Reviewerly/exHarmony huggingface dataset:
Gray Learning from Non-IID Data with Out-of-distribution Samples

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed \textit{Gray Learning} (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics.
Internet of things based mobile application to improve citizen security

Citizen insecurity is a social problem that has increased considerably around the world. To combat it, in this research a mobile application based on IOT has been developed with the objective of mapping crimes and incident alerts to improve citizen security. Scrum methodology was used and a significant improvement can be seen with respect to the following indicators: number of reports of dangerous places, with an increase of 102.7%; the second indicator: number of reports by type of crime, with an increase of 25.34%; and the indicator: response time to attention, with an increase of 23.5%. It is determined that there is a significant positive influence of the mobile application developed to improve citizen security.
3D Object Detection for Autonomous Driving: A Comprehensive Survey

Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area.
Attention-based Dynamic Subspace Learners for Medical Image Analysis

Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of object attributes such as color, shape, or artifacts. Encoding such attributes using a single metric learner is inadequate and may fail to generalize. Instead, multiple learners could focus on separate aspects of these attributes in subspaces of an overarching embedding. This, however, implies the number of learners to be found empirically for each new dataset. This work, Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by removing the need of knowing apriori the number of learners and aggregating new subspace learners during training. Furthermore, the visual interpretability of such subspace learning is enforced by integrating an attention module into our method. This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features. The benefits of our attention-based dynamic subspace learners are evaluated in the application of image clustering, image retrieval, and weakly supervised segmentation. Our method achieves competitive results with the performances of multiple learners baselines and significantly outperforms the classification network in terms of clustering and retrieval scores on three different public benchmark datasets. Moreover, our attention maps offer a proxy-labels, which improves the segmentation accuracy up to 15% in Dice scores when compared to state-of-the-art interpretation techniques.
Augment with Care: Contrastive Learning for Combinatorial Problems

Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due to exponential worst-case complexity. Inspired by the recent success of contrastive pre-training for images, we conduct a scientific study of the effect of augmentation design on contrastive pre-training for the Boolean satisfiability problem. While typical graph contrastive pre-training uses label-agnostic augmentations, our key insight is that many combinatorial problems have well-studied invariances, which allow for the design of label-preserving augmentations. We find that label-preserving augmentations are critical for the success of contrastive pre-training. We show that our representations are able to achieve comparable test accuracy to fully-supervised learning while using only 1% of the labels. We also demonstrate that our representations are more transferable to larger problems from unseen domains. Our code is available at https://github.com/h4duan/contrastive-sat.
Research of Paraphrasing for Chinese Complex Sentences Based on Templates

Based on the paraphrasing of Chinese simple sentences, the complex sentence paraphrasing by using templates are studied. Through the classification of complex sentences, syntactic analysis and structural analysis, the proposed methods construct complex sentence paraphrasing templates that the associated words are as the core. The part of speech tagging is used in the calculation of the similarity between the paraphrasing sentences and the paraphrasing template. The joint complex sentence can be divided into parallel relationship, sequence relationship, selection relationship, progressive relationship, and interpretive relationship’s complex sentences. The subordinate complex sentence can be divided into transition relationship, conditional relationship, hypothesis relationship, causal relationship and objective relationship’s complex sentences. Joint complex sentence and subordinate complex sentence are divided to associated words. By using pretreated sentences, the preliminary experiment is carried out to decide the threshold between the paraphrasing sentence and the template. A small scale paraphrase experiment shows the method is availability, acquire the coverage rate of paraphrasing template 40.20% and the paraphrase correct rate 62.61%.
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region. The synthetic dataset used in this stage is procured using the UnityEyes simulator, and consists of 80,000 eye images. Successive to training, the eye region isolation network is then transferred to the real domain for generating masks for the real-world eye images. In order to successfully make the transfer, we exploit domain randomization in the training process, which allows for the synthetic images to benefit from a larger variance with the help of augmentations that resemble artifacts. The generated eye region masks along with the raw eye images are then used together as a multistream input to our gaze estimation network, which consists of wide residual blocks. The output embeddings from these encoders are fused in the channel dimension before feeding into the gaze regression layers. We evaluate our framework on three gaze estimation datasets and achieve strong performances. Our method surpasses the state-of-the-art by 7.57% and 1.85% on two datasets, and obtains competitive results on the other. We also study the robustness of our method with respect to the noise in the data and demonstrate that our model is less sensitive to noisy data. Lastly, we perform a variety of experiments including ablation studies to evaluate the contribution of different components and design choices in our solution.
Types and Tools of Steganography

