Reviewerly/exHarmony huggingface dataset:
Anisotropic Additive Quantization for Fast Inner Product Search

Maximum Inner Product Search (MIPS) plays an important role in many applications ranging from information retrieval, recommender systems to natural language processing and machine learning. However, exhaustive MIPS is often expensive and impractical when there are a large number of candidate items. The state-of-the-art approximated MIPS is product quantization with a score-aware loss, which weighs more heavily on items with larger inner product scores. However, it is challenging to extend the score-aware loss for additive quantization due to parallel-orthogonal decomposition of residual error. Learning additive quantization with respect to this loss is important since additive quantization can achieve a lower approximation error than product quantization. To this end, we propose a quantization method called Anisotropic Additive Quantization to combine the score-aware anisotropic loss and additive quantization. To efficiently update the codebooks in this algorithm, we develop a new alternating optimization algorithm. The proposed algorithm is extensively evaluated on three real-world datasets. The experimental results show that it outperforms the state-of-the-art baselines with respect to approximate search accuracy while guaranteeing a similar retrieval efficiency.
Deeply Tensor Compressed Transformers for End-to-End Object Detection

DEtection TRansformer (DETR) is a recently proposed method that streamlines the detection pipeline and achieves competitive results against two-stage detectors such as Faster-RCNN. The DETR models get rid of complex anchor generation and post-processing procedures thereby making the detection pipeline more intuitive. However, the numerous redundant parameters in transformers make the DETR models computation and storage intensive, which seriously hinder them to be deployed on the resources-constrained devices. In this paper, to obtain a compact end-to-end detection framework, we propose to deeply compress the transformers with low-rank tensor decomposition. The basic idea of the tensor-based compression is to represent the large-scale weight matrix in one network layer with a chain of low-order matrices. Furthermore, we propose a gated multi-head attention (GMHA) module to mitigate the accuracy drop of the tensor-compressed DETR models. In GMHA, each attention head has an independent gate to determine the passed attention value. The redundant attention information can be suppressed by adopting the normalized gates. Lastly, to obtain fully compressed DETR models, a low-bitwidth quantization technique is introduced for further reducing the model storage size. Based on the proposed methods, we can achieve significant parameter and model size reduction while maintaining high detection performance. We conduct extensive experiments on the COCO dataset to validate the effectiveness of our tensor-compressed (tensorized) DETR models. The experimental results show that we can attain 3.7 times full model compression with 482 times feed forward network (FFN) parameter reduction and only 0.6 points accuracy drop.
Channel Pruning Base on Joint Reconstruction Error for Neural Network

In this paper, we propose a neural network channel pruning method based on the joint reconstruction error (JRE). To preserve the global discrimination ability of a pruned neural network, we propose the global reconstruction error. To ensure the integrity of information in the forward propagation process of a neural network, we propose the local reconstruction error. Finally, through normalization, the two magnitude mismatched losses are combined to obtain the joint error. The baseline network and pruned network are symmetrical structures. The importance of each channel in the pruned network is determined by the joint error between the channel and the corresponding channel in the baseline network. The proposed method prunes the channels in the pruned network according to the importance score and then restores its accuracy. The proposed method reduces the scale of the neural network and speeds up the model inferring speed without losing the accuracy of the neural network. Experimental results show the effectiveness of the method. For example, on the CIFAR-10 dataset, the proposed method prunes 50% of the channels of the VGG16 model, and the accuracy of the pruned model is 0.46% higher than that of the original model.
GRU4RecBE: A Hybrid Session-Based Movie Recommendation System (Student Abstract)

We present a novel movie recommendation system, GRU4RecBE, which extends the GRU4Rec architecture with rich item features extracted by the pre-trained BERT model. GRU4RecBE outperforms state-of-the-art session-based models over the benchmark MovieLens 1m and MovieLens 20m datasets.
Exploring Uplift Modeling with High Class Imbalance

Abstract Uplift modeling refers to individual level causal inference. Existing research on the topic ignores one prevalent and important aspect: high class imbalance. For instance in online environments uplift modeling is used to optimally target ads and discounts, but very few users ever end up clicking an ad or buying. One common approach to deal with imbalance in classification is by undersampling the dataset.In this work, we show how undersampling can be extended to uplift modeling.We propose three new undersampling methods for uplift modeling and one novel calibration method.We compare the proposed methods empirically and show when some methods have a tendency to break down.One key observation is that accounting for the imbalance is particularly important for uplift random forests, which explains the poor performance of the model in earlier works. Undersampling is also crucial for class-variable transformation based models.
Towards Robust Video Object Segmentation with Adaptive Object Calibration

