Reviewerly/exHarmony huggingface dataset:
Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms' convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods' time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.
Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2

In this paper we study Graphol, a fully graphical language inspired by standard formalisms for conceptual modeling, similar to the UML class diagram and the ER model, but equipped with formal semantics. We formally prove that Graphol is equivalent to OWL 2, i.e., it can capture every OWL 2 ontology and vice versa. We also present some usability studies indicating that Graphol is suitable for quick adoption by conceptual modelers that are familiar with UML and ER. This is further testified by the adoption of Graphol for ontology representation in several industrial projects.
Physics-based Differentiable Depth Sensor Simulation

Gradient-based algorithms are crucial to modern computer-vision and graphics applications, enabling learning-based optimization and inverse problems. For example, photorealistic differentiable rendering pipelines for color images have been proven highly valuable to applications aiming to map 2D and 3D domains. However, to the best of our knowledge, no effort has been made so far towards extending these gradient-based methods to the generation of depth (2.5D) images, as simulating structured-light depth sensors implies solving complex light transport and stereo-matching problems. In this paper, we introduce a novel end-to-end differentiable simulation pipeline for the generation of realistic 2.5D scans, built on physics-based 3D rendering and custom block-matching algorithms. Each module can be differentiated w.r.t. sensor and scene parameters; e.g., to automatically tune the simulation for new devices over some provided scans or to leverage the pipeline as a 3D-to-2.5D transformer within larger computer-vision applications. Applied to the training of deep-learning methods for various depth-based recognition tasks (classification, pose estimation, semantic segmentation), our simulation greatly improves the performance of the resulting models on real scans, thereby demonstrating the fidelity and value of its synthetic depth data compared to previous static simulations and learning-based domain adaptation schemes.
SaaS or not: optimal versioning strategy of releasing enterprise software

Purpose With the rapid development of cloud computing, most software firms face the significant choice of whether they should change the versioning strategy of enterprise software from releasing the on-premise version to the software-as-a-service (SaaS) version. Data being generated and hosted on SaaS vendors' servers brings multiple effects. It enables customers to enjoy the flexibility of accessing data and using the software remotely, named the “portability” effect. However, on the other hand, the cumulative data resources on the cloud also provide a clear target for external attacks, leading to the concern of information security. Considering these, the authors hope to offer insights for software firms by exploring the strategy selection problem. Design/methodology/approach Taking the portability effect and security risks of the SaaS licensing model into account, the authors develop a two-period model to figure out the market segmentation and identify the feasible conditions for employing three alternative strategies. Comparative statics analyses are conducted to explore the influencing mechanism of exogenous factors on strategy selection. The authors also discuss the strategy selection in the presence of the network effect and the security loss faced by users of on-premise software. Findings One significant finding is that the on-premise strategy can be excluded when the potential loss from security risks is small. Under this circumstance, the dual version strategy is optimal provided that the increase of customer valuation caused by portability effect is below a threshold. Otherwise, the SaaS strategy generates the highest profit. When the potential loss from security risks turns large, the on-premise strategy, the dual version strategy and the SaaS strategy are the optimal options in order as the portability effect on customer valuation gets stronger. Originality/value Previous literature has insufficiently addressed the versioning issue of enterprise software. In this paper, the distinctive features of the SaaS model are considered, and differentiated results compared with previous work are obtained. The research results provide guidelines for software firms in deciding their product releases in the future.
Learning Dynamics for Video Facial Expression Recognition

Video-based facial expression recognition has always been a focus of attention in the computer vision community for decades. It aims to automatically identify one of several emotions represented by the video according to the input audio or visual information. Capturing the dynamics, namely motion pattern plays an important role in video-based facial expression recognition. In this paper, we explore an effective and efficient motion pattern to model temporal relationships called Diff-based Canny Operator (DCO) to guide the inter-frame aggregation and generate a novel feature modality. Our proposed DCO add rare computational consumption and can be easily inserted into any frameworks, so we incorporate it with exist networks to form a unified structure for video-based facial expression recognition task, which enable the network to Ideally extract temporal information. With extensive experiments on CK+ and AFEW dataset, our proposed method shows its superiority with better or comparable performance compared to the state-of-the-art approaches at low FLOPs.
You Don’t Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking

