Reviewerly/exHarmony huggingface dataset:
Modeling Handwritten Digits Dataset Using the Matrix Variate t Distribution

In this paper, we consider matrix variate t distribution and explore some of its distributional properties to model handwritten digits dataset. In particular, we show that the marginal and the conditional distributions are also matrix variate t distribution. We provide parameter estimation of the matrix variate t distribution using the EM algorithm. We give a small simulation study to show the performance of the proposed EM algorithm for finding the estimates for the parameters of interests. A real data example illustrates that the matrix variate t distribution can be used as a robust alternative to the matrix variate normal distribution for modeling matrix variate datasets with some atypical observations.
Power Overwhelming: Quantifying the Energy Cost of Visualisation

GPUs are the power-hungry tool of many visualisation researchers. However, their energy consumption has mostly been investigated outside the visualisation community, albeit our algorithms can generate more complex workloads than compute kernels. Additionally, a raising number of web-based visualisations potentially makes consumers other than the GPU more relevant. We present measurement setups for quantifying the energy cost of visualisation, ranging from software sensors over external power meters and micro controller-based setups to using oscilloscopes. These setups cover energy consumption of GPUs, CPUs and other components of a computing system. Using raycasting of spherical glyphs, volume rendering and D3 visualisations as examples, we show that there are viable options for evaluating most kinds of visualisations. We conclude by stating the challenges to a broader application of these techniques and by making recommendations on how to overcome these.
Context-aware Method for Small Object Segmentation in Road Scenes

Thanks to the rapid development of deep learning algorithms, autonomous driving perception systems are now able to handle most common road scenes. But the perception in some corner scenes is still a great challenge. In this paper, we focus on the perception of small road objects. Perception of small objects at long distance is diffcult due to their low resolution in images. In order to address this issue, we firstly build a large dataset for small object segmentation in road scenes, which could be a data support for deep learning-based method. Then, we propose a method that contains three modules: transformer-based attention module, feature fusion module and atrous convolution-based auxiliary loss module to divide small obstacles from road area. The transformer-based attention module converts images to sequences and exploits global context information adequately. Feature fusion module is designed to combine texture information and semantic information. Auxiliary loss module takes atrous convolution with multiple dilation rates to catch context information as much as possible. Finally, experiments are conducted on both self-collected dataset and generic segmentation dataset, which all show the superiority of our method in segmenting small objects.
Sentiment Analysis as Assessment of the COVID-19 Social Assistance Pollemic using Random Forest Algorithm

The government’s endeavors in organizing the COVID-19 Social Assistance program often encounter problems and lead to the opinion of many parties. One of the opinions expressed on social media is twitter. Sentiments from these opinions were then analyzed to find out the assessment and discussion of each sentiment that can be used as evaluation material for the Social Assistance program. In this study, the sentiment of each preprocessed text was obtained using a labeling process with an assessment of polarity and subjectivity from TextBlob library. The results of neutral, positive, and negative sentiment assessments were weighted using TFIDF. Words that have been formatted into numeric then classified using the Random Forest algorithm. The parameters in this case were in accordance with the documentation on sklearn. An evaluation of the algorithm was also carried out using the 10 kfold cross validation method as a performance validation of the results of testing each piece of data. The performance obtained is quite satisfactory.
Augmented Reality in Clothing Consumer Customization in COVID-19 Pandemic: A Preliminary Study

Emergencies produce significant changes in people’s habits and lifestyles. Further, it also impacts our clothing tastes and preferences because social interaction is reduced, and everyday activities are performed at home. Thus, this paper shows a psychographic analysis to determine the type, style, color, and other clothing characteristics users preferred across the pandemic. In addition, some considerations have been taken into account to understand the clothing characteristics users will look for in post-pandemic presential jobs. In this context, the first version of a smartphone app using augmented reality (AR) has been developed. Three-dimensional objects were designed using Blender, while the smartphone app was set up using Unity, accompanied by modules such as AR Foundation, ARCore XR Plugin, and DOTween. The application allows the overview of the selected three-dimensional garment and relevant information about its components. Statistical analysis shows a vital essential between the monthly income of the participants and their purchasing decision; likewise, between sex and upper garments usage. Experimental tests inside a retail store validate this proposal throughout a new sample of 44 people. Ultimately, they filled out a usability test (SUS) that confirmed the application acceptance with an 82.73%. Finally, they gave the corresponding feedback on their experience using the app. Consequently, relevant information that might be used in future research to understand the consumer needs in a matter of clothing emergencies or confinement is passing through has been exposed.
Optimizing Cache Accesses with Tensor Memory Format Search for Transformers in TVM

