Reviewerly/exHarmony huggingface dataset:
Data augmentation with mixtures of max-entropy transformations for filling-level classification

We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties such as shape or transparency of test-time containers (cup or drinking glass) may differ from those represented in the training data. Dealing with such distribution shifts using standard augmentation schemes is challenging and transforming the training images to cover the properties of the test-time instances requires sophisticated image manipulations. We therefore generate diverse augmentations using a family of max-entropy transformations that create samples with new shapes, colors and spectral charac-teristics. We show that such a principled augmentation scheme, alone, can replace current approaches that use transfer learning or can be used in combination with transfer learning to improve its performance.
Graph Neural Network-Based Coflow Scheduling in Data Center Networks

In this chapter, we introduce DeepWeave, a DRL framework to generate coflow scheduling policies. DeepWeave works for both the intra-coflow scheduling and inter-coflow scheduling. To improve the inter-coflow scheduling ability in the job, DeepWeave employs a GNN to process directed-acyclic graph information. DeepWeave learns from the historic workload trace to train the neural networks of the DRL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model.
A sparse representation face classification method based on convolutional neural network

Convolutional neural network has a very important role in feature extraction, with stronger feature learning and feature expression ability. Therefore, to address the lack of robustness of sparse representation and the changes of environment and age in the face feature classification, at the feature level, this paper studies the convolutional neural network. We proposed a face feature extraction method based on convolutional neural network and then face classification using sparse representation. This method combines convolutional neural network extraction and sparse representation, taking full advantages of convolutional neural network in deep feature extraction and sparse representation in face feature recognition classification. The experimental results show that acquiring features through the convolutional neural network can represent faces linearly, which is more robust, and satisfies the assumption conditions of the face classification method based on sparse representation. The final experimental results show that the proposed face classification method is 9%~20% higher than the traditional feature extraction method.
Control system design for smart wheelchair robot

Over 1 billion people or approximately 15% of the world population that have disabilities levels and lesser access to health facilities. The majority of this population with physical disabilities, i.e. around 80%, is from low-income countries; and it is estimated only 5% up to 15% in developing countries, including Indonesia, that have access to assistive device technologies. Indonesia, for instance, has 20% of the total 240 million people who have disabilities in daily and social activities, and about 10% or 4.8 million people require wheelchair due to inability to walk. One major factor affecting limited access to this technology is poverty level. Besides, the ones available on the market are manually controlled by joysticks which are not user-friendly especially diffable with motoric problems. This paper highlights the control system design for a smart wheelchair prototype and the smart interface utilising voice-based and vision-based mechanism, namely voice and image guided direction. The results show that during image guided direction scenario, the system functions accurately with the intensity of light 0.4 to 400 lux without direct sun light, whilst voice guided direction functions effectively regardless any light source disturbances. However, there are some delays between 1.11 to 3.66 seconds for commands to activate the sensor while commands for actuating the motors will generate a delay of 2.19 to 4.62 seconds. Some discrepancies occur during motions, trajectory deviates in X ordinate direction, 83° in forward motion, 63° in reverse motion, 39° in left and 51° in right. These situations could be compensated by implementing feedback control as the current one is only an open-loop control.
Post-Attention Modulator for Dense Video Captioning

Dense video captioning (VC) aims at generating a paragraph-long description for events in video segments. Borrowing from the success in language modeling, Transformer-based models for VC have been shown effective also in modeling cross-domain video-text representations with cross-attention (Xatt). Despite Xatt’s effectiveness, the queries and outputs of attention, which are from different domains, tend to be weakly related. In this paper, we argue that the weak relatedness, or domain discrepancy, could impede a model from learning meaningful cross-domain representations. Hence, we propose a simple yet effective Post-Attention Modulator (PAM) that post-processes Xatt’s outputs to narrow the discrepancy. Specifically, PAM modulates and enhances the average similarity over Xatt’s queries and outputs. The modulated similarities are then utilized as a weighting basis to interpolate PAM’s outputs. In our experiments, PAM was applied to two strong VC baselines, VTransformer and MART, with two different video features on the well-known VC benchmark datasets ActivityNet Captions and YouCookII. According to the results, the proposed PAM brings consistent improvements in, e.g., CIDEr-D at most to 14.5%, as well as other metrics, BLEU and METEOR, considered.
Applications of Linear Programming: A Case Study in Cottage Industry