Humans are able to continuously detect and track surrounding objects by constructing a spatial-temporal memory of the objects when looking around. In contrast, 3D object detectors in existing tracking-by-detection systems often search for objects in every new video frame from scratch, without fully leveraging memory from previous detection results. In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames. This memory, together with the ego-motion from back-end odometry, guides the detector to achieve more efficient object proposal generation and more accurate object state estimation. The experiments demonstrate the effectiveness of the proposed system and its performance on the ScanNet and KITTI datasets. Moreover, the proposed system produces stable bounding boxes and pose trajectories over time, while being able to handle occluded and truncated objects. Code is available at the project page: https://zju3dv.github.io/UDOLO.
Nonasymptotic Analysis of Monte Carlo Tree Search

In “Nonasymptotic Analysis of Monte Carlo Tree Search,” D. Shah, Q. Xie, and Z. Xu consider the popular tree-based search strategy, the Monte Carlo Tree Search (MCTS), in the context of the infinite-horizon discounted Markov decision process. They show that MCTS with an appropriate polynomial rather than logarithmic bonus term indeed leads to the desired convergence property. The authors derive the results by establishing a polynomial concentration property of regret for a class of nonstationary multiarm bandits. Furthermore, using this as a building block, they demonstrate that MCTS, combined with nearest neighbor supervised learning, acts as a “policy improvement” operator that can iteratively improve value function approximation.
A Deep Learning-Based Password Security Evaluation Model

It is very important to consider whether a password has been leaked, because security can no longer be guaranteed for passwords exposed to attackers. However, most existing password security evaluation methods do not consider the leakage of the password. Even if leakage is considered, a process of collecting, storing, and verifying a huge number of leaked passwords is required, which is not practical in low-performance devices such as IoT devices. Therefore, we propose another approach in this paper using a deep learning model. A password list was made for the proposed model by randomly extracting 133,447 words from a total of seven dictionaries, including Wikipedia and Korean-language dictionaries. After that, a deep learning model was created by using the three pieces of feature data that were extracted from the password list, as well as a label for the leakage. After creating an evaluation model in a lightweight file, it can be stored in a low-performance device and is suitable to predict and evaluate the security strength of a password in a device. To check the performance of the model, an accuracy evaluation experiment was conducted to predict the possibility of leakage. As a result, a prediction accuracy of 95.74% was verified for the proposed model.
Computational Abstraction

Representation and abstraction are two of the fundamental concepts of computer science. Together they enable “high-level” programming: without abstraction programming would be tied to machine code; without a machine representation, it would be a pure mathematical exercise. Representation begins with an abstract structure and seeks to find a more concrete one. Abstraction does the reverse: it starts with concrete structures and abstracts away. While formal accounts of representation are easy to find, abstraction is a different matter. In this paper, we provide an analysis of data abstraction based upon some contemporary work in the philosophy of mathematics. The paper contains a mathematical account of how Frege’s approach to abstraction may be interpreted, modified, extended and imported into type theory. We argue that representation and abstraction, while mathematical siblings, are philosophically quite different. A case of special interest concerns the abstract/physical interface which houses both the physical representation of abstract structures and the abstraction of physical systems.
An Empirical Study of Geography Learning on Students’ Emotions and Motivation in Immersive Virtual Reality