Transformer-based models have achieved great success in natural language processing and computer vision applications. These models, however, often comprise a large number of parameters. Furthermore, tend to be computationally intensive. This presents a challenge in deploying them on resource-constrained devices. Using deep learning compilers, e.g. TVM, to compile these models can reap the performance benefit gained by tailoring CUDA kernels specifically for the target GPU devices. In this paper, we focus on complementing existing compiler optimization passes in TVM by further exploring the impact of tensor memory formats used by intermediate activations on cache accesses and its performance implications. First, building on top of the graph-based abstraction, we express each layer node, e.g. multi-layer perceptron (MLP) and self-attention layers, using Einstein summation or Einsum-based notations. Edges formed by intermediate tensors thus connecting layer nodes as their inputs and outputs. As intermediate tensors are typically stored in memory contiguously, their memory formats in terms of the ordering of its dimensions, may exhibit the notable effect on cache access behavior, and strided memory accesses are typically slower than contiguous ones. Yet existing compiler frameworks focus on layer-wise optimizations, and often neglected the impact of tensor memory formats of the layer’s inputs and outputs on the performance of the resulting kernels. To this end, this paper proposes to optimize the performance of compiled models by searching for optimal memory formats for all intermediate tensors. We then use the MLP-Mixer model architecture as a case study of the optimization process and deploy the resulting optimized models with TVM on target GPUs. As exhaustive searching requires a substantial computational cost, we thus propose algorithms to efficiently navigate the search space of memory formats of intermediate tensors. Applying the algorithm on an MLP-Mixer model with 42 mixer-layers, we can achieve 23.7% inference performance enhancement.
The Effect of The Thematic Storytelling Method on The Reading Liteacy of Children 5-6 Years Old at Kindergarten

This study aims to determine the influence and how much influence the thematic storytelling method has on the reading literacy of children aged 5-6 years in InsanMandiri Kindergarten Sidoarjo. This research is an experimental research that requires experimental and control samples as data analysis. The population in this study was students at InsanMandiri Kindergarten, with a sample of 20 children each as an experimental sample and a control sample. Data collection techniques used are tests and documentation. Data analysis method using independent test t.Based on the results of the study obtained a significance value of 0.000 &lt;0.05, it can be concluded that there is an average difference between the research samples. Meanwhile, from the mean, it can be seen that the test result score for the experimental class is 9.55 higher than the control class, which is 6.35. So it can be concluded that there is an effect of the thematic storytelling method on the reading literacy of children aged 5-6 years in TK InsanMandiriSidoarjo, amounting to 9.55. The student's thematic storytelling activity is stimulated to understand stories that have been read or heard, and it may help the student to answer questions about the reading and thus make it easier for the student to retell the reading or the story in his own language.
Authentication Mechanism Based on Physical Layer Security in Industrial Wireless Sensor Networks

When wireless network technology is applied to industrial scenes, the open channel environment makes industrial equipment more vulnerable to attacks and threats from illegal nodes, such as eavesdropping, deception and identity information forgery. The complexity and variety of attack methods make the supervised machine learning algorithm insufficient to establish a low complexity, lightweight and high security authentication mechanism in industrial wireless sensor networks. Moreover, the wireless electromagnetic wave will be affected by additive noise and fading in the propagation process, making the wireless channel in a dynamic state. Based on this, we study a new authentication mechanism based on physical layer security for wireless sensor networks in dynamic industrial scenarios. Using more precise physical layer channel information, and building an authentication model around positive-unlabeled (PU) learning and bootstrap aggregating (bagging) strategy, we can accurately distinguish legal nodes and illegal nodes in the received channel information in the industrial scene where only the channel information of legal nodes is known. Finally, the effectiveness of the scheme is verified by using the public data set collected by the national institute of standards and technology (NIST) in a real industrial scene.
Disruption Is On the Way A cartoon sketch of a man in the middle of four stranger models. The text reads Between three rocks and a hard place, I love to be challenged.

