Studnet Team Achievement Division (STAD) learning model, there were 30 students (96%) who did not complete with a score of 44.46-66.67. and 6 (4%) students 78.78 - 88.89 completed with grades. And after applying the Student Team Achievement Division (STAD) learning model, in the first cycle there were 35 students (99%) who scored 78.78. 88.89 this value has exceeded the KKM score of 75% and 1 student (1%) did not complete. There was an increase of 26.15. The conclusion is that the application of the Student Team Achievement Division learning model is very effective in improving students' speaking skills.
Multimodal Critical Discourse Analysis on the advertisement of Colgate

In semiotics there is an analysis called multimodal analysis. This study aimed to analyze the advertisement of Colgate in audiovisual formed, when Colgate advertisement is one of the Super Bowl commercial breaks in USA. This study applied on semantic approach focused on the multimodal discourse analysis. The method applied in this study was descriptive qualitative method. The result of this study was that Colgate Total advertisement covers five aspects in a multimodal semiotic system, namely linguistic, visual, audio, gestural, and location aspects. These five aspects come together to make the ad appearance very attractive and easy to remember. A product that is advertised, if many people are able to remember the words, visuals, accompanying music, and actors in the advertisement, it means that the message in the product has successfully reached the audience. It could be predicted that these products would be able to survive in the market. This ad for Colgate Total toothpaste products displays clear and interesting information about what people want from a product, namely its ingredients and the benefits of using it. Messages in the form of language carried out through spoken language and written language make the audience understand more about what is being promoted. This ad makes a unique impression on the audience because of its short duration but important overall message. The linguistic, visual, audio, gestural, and location aspects of a multimodal semiotic system were all addressed in this advertisement. These five elements combine to create a visually appealing and memorable advertisement.&#x0D; Keywords: multimodality, advertisement, analysis
Logit Perturbation

Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) feature perturbation can improve the robustness or even generalization capability of learned models. However, limited studies have explicitly explored for the perturbation of logit vectors. This work discusses several existing methods related to logit perturbation. Based on a unified viewpoint between positive/negative data augmentation and loss variations incurred by logit perturbation, a new method is proposed to explicitly learn to perturb logits. A comparative analysis is conducted for the perturbations used in our and existing methods. Extensive experiments on benchmark image classification data sets and their long-tail versions indicated the competitive performance of our learning method. In addition, existing methods can be further improved by utilizing our method.
On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections

We propose a new structured pruning framework for compressing Deep Neural Networks (DNNs) with skip-connections, based on measuring the statistical dependency of hidden layers and predicted outputs. The dependence measure defined by the energy statistics of hidden layers serves as a model-free measure of information between the feature maps and the output of the network. The estimated dependence measure is subsequently used to prune a collection of redundant and uninformative layers. Extensive numerical experiments on various architectures show the efficacy of the proposed pruning approach with competitive performance to state-of-the-art methods.
Peningkatan Pelayanan Administrasi Masyarakat di Desa Loa Duri Ulu Menggunakan Sistem Informasi Berbasis Web (SIMPELDES)

Administrative control is a way for an institution to archive or discipline its administration correctly. Loa Duri Ulu Village is one of the villages located in Loa Janan District, Kutai Kartanegara Regency, East Kalimantan. Currently, the service administration system in the village of Loa Duri Ulu is still manual. Every community that needs documents related to a population still has to come to the village office to take care of these documents and record them into a book manually to impact the village office in carrying out their duties. This community service aims to overcome administrative service problems in Loa Duri Ulu Village by creating an administrative service information system. In this research-based community service, an iterative model strategy is used in the Software Development Life Cycle (SDLC), including several stages. This method consists of 5 stages: Planning Stages, Analysis Stages, Web Design Stages, SIMPELDES web Implementation Stages, and Maintenance Stages. The result of this service is that a web called the SIMPELDES web has been created and designed where this web has service features for various types of inputting and archiving letters in Loa Duri village. With an administrative application system and community service that is connected to the community database, it will be faster to get the required letters, both new letters, and old ones.
Selective Weighted Adaptive Coding

Unlike the uniform treatment of symbols in different locations of an input file, a new approach is taken in the weighted dynamic compression method suggested in [1], which assigns higher priority to closer to be encoded symbols by means of an increasing weight function. The weighted method is especially suited for the encoding of files with locally skewed distributions. Empirical results have shown that weighted techniques can improve beyond the lower bound given by the entropy for static encoding.
A Study on the Word Order Constraints of Event Quantifier Phrase and Object from the Viewpoint of Information Structure — Focus on Event Quantifier “cì”

The event quantifier phrase can occur either before objects or after objects in Mandarin Chinese, which can be expressed as “V+Q+N” and “V+N+Q”. Using the theory of “the mental representations of discourse referents” proposed by Lambrecht (1994), this paper discusses the mechanism of this kind of word order variation. The category of mental representations of discourse referents involves two important concepts――identifiability and activation states. The continuum from unidentifiable referents to identifiable referents reflects the way mankind perceive the world. I exhaustively extracted all instances containing two word orders of “V+N+Xci” and “V+Xci+N” from the closed corpus. After analyzing the cognitive state of all N in each instance and making statistics, I draw the following conclusions: “V+N+Xci” requires N to be highly identifiable or highly activated, while “V+Xci+N” requires that N is relatively unidentifiable or has a low activation degree. This shows that the mental representation state of N is a key factor affecting word order choice. We believe that human cognition is not discrete, but presents a continuum. Therefore the conclusion of this paper expresses not an absolute standard but only a tendency relating to the choice of word order.
Towards Multimodal Vision-Language Models Generating Non-generic Text