&#x0D; &#x0D; &#x0D; Cottage industries are in limelight both in urban and rural areas of Pakistan. The handmade shawls and hats are in demand from within the country and outside world. Cottage goods have added its share to the export of the nation in the recent time despite the challenges the wearing sector faces in the present time. From many writings it could be deduced that the area has still a room for improvement if the prevailing production situation is improved. In this connection, publication in apparel sector could not only resuscitate the financial system but also attract many to this side. Dresses connected with different culture, rituals and sites could also boast the business on foreign stage, which is an additional stride. The purpose of this paper is to make suitable changes in the consumption of the available resources in order to make them more significant and to make more profit out of the given available resources.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; As a case study, taking the cottage industry in Swat, Pakistan. The data gathered was used to estimate the parameters of the linear programming model. The model was solved using POM QM software. The findings of the study show that the profit of the company can be improved by 15% per month by applying linear programming models.&#x0D; &#x0D; &#x0D;
A Hierarchical Parallel Discrete Gaussian Sampler for Lattice-Based Cryptography

Discrete Gaussian sampling is one of the important components in lattice-based cryptosystems which are promising candidates for post-quantum cryptographic algorithms. For sufficient security and satisfactory performance, the Knuth-Yao algorithm is an efficient way to implement discrete Gaussian samplers. Nevertheless, most polynomials in lattice-based cryptography have 256 coefficients or more, which suffers from long latency to complete the sample generation. In this paper, the first parallel discrete Gaussian sampler with hierarchical structure is proposed, while keeping statistical distance to the actual distribution. Based on the imbalanced visiting frequency of the probability matrix, a three-stage generation strategy is adopted with hierarchical bit search units (BSUs) that can greatly reduce area consumption of the repeated costly lookup tables. Besides the architecture improvement, a lowest-set-bit scanning scheme is introduced to BSUs. Moreover, the parallelism of our design provides obfuscation ability against side-channel attacks (SCAs). A practical hardware implementation of discrete Gaussian distributions with $\sigma$=3.33 on the Xilinx Virtex-5 XC5VLX30 FPGA device spends 26.12 ns on average to generate 256 samples, consuming 994 slices. Results have verified its advantages of area efficiency over the state-of-the-arts (SOAs).
Content Based Recommender System Using Machine Learning

Due to the impact of high internet speeds, multimedia has become one of the best forms of entertainment for the user. Nowadays, the best source to make our day enjoyable is entertainment. Even Movie websites are most in demand as in this pandemic; it is very difficult for the user to go to the cinema hall and to watch movies. In order to watch favourable movies on OTT platforms, users can utilize movie recommender systems that are quite reliable. To search the preferred movies it generally requires a lot of time. In those cases, a system that can suggest a set of movies to the users according to their interest, or the popularity of the movies. The data is retrieved from the datasets and also from TMDb website. This is a user-friendly model which is easy and flexible to use. The technique used here is Content based filtering and the Cosine similarity measure algorithm which is present in the research method and comparative results and has been displayed which depicts that the proposed method/algorithm has better accuracy, flexibility and scalability of the proposed system. It focuses on providing the best possible recommendation of movies to the user.
Hyperintensional Models and Belief Change

Formal frameworks for Epistemology need to have enough logical structure to enable interesting conclusions regarding epistemic phenomena and to be expressive enough to model competing positions in the philosophical and logical literature. While beliefs are commonly accepted as hyperintensional attitudes, most work on standard epistemic logic has relied on idealised and intensional agents. This is particularly true in the area of AGM-inspired Belief Change. In this work, we investigate hyperintensional belief change operations providing a semantic framework based on impossible worlds semantics to hyperintensional variants of belief change operations. In doing so, we provide the basis for deepening the connection between AGM-inspired Belief Change literature and current discussions on Formal Epistemology and Metaphysics.
Do Neural Networks Recognize Patterns as well as Students?

We compare the similarity of humans and one of the current convolutional deep neural network (CNN) LeNet-5 on handwritten digits (MNIST database) recognition under stricter formulated recognition tasks and image degradations caused by a generative adversarial network (GAN). First, a conditional GAN neural network was trained on the MNIST database and generator seeds with normal probability distribution were used. The images for recognition were obtained by the generator that is part of this trained GAN network. Different image degradations were obtained by scaling the length of the input random vector for the generator. Second, as a criterion for the recognition task, we asked the question: Did a person write this digit (without thinning, thickening, protrusions or interruption of the line, no additional image artefacts which a human cannot write with a smooth stroke of the pen)? Human is more robust in most cases of recognizing degraded images. This is since a person who learns to recognize images throughout his life can better comply with the decision criterion than a neural network, which has a much simpler structure than the human brain and learns to recognize only on a limited training set. On the other hand, if we focus recognition criteria on the handwriting of digits, human is more precise, and CNN generalizes the recognised digit more behind this limitation. Our similarity graphs between CNNs and humans can be further used to find the boundaries of the handwritten digits domain and to create a system for recognizing anomalous images.
DRAKE: Deep Pair-Wise Relation Alignment for Knowledge-Enhanced Multimodal Scene Graph Generation in Social Media Posts

