Reviewerly/exHarmony huggingface dataset:
Skew cyclic codes over $\mathbb{Z}_4+v\mathbb{Z}_4$ with derivation: structural properties and computational results

In this work, we study a class of skew cyclic codes over the ring $R:=\mathbb{Z}_4+v\mathbb{Z}_4,$ where $v^2=v,$ with an automorphism $\theta$ and a derivation $\Delta_\theta,$ namely codes as modules over a skew polynomial ring $R[x;\theta,\Delta_{\theta}],$ whose multiplication is defined using an automorphism $\theta$ and a derivation $\Delta_{\theta}.$ We investigate the structures of a skew polynomial ring $R[x;\theta,\Delta_{\theta}].$ We define $\Delta_{\theta}$-cyclic codes as a generalization of the notion of cyclic codes. The properties of $\Delta_{\theta}$-cyclic codes as well as dual $\Delta_{\theta}$-cyclic codes are derived. As an application, some new linear codes over $\mathbb{Z}_4$ with good parameters are obtained by Plotkin sum construction, also via a Gray map as well as residue and torsion codes of these codes.
A faster algorithm for counting the integer points number in $Δ$-modular polyhedra (corrected version)

Let a polytope $P$ be defined by a system $A x \leq b$. We consider the problem of counting the number of integer points inside $P$, assuming that $P$ is $Δ$-modular, where the polytope $P$ is called $Δ$-modular if all the rank sub-determinants of $A$ are bounded by $Δ$ in the absolute value. We present a new FPT-algorithm, parameterized by $Δ$ and by the maximal number of vertices in $P$, where the maximum is taken by all r.h.s. vectors $b$. We show that our algorithm is more efficient for $Δ$-modular problems than the approach of A. Barvinok et al. To this end, we do not directly compute the short rational generating function for $P \cap Z^n$, which is commonly used for the considered problem. Instead, we use the dynamic programming principle to compute its particular representation in the form of exponential series that depends on a single variable. We completely do not rely to the Barvinok's unimodular sign decomposition technique. Using our new complexity bound, we consider different special cases that may be of independent interest. For example, we give FPT-algorithms for counting the integer points number in $Δ$-modular simplices and similar polytopes that have $n + O(1)$ facets. As a special case, for any fixed $m$, we give an FPT-algorithm to count solutions of the unbounded $m$-dimensional $Δ$-modular subset-sum problem.
What is understandable in Bayesian network explanations?

Explaining predictions from Bayesian networks, for example to physicians, is non-trivial. Various explanation methods for Bayesian network inference have appeared in literature, focusing on different aspects of the underlying reasoning. While there has been a lot of technical research, there is very little known about how well humans actually understand these explanations. In this paper, we present ongoing research in which four different explanation approaches were compared through a survey by asking a group of human participants to interpret the explanations.
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping

Using an extended and formalized version of the Q/C map analysis of Poole et al. (2016), along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the "shape" of the network's initialization-time kernel function. We then develop a method called Deep Kernel Shaping (DKS), which accomplishes this using a combination of precise parameter initialization, activation function transformations, and small architectural tweaks, all of which preserve the model class. In our experiments we show that DKS enables SGD training of residual networks without normalization layers on Imagenet and CIFAR-10 classification tasks at speeds comparable to standard ResNetV2 and Wide-ResNet models, with only a small decrease in generalization performance. And when using K-FAC as the optimizer, we achieve similar results for networks without skip connections. Our results apply for a large variety of activation functions, including those which traditionally perform very badly, such as the logistic sigmoid. In addition to DKS, we contribute a detailed analysis of skip connections, normalization layers, special activation functions like RELU and SELU, and various initialization schemes, explaining their effectiveness as alternative (and ultimately incomplete) ways of "shaping" the network's initialization-time kernel.
VTAMIQ: Transformers for Attention Modulated Image Quality Assessment

