Reviewerly/exHarmony huggingface dataset:
Single Depth-image 3D Reflection Symmetry and Shape Prediction

In this paper, we present Iterative Symmetry Completion Network (ISCNet), a single depth-image shape completion method that exploits reflective symmetry cues to obtain more detailed shapes. The efficacy of single depth-image shape completion methods is often sensitive to the accuracy of the symmetry plane. ISCNet therefore jointly estimates the symmetry plane and shape completion iteratively; more complete shapes contribute to more robust symmetry plane estimates and vice versa. Furthermore, our shape completion method operates in the image domain, enabling more efficient high-resolution, detailed geometry reconstruction. We perform the shape completion from pairs of viewpoints, reflected across the symmetry plane, predicted by a reinforcement learning agent to improve robustness and to simultaneously explicitly leverage symmetry. We demonstrate the effectiveness of ISCNet on a variety of object categories on both synthetic and real-scanned datasets.
Do DALL-E and Flamingo Understand Each Other?

The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence of sophisticated models dedicated to image captioning at scale, such as the notable Flamingo model and text-to-image generative models, with DALL-E serving as a prominent example. An interesting question worth exploring in this domain is whether Flamingo and DALL-E understand each other. To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image. We argue that these models understand each other if the generated image is similar to the given image. Specifically, we study the relationship between the quality of the image reconstruction and that of the text generation. We find that an optimal description of an image is one that gives rise to a generated image similar to the original one. The finding motivates us to propose a unified framework to finetune the text-to-image and image-to-text models. Concretely, the reconstruction part forms a regularization loss to guide the tuning of the models. Extensive experiments on multiple datasets with different image captioning and image generation models validate our findings and demonstrate the effectiveness of our proposed unified framework. As DALL-E and Flamingo are not publicly available, we use Stable Diffusion and BLIP in the remaining work. Project website: https://dalleflamingo.github.io.
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification

"A picture is worth a thousand words", significantly beyond mere a categorization. Accompanied by that, many patches of the image could have completely irrelevant meanings with the categorization if they were independently observed. This could significantly reduce the efficiency of a large family of few-shot learning algorithms, which have limited data and highly rely on the comparison of image patches. To address this issue, we propose a Class-aware Patch Embedding Adaptation (CPEA) method to learn "class-aware embeddings" of the image patches. The key idea of CPEA is to integrate patch embeddings with class-aware embeddings to make them class-relevant. Furthermore, we define a dense score matrix between class-relevant patch embeddings across images, based on which the degree of similarity between paired images is quantified. Visualization results show that CPEA concentrates patch embeddings by class, thus making them class-relevant. Extensive experiments on four benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and FC-100, indicate that our CPEA significantly outperforms the existing state-of-the-art methods. The source code is available at https://github.com/FushengHao/CPEA.
Online Crime Reporting System for Digital Forensics

The occurrence of unlawful acts, commonly known as crimes, is on the rise in our society. However, the process of reporting such crimes to law enforcement agencies through traditional procedures is extremely complicated and time-consuming for victims. Additionally, law enforcement agencies face difficulties in manually investigating and maintaining criminal records. To address these issues, an online crime reporting system can be implemented, which will not only simplify the process but also keep the users informed of any updates or instructions from law enforcement agencies regarding their complaints. Such a system will facilitate communication between the law enforcement agencies and victims, ultimately enhancing security in the community. On the other hand, there is a need to implement an interactive platform to help the investigator manage crime investigation processes using tools such as statistical dashboard, database management tools, a crime type classifier and also a tool to extract valuable information from the crime documents using a powerful technique such as natural Languages processing. It can be stated that the proposed system has demonstrated satisfactory outcomes.
Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction

Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep unfolding framework by formulating the problem into a data module and a prior module. Nevertheless, existing methods still face the problem of insufficient matching with HSI data. The issues lie in three aspects: 1) fixed gradient descent step in the data module while the degradation of HSI is agnostic in the pixel-level. 2) inadequate prior module for 3D HSI cube. 3) stage interaction ignoring the differences in features at different stages. To address these issues, in this work, we propose a Pixel Adaptive Deep Unfolding Transformer (PADUT) for HSI reconstruction. In the data module, a pixel adaptive descent step is employed to focus on pixel-level agnostic degradation. In the prior module, we introduce the Non-local Spectral Transformer (NST) to emphasize the 3D characteristics of HSI for recovering. Moreover, inspired by the diverse expression of features in different stages and depths, the stage interaction is improved by the Fast Fourier Transform (FFT). Experimental results on both simulated and real scenes exhibit the superior performance of our method compared to state-of-the-art HSI reconstruction methods. The code is released at: https://github.com/MyuLi/PADUT
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement

Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose a two-stage motion prediction method, called R-Pred, designed to effectively utilize both scene and interaction context using a cascade of the initial trajectory proposal and trajectory refinement networks. The initial trajectory proposal network produces M trajectory proposals corresponding to the M modes of the future trajectory distribution. The trajectory refinement network enhances each of the M proposals using 1) tube-query scene attention (TQSA) and 2) proposal-level interaction attention (PIA) mechanisms. TQSA uses tube-queries to aggregate local scene context features pooled from proximity around trajectory proposals of interest. PIA further enhances the trajectory proposals by modeling inter-agent interactions using a group of trajectory proposals selected by their distances from neighboring agents. Our experiments conducted on Argoverse and nuScenes datasets demonstrate that the proposed refinement network provides significant performance improvements compared to the single-stage baseline and that R-Pred achieves state-of-the-art performance in some categories of the benchmarks.
Advancing Referring Expression Segmentation Beyond Single Image

Referring Expression Segmentation (RES) is a widely explored multi-modal task, which endeavors to segment the pre-existing object within a single image with a given linguistic expression. However, in broader real-world scenarios, it is not always possible to determine if the described object exists in a specific image. Generally, a collection of images is available, some of which potentially contain the target objects. To this end, we propose a more realistic setting, named Group-wise Referring Expression Segmentation (GRES), which expands RES to a group of related images, allowing the described objects to exist in a subset of the input image group. To support this new setting, we introduce an elaborately compiled dataset named Grouped Referring Dataset (GRD), containing complete group-wise annotations of the target objects described by given expressions. Moreover, we also present a baseline method named Grouped Referring Segmenter (GRSer), which explicitly captures the language-vision and intra-group vision-vision interactions to achieve state-of-the-art results on the proposed GRES setting and related tasks, such as Co-Salient Object Detection and traditional RES. Our dataset and codes are publicly released in https://github.com/shikras/d-cube.
Modular Approach to Educational Opportunities of Chess in the Training Process of Kindergarten Teachers

The success of the modern educational system often depends on the investment of new educational tools. A number of recent studies justified the use of chess as an effective educational tool in the development process of preschoolers. Introduction of educational possibilities of chess to educators during trainings, stimulates their professional competences. The article discusses a training model for preschool teachers aimed at the formation of professional competences through acquiring prior knowledge on chess. The aim of the article is to describe a training model for preschool teachers. The methodological basis of the proposed training program was to stimulate self-realization of the participants by introducing prior knowledge on chess in all spheres of a preschooler’s development. The effectiveness of the program was revealed by PARLA method, and the difficulties - through the Fishbone method by K. Ishikawa. The method revealed the insufficient formation of preschool teachers’ personal competences. The methods were used among 60 preschool teachers during 1,5 years. The experimental results justified the possibility of developing professional competences emphasizing personal competences and the importance of acquiring acme-qualitative results. The novelty of the research is clarified by the choice of sequential modules leading to the formation of professional competences. Practical significance - the proposed methodology can be used in various training programs for preschool teachers.
A Contracting Dynamical System Perspective toward Interval Markov Decision Processes

Interval Markov decision processes are a class of Markov models where the transition probabilities between the states belong to intervals. In this paper, we study the problem of efficient estimation of the optimal policies in Interval Markov Decision Processes (IMDPs) with continuous action- space. Given an IMDP, we show that the pessimistic (resp. the optimistic) value iterations, i.e., the value iterations under the assumption of a competitive adversary (resp. cooperative agent), are monotone dynamical systems and are contracting with respect to the e(X)-norm. Inspired by this dynamical system viewpoint, we introduce another IMDP, called the action-space relaxation IMDP. We show that the action-space relaxation IMDP has two key features: (i) its optimal value is an upper bound for the optimal value of the original IMDP, and (ii) its value iterations can be efficiently solved using tools and techniques from convex optimization. We then consider the policy optimization problems at each step of the value iterations as a feedback controller of the value function. Using this system-theoretic perspective, we propose an iteration-distributed imple-mentation of the value iterations for approximating the optimal value of the action-space relaxation IMDP.
The Singing Voice Conversion Challenge 2023