In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. Most existing methods build pixel-wise reference-target correlations and then perform pixel-wise tracking to obtain target masks. Due to neglecting object-level cues, pixel-level approaches make the tracking vulnerable to perturbations, and even indiscriminate among similar objects. Towards robust VOS, the key insight is to calibrate the representation and mask of each specific object to be expressive and discriminative. Accordingly, we propose a new deep network, which can adaptively construct object representations and calibrate object masks to achieve stronger robustness. First, we construct the object representations by applying an adaptive object proxy (AOP) aggregation method, where the proxies represent arbitrary-shaped segments at multi-levels for reference. Then, prototype masks are initially generated from the reference-target correlations based on AOP. Afterwards, such proto-masks are further calibrated through network modulation, conditioning on the object proxy representations. We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively. Extensive experiments are conducted on the standard VOS benchmarks, YouTube-VOS-18/19 and DAVIS-17. Our model achieves the state-of-the-art performance among existing published works, and also exhibits superior robustness against perturbations.
Indoor Scene Recognition via Object Detection and TF-IDF

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.
Answering Count Queries with Explanatory Evidence

A challenging case in web search and question answering are count queries, such as"number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.
Blockchain-Based Smart Renewable Energy: Review of Operational and Transactional Challenges

Blockchain has peculiar characteristics among various digital technologies due to its decentralised and cryptographic properties. The combination of intelligent energy systems and blockchain can innovate new forms of transactive energy and navigate the digital journey to transform the future of renewable energy systems. This review studies various blockchain implementations in the smart energy domain and presents the findings on operational and transactional challenges in a blockchain-based smart renewable energy system. We also identify the differences between operations and transactions in smart energy systems. Furthermore, we identify the most pronounced cryptocurrencies in different studies. The findings highlighted various challenges concerning the implementation of blockchain-based smart energy systems. We identified how these challenges spawn across operational and transactional deliverables. Building on these findings, we discuss various challenges impacting the operational and transactional domains, which we believe have significant value for researchers, practitioners, policy makers, entrepreneurs, and start-ups. It will provide long-term benefits to humankind in fulfilling energy requirements, promoting sustainable energy use by developing countermeasures to combat identified challenges and leveraging the optimal use of blockchain technology.
Benefits and Challenges of Using NodeJS

Node.js, the JavaScript runtime environment is very popular among Full Stack Developers for building Web applications. It gives the developer to write server-side code in JavaScript and able to manage both client and server side. Node.js ensures outstanding performance, it applies event-driven, non-blocking input output and asynchronous paradigm. The study is based on benefits and challenges of using Node.js. The results of study are from comparing different web development programming languages with JavaScript which is used in Node.js. And in the end some suggestions on how to improve those areas and make it more feasible.
Content Based VGG16 Image Extraction Recommendation

Data transfer across numerous platforms has increased dramatically due to the enormous number of visitors or users of the present e-commerce platform. With the rise of increasingly massive data, consumers are finding it challenging to obtain the right data. The recommendation engine may be used to make it simpler to find information that is relevant to the user's needs. Clothing, gadgets, autos, furniture, and other e-commerce items rely on product visualization to entice shoppers. There are millions of images in these items. Displaying the information sought by clients based on visual data is a difficult challenge to address. One strategy that is simple to use in a recommendation system is content-based filtering. This approach will eventually make suggestions to consumers based on previously accessible goods or product descriptions. Content-based filtering works by searching for similarities based on the properties of a product item. User interactions with a product will be recorded and analyzed in order to recommend certain similarities to users. Text-based datasets are used in the majority of content-based filtering studies. In this study, however, we attempt to leverage a dataset received from Kaggle in the form of images of futsal shoes. Then, VGG16 architecture is used to extract the image dataset. The top 5 most relevant item rankings are generated by this recommendation method using cosine similarity. In addition, the NDCG (Normalized Discounted Cumulative Gain) approach is used to assess the results of the suggestions. The NDCG was evaluated in ten test scenarios, with an average NDCG value of 0.855, indicating that the system delivers a reasonable performance suggestion.
A New Line Matching Approach for High-Resolution Line Array Remote Sensing Images

In this paper, a new line matching approach for high-resolution line array remote sensing images is presented. This approach establishes the correspondence of straight lines on two images by combining multiple constraints. Firstly, three geometric constraints, epipolar, direction and the point-line geometric relationship, are used in turn to reduce the number of matching candidates. After this, two similarity constraints, the double line descriptor and point-line distance, are used to determine the optimal matches. Finally, the co-linearity constraint is used to check the one-to-many and many-to-one correspondences in the results. The proposed approach is tested on eight representative image patches selected from the ZY-3 line array satellite images, and the results are compared with those of two state-of-the-art approaches. Experiments demonstrate the superiority and potential of the proposed approach due to its higher accuracy and greater number of matches in most cases.
Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional Network