Scene Graph Generation (SGG) is a typical computer vision task that detects objects and corresponding predicates in an image. Existing SGG methods focus on modeling visual contexts to generate scene graphs and are conducted on well-annotated datasets with high-quality images. However, the quality is unguaranteed for images in social media posts, so that some images may be incomplete or occluded by some obstacles, hence might not provide sufficient visual context for SGG. Therefore, previous methods might result in missing or false visual relationship detection due to lacking visual contexts. To effectively generate the scene graphs in social media, we study multimodal scene graph generation (MSG) in this paper. MSG aims to develop visual scene graphs from images in social media posts with the support of text sentences. However, leveraging textual contents by simple multimodal alignment such as object-level alignment neglects the inherent pair-wise mapping between multimodal object pairs. To address the limitations, we propose a method named Deep pair-wise Relation Alignment for Knowledge-Enhanced (DRAKE) multimodal scene graph generation. The model supplements the missing visual contexts with well-aligned textual knowledge. It first represents the textual information into object-aware knowledge representation with the help of vision data. Furthermore, our proposed DRAKE facilitates the interaction of the info between multimodal pair-wise representations. A multimodal context enhancement layer can be devised to help the model generate the scene graph. To evaluate the model performance of SGG on social media images, we propose a social media SGG dataset called MSG. We comprehensively analyze the effectiveness of our proposed method on the MSG dataset. The experimental results on the MSG dataset indicate that our model outperforms the previous methods. To fairly compare our method with other SGG models, we also conduct experiments on the Visual Genome dataset for more analysis The MSG dataset is released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FuZe4ever/MSG</uri> .
Evaluating Neural Dialogue Systems Using Deep Learning and燙onversation燞istory

Neural talk models play a leading role in the growing popular building of conversational managers.A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently.The development of profound concentration on innovations has increased the use of neural models for a discussion display.In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques.The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment.In this paper, we mainly focus on DL-based dialogue systems.The issue to be overcome under this publication would be dialogue supervision, which will determine how the framework responds to recognizing the needs of the user.The dataset utilized in this research is extracted from movies.The models implemented in this research are the seq2seq model, transformers, and GPT while using word embedding and NLP.The results obtained after implementation depicted that all three models produced accurate results.In the modern revolutionized world, the demand for a dialogue system is more than ever.Therefore, it is essential to take the necessary steps to build effective dialogue systems.
SiamSampler: Video-Guided Sampling for Siamese Visual Tracking

We present SiamSampler, the first to our knowledge investigating video sampling in visual object tracking. We observe that the random sampling applied in Siamese-based trackers cannot focus on important data or ensure data diversity, hindering the effective training of networks. This paper proposes the Video-Guided Sampling Strategy to solve the problems in random sampling from both inter and intra-video levels. At the inter-video level, we propose Modified Gaussian Sampling Strategy (MGSS) to automatically assign higher sampling probabilities to longer and more difficult videos and reduce the sampling probabilities of shorter and easier videos. At the intra-video level, the Farthest Image Pair Sampling Strategy (FPSS) is proposed to increase the diversity of training data. Extensive experiments on general benchmarks demonstrate the effectiveness of our method. Compared with the baseline model, our method improves tracking performance on five datasets, without affecting the testing speed.
Anti-Counterfeiting Digital Pen for Multi-Dimensional Force Information Measurement Based on Biometric Technology

Do you remember, in the James Bond movie, James Bond carried a smart pen with him. This is an amazing and fun pen that shoots narcotic darts and turns into a mini grenade. This pen looks amazing and is super fun. Some companies in the United Kingdom and the United States have set up some digital pens imitating the shape of the 007 agent pen, and have anti-counterfeiting functions. The smart pen is a ballpoint pen. When writing, the sensor in the pen will acquire the three-dimensional force, three-dimensional acceleration and inclination angle during use. These data are transmitted to the PC by wireless data communication lines. This is a unique, fun pen. The method used in this paper is biometric technology. With the development of Internet of Things technology, artificial intelligence, big data and other technologies, more and more network devices are integrated into our daily life. Only using traditional security solutions will inevitably have limitations. The latest biometric technology can improve the security in the authentication process to a certain extent.
A Real-Time Approach to Recognition of Kazakh Sign Language

