Reviewerly/exHarmony huggingface dataset:
Watermarking Pre-trained Encoders in Contrastive Learning

Contrastive learning has become a popular technique to pre-train image encoders, which could be used to build various downstream classification models in an efficient way. This process requires a large amount of data and computation resources. Hence, the pre-trained encoders are an important intellectual property that needs to be carefully protected. It is challenging to migrate existing watermarking techniques from the classification tasks to the contrastive learning scenario, as the owner of the encoder lacks the knowledge of the downstream tasks which will be developed from the encoder in the future. We propose the first watermarking methodology for the pre-trained encoders. We introduce a task-agnostic loss function to effectively embed into the encoder a backdoor as the watermark. This backdoor can still exist in any downstream models transferred from the encoder. Extensive evaluations over different contrastive learning algorithms, datasets, and downstream tasks indicate our watermarks exhibit high effectiveness and robustness against different adversarial operations.
Sentiment Analysis of Review Sestyc Using Support Vector Machine, Naive Bayes, and Logistic Regression Algorithm

The growth of internet users in Indonesia experiences a very high increase every year, with the increase in internet users in Indonesia also resulting in many people using social media. Sestyc is a social media application created by a group of millennial children in Indonesia. This study was conducted to analyze the sentiment of users of Sestyc using text data in the form of a review obtained from the Google Play Store. The purpose of this research is to analyze sentiment towards the sestyc application and find the best algorithm for classifying sentiment. The algorithm used in analyzing sentiment in this study consists of Support Vector Machine, Logistic regression, and Naive Bayes. The results of sentiment class labeling on the sestyc review data obtained 8000 reviews with a total of 4719 positive reviews and 3281 negative reviews. The results of this study indicate that the Support Vector Machine algorithm has the highest accuracy value compared to other algorithms, where the Support Vector Machine gets an accuracy value. by 87.81%.
Studying the impact of continuous delivery adoption on bug-fixing time in apache's open-source projects

Buggy software impacts people's lives and businesses. Nowadays, a huge portion of a software project's cost is spent on debugging (finding and fixing bugs). Therefore, reducing the time needed to release new software versions free from bugs becomes crucial. Continuous delivery (CD) arises as an alternative to traditional software release engineering by providing the capability to faster and continuously release software to customers through automated pipelines. Previous studies claim that CD adoption leads to a reduction in the software release cycle time, including the time lag to fix reported bugs (bug-fixing time) and apply correction patches in the affected versions. However, there is a lack of empirical evidence supporting (or not) this claim. To fulfill this gap, we conducted an empirical study to evaluate the impact of CD adoption in the bug-fixing time. We study 25 open-source projects comparing the bug-fixing time before and after adopting CD. Our results show that bug-fixing time after CD adoption becomes shorter (with statistical significance) than the bug-fixing time before CD adoption.
Subjective Quality Assessment for Images Generated by Computer Graphics

With the development of rendering techniques, computer graphics generated images (CGIs) have been widely used in practical application scenarios such as architecture design, video games, simulators, movies, etc. Different from natural scene images (NSIs), the distortions of CGIs are usually caused by poor rending settings and limited computation resources. What's more, some CGIs may also suffer from compression distortions in transmission systems like cloud gaming and stream media. However, limited work has been put forward to tackle the problem of computer graphics generated images' quality assessment (CG-IQA). Therefore, in this paper, we establish a large-scale subjective CG-IQA database to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs through previous databases and personal collection. After data cleaning, we carefully select 1,200 CGIs to conduct the subjective experiment. Several popular no-reference image quality assessment (NR-IQA) methods are tested on our database. The experimental results show that the handcrafted-based methods achieve low correlation with subjective judgment and deep learning-based methods obtain relatively better performance. The current NR-IQA models are not suitable for CG-IQA tasks and more effective models are urgently needed.
Detection of Sickle Cell Anemia in Blood Smear using YOLOv3