Following the major successes of self-attention and Transformers for image analysis, we investigate the use of such attention mechanisms in the context of Image Quality Assessment (IQA) and propose a novel full-reference IQA method, Vision Transformer for Attention Modulated Image Quality (VTAMIQ). Our method achieves competitive or state-of-the-art performance on the existing IQA datasets and significantly outperforms previous metrics in cross-database evaluations. Most patch-wise IQA methods treat each patch independently; this partially discards global information and limits the ability to model long-distance interactions. We avoid this problem altogether by employing a transformer to encode a sequence of patches as a single global representation, which by design considers interdependencies between patches. We rely on various attention mechanisms -- first with self-attention within the Transformer, and second with channel attention within our difference modulation network -- specifically to reveal and enhance the more salient features throughout our architecture. With large-scale pre-training for both classification and IQA tasks, VTAMIQ generalizes well to unseen sets of images and distortions, further demonstrating the strength of transformer-based networks for vision modelling.
Matching Models for Graph Retrieval

Graph Retrieval has witnessed continued interest and progress in the past few years. In thisreport, we focus on neural network based approaches for Graph matching and retrieving similargraphs from a corpus of graphs. We explore methods which can soft predict the similaritybetween two graphs. Later, we gauge the power of a particular baseline (Shortest Path Kernel)and try to model it in our product graph random walks setting while making it more generalised.
Architecture of Network Camera Photo Authentication Scheme using Steganography Approach

The aim of integrity protection process is not only to secure the send message, but also ensures that the original message is not modified during sending the message. In this paper, a novel proposed model is proposed to verify that the sent message was not modified. The proposed model starting with encrypting the original message and applying one of the hash functions on the message. The ciphered message, private key, and the output of hash function was reshaped to apply Discrete Cosine Transform (DCT), Zig-zag ordering, and Huffman coding to obtain a compressed text, this compressed text is then hidden in an image. The same steps with a reverse order are applied on the receiver to obtain the decrypted message and then apply the same hash function to verify that the message was not changed. Different experiments were conducted and the results demonstrated that the proposed model achieved high integrity protection.
SDR: Efficient Neural Re-ranking using Succinct Document Representation

BERT based ranking models have achieved superior performance on various information retrieval tasks. However, the large number of parameters and complex self-attention operation come at a significant latency overhead. To remedy this, recent works propose late-interaction architectures, which allow pre-computation of intermediate document representations, thus reducing the runtime latency. Nonetheless, having solved the immediate latency issue, these methods now introduce storage costs and network fetching latency, which limits their adoption in real-life production systems. In this work, we propose the Succinct Document Representation (SDR) scheme that computes highly compressed intermediate document representations, mitigating the storage/network issue. Our approach first reduces the dimension of token representations by encoding them using a novel autoencoder architecture that uses the document's textual content in both the encoding and decoding phases. After this token encoding step, we further reduce the size of entire document representations using a modern quantization technique. Extensive evaluations on passage re-reranking on the MSMARCO dataset show that compared to existing approaches using compressed document representations, our method is highly efficient, achieving 4x-11.6x better compression rates for the same ranking quality.
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, i.e., the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best model achieves F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of our dataset. Our dataset and code is available at https://mcgill-nlp.github.io/topiocqa
SPEC: Seeing People in the Wild with an Estimated Camera

Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspective projection, large constant focal length, and zero camera rotation. These assumptions often do not hold and we show, quantitatively and qualitatively, that they cause errors in the reconstructed 3D shape and pose. To address this, we introduce SPEC, the first in-the-wild 3D HPS method that estimates the perspective camera from a single image and employs this to reconstruct 3D human bodies more accurately. First, we train a neural network to estimate the field of view, camera pitch, and roll given an input image. We employ novel losses that improve the calibration accuracy over previous work. We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose. SPEC is more accurate than the prior art on the standard benchmark (3DPW) as well as two new datasets with more challenging camera views and varying focal lengths. Specifically, we create a new photorealistic synthetic dataset (SPEC-SYN) with ground truth 3D bodies and a novel in-the-wild dataset (SPEC-MTP) with calibration and high-quality reference bodies. Both qualitative and quantitative analysis confirm that knowing camera parameters during inference regresses better human bodies. Code and datasets are available for research purposes at https://spec.is.tue.mpg.de.
Stochastic Contrastive Learning

While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent variables. In contrast, prescribed latent variable (LV) models enable attributing uncertainty, inducing task specific compression, and in general allow for more interpretable representations. In this work, we introduce LV approximations to large scale contrastive SSL models. We demonstrate that this addition improves downstream performance (resulting in 96.42% and 77.49% test top-1 fine-tuned performance on CIFAR10 and ImageNet respectively with a ResNet50) as well as producing highly compressed representations (588x reduction) that are useful for interpretability, classification and regression downstream tasks.
Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?