Abstract: Steganography is the practice of concealing a secret message inside of another message. It is a form of covert communication and can involve the use of any medium like a computer file, message, image, or video within the other file, message, image or video. It is not the same as cryptography where cryptography means secret writing and steganography which is not as popular as cryptography means covered writing. Many different file formats are often used but digital images or media files are the most popular due to their most-used on the web and the large size of media files. There exist many Steganography techniques for hiding secret information in images where some of them are more complex than others. Steganography works by changing bits of useless or used data in regular computer files such as graphics, sound, and text with bits of different and invisible information. Steganography technique refers to methods in which data hiding is performed directly on every hundredths or thousandths pixel value of cover image by replacing with corresponding to letter in the alphabet in such a way that the effect of message is not visible on the cover image and unnoticeable to someone. This paper is intended to illustrate the different steganography techniques and the purpose of techniques.
Great Expectations: Unsupervised Inference of Suspense, Surprise and Salience in Storytelling

Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. The thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating original stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow-up work, the insights could help improve computer models for tasks such as automatic story writing and assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, and listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach for applying machine learning in many areas of interest.
Purwarupa Sistem Pemesanan Menu Makanan Dan Minuman Menggunakan Qr-Code Berbasis Web

The combination of the web and QR-Code technology can create a food and beverage menu ordering system that can help the waiter's duties so that services can be carried out evenly by utilizing Qr-Code technology for ordering menus with the scanner device being an android smartphone. Payments can also be made using your name or table number and can be made at the cashier or customers simply request a bill at the table. That way the waiter doesn't need to take orders one by one and it's easier for customers to make payments
MIAMI: A Mixed Reality Interface for AI-based Music Improvisation

In this research, we created a mixed reality interface and system for improvisation with deep generative music models, named MIAMI, which stands for "a Mixed reality Interface for Ai-based Music Improvisation". This was achieved by linking a 3D interface using Microsoft Hololens, a Python program that loads and executes generative models, Ableton Live which is a Digital Audio Workstation (DAW), and a Max for Live(M4L) device running on it. By incorporating the interface itself into the live space, we are able to achieve physical movements and visual augmentation that were not possible in conventional improvisational performances using music generation interfaces, demonstrating the possibility of a new musical experience.
Outlier tolerant adaptive sampling rate approach for wireless sensor node

Nowadays the wireless sensor network (WSN) has been used for variety of applications and still lot of research in progress around the corner for the betterment of the wireless sensor network technology. In this paper, one such issues related to energy consumption in sensor node due to fixed sampling interval of sensing unit and its impact on redundant data is discussed with a possible solution. The association of sampling interval and its impact on energy dissipation in sensor node enforces the need for study on energy efficient adaptive sampling interval approach. The lack of serious consideration of outlier in sensor data degrades the performance of the existing adaptive sampling interval approach. The result of the proposed approach of in-network clustering algorithm shows the better efficiency towards detecting the outlier in real time. The results also showcase the better efficiency of proposed approach in terms of rapid optimization of sampling interval compared to simple variance based approach.
CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness

We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive language of the Why3 verification platform. Moreover, CAISAR orchestrates and composes state-of-the-art machine learning verification tools which, individually, are not able to efficiently handle all problems but, collectively, can cover a growing number of properties. Our aim is to assist, on the one hand, the V\&V process by reducing the burden of choosing the methodology tailored to a given verification problem, and on the other hand the tools developers by factorizing useful features-visualization, report generation, property description-in one platform. CAISAR will soon be available at https://git.frama-c.com/pub/caisar.
Understanding the Teachers’ Lived Experience in Teaching Technology and Livelihood Education (TLE) Using Modular Distance Learning in Now Normal