Object recognition algorithms such as the YOLOv3 algorithm have a lot of possible applications in the field of medicine. One such application is the study's aim of recognizing the presence of sickle cell-shaped red blood cells (sickle cell disease) in blood smear samples. As YOLOv3 uses features learned from a trained model using deep convolutional neural networks, it is possible to use it in automating the process of morphologically recognizing the presence of sickle cells in blood smear samples. Additionally, as blood smear samples may have different staining methods, the images must be processed prior to recognition which will result in a grayscale image frame. Based on the results generated by the system when processing 12 blood smear samples, a confidence level of 50% corresponding to a threshold value of 0.5 provided the highest accuracy rate when using YOLOv3-Tiny custom weights. Further testing by the researchers showed that the detections in unstained test slides have a higher confidence level per detection than in test slides stained using a darker pigment caused by the stain's effect on the illumination of the image. Overall, the design system was able to achieve 100% accuracy in detecting the presence of sickle cell-shaped RBCs in 12 blood smear samples.
Comparison of Garbage Classification Frameworks Using Transfer Learning and CNN

With the never-ending increase in the population, garbage and other waste materials have become one of the major hurdles in forming a healthy environment. The proliferation in the development of such schemes and integration of technology brings up the concept of smart waste management based on its biodegradability. These proposed models can be found useful to the smart waste development program and other likely schemes which require the classification of garbage based on their images. The experiment uncovers the reasons behind the working of VGG19 and A9 architecture CNN-based models which were found to provide the best results in accurately detecting the type of garbage. Experimental evaluation was based on 27 models including out of which A9 and VGG19 models were found to be the most efficient ones with 92.24% and 86.35% accuracy, respectively, which are further compared in detail for understanding these models better.
Short Texts Classification Based an Improved BiLSTM

The most important thing of Chinese short text classification is to extract and represent the features of short text. In the traditional representation methods, there are some problems, such as short text length and sparse features. Therefore, a short text classification model based on improved bilstm is proposed. The model includes word vector input layer, bilstm layer and attention layer. In the input layer, the pre trained word vector is used to transform the original text, and the intermediate vector representation with context timing information is obtained through the two direction (long-term and short-term) memory network; In the attention mechanism layer, the forward and reverse features are fused and weighted to obtain the short text vector with deep semantic features. The model is compared with other baseline models, and the experimental results show that its accuracy is significantly improved.
MM-DOS: A Novel Dataset Of Workout Activities

Human Activity Recognition (HAR) is now one of the most widespread fields in research and application, especially within the area of artificial intelligence. A wide variety of applications are based on HAR, including healthcare, entertainment, and sports performance analysis. Such systems can be used as a reliable source of performance feedback for sports. Hence, coaching and the athletes' performance enhancement became the main application for HAR. Considering a data-oriented approach, the need for quality datasets increased to cover these springing needs. In this paper, we present a new multi-modal dataset MMDOS; the dataset contains a variety of data forms from different sensors, including Red-Green-Blue (RGB) videos, inertial motion data, depth, and thermal data, all synchronized with regard to the activities/exercises performed. The dataset consists of four workout exercises: free squats, shoulder presses, push-ups, and lunges. The exercises are performed by 50 participants indoors at a fitness center. Professional trainers label the data with the type of activity and the mistakes that the participant made. In this paper, we offer further details about the nature of the data and how the dataset was collected and post-processed. Finally, the paper shows some analysis performed to evaluate the dataset quality to recognize the different exercises. We reached accuracy up to 96% for RGB videos, 90% for depth videos, and 91% for IMU data.
Robot Dance Generation with Music Based Trajectory Optimization

Musical dancing is an ubiquitous phenomenon in the human society. Providing robots the ability to dance has the potential to make the human robot co-existence more acceptable in our society. Hence, dancing robots have generated a considerable research interest in the recent years. In this paper, we present a novel formalization of robot dancing as planning and control of optimally timed actions based on beat timings and additional features extracted from the music. We showcase the use of this formulation in three different variations: with input of human expert choreography, imitation of a predefined choreography, and automated generation of a novel choreography. Our method has been validated on four different musical pieces, both in simulation and on a real robot, using the upper-body humanoid robot RH5 Manus.
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution

Online processing of compressed videos to increase their resolutions attracts increasing and broad attention. Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. However, state-of-the-art recurrent VSR models still require significant computation to obtain a good performance, mainly because of the complicated motion estimation for frame/feature alignment and the redundant processing of consecutive video frames. In this paper, considering the characteristics of compressed videos, we propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos. Firstly, the framework reuses the coded video information of Motion Vectors to model the temporal relationships between adjacent frames. Experiments demonstrate that the models with Motion Vector based alignment can significantly boost the performance with negligible additional computation, even comparable to those using more complex optical flow based alignment. Secondly, by further making use of the coded video information of Residuals, the framework can be informed to skip the computation on redundant pixels. Experiments demonstrate that the proposed framework can save up to 70% of the computation without performance drop on the REDS4 test videos encoded by H.264 when CRF is 23.
Window Canaries: Re-Thinking Stack Canaries for Architectures With Register Windows