Despite the success of a number of recent techniques for visual self-supervised deep learning, there has been limited investigation into the representations that are ultimately learned. By leveraging recent advances in the comparison of neural representations, we explore in this direction by comparing a contrastive self-supervised algorithm to supervision for simple image data in a common architecture. We find that the methods learn similar intermediate representations through dissimilar means, and that the representations diverge rapidly in the final few layers. We investigate this divergence, finding that these layers strongly fit to their distinct learning objectives. We also find that the contrastive objective implicitly fits the supervised objective in intermediate layers, but that the reverse is not true. Our work particularly highlights the importance of the learned intermediate representations, and raises critical questions for auxiliary task design.
New Evolutionary Computation Models and their Applications to Machine Learning

Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability have increased exponentially, but the software is years behind. The demand for software has also increased significantly, but it is still written in old fashion: by using humans. There are multiple problems when the work is done by humans: cost, time, quality. It is costly to pay humans, it is hard to keep them satisfied for a long time, it takes a lot of time to teach and train them and the quality of their output is in most cases low (in software, mostly due to bugs). The real advances in human civilization appeared during the industrial revolutions. Before the first revolution, most people worked in agriculture. Today, very few percent of people work in this field. A similar revolution must appear in the computer programming field. Otherwise, we will have so many people working in this field as we had in the past working in agriculture. How do people know how to write computer programs? Very simple: by learning. Can we do the same for software? Can we put the software to learn how to write software? It seems that is possible (to some degree) and the term is called Machine Learning. It was first coined in 1959 by the first person who made a computer perform a serious learning task, namely, Arthur Samuel. However, things are not so easy as in humans (well, truth to be said - for some humans it is impossible to learn how to write software). So far we do not have software that can learn perfectly to write software. We have some particular cases where some programs do better than humans, but the examples are sporadic at best. Learning from experience is difficult for computer programs. Instead of trying to simulate how humans teach humans how to write computer programs, we can simulate nature.
The Impact of Implementing Contextual Guessing Strategy on Improving EFL Learners’ Vocabulary Competence and Reading Comprehension

The present study aims to measure the effect of implementing the contextual guessing strategy on improving the learners’ vocabulary competence and reading comprehension. In other words, the researchers try to investigate whether there was a major difference of EFL learners’ vocabulary competence and reading comprehension after implementing the treatment by using guessing meaning from context strategy. This study adopted the experimental design. The participants of this study consist of sixty learners who studying the English Course, advanced reading and summarizing, divided into two groups (30 in the control group and 30 in the experimental group). The results of the study showed that there was a significant difference of learners’ vocabulary competence and reading comprehension achievements between the control and experimental groups due to the treatment.
Classification of Determination the Recipients of the Program Keluarga Harapan (PKH) Using K-Nearest Neighbor Algorithm

Classification is one method of data mining techniques for classifying data by system according to predetermined rules. In this study, the algorithm used was K-Nearest Neighbor (KNN) algorithm and the data used in this study was the community data obtained from Kantor Desa Gampong Uteun Geulinggang, Dewantara, in Aceh Utara. The main focus of this study was to analyzing and applying K-NN algorithm in a web-based system to classify data on beneficiaries of the Program Keluarga Harapan (PKH) in Gampong Uteun Geulinggang, Dewantara, Aceh Utara. This study used 16 (sixteen) criterias, namely house status, floor area, floor type, gas cylinder, refrigerator, air conditioner, water heater, television, jewelry/gold (10 grams), computer/laptop, bicycle, motorcycle, car, cow, buffalo and goat. The data was classified into 3 classes, namely worthy, not unworthy and very unworthy. The results of this study with a value of k = 3, obtained a precision value of 97%, recall value of 95% and the accuracy value of 97%.
ANIMATION INTRODUCTION OF PROFILE OF SMK NEGERI 5 JAYAPURA USING MULTIMEDIA ANIMATION COMPUTACION METHOD

