Reviewerly/exHarmony huggingface dataset:
PreyNet: Preying on Camouflaged Objects

Species often adopt various camouflage strategies to be seamlessly blended into the surroundings for self-protection. To figure out the concealment, predators have evolved excellent hunting skills. Exploring the intrinsic mechanisms of the predation behavior can offer more insightful glimpse into the task of camouflaged object detection (COD). In this work, we strive to seek answers for accurate COD and propose a PreyNet, which mimics the two processes of predation, namely, initial detection (sensory mechanism) and predator learning (cognitive mechanism). To exploit the sensory process, a bidirectional bridging interaction module (BBIM) is designed for selecting and aggregating initial features in an attentive manner. The predator learning process is formulated as a policy-and-calibration paradigm, with the goal of deciding on uncertain regions and encouraging targeted feature calibration. Besides, we obtain adaptive weight for multi-layer supervision during training via computing on the uncertainty estimation. Extensive experiments demonstrate that our model produces state-of-the-art results on several benchmarks. We further verify the scalability of the predator learning paradigm through applications on top-ranking salient object detection models. Our code is publicly available at \urlhttps://github.com/OIPLab-DUT/PreyNet.
Continual Multi-view Clustering

With the increase of multimedia applications, data are often collected from multiple sensors or modalities, encouraging the rapid development of multi-view (also called multi modal) clustering technique. As a representative, late fusion multi-view clustering algorithm has attracted extensive attention due to its low computation complexity yet promising performance. However, most of them deal with the clustering problem in which all data views are available in advance, and overlook the scenarios where data observations of new views are accumulated over time. To solve this issue, we propose a continual approach on the basis of late fusion multi-view clustering framework. In specific, it only needs to maintain a consensus partition matrix and update knowledge with the incoming one of a new data view rather than keep all of them. This benefits a lot by preventing the previously learned knowledge from recomputing over and over again, saving a large amount of computation resource/time and labor force. Nevertheless, we design an alternate and convergent strategy to solve the resultant optimization problem. Also, the proposed algorithm shows excellent clustering performance and time/space efficiency in the experiment.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able to properly handle multi-object tracking challenges such as occlusion, in part because they ignore long-term temporal information. To address these shortcomings, we present MO3TR: a truly end-to-end Transformer-based online multi-object tracking (MOT) framework that learns to handle occlusions, track initiation and termination without the need for an explicit data association module or any heuristics. MO3TR encodes object interactions into long-term temporal embeddings using a combination of spatial and temporal Transformers, and recursively uses the information jointly with the input data to estimate the states of all tracked objects over time. The spatial attention mechanism enables our framework to learn implicit representations between all the objects and the objects to the measurements, while the temporal attention mechanism focuses on specific parts of past information, allowing our approach to resolve occlusions over multiple frames. Our experiments demonstrate the potential of this new approach, achieving results on par with or better than the current state-of-the-art on multiple MOT metrics for several popular multi-object tracking benchmarks.
Benchmarking state-of-the-art imbalanced data learning approaches for credit scoring

The goal of credit scoring is to identify abnormalities, aiding decision making and maintaining the order of financial transactions. Due to the small number of default records, one inevitably faces a class imbalance problem when handling financial data. The class imbalance problem has received a lot of attention because of the economic loss that can occur when one fails to accurately identify default samples. To solve this problem, there are various classic and mature approaches to learning imbalanced data, including resampling approaches, cost-sensitive strategies, and so on. Especially in recent years, generative adversarial networks (GANs) have attracted the attention of researchers to explore these networks’ effects as imbalanced data learning tools. However, no attention has been paid to the systematic scoring and comparison of these traditional and state-of-the-art imbalanced data learning approaches in relation to credit scoring. Therefore, choosing several related datasets, we compare the performance of the traditional approaches and GANs in solving the class imbalance problem of credit scoring; at the same time, with the help of benchmark analysis, we provide some suggestions for relevant research.
A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.
PhishNot: A Cloud-Based Machine-Learning Approach to Phishing URL Detection

Phishing is constantly growing to be one of the most adopted tools for conducting cyber-attacks. Recent statistics indicated that 97% of users could not recognize a sophisticated phishing email. With over 1.5 million new phishing websites being created every month, legacy black lists and rule-based filters can no longer mitigate the increasing risks and sophistication level of phishing. Phishing can deploy various malicious payloads that compromise the network's security. In this context, machine learning can play a crucial role in adapting the capabilities of computer networks to recognize current and evolving phishing patterns. In this paper, we present PhishNot, a phishing URL detection system based on machine learning. Hence, our work uses a primarily "learning from data" driven approach, validated with a representative scenario and dataset. The input features were reduced to 14 to assure the system's practical applicability. Experiments showed that Random Forest presented the best performance with a very high accuracy of 97.5%. Furthermore, the design of our system also lends itself to being more adoptable in practice through a combination of high phishing detection rate and high speed (an average of 11.5μs per URL) when deployed on the cloud.
Training Logistic Regression Model by Enhanced Moth Flame Optimizer for Spam Email Classification