We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) systems based on a common dataset. This year we shifted our focus to singing voice conversion (SVC), thus named the challenge the Singing Voice Conversion Challenge (SVCC). A new database was constructed for two tasks, namely in-domain and cross-domain SVC. The challenge was run for two months, and in total we received 26 submissions, including 2 baselines. Through a large-scale crowd-sourced listening test, we observed that for both tasks, although human-level naturalness was achieved by the top system, no team was able to obtain a similarity score as high as the target speakers. Also, as expected, cross-domain SVC is harder than in-domain SVC, especially in the similarity aspect. We also investigated whether existing objective measurements were able to predict perceptual performance, and found that only few of them could reach a significant correlation.
IMPLEMENTATION OF ASSOCIATION RULES USING APRIPORI ALGORITHM FOR ANGKRINGAN

Angkringan is a culinary business that is often found, especially in Surakarta and Yogyakarta in particular and Central Java in general. Most angkringan include small and medium enterprises (SMES). Food and drink merchandise mapping is an important thing in angkringan. In this research, food and drink mapping was carried out at several snack bars in Jebres sub-district, Surakarta city using word clouds and the Apriori algorithm. The word cloud will produce the dominant food and beverage itemsets which will then be processed using the Apriori algorithm. The results of mapping with the Apriori algorithm for food yield support(tempeh) = support (tofu) = 95 which is the highest. Relationship value between dominant itemsets: Confidence(tempeh, tofu→satay, milkfish rice, quail egg, bakwan) = 21. Results for drinks have the highest support value for tea, support(tea) = 100. Relationship value between dominant itemsets: Confidence(tea → coffee, milk, ginger) = 15.
Image Processing-Based Real-Time Detection and Tracking of Human

The “Image Processing-Based Real-Time Detection and Tracking of Human” system were developed as a real-time approach to detect, identify and track people from a given video field or live stream. This can be used for security, surveillance, or missing person identification. Monitor people through CCTV and webcam video recordings. The system uses technologies such as OpenCV to process videos and images and produce expected results. The main goal of this system is to reduce human effort in manually identifying people in video streams. Using image processing techniques such as OpenCV, the system will be able to identify and track the person being trained.
Feature Importance for Uncertainty Quantification In Agent-Based Modeling

Simulation models are subject to uncertainty and sensitivity, meaning that even small variations of input can cause considerable fluctuations in the output results. Consequently, this can amplify the uncertainty associated with the simulation, thereby limiting the confidence one can have in its outcomes. To mitigate these effects, this paper suggests using a cooperative game theory-based feature importance method, which can identify uncertainty in a dataset, and provide additional insights that could be used in the development or analysis of a simulation model. A predator-prey scenario was considered, demonstrating its usefulness in identifying important parameters or features. By identifying the most influential parameters or features, this approach can help improve the accuracy, explainability, and reliability of simulation models as well as other models with highly variable input parameters.
Analyzing the Relationship between Employee Engagement and Job Performance

This study presents a brief overview of the research's investigation of the correlation between employee engagement and work performance. This observation underscores the interconnectedness of these two variables and their substantial influence on the achievement of an organization. The correlation between employee engagement and job performance is an intricate and mutually beneficial association. Employees that are actively involved, displaying strong dedication and motivation, are more likely to demonstrate superior work performance, so making a favorable impact on an organization's productivity and overall success. On the contrary, a lack of employee involvement can have adverse effects on job performance, hence presenting a multitude of difficulties for the firm. Comprehending and nurturing this association is crucial for organizations seeking to optimize their performance and establish a flourishing work milieu. The concept of worker engagement is not universally applicable and instead exhibits variations across different firms. Adopting a tailored approach that considers the unique cultural, operational, and strategic aspects of individual firms is crucial. Ultimately, organizations that prioritize and allocate resources towards fostering employee engagement are more inclined to see enhanced staff retention rates, heightened productivity levels, elevated customer satisfaction levels, and overall improved performance. Performance appraisals, peer reviews, and self-assessments are widely utilized methods by which employees examine their own work performance. The assessments serve as a foundation for managerial and supervisory decision-making pertaining to several aspects, including but not limited to, promotions, salary increments, and further professional growth opportunities. The performance of employees can be improved and their contribution to the overall success of the organization can be facilitated via the implementation of good management practices and the provision of support from their superiors.
PENERAPAN MODEL PEMBELAJARAN MAKE A MATCH UNTUK MENINGKATKAN KETERAMPILAN MEMBACA PEMAHAMAN SISWA DI SEKOLAH DASAR