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing (e.g., public transport networks) have been conducted. In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassifications have an equal loss and thus seek to maximize the posterior probability of the sample nodes under labeled classes. The graph data used in realistic scenarios tend to follow unbalanced long-tailed class distributions, where a few majority classes contain most of the vertices and the minority classes contain only a small number of nodes, making it difficult for the GNN to accurately predict the minority class samples owing to the classification tendency of the majority classes. In this paper, we propose a dual cost-sensitive graph convolutional network (DCSGCN) model. The DCSGCN is a two-tower model containing two subnetworks that compute the posterior probability and the misclassification cost. The model uses the cost as ”complementary information” in a prediction to correct the posterior probability under the perspective of minimal risk. Furthermore, we propose a new method for computing the node cost labels based on topological graph information and the node class distribution. The results of extensive experiments demonstrate that DCSGCN outperformed other competitive baselines on different real-world imbalanced long-tailed graphs.
Study on Non-Fungible Tokens (NFT)

Abstract: This study examines the risk and return characteristics of the NFT-based startups listed on the cryptocurrency exchange. Our investigation is motivated by the recent surge in the NFT activity on the part of creators, investors, and traders. We begin by proposing novel classification ofthe existing NFTs that range from NFT blockchains through NFT metaverse to NFT DeFi. Next, we establishthat NFTs: 1) earn 130% on the first-listing-day; 2) yield an average investment multiple of 40 (roughly 4,000%) over long-term, which is four times higher than bitcoin during the same period; 3) deliver positive and significantalpha and exhibit above-average beta. We also show thatthe NFT segment of the cryptocurrency market leads market recovery following the mid-2021 crash and generate a return of close to 350%.
Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed.In the US, the current 5-year survival rate of 11%.Therefore, PC has to be detected as early as possible.Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy.The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem.We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches.Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values.These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses.Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multiclass classification approaches.This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection.Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection.The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence.This further aids in determining if more tests are required for better diagnosis.Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure.Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.
Automated app-based augmented reality cognitive behavioral therapy for spider phobia: Study protocol for a randomized controlled trial

Background Fear of spiders, or Arachnophobia, is one of the most common specific phobias. The gold standard treatment, in vivo exposure therapy, is effective, but comes with significant limitations, including restricted availability, high costs, and high refusal rates. Novel technologies, such as augmented reality, may help to overcome these limitations and make Exposure Therapy more accessible by using mobile devices. Objective This study will use a Randomized Controlled Trial design to investigate whether ZeroPhobia: Arachnophobia, a 6-week Augmented Reality Exposure Therapy smartphone self-help application, can effectively reduce spider phobia symptoms. Additionally, we will examine user-friendliness of the application and the effect of usage intensity and presence on treatment outcome. Methods This study is registered in the Netherlands Trial Registry under NL70238.029.19 (Trial NL9221). Ethical approval was received on October 11, 2019. One-hundred-twelve participants (age 18–64, score ≥ 59) on the Fear of Spiders Questionnaire [FSQ] will be recruited from the general Dutch population and randomly assigned to a treatment or waitlist control group. The ZeroPhobia application can be accessed on users’ smartphone. Baseline, post-test (i.e., at six weeks), 3- and 12-month follow-up assessments will be done, each including the Fear of Spiders Questionnaire as the main outcome measure as well as additional measures of anxiety, depression, user-friendliness, and presence as secondary measures and covariates. Results The study was funded on September 25, 2018. Data collection started in September 2021 and the study is expected to run until September 2022. Conclusions Our study will improve our understanding of the efficacy and feasibility of providing Exposure Therapy for spider phobia using an Augmented Reality self-help application, with the intention of making mental health care more accessible.
Informatics and Artificial Intelligence (AI) Education in Korea: Situation Analysis Using the Darmstadt Model

The Korean government has implemented various policies to support software (SW) and artificial intelligence (AI) education to secure national competitiveness since 2015. As the social impact of AI technology increased, AI became an integral component of the computer education curricula. SW and AI education projects have been promoted jointly to develop technical and human infrastructure supporting the â€œinformaticsâ€â€”the subject name of computer science (CS) education in Korea. However, a survey conducted by the Korean government showed that only a few respondents had AI education provided by public institutions such as schools. Therefore, this paper analyzes why AI education has not been well implemented and proposes discussions for improvement. The Darmstadt model, a systematic framework for analyzing CS education in the country, was chosen to analyze the documents. Between 2015 and 2021, 72 documents related to the computer education system, sociocultural factors, curricula, policies, teaching environment, tests, extra curriculum activities, and media were collected and qualitatively analyzed. The results were presented regarding the educational system, sociocultural factors, curriculum, policies, teachers, tests, extracurricular activities, and media. The results reveal that systematic and coherent curricula are required at all school levels, and it is also necessary to secure more education time to implement the curriculum. The number of informatics-computer teachers should be increased, and research to verify the teaching capacity of teachers is demanded. Finally, it is vital to build and manage a technical infrastructure.