This paper presents Window Canaries, a novel approach to Stack Canaries for architectures with a register window that protects return addresses and stack pointers without the need of adding additional instruction to each potentially vulnerable function. Instead, placement and check of the canary word is moved to window exception handlers that are responsible to handle register window overflows and underflows. The approach offers low performance overhead while guaranteeing that return addresses are protected by stack buffer overflows without relying on a heuristic that decides which functions to instrument. The contributions of this paper are a complete implementation of the approach for the Xtensa LX architecture with register window option as well as a performance evaluation and discussion of advantages and drawbacks.
Multi-Sourced Knowledge Integration for Robust Self-Supervised Facial Landmark Tracking

Expensive annotation costs significantly hinder the development of facial landmark tracking owing to the frame-by-frame labeling of dense landmarks. The most promising approach to address this problem is to develop a self-supervised tracker for large-scale unlabeled videos. However, existing self-supervised trackers trained using single-sourced knowledge are unstable under unconstrained environments. Herein, we propose multi-sourced knowledge integration (MSKI), a robust self-supervised tracking method. It integrates knowledge from multiple sources to provide supervisory signals, thereby improving the stability of the self-supervised tracker. Specifically, the proposed MSKI comprises two complementary modules: a temporal knowledge reasoning (TempRes) module and an interactive knowledge distillation (KnowDist) module. The TempRes module enforces the tracker to achieve cycle-consistent tracking, allowing the tracker to learn temporal correspondence based on the cycle-consistency of time. To exploit facial geometry knowledge against various occlusions, our tracker imposes a multi-level shape constraint over the structure of facial landmarks by leveraging adversarial shape learning, thereby enabling the tracking of occluded faces. Moreover, the tracker interacts with an initialization detector to further develop complementary knowledge via KnowDist. The KnowDist module distills the spatial and temporal knowledge provided by the detector and tracker to generate plausible labels automatically. Finally, these generated labels are utilized to fine-tune the detector, such that it provides high-quality initial landmarks for the cycle-consistent tracking of the tracker on unlabeled videos. The experimental results show that the proposed MSKI can stabilize the tracking trajectory and improve the robustness against various occlusions.
Optimization of Quantum Read-Only Memory Circuits

Quantum computing is a rapidly expanding field with applications ranging from optimization all the way to complex machine learning tasks. Quantum memories, while lacking in practical quantum computers, have the potential to bring quantum advantage. In quantum machine learning applications for example, a quantum memory can simplify the data loading process and potentially accelerate the learning task. Quantum memory can also store intermediate quantum state of qubits that can be reused for computation. However, the depth, gate count and compilation time of quantum memories such as, Quantum Read Only Memory (QROM) scale exponentially with the number of address lines making them impractical in state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers beyond 4-bit addresses. In this paper, we propose techniques such as, pre-decoding logic and qubit reset to reduce the depth and gate count of QROM circuits to target wider address ranges such as, 8-bits. The proposed approach reduces the number of gates and depth count by at least 2X compared to the naive implementation at only 36% qubit overhead. A reduction in circuit depth and gate count as high as 75X and compilation time by 85X at the cost of a maximum of 2.28X qubit overhead is observed. Experimentally, the fidelity with the proposed pre-decoding circuit compared to existing optimization approach is also higher (as much as 73% compared to 40.8%) under reduced error rates.
Multi-Level Task Learning Based on Intention and Constraint Inference for Autonomous Robotic Manipulation

To perform tasks in unstructured environments, robots need to be able to apply learned skills to different contexts and to autonomously make decisions online. We, therefore, developed a novel data-driven task learning approach that segments a task demonstration into simpler skills and structures them in a high-level task graph. In contrast to other state-of-the-art methods, the presented approach can not only infer the low-level skills and their respective subgoals but also multimodal feature constraints fitted individually to each skill. The inferred feature constraints allow to detect anomalies during autonomous task execution, which can be automatically resolved by a recovery behavior of the task graph. The subgoals encode each skill's intention and thereby enable to flexibly transition between skills and to generalize the behavior to new setups. By separating the subgoal and constraint inference, we achieve a reduced computational complexity and an increased performance compared to state-of-the-art task learning approaches. In a real-world manipulation task, we demonstrate the reusability of skills as well as the autonomous decision-making of our approach.
Recommendations for Developers Identifying Code Smells