While text generated by current vision-language models may be accurate and syntactically correct, it is often general. Recent work has used optical character recognition to supplement visual information with text extracted from an image. In many cases, using text in the image improves the specificity and usefulness of generated text. We contend that vision-language models can benefit from additional information extracted from an image. We modify previous multimodal frameworks to accept relevant information from a number of auxiliary classifiers. In particular, we focus on person names as an additional set of tokens and create a novel image-caption dataset to facilitate captioning with person names. The dataset, Politicians and Athletes in Captions (PAC), consists of captioned images of well-known people in context. By fine-tuning pretrained models with this dataset, we demonstrate a model that can naturally integrate facial recognition tokens into generated text by training on limited data.
PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

We propose PaintTeR, our Paintings TextRank algorithm for extracting art-related text spans from passages on paintings. PaintTeR combines a lexicon of painting words curated automatically through distant supervision with random walks on a large-scale word co-occurrence graph for ranking passage spans for artistic characteristics. The spans extracted with PaintTeR are used in state-of-the-art Question Generation and Reading Comprehension models for designing an interactive aid that enables gallery and museum visitors focus on the artistic elements of paintings. We provide experiments on two datasets of expert-written passages on paintings to showcase the effectiveness of PaintTeR. Evaluations by both gallery experts as well as crowdworkers indicate that our proposed algorithm can be used to select relevant and interesting art-centered questions. To the best of our knowledge, ours is the first work to effectively fine-tune question generation models using minimal supervision for a low-resource, specialized context such as gallery visits.
Errors Found in Ayuna Silver's Emails

This paper discussed an analysis of correspondence especially about the kinds of error made by the staff in replying to emails from the customer. All the data were mostly analyzed based on the theory applied by Dulay, Heidi C, and Marina Burt in their book entitled Language Two (1982) and it was also supported by other theories. They were Error Analysis and Interlanguage written by S. P. Corder (1981), Error Analysis Perspective on Second Language Acquisition written by Richard (1974) and beside the other references about, several supporting references that were considered relevant to the topic were also used in this paper. In conducting this research, the library research was applied and there were three steps for the methodology. They were data collection, data analysis, and data source. The information for this paper came from emails written by Ayuna Silver employees. The data was then gathered by reading carefully and then classified based on the types of errors according to the theory used in this qualitative paper. Based on the findings of the study, it can be concluded that Ayuna Silver’s staff made some mistakes, and the factor that caused the errors. There were omissions, additions, misformations, and misordering errors. Overgeneralization, incomplete application of rules, ignorance of rule restrictions, and false concept hypothesized were the factors that caused errors.&#x0D; Keywords: syntax, error analysis, e-mail
Inferring Multiple Tissue Properties from Magnetic Resonance Fingerprinting Images

Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that is a cornerstone of diagnostic radiology. Clinical MRI scans capture a single image to highlight a single tissue property. The intensity difference between different regions of this image shows disease states that a radiologist can interpret. Magnetic Resonance Fingerprinting (MRF) is a recently proposed novel MRI technique. MRF allows the capture of multiple MR images in a single scan. This enables clinicians to analyze multiple tissue properties, potentially increasing the sensitivity of diagnosis and also allowing for the diagnosis of novel diseases. However, it is more challenging to analyze MRF images, because MRF produces much larger and noisier data than MRI. In this paper, we show how AI techniques can help solve this problem. Using a hybrid search strategy combining simulated annealing with pattern search, we show it is possible to tractably reconstruct multiple tissue properties from a single MRF image. This is a key step towards the deployment of MRF for radiological diagnosis.
Grammatical Alliance of South Nias Dialect

This research aimed to understand (1) the types of complex sentence structures in BNDS, and (2) the determination of grammatical alliance system. This study used a qualitative descriptive method and applied the theoretical framework of language typology proposed by Van Vallin Jr, Lapolla (1999) and Dixon (1994) as the main theory. The data of this research were in the form of clauses and sentences. Based on the results of data analysis that has been carried out, it was found that syntactically BNDS is included in the accusative language group that treats the subject the same as the agent, and gives different treatment to the patient (S = A, ≠ P). BNDS is a language group that works with the S/A pivot system. This grammatical alliance system showed that BNDS is syntactically a nominative-accusative language. Paying attention to S’s behavior in intransitive clauses with A and P’s behavior in BNDS transitive clauses which showed a tendency to be nominative-accusative language.&#x0D; Keywords: clauses, complex sentences, pivot test and grammatical alliance
Effects of LNU Student's Mental Health with the Implemented Online Learning amidst pandemic of Social Studies

Irawan et al. (2020) where they mentioned one of the contributing factors to students' stress the pressure of online learning task which goes to learned used online media instantly.Studies reveal that students faced challenges during online learning classes these includes anxiety, depression, poor internet service, unfavorable home learning environment which students from remote areas (Kapasia et al. 2020).Stress can bring with it significant harm to the students' academic performance (e.g., reduced ability to pay attention or to memories, less dedication to study, unmotivated and more absences from class) (Chou et al., 2011).The researcher conducted the study to determine the effects of online learning to the student's mental health amidst pandemic.Thus, study employed a narrative research design in order to determine the student's effects of mental health related problems.The participants were selected using purposeful sampling with a total of ten (10) participants and the data gathered using indepth interview with open ended questions.The data gathered were summarize and thoroughly analyzed using thematic analysis.It reveals from the participant's responses that they encounter various online learning barriers where it leads them to mental stress.240 www.ijrp.org