This paper assesses the impact of emotional arousal on learning through a virtual reality video of geography immersion learning. Validity was measured with traditional questionnaire data and using electroencephalography (EEG). Twenty-four human subjects were recruited and presented with eight geography immersive learning videos with different affective tendencies. EEG data of the subjects were collected while they were watching the videos. After the video watching, subjects were requested to fill in an emotion scale, a learning motivation scale, and a mind flow experience. The results of the study show that VR video learning materials can well induce the positive and negative emotions of the subjects. Compared with negative emotions, the alpha band power in the frontal lobe of the brain, beta and gamma bands in the temporal lobe region is significantly higher under positive emotions. In addition, the results of the subjective scales indicate that subjects have stronger intrinsic motivations and better flow experiences under positive emotions. However, there was no significant difference for the immersion under positive and negative emotions. Our findings demonstrate the usability of teaching virtual reality situational geography stories and the broad value of using this teaching method for future instruction.
GPUReplay: a 50-KB GPU stack for client ML

GPUReplay (GR) is a novel way for deploying GPU-accelerated computation on mobile and embedded devices. It addresses high complexity of a modern GPU stack for deployment ease and security. The idea is to record GPU executions on the full GPU stack ahead of time and replay the executions on new input at run time. We address key challenges towards making GR feasible, sound, and practical to use. The resultant replayer is a drop-in replacement of the original GPU stack. It is tiny (50 KB of executable), robust (replaying long executions without divergence), portable (running in a commodity OS, in TEE, and baremetal), and quick to launch (speeding up startup by up to two orders of magnitude). We show that GPUReplay works with a variety of integrated GPU hardware, GPU APIs, ML frameworks, and 33 neural network (NN) implementations for inference or training. The code is available at https://github.com/bakhi/GPUReplay.
Omni-GAN: On the Secrets of cGANs and Beyond

The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse. This paper presents Omni-GAN, a variant of cGAN that reveals the devil in designing a proper discriminator for training the model. The key is to ensure that the discriminator receives strong supervision to perceive the concepts and moderate regularization to avoid collapse. Omni-GAN is easily implemented and freely integrated with off-the-shelf encoding methods (e.g., implicit neural representation, INR). Experiments validate the superior performance of Omni-GAN and Omni-INR-GAN in a wide range of image generation and restoration tasks. In particular, Omni-INR-GAN sets new records on the ImageNet dataset with impressive Inception scores of 262.85 and 343.22 for the image sizes of 128 and 256, respectively, surpassing the previous records by 100+ points. Moreover, leveraging the generator prior, Omni-INR-GAN can extrapolate low-resolution images to arbitrary resolution, even up to ×60+ higher resolution. Code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Dissecting Image Crops

The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optics, lenses will leave behind certain clues, notably chromatic aberration and vignetting. Photographers also leave behind other clues relating to image aesthetics and scene composition. We study how to detect these traces, and investigate the impact that cropping has on the image distribution. While our aim is to dissect the fundamental impact of spatial crops, there are also a number of practical implications to our work, such as revealing faulty photojournalism and equipping neural network researchers with a better understanding of shortcut learning. Code is available at https://github.com/basilevh/dissecting-image-crops.
Unsupervised Sparse, Nonnegative, Low Rank Dictionary Learning for Detection of Driver Cell Phone Usage

One of the most significant problem in traffic accidents is distracted driving that causes a higher number of deaths and injury every year. In recent times car manufacturing companies are integrating driver distraction detection systems for detecting distracted behavior. In this work we have suggested an mobile usage detection system in driving images taken from inside of a car. Previously proposed distracted driver detection systems that focus only on gaze and head poses. We have suggested another alternative using driver hand images by which distracted driving detection system can be benefited. If the driver looks at the road ahead, previous head pose and gaze based systems are unable to detect distractions caused by cell phone use. To reliably detect these modes of distraction, we trained an unsupervised low rank nonnegative dictionary and used a threshold based criterion over reconstruction error. We have created a new driving dataset based on YouTube videos that includes real-world driving scenarios as well as mobile phone usage while driving. We have also tested our method using a number of publicly available driving datasets, including the State farm driving dataset, the AUC driving dataset, the VIVA dataset, and the CVRR-3D dataset. According to our knowledge of the current literature, this method is the first to use an unsupervised approach to address the problem of cell phone usage while driving. We achieved a competitive test accuracy of 79.17%, 77.19%, 84.0%, 76.0%, 75.45% in the proposed Youtube ego driving, State farm driving, CVRR-3D, VIVA, AUC distracted driver dataset, respectively.
Automated Detection of Anti-National Textual Response to Terroristic Events on Online Media