Chapter 1 summarizes some of the important trends in our environment and helps us understand the disruptions that will fundamentally threaten our firm—sooner or later.
Tracking Multi-objects with Anchor-Free Siamese Network

Single object tracker based on siamese neural network have become one of the most popular frameworks in this field for its strong discrimination ability and high efficiency. However, when the task switch to multi-object tracking, the development of siamese-network based tracking methods is limited by the huge calculation cost comes from repeatedly feature extract and excessive predefined anchors. To solve these problems, we propose a siamese box adaptive multi-object tracking (SiamBAN-MOT) method with parallel detector and siamese tracker branch, which implementation in an anchor-free manner. Firstly, ResNet-50 is constructed to extract shared features of the current frame. Then, we design a siamese specific feature pyramid networks (S-FPN) to fuse the multi-scale features, which improves detection and tracking performance with the anchor-free architecture. To alleviate the duplicate feature extraction, RoI align is operated to extract all trajectories’ template feature and search region feature in a single feature map at once. After that, anchor-free based Siamese tracking network outputs the tracking result of each trajectory according to its template and search region feature. Meanwhile, current frame’s detection results are obtained from the detector for the target association. Finally, a simple novel IOU-matching scheme is designed to map the tracking results to the detection results so as to refine the tracking results and suppress the drifts caused by siamese tracking network. Through experimental verification, our method achieves competitive results on MOT17.
Character-Level Chinese Toxic Comment Classification Algorithm Based on CNN and Bi-GRU

At present, the classification of "toxic comment" is mainly studied in the English context, whereas Chinese context is less explored and even lacks a public corpus. As many comment are short texts with sparse features and strong context dependence, this study proposes a character-level embedded neural network model based on the convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Then, the classification of toxic comment based on the Chinese toxic comment dataset is developed. In our proposed model, the CNN, which combines character- and word-level vectors, is used to fully obtain the local important features of the text, and then the bidirectional timing information acquisition ability of Bi-GRU is used to improve the accuracy of the Chinese toxic comment classification. Experimental results show that the F1 score of our proposed model can reach 0.8081, which is better than the correlation comparison models.
BECT: Beacon-based Contact Tracing for Detecting Direct &amp; Indirect Contacts

Digital Contact tracing with smartphone apps may help control the spread of serious pathogens, such as COVID-19. Such apps typically use peer-to-peer Bluetooth data transfer to record a contact. However, they suffer from low adoption rates, high false alarm contact indications, battery drain, and user privacy concerns. This paper proposes BECT or BEacon-based Contact Tracing, a contact tracing framework using static Bluetooth beacon devices installed in public or private places that periodically broadcast packets to nearby users that are stored as coins. Users that are positively diagnosed submit their coin IDs to a third-party service (e.g., local health authority) which can mark these coins as infected and disseminate them to other users. A match between a user's stored coins and an infected coin implies that the user has come in direct or indirect contact with an infected person. The BECT framework does not expose users' private data and conserves the device battery. We use MATLAB simulations to compare the performance of the BECT framework to phone-phone apps in a restaurant scenario and show that BECT has superior contact tracing performance. We also provide general deployment guidelines.
Elementary Logic

This chapter provides the basics of the elementary logic. The key concepts, ideas, and topics that are discussed, among others, include: (i) Sentential connectives; (ii) Truth tables of sentential formulas; (iii) Notions of tautology, contradiction, and logical equivalence; (iv) Three proof methods, namely, Direct Proof, Proof by Contradiction, and Conditional Proof; (v) Existential and Universal quantifiers; (vi) Validity and invalidity of inferences.
Improving English to Chinese machine translation performance via adversarial training

The transformer has improved slow training, the most criticized disadvantage, of RNNs and achieves fast parallelism by self-attention mechanism. Also, the transformer can be enhanced to a great depth to fully exploit the characteristics of the deep neural network model and improve the model’s accuracy, which is widely used in machine translation. Deep models, however, have the inevitable disadvantage of being prone to overfitting. We consider introducing the adversarial training method in the translation model to improve the robustness of the model through regularization to alleviate the overfitting problem. We conducted analytical experiments on the English-Chinese translation dataset which shows that the adversarial training approach can improve translation quality.
Scene Text Detection and Recognition Based on Iterative Correction

There are a number of challenges for text detection and recognition because of the complexity of text presentation in natural scene images. This paper proposes a scene text detection and recognition model based on iterative correction to reduce the impact of the curvature of complex text on text detection and recognition. The model comprises a rectification network and a recognition network. An innovative rectification network is developed which employs a novel Bezier curve to predict the bounding rectangle of the scene text, and uses TPS transform to iteratively refine the correction of image. The recognition network adopts ResNet added to Bi-LSTM for encoding and LSTM with attention mechanism for decoding. Experiments on several benchmark datasets demonstrate that the proposed method can effectively improve the text recognition rate, especially on complex curved text.
Screening for The Risk of Dyslexia in Children through a Redesigned Game