This research was motivated by the low reading comprehension skills of third grade students at SDN 035 Indra Puri, Tapung District, Kampar Regency. One solution to overcome this problem is to use the make a match learning model. The purpose of this study was to determine the planning, implementation and improvement of the make a match learning model in improving reading comprehension skills through third grade students at SDN 035 Indrapuri. This research method is classroom action research (PTK) which is carried out in two cycles. Each cycle consists of two meetings and four stages, namely planning, implementing, observing and reflecting. The results of data analysis showed that the average value from cycle I to cycle II increased by 17.5 from 59.23 to 76.73. This means that there was a significant increase. Similarly, student completeness also increased from 54% to 85%. Thus it can be concluded that the make a match learning model can improve the reading skills of class III students at SDN 035 Inra Puri, especially in the theme of Love the Animals and Plants Around..
An algorithm of blood typing using serological plate images

This paper describes an in vitro medical express diagnostic system designed to determine the blood group by analyzing the agglutination reaction (gluing of erythrocytes). The medical staff only needs to take a blood sample, put it on a serological plate, placing it in a special scanner for the blood group to be automatically determined. Data digitizing and machine-assisted plate identification allows two critical tasks to be addressed at once: storing the analysis results and controlling the human factor. The proposed recognition algorithm allows the alveolus boundaries to be accurately determined and the agglutination degree to be evaluated using a lightweight convolutional neural network. A unique dataset was collected with the independent assessment of agglutination degree conducted by medical experts. The agglutination estimation accuracy on the collected dataset of 3231 alveole was comparable to the accuracy of an average medical expert and equal to 0.98.
Indirect truth marking via backgrounding: evidence from Bantu

Abstract Verum focus has long been analysed as part of information structure, being accounted for within a theory of focus as generation of alternatives on a polarity value. Recently, however, this view has been challenged, with some authors arguing for a separation between verum and focus. The empirical debate focuses on two types of strategies: either verum is marked identically to focus (e.g., pitch accent in German, English) or through a dedicated strategy (e.g., dedicated markers in Gitksan, Bura, South Marghi). On the basis of the second type, it has been argued that the link between focus and verum is only superficial, with verum instead arising from a lexical operator VERUM. In this paper, we provide evidence in support of the traditional link between truth marking and focus, using original field data on the morphosyntactic expression of information structure in 11 Bantu (Niger-Congo) languages. By working from function to form, we show that these languages use a variety of grammatical strategies for truth expression, strategies that are used elsewhere in the information structural system for backgrounding. In this way, verum is not directly marked by focal accent but marked indirectly via backgrounding, which we formulate as the Backgrounding and Underspecification Thesis (BUT). On the basis of this Bantu evidence, we add a third type of strategy to the typology, where verum is marked indirectly via backgrounding. We use this updated typology to argue in favour of maintaining a conceptual link between verum, polarity focus, and predicated-centred focus more broadly.
Modelling the Understanding of Noun-Noun Compounds: The Role of Familiarity

Concept compounds play an important role in expanding language through the coining of new terms. The understanding of novel compounds also provides a key perspective on the inherent generativity of natural languages. In this paper, we report three experiments examining the effects of familiarity on the understanding of noun-noun compounds. We show that familiar compounds are judged to be sensible/comprehensible more quickly than unfamiliar ones. We also show, contrary to some previous proposals, that the production of relational/property interpretations to these compounds is equally “natural”. These findings have important implications for computational models of concept combination, as they support models that can produce both interpretation types as a matter of course.
Fundamental Properties of Pair-Wise Interactions Naturally Lead to Quarks and Quark Confinement: A Theorem Motivated by Neural Universal Approximation Results

In traditional mechanics, most interactions are pair-wise; if we omit one of the particles from our description, then the original pair-wise interaction can sometimes only be represented as interaction between triples, etc. It turns out that, vice versa, every possible interaction between N particles can be represented as pair-wise interaction if we represent each of the original N particles as a triple of new ones (and two new ones are not enough for this representation). The resulting three “particles” actually represent a single directly observable particle and in this sense, cannot be separated. So, this representation might ultimately explain the three-quark model of basic baryons, and explain quark confinement. The representation is based on a deep mathematical result (namely, a minor modification of Kolmogorov’s solution to Hilbert’s 13th problem) which has already been used in foundations of neural networks and in foundations of physics—to explain why fundamental physical equations are of second order, and why all these fundamental equations naturally lead to non-smooth solutions like singularity.