The advent of internet has led to prodigious growth in popularity of social media platforms for people to communicate and opinionate on topics of their interests. And, terroristic events being a topic of national importance, receives enormous response from the citizens. Unfortunately, miscreants with anti-national agendas incite the large available audience on these platforms against the country by inducing anti-national content amid terrorist attacks. The social media platforms being of informal use are commonly observed to have users opinionating using multiple languages in same sentence called code-mixing. The over-arching goal of research done is to identify anti-national code-mix textual content on YouTube in the form of comments on terrorism-related videos. We collected YouTube comments on videos related to terroristic events in Kashmir region of India, which consisted of code-mix comments in Hindi (native language of India) and English languages. The paper presents a novel deep-learning-based transformer model HE-CM-BERT, i.e. Hindi-English code-mix BERT, where we extend the vocabulary of pre-trained multilingual BERT with code-mix vocabulary extracted from the collected data to automate the detection of anti-national code-mix text. The comparative analysis of the proposed model with the state-of-the-art machine learning and deep learning models depicts that it outperforms the existing ones.
Toward Steganographic Payload Location via Neighboring Weight Algorithm

Modern steganalysis has been widely investigated, most of which mainly focus on dealing with the problem of detecting whether an inquiry image contains hidden information. However, few articles in the literature study the location of secret bits hidden by modern adaptive steganography. In this paper, we propose a novel algorithm for locating steganographic payload in the spatial domain. We first predict the steganographic scheme and its payload, which is used for generating a random bitstream. Then, the random bits are embedded in the stego image based on the cost matrix in the framework of Syndrome-Trellis Codes (STCs). Next, relying on the differences between two stego images, the extended modification map in couple with the neighboring weight algorithm can be acquired, leading to the location of the hidden bits. Compared with the prior art, the extensive experiments verify that our proposed locating algorithm performs better, in terms of locating accuracy and efficiency.
Hate Speech Detection using mono BERT model in custom Content-Management-System

Detecting hate speech is more or less natural from the point of view of a human being. Slurs and hateful symbols usually represent attacks on a person's opinion, race or religion. Being in the 21st century and the emergence of E-content has only provided a means of distributing hate speech to more people for a quarter of the effort. It is not hard to find hateful content while scrolling through out social media feed or online forums. On the other side of the coin, the rapid distribution of hate speech online means that there is a standardised way of creating and publishing content through platforms (Forums, blogs, social media, etc …) With the right set of tool, this standardised means can be targeted and fitted with the appropriate means to prevent the spread of such information. Here an open-source content management system is developed for creating and managing content with integrated hate speech detection using the state-of-the-art mono BERT model. In contrast to older language models, which relied on the previous token to predict the next one, BERT is specialized for predicting the next and previous tokens simultaneously. This makes it appropriate for tasks such as hate-speech detection. The model was trained on a combined dataset of Hatebase Twitter and StormFront. This project is mostly targeted to hindering the spread of hateful content in the form of blogs, forum responses and so on.
De-rendering Stylized Texts

Editing raster text is a promising but challenging task. We propose to apply text vectorization for the task of raster text editing in display media, such as posters, web pages, or advertisements. In our approach, instead of applying image transformation or generation in the raster domain, we learn a text vectorization model to parse all the rendering parameters including text, location, size, font, style, effects, and hidden background, then utilize those parameters for reconstruction and any editing task. Our text vectorization takes advantage of differentiable text rendering to accurately reproduce the input raster text in a resolution-free parametric format. We show in the experiments that our approach can successfully parse text, styling, and background information in the unified model, and produces artifact-free text editing compared to a raster baseline.