Advances in science and technology can help an institution to support information and promotion, such as creating school profile recognition animations. Sala one vocational secondary in Papua, SMK Negeri 5 Jayapura at this time for the process of introducing school profiles using a website in the form of text so that it would be better if using multimedia components such as animation to clarify school profiles. The purpose of this research is to design animation to help promote SMK Negeri 5 Jayapura to the community to help the school to be able to provide better promotion and service to the community. This animation was created by the multimedia development life cycle (MDLC) development model of Luther Sutopo's multimedia development model which consists of six stages namely Concept, Design, Material Collection, Manufacturing, Testing and Distribution. The software used in the animation process is Blender V 2.79b, Photoshop CS5 and Adobe Audition CS6. The results of the study in the form of animated videos are 6 minutes long and not interactive. The profile creation of SMK Negeri 5 Jayapura has been successfully built to help the school to provide information about SMK Negeri 5 Jayapura, especially the introduction of school profiles to the public.
Penerapan Metode Convolution Neural Network (CNN) Pada Aplikasi Automatic Lip Reading

AbstractA Prototype of Automatic Lip-Reading or a prototype of automatic lip-reading is a device that is needed by people with hearing disabilities or the Deaf. The prototype will help people with hearing impairment move independently without depending on others. Advances in Nano technology have driven the development of Computer Vision and software that enables the creation of prototypes of Automatic Lip-Reading. The image processing applied to this prototype is focused on the movement of the lips, tongue, and the area around a person's mouth. Furthermore, the results of the image recognition will compare with the database that has been provided to produce certain words. The prototype design consists of a camera, Python with Tensorflow used as an image processing programming language with the Convolutional Neural Network (CNN) method as an image recognition method, and Machine Learning Technology used as processing and decision-making systems. This study, numbers and alphabets were used as trials or predictions of the Automated Lip-Reading system. By using CNN and Machine Learning methods, the test results show that in general, the system designed can predict numbers and alphabet with not quite high or less than 35%. Â Â
Intelligent Decision Support System in the Selection of Children's School Types Based on IQ, SQ, and EQ in Aceh

Schools are institutions for students to carry out early stages of education that are tailored to their level. Developments that continue to show their existence have a very rapid impact, for example school institutions. Currently, there are permanent schools (pondok/pesantren) or public schools (state/private) with their respective performances. However, the tendency of parents to choose the best school according to him without paying attention to the child's growth and development first. This often occurs after the transition from elementary school to junior high school (SMP). Especially for the Aceh area, where the majority of children are directly in hostel with the hope that the child can grow optimally without reviewing the advantages and disadvantages. The rapid development of technology in Indonesia has contributed a lot to the problems experienced by society with the birth of various kinds of intelligent systems. The development of an intelligent system based on this expert system can be a solution to the problems that arise for parents now, can determine the type of school children will go to in the future. by implementing a method that is able to make decisions by providing the best solution. The purpose of this study is to build an intelligent system that is able to provide decision support in determining the type of school. By providing services to parents in the form of a web service-based interface system. As well as understanding the type of child's ability before deciding to be in a boarding school or public school, especially at the junior high school level.
Classification of Public Figures Sentiment on Twitter using Big Data Technology

Public figures often receive widespread public attention because they can exert a meaningful influence. On Twitter, the users can freely express their opinion through tweets. There are about 456,000 tweets sent in a minute which with this large and diverse number will make Big Data. Big Data has valuable potential for better decision-making. This large amount of tweet data can yield valuable information through sentiment analysis. This study aims to conduct a sentiment classification of Indonesian public figures using Twitter's data. This study used 1,034,329 tweets collected from Twitter in the period November 2021 until March 2022. Tweet classification is done by building a classification model using the Bidirectional Long Short-Term Memory algorithm. Sentiment toward public figures in Indonesia is 45.98% negative sentiment, 28.04% positive sentiment, and 25.98% neutral sentiment resulting from this study. The highest positive sentiment is obtained by public figures when there is content or news that is relevant to the public figure, while the highest negative sentiment is obtained when there is content or news that contradicts the image of the public figure.
Predicting Consumer Interest in All You Can Eat Restaurants with Gradient Boosting Algorithm

The rise of businesses in the food industry makes industry players think creatively. One of the trends in this industry is the â€œAll You Can Eatâ€ restaurant with its various variations. This type of restaurant is considered capable of being an attraction for consumers because consumers can eat every dish as much as they want without a limit on the amount. However, it is difficult to map out the factors that make consumers want to come back to the restaurant. This research will build a web application with machine learning features using the Gradient Boosting algorithm that can map whether consumers will come back or not, so that restaurant businesses can use this application to continuously improve the performance of their restaurants.