The outcomes of this work provide a comprehensive view of the task and emerging findings, such as the developers’ major beliefs, values, and ideas about identifying code smells. Based on these outcomes, we present practical recommendations to developers to optimize efforts in identifying code smells.
Knowledge Graph Construction for Foreign Military Unmanned Systems

Unmanned systems have become a significant component of modern military forces and play a more and more important role in various military operations. It mainly consists of four major domains which are unmanned aerial system (UAS), unmanned ground vehicle (UGV), unmanned underwater vehicle (UUV), and unmanned surface vessel (USV). This paper focuses on the construction of a high-quality knowledge graph on foreign unmanned systems and proposes an effective method to complete the construction. The method first analyses the data provided by CCKS2022 evaluation organizers and builds a schema. Then not only data provided are used to construct the knowledge graph but also external data are crawled and extracted as triples under constraints of the schema. After that, entities are aligned and logic rules are also utilized to knowledge graph completion. Finally, the knowledge graph constructed is stored and visualized in the Neo4j database and evaluated by the question-answering tasks. This paper presents our technics for the 13th task of CCKS2022 evaluation (i.e. knowledge graph construction and evaluation on foreign military unmanned systems) and our team win the 3rd place in this task.
2D Amodal Instance Segmentation Guided by 3D Shape Prior

Amodal instance segmentation aims to predict the complete mask of the occluded instance, including both visible and invisible regions. Existing 2D AIS methods learn and predict the complete silhouettes of target instances in 2D space. However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances. With the 2D masks learned, 2D amodal methods are hard to generalize to new viewpoints not included in the training dataset. To tackle these problems, we are motivated by observations that (1) a 2D amodal mask is the projection of a 3D complete model, and (2) the 3D complete model can be recovered and reconstructed from the occluded 2D object instances. This paper builds a bridge to link the 2D occluded instances with the 3D complete models by 3D reconstruction and utilizes 3D shape prior for 2D AIS. To deal with the diversity of 3D shapes, our method is pretrained on large 3D reconstruction datasets for high-quality results. And we adopt the unsupervised 3D reconstruction method to avoid relying on 3D annotations. In this approach, our method can reconstruct 3D models from occluded 2D object instances and generalize to new unseen 2D viewpoints of the 3D object. Experiments demonstrate that our method outperforms all existing 2D AIS methods.
Research on E-learning Behavior Evaluation of Students Based on Three-way Decisions Classification Algorithm

With the popularization and application of E-learning platform, it is feasible to collect learning behavior data of students, which provides data basis for analyzing the knowledge and rules contained in learning process of students. At present, the common e-learning platform only realizes the simple statistics and display function for the data, and does not do further in-depth calculation. The data observed by educators are still superficial learning phenomena, and they cannot see the learning rules behind the appearance. It is difficult to effectively guide students, change their learning routes and feedback teaching strategies. In view of this, the main characteristics of learning behavior deeply mined from learning activities and learning evaluation, and the important data feature items affect E-learning effect obtained. Three decision classification algorithms applied to establish prediction and evaluation model for students with learning behavior risk. The overall accuracy of model test is in line with the expectation. This provides a practical application modeling method for further implementation of personalized learning service.
FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks

Federated learning (FL) enables clients to collaboratively train a model, while keeping their local training data decentralized. However, high communication costs, data heterogeneity across clients, and lack of personalization techniques hinder the development of FL. In this paper, we propose FedLTN, a novel approach motivated by the well-known Lottery Ticket Hypothesis to learn sparse and personalized lottery ticket networks (LTNs) for communication-efficient and personalized FL under non-identically and independently distributed (non-IID) data settings. Preserving batch-norm statistics of local clients, postpruning without rewinding, and aggregation of LTNs using server momentum ensures that our approach significantly outperforms existing state-of-the-art solutions. Experiments on CIFAR-10 and Tiny ImageNet datasets show the efficacy of our approach in learning personalized models while significantly reducing communication costs.