The interaction between man and computer is expanded with computer technology development. Hand gesture recognition is an essential technology for human-computer interaction, and it allows the recording and interpretation of hand gestures without some devices' use. Since the hand is a man's primary working tool, its position and orientation in space are crucial for many potential applications, such as interaction with virtual reality devices. Hand position estimation is a substantial process of hand gesture recognition. However, there are many difficulties in 3D hand pose estimating from a single RGB image. Media Pipe is a high-quality finger and hand-tracking framework. It uses in machine learning to detect 3D landmarks. The proposed paper provides a real-time approach to recognizing Kazakh gestures performance using a conventional camera and Media Pipe.
CoMBiNED: Multi-Constrained Model Based Planning for Navigation in Dynamic Environments

Recent deep reinforcement learning (DRL) approaches have achieved high success rate in map-less dynamic obstacle avoidance tasks. However, navigation in unseen dynamic scenarios without a pre-built map in the presence of dynamic obstacles still remains an open challenge. Since, learning accurate models for complex robotic scenarios such as navigation directly from high dimensional sensory measurements requires a large amount of data and training. Furthermore, even a small change on robot configuration such as kino-dynamics or sensor in the inference time requires re-training of the policy. In this paper, we address these issues in a principled fashion through a multi-constraint model based online planning (CoMBiNED) framework that does not require any retraining or modifications on the existing policy. We disentangle the given task into sub-tasks and learn dynamical models for them. Treating these dynamical models as soft-constraints, we employ stochastic optimisation to jointly optimize these sub-tasks on-the-fly at the inference time. We consider navigation as central application in this work and evaluate our approach on publicly available benchmark with complex dynamic scenarios and achieved significant improvement over recent approaches both in the cases of with-and-without given map of the environment.
Potential of basalt rock as a filler in polymer composite through the process of melting basalt combined with slow cooling

Basalt rock is a volcanic rock that has potential as a main composite material. One of the processing methods is by recrystallizing the crystal composition by burning it over its critical temperature and heat annealing to be made into powder as a filler on particleboard polyester composites. In this study, variations in basalt powder particles passed mesh 60, 100, 150, and 200, and variations in the volume of 30%, 40%, and 50% polyester resin were carried out in making particle board samples. Composite specimens were molded according to ASTM D695-15 standards for compressive strength tests and JIS A 5908-2003 standards for water absorption tests (DSA), MOE flexural strength tests, and MOR fracture strength tests. The purpose of this study was to obtain information about the optimum composition of the particle size variations of annealed basalt powder and polyester resin on physical and mechanical properties. The lowest optimum physical water absorption results were found in 200 mesh with a 50% resin volume variation with a value of 0.001%. The optimum results of the mechanical test of compressive strength showed the highest strength value of 6.81 kgf/cm2 found in 150 mesh particle variations and 50% resin variations; for testing, the MOE test showed the highest value of 8798.13 kgf / cm2 on 100 mesh particles variations and 50% resin variations. At the same time, the MOR test results showed the highest value of 62.76 kgf/cm2 at 150 mesh particles variation and 50% resin variation.
Flat and Texture Region Segmentation in Image Denoising

Existing image denoising methods have shown to be effective in suppressing noise, however, they over-smooth edge and texture details excessively because it is difficult to distinguish signal and noise while denoising. In this paper, we propose a method to segment an image into texture and flat region for enhancing the performance of denoising. To do this, texture map is generated using Sobel gradients, and is utilized in the loss function of the proposed network to learn the residual of the flat regions. The proposed method can control the level of noise in the texture region while suppressing the noise in the flat region as much as possible.
Unsupervised Sketch-to-Image Translation Network for Single Category Image Synthesis

Sketch-based image synthesis is a challenging cross-modal translation problem in the field of computer vision. Existing supervised learning methods either rely on edge information or resort to category labeling. However, the amount of sketch-photo pair training data is limited. Accordingly, in this paper, we propose an unsupervised two-stage synthesis model to accomplish fine-grained sketch image translation at the pixel level. More precisely, we boldly use the grayscale image as an intermediate result of the synthesis task between the geometric transformation and the color filling process. In addition, we propose a new perceptual discriminator on the unpaired datasets to better learn the geometry of sketches, while incorporating an attention mechanism that preserves the main information of sketches to handle sketch-specific abstractions and stylistic variations. The final experimental results indicate that our approach achieves more promising results, obtaining more realistic and diverse generated images with significantly better FID scores.