Dyslexia is the disorder affecting reading and learning language. Screening is a necessary, yet the discouraging and lengthy, process to distinguish dyslexia children early to treat and help them accordingly. In this study, the game to detect dyslexia risk called the picture rotation game is redesigned. The aim is to create a dyslexia risk detection tool that encourages children to stay motivated and achieve more accurate detection results. The indicator used is visual perception which identifies problems with directions, shapes, and distinction of similar or symmetrical figures. The redesigned game could differentiate the risk group from the non-risk group when a difficulty mechanism was implemented.
The Impacts of Social Media in Higher Education Institutions

Today, social networks have become an important part of an individual's life. Most people use social networks to interact and communicate not only with people, but also with different companies or institutions in search of information. This includes higher education institutions. The role of social networks in a higher education institution is important; It helps show what the university does, what services it offers, the achievements of its students, what achievements they get, etc. Social networks, with the right strategies, can create a great impact on how students see the university and what to do, and also they become an admission tool to generate interest in future students.
Causality-aided Recommendation Systems

Recommendation systems often face the challenge of cold-start problem where the two parties are completely new to the systems. Previous works introduce the integration of side information of items and users into the systems so that they can learn a representation of the inputs with better generalization, thus potentially solving the cold-start problem. However, using users' or items' side information brings another problem to deep learning based collaborative filtering models known as spurious correlations. In this case, deep learning models fail to adequately generalize and simply capture the idiosyncrasies of the training datasets. In short, deep learning models find a ‘short-cut’ created by the hidden bias in the datasets and make the spurious correlations between specific features and the outcome. In this work, we propose the counterfactual constraint on user/item attributes, allowing the systems to avoid hidden bias in the datasets, thus having the robustness and generalization. The goal is to discourage the disruptive distribution change in the prediction when the distribution shift occurs in the user/item attribute spaces. The proposed model compares favorably with conventional baseline on both synthetic and real-world datasets.
Weighting Ratings: Are People Adjusting for Bias in Extreme Reviews?

Consumer ratings distributions have increased in importance due to the ever-increasing proportion of transactions that are made online. This raises the question of whether consumers are adjusting for potentially fake or biased reviews when evaluating products from ratings. Two online experiments tested treatments that manipulated how the extremes of rating distributions (i.e. 1-star and 5-star) were presented. Neither removing extreme reviews while preserving the mean rating, flagging suspicious extreme reviews in the distribution, nor priming individuals about review manipulation significantly affects perceived product quality on average (when the mean rating is accounted for). Furthermore, people appear to apply different weights to ratings of different scores to evaluate product quality. The estimated weighting profile is hump-shaped and asymmetric, implying that consumers are particularly susceptible to being misled by disingenuous 1-star reviews. These results may indicate a need to change how summary rating statistics are conveyed in order to prevent undesirable or wasteful purchases.
Transmissibility-based DAgger For Fault Classification in Connected Autonomous Vehicles

Fault mitigation in Connected Autonomous Vehicle (CAV) platoons is faster and more reliable if the fault structure is known. In this paper we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmissibility operators were shown to be exceptional in signals estimation; however, its also shown to be noncausal and thus can only be used offline. To this end, we propose using Data Aggregation (DAgger), which is an extension in imitation learning to transfer the classification experience from transmissibility operators to a novice machine learning agent to be used online. A heterogeneous CAV platoon was modeled with three different faults separately. These faults are actuator disturbances, false data injection attacks, and communication time delay. The proposed algorithm is then tested on the platoon model and then applied to an experimental setup that consists of three autonomous robots. The overall classification accuracy achieved was 95.8% for the experiment.
ROSETTA: A Resource and Energy-Efficient Inference Processor for Recurrent Neural Networks Based on Programmable Data Formats and Fine Activation Pruning

Recurrent neural networks (RNNs) are extensively employed to perform inference based on the temporal features of the input data. However, their computational workload and power consumption involved in inference are prohibitively high in practice, which may be problematic to achieve a high-speed inference in devices with tight limitations in the available silicon resources and power supply. This paper presents an efficient inference processor for RNNs, named ROSETTA. ROSETTA supports multiple data formats programmable for each vector operand to achieve a wide range or high precision with a limited data size. ROSETTA consistently performs every vector operation based on homogeneous processing units with a high utilization rate. Moreover, ROSETTA skips operations and reduces memory accesses to achieve high energy efficiency by pruning the activation elements in a fine-grained manner. Implemented in a low-cost 28 nm field-programmable gate array, ROSETTA exhibits a resource and energy efficiency as high as 2.51 - 1.14 MOP/s/LUT and 434.01 - 113.29 GOP/s/W, respectively, while producing near-floating-point inference results. The resource and energy efficiency of ROSETTA are higher than those of the previous processor implemented in the same device by up to 206.1% and 304.0%, respectively. The functionality has been verified for several RNN models of various types under a fully-integrated inference system.