Spam email is a massive issue that bothers and consumes receivers’ time and effort. Because of its effectiveness in identifying mail as wanted or unwanted, machine learning approaches have become a popular technique in spam detection. Current spam detection methods, on the other side, typically have low detection performance and are incapable of handling high-dimensional information easily. As a result, a unique spam detection approach that combines an improved moth flame optimization algorithm and a logistic regression classification model was proposed in this paper. The research evidence on two accessible datasets (CSDMC2010, Enron) indicates that the suggested methodology can tackle high-dimensional data due to its very powerful local and global search skills. The suggested technique was evaluated for spam detection accuracy to that of logistic regression, naive Bayes classifiers, and support vector machine, as well as the performance of earlier research’ that includes state-of-the-art approaches. In terms of classification performance, the suggested methodology outperforms the other spam detection algorithms examined in this work.
Real-time Short Video Recommendation on Mobile Devices

Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features.
Integrating scientific knowledge into machine learning using interactive decision trees

Decision Trees (DT) describe a type of machine learning method that has been widely used in the geosciences to automatically extract patterns from complex and high dimensional data. However, like any data-based method, the application of DT is hindered by data limitations, such as significant biases, leading to potentially physically unrealistic results. We develop interactive DT (iDT) that put humans in the loop to integrate the power of experts' scientific knowledge with the power of the algorithms to automatically learn patterns from large datasets. We created an open-source Python toolbox that implements the iDT framework. Users can interactively create new composite variables, change the variable and threshold to split, prune and group variables based on their physical meaning. We demonstrate with three case studies how iDT overcomes problems with current DT thus achieving higher interpretability and robustness of the result.
Fooling MOSS Detection with Pretrained Language Models

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect similarities between pieces of software. We find that a student using GPT-J [60] can complete introductory level programming assignments without triggering suspicion from MOSS [2], a widely used software similarity and plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.
A Mixed Reality Application for the On-Site Assessment of Building Renovation: Development and Testing

This paper contributes to a sustainable construction design management approach to increase the successful renovation rate of existing residential building stock. Indeed, coupling BIM with mixed reality can speed up and improve the quality of the renovation design processes, because it can display virtual models of alternative design scenarios superimposed over the existing physical facility. To this purpose, a sample of technicians was enrolled to test the reliability of this technology. A prototype was developed that enables cooperation among stakeholders and the implementation of an efficient workflow. The volunteers carried out real-life tests in a building demonstrator in Caceres (Spain) and filled in two questionnaires with their feedback. The results showed that an MR-based platform can involve interested stakeholders in the assessment of renovation design projects, that speeds up the decision-making process and increases the quality of those projects. Moreover, technicians can master the technology quickly, provided that it is included in the current renovation workflow and some technology gaps are covered. However, the main limitations of this study are that these findings are valid for building renovation design only, and the tests were performed in a controlled, yet full scale, experimental environment. Finally, this paper deals with a few open technical issues, such as the efficient alignment of holograms, transformation of BIM models into a format suitable for mixed reality applications and sharing feedbacks in an on-line repository to foster collaboration.
Exploring blockchain-supported authentication based on online and offline business in organic agricultural supply chain

• We first conceptualize two models BTM and BEM according to actual business marketing. • We quantitatively analyze each model and report their best performance. • We answer the issue which model generate more benefit to the farmers and the consumers. • Our findings can apply to other industrial products that require such services. The rise of blockchain technology has disrupted the traditional traceability method of organic agricultural products supply chain (OASC) by emerging authentication or certification. For an offline business scenario, a blockchain certification traceability model (BTM) is popular, in which consumers check the product information before purchasing by convenience scanning bar-code. Similarly, for an online business scenario, a blockchain-based e-commerce model (BEM) is popular, in which consumers verify and book the product while surfing the web. However, few studies have an insight into the potential OASCs business model and employ quantitative methods to confirm the benefits of the adoption of blockchain. According to a current real-world online and offline business, we first conceptualize two models in this paper. We employ the Stackelberg game to compare the supply chain profit and consumer surplus. Furthermore, we extend the analysis by using the case of Red Beauty orange, which proves that the blockchain e-commerce model is a good supply chain model under the condition of high shopping convenience and low operating cost of the blockchain platform. Our findings are probably generalizable to apply to other industrial products that require such services.
Sustainability standards and blockchain in agro-food supply chains: Synergies and conflicts