This qualitative study examined the lived experiences of teachers of teaching Technology and Livelihood Education (TLE) using modular distance learning in now normal utilizing daily. A review of literature was completed concerning the areas of teachers and the history of modular distance learning. The research question for the study was, what are the participants’ lived experience in teaching and assessing students in modular distance learning class of TLE in the now normal? And how do they cope up with the challenges of teaching and assessing students in modular distance learning class of TLE in the now normal? The research design for the study was Phenomenological qualitative research in interpretive design. Interviews were completed. The data were disaggregated according to themes and patterns. The participants of the study consisted of eight (8) participants’ experiences of TLE teachers on modular distance learning in the Department of Education in the Division of the City of Biñan. After data collection, the researcher found seven (6) common themes. Each of these themes was discussed in terms of supporting or not supporting the review of literature. The majority of the findings of the study supported by participants served as authentic guidelines of the study. A major contradiction in the findings was the fact that none of the teachers in the study felt a need for more professional development and community involvement.
Using binary classification to evaluate the quality of machine translators

Machine translators have become increasingly popular and currently play an important role because of their great assistance in cross-cultural communication. However, machine translators often produces some unnatural texts, and an evaluation of machine translators is thus needed to avoid the abuse of machine-translated texts. This paper presents the use of binary classification to evaluate the quality of machine translators without references. First, we construct a large-scale dataset including humangenerated texts and machine-translated texts. Second, the dataset is used to train the multiple binary classifiers, e.g., decision tree, random forest, extreme gradient boosting, support vector machines, logistic regression, etc. Finally, these trained classifiers constitute the ensemble model by majority voting, and this ensemble model is used to evaluate the qualities of machine-translated texts. Experimental results show that the proposed evaluation method better measures the qualities of translated texts by some commercial machine translators.
Capture the Flag for Team Construction in Cybersecurity

Team collaboration among individuals with diverse sets of expertise and skills is essential for solving complex problems. As part of an interdisciplinary effort, we studied the effects of Capture the Flag (CTF) game, a popular and engaging education/training tool in cybersecurity and engineering, in enhancing team construction and collaboration. We developed a framework to incorporate CTF as part of a computer-human process for expertise recognition and role assignment and evaluated and tested its effectiveness through a study with cybersecurity students enrolled in a Virtual Teams course. In our computer-human process framework, the post-CTF algorithm using the CTF outcomes assembles the team (assigning individuals to teams) and provides the initial role assignments, which then gets updated by human-based team discussions. This paper shares our insights, design choices/rationales, and analyses of our CTF-incorporated computer-human process framework. The students' evaluations revealed that the computer-human process framework was helpful in learning about their team members' backgrounds and expertise and assigning roles accordingly made a positive impact on the learning outcomes for the team collaboration skills in the course. This experience report showcases the utility of CTF as a tool for expertise recognition and role assignments in teams and highlights the complementary roles of CTF-based and discussion-based processes for an effective team collaboration among engineering students.
Vehicle Counting Method Based On Gaussian Mixture Models And Blob Analysis

Combination of a Gaussian Mixture Models and Entropy to detect vehicles in motion from any sort of video sequences. The proposed method is composed of four major steps: background subtraction, improved GMM, Blob Analysis, Voting Scheme processing. An implementation of proposed technique has been performed using MATLAB R2017a. Vehicle detection process provides relevant information about traffic patterns, crash occurrences and traffic peak times in roadways. Detection of number of vehicles and traffic monitoring will be helpful by implementing motion and object detection using background subtraction, Monitoring traffic from video footages will provide a greater reality in terms of vehicle count. Gaussian Mixture Model is most advanced model in motion detection due to the reliability that it has shown in the background extraction and foreground segmentation process. Here we are going to find motion of object in video sequences under both static camera arrangement and dynamic background sequences by using background subtraction method and blob analysis approach and analyzing them
Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities. However, current deep learning models takes as input distributed representations or vectors. Thus, the graph is compressed in a vectorized representation. We conduct a study to examine if the deep learning model can compress a graph and then output the same graph with most of the semantics intact. Our experiments show that Transformer models are not able to express the full semantics of the input knowledge graph. We find that this is due to the disparity between the directed, relationship and type based information contained in a Knowledge Graph and the fully connected token-token undirected graphical interpretation of the Transformer Attention matrix.