Blockchain-based technologies have emerged as a mechanism for governing sustainability in agro-food supply chain, where voluntary sustainability standards have been the main governance mechanisms over the past decades. Despite a growing body of research on blockchain-based technologies, the relationship between these two mechanisms for supply chains remains poorly understood. Therefore, this study aims at addressing this research gap and explaining their interaction. We described and assessed 16 cases of blockchain-based technologies and voluntary sustainability standards against twelve sustainability-related assessment criteria. The results show that the relationship between blockchain-based technologies and voluntary sustainability standards can be co-existing, synergistic, and antagonistic. While most cases fall under the co-existing relationship, we identified a few cases with synergistic relationships, and one case with an antagonistic relationship. We explain each type of relation and show how the system architecture and goal of a blockchain-based technology implementation are key determinants of this relationship. This study can support stakeholders in agro-food supply chain in better understanding the application of blockchain-based technologies for sustainability governance in relation to existing voluntary sustainability standards. It can further inform those stakeholders of possibilities to constructively collaborate and focus on positive social and environmental impacts within agro-food supply chains.
Forward search optimization and subgoal-based hybrid path planning to shorten and smooth global path for mobile robots

The characteristics of the path in length, smoothness, and safety are critical to the behavior of autonomous mobile robots. However, in searching-based global path planning, the potential heading of the robot is artificially restricted, so the resulting path typically consists of several polylines containing undesirable sharp bends, which sacrifices optimality and practicality in continuous space. To improve the optimality, we propose a Forward Search Optimization (FSO) algorithm to shorten the path planned by searching-based global algorithms such as Dijkstra, A*, D*, and D* Lite. To improve the practicability, we develop a Subgoal-based Hybrid Path Planning (SHPP) approach to smooth the path and keep it safe and comfortable from obstacles. Rather than formulating the smoothing process as a mathematical optimization problem, the SHPP approach combines subgoals and local algorithms (i.e., fuzzy inference system and artificial potential field) to meet the safety and smoothness requirements, which is essential for the practical application of the algorithm. Moreover, the guidance of subgoals solves the local minimum and goal unreachable problems of local path planning algorithms in complex environments. The simulation results show that the FSO and SHPP algorithms generate a short, smooth, and safe path.
Developing an Energy Cost Calculator for Solar

One of the most important variables affecting a country’s socioeconomic maturity is access to electricity. Bangladesh is now suffering from a severe electricity shortage. Around 65% of people do not have access to power, and most of them live in villages. The generated electricity was unable to meet demand, resulting in load shedding of up to 1500 MW. Solar Home Systems technology can be a wise effort to fix this problem in this case by harnessing energy from the country’s free-flowing renewable source. The main purpose of this paper is to make people more aware of using the solar system.
Agricultural Lightweight Embedded Blockchain System: A Case Study in Olive Oil

In Tunisia, one of the major problems of the olive oil industry is marketing. Several factors have an impact, such as quality, originality, lobbying, subsidies and the certification of extra virgin olive oil. The major problem remains the traceability of the production process to guarantee the origin of the food at all times. This fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that could bring visibility to the oil supply chain. It is proposed in order to guarantee the veracity of the product information at different stages. In this paper, a multi-Blockchain, multi-sensor traceability system using IoT will be presented. Two Blockchains that can be programmed via Smart Contract will be used. The first one is Quorum, which is a private Blockchain used by the actors of our system, and the second one is Ethereum, which is public and connects the different actors who have access to our system. This smart contract allows us to conta our system to track the olive oil manufacturing process from the farmer, through the oil mill, the transporter and the quality controller to the customer. A general approach for managing the olive oil supply chain is presented. This approach offers the possibility for the system to be configurable. It is based on smart contracts and applications that interact with the same smart contracts. The IoT is used to configure sensors. These sensors are the source of data for the supply chain process. These sensors are connected to the embedded platforms that host Quorum.
Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation

• SLMDS uses the self-expression model to preserve global label correlations. • A high-quality dynamic graph is designed to preserve the local label correlations. • An inner product term is used to select low-redundant features. • An efficient optimization method with provable convergence is designed. Recent years, joint feature selection and multi-label learning have received extensive attention as an open problem. However, there exist three general issues in previous multi-label feature selection methods. First of all, existing methods either consider local label correlations or global label correlations when they design multi-label feature selection methods, in fact, both two types of label correlations are significant for feature selection; second, previous methods use the low-quality graph to excavate local label correlations so that the results of these methods are under-performing; third, feature redundancy is ignored by most of the sparse learning methods. To overcome these challenges, we preserve global label correlations and dynamic local label correlations by preserving graph structure. Additionally, the l 2 , 1 -norm and an inner product regularization term are imposed onto the objective function to preserve robust high row-sparsity and to select low redundant features. All the above terms are integrated into one learning framework, and then we utilize a simple yet effective scheme to optimize the framework. Experimental results demonstrate the classification superiority of the proposed method.