Reviewerly/exHarmony huggingface dataset:
Turn-of-the-month effect in cryptocurrencies

Purpose This study examines the turn-of-the-month (TOM) effect in Bitcoin (BIT), Ethereum (ETH) and Litecoin (LIT) cryptocurrencies from August 2015 to August 2021. Design/methodology/approach Dummy regression model is used to examine the presence of the TOM effect and to test the efficiency of the cryptocurrency market. The characteristics of the returns during TOM days are compared with that of the non-non-TOM trading days. The authors also develop a trading strategy to earn abnormal returns using the TOM effect. Findings The authors show that TOM returns are positive and significantly higher than that of non-TOM returns. Interestingly, the authors empirically show that the TOM effect is not driven by the day-of-the-week (DOW) effect or the January effect. Based on the significant TOM effect, the authors formulate a trading strategy that annually outperforms the buy-and-hold strategy for BIT by 21.77% and for LIT by 47.10%. Finally, the results are robust to using a Generailzed Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model and the January 2018 sell-off. Practical implications The results have important implications for both traders and investors. The findings suggest that the investors might be able to earn excess profits by timing their positions in BIT and LIT taking the advantage of the TOM effect. Originality/value First, the authors provide the only study to report the evidence of the TOM effect in three leading cryptocurrencies, viz., BIT, LIT and ETH. Second, the authors control for the DOW effect and the January effect while investigating the TOM effect in cryptocurrency market. Finally, this study develops a trading strategy based on which the investors can time the cryptocurrency markets as indicated by the pattern of the TOM effect during the studied time period.
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection

Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs).The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is laborconsuming.In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED.The UED mines the literal semantic information to generate pseudo entity pairs and globally guided alignment information for EA and then utilizes the EA results to assist the DED.We construct a medical crosslingual knowledge graph dataset, MedED, providing data for both the EA and DED tasks.Extensive experiments demonstrate that in the EA task, UED achieves EA results comparable to those of state-of-the-art supervised EA baselines and outperforms the current state-ofthe-art EA methods by combining supervised EA data.For the DED task, UED obtains highquality results without supervision.
Learning Confidence for Transformer-based Neural Machine Translation

Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.A well-calibrated confidence estimate enables accurate failure prediction and proper risk measurement when given noisy samples and out-of-distribution data in real-world settings.However, this task remains a severe challenge for neural machine translation (NMT), where probabilities from softmax distribution fail to describe when the model is probably mistaken.To address this problem, we propose an unsupervised confidence estimate learning jointly with the training of the NMT model.We explain confidence as how many hints the NMT model needs to make a correct prediction, and more hints indicate low confidence.Specifically, the NMT model is given the option to ask for hints to improve translation accuracy at the cost of some slight penalty.Then, we approximate their level of confidence by counting the number of hints the model uses.We demonstrate that our learned confidence estimate achieves high accuracy on extensive sentence/word-level quality estimation tasks.Analytical results verify that our confidence estimate can correctly assess underlying risk in two real-world scenarios:(1) discovering noisy samples and (2) detecting out-of-domain data.We further propose a novel confidence-based instance-specific label smoothing approach based on our learned confidence estimate, which outperforms standard label smoothing 1 .
Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking

Situation-aware technologies enabled by multiobject tracking (MOT) methods will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-the-art method for Bayesian MOT that relies on a statistical model and preprocessed sensor measurements. In this paper, we establish a hybrid method for model-based and data-driven MOT. The proposed neural enhanced belief propagation (NEBP) approach complements BP by information learned from raw sensor data with the goal to improve data association and to reject false alarm measurements. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can outperform state-of-the-art reference methods.
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-resolution

In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device. However, this pipeline is redundant and inefficient for the independent processes, and some inner features could have been shared. Therefore, we present an efficient paradigm to perform Simultaneously Image Colorization and Super-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal. The proposed method consists of two parts: colorization branch for learning color information that employs the proposed plug-and-play Pyramid Valve Cross Attention (PVCAttn) module to aggregate feature maps between source and reference images; and super-resolution branch for integrating color and texture information to predict target images, which uses the designed Continuous Pixel Mapping (CPM) module to predict high-resolution images at continuous magnification. Furthermore, our SCSNet supports both automatic and referential modes that is more flexible for practical application. Abundant experiments demonstrate the superiority of our method for generating authentic images over state-of-the-art methods, e.g., averagely decreasing FID by 1.8 and 5.1 compared with current best scores for automatic and referential modes, respectively, while owning fewer parameters (more than x2) and faster running speed (more than x3).
Towards Fast and Accurate Federated Learning with Non-IID Data for Cloud-Based IoT Applications

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in the Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of the vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. Comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data.
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue

Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.Recent works have leveraged natural language descriptions of schema elements to enable such systems; however, descriptions only indirectly convey schema semantics.In this work, we propose Show, Don't Tell, which prompts seq2seq models with a labeled example dialogue to show the semantics of schema elements rather than tell the model through descriptions.While requiring similar effort from service developers as generating descriptions, we show that using short examples as schema representations with large language models results in state-of-the-art performance on two popular dialogue state tracking benchmarks designed to measure zeroshot generalization -the Schema-Guided Dialogue dataset and the MultiWOZ leave-oneout benchmark.
Blockchain and medicine: From digital promise to frontline practice

The promise of blockchain has far-reaching ramifications for health-care stakeholders. Blockchain technology has the potential to improve health care by putting the patient at the centre of the system and improving health data security, privacy, and interoperability. This is a reflective piece to highlight the opportunities for applications of blockchain technology in the future of healthcare.
Assessing non-LUS stutter in DNA sequence data

Forensic DNA analysis is among the most well-recognized and well-developed forensic disciplines. The field’s use of DNA markers known as short tandem repeats (STRs) offer a robust means of discriminating individuals while also introducing challenges to the analysis. One of these challenges, stutter, is the result of a non-biological artifact introduced during PCR. The formation and amplification of these stutter products can occur at rates as high as 15–20% of the parent allele. The challenge inherent in this process is differentiating stutter artifacts from true alleles, particularly in the presence of a minor contributor. Traditionally, DNA profiles are obtained using capillary electrophoresis (CE), where amplified DNA fragments are separated by size, not sequence, and the identification of stutter is performed on a locus-specific level. The use of CE-based fragment data rather than sequence-based data, has limited the community’s understanding of the precise behavior of stutter. Massively parallel sequencing (MPS) data provides an opportunity to better characterize stutter, permitting a more accurate means of detecting both size- or longest uninterrupted stretch (LUS)-based stutter but also allele and motif-specific stutter characteristics. This study sheds light on the value of characterizing motif- and allele-specific stutter, including non-LUS stutter, when using MPS methods. Analysis and characterization of stutter sequences was performed using data generated from 539 samples amplified with the ForenSeq and PowerSeq 46GY library preparation kit and sequenced on the Illumina MiSeq FGx. Assessment of non-LUS stutter begins with calculating stutter rates for all potential stutter products at a given locus (and allele), additionally, the occurrence of these discrete stutter products were quantified. Results show that although the LUS sequence stutters at a higher rate than non-LUS motifs, the non-LUS stutter products do occur at detectable levels and potentially influence sequence-based mixture analysis. The data indicate that the stutter from one motif or allele can be distinguished from another motif or allele based on their unique stutter rates; however, the number of stutter products from each motif or allele may similarly make up the overall pool of stutter products. Motif- and allele-specific stutter models provide the most comprehensive analysis of sequence stutter rates and provide the ability to differentiate stutter sequences more accurately from true allele stutter. This information provides a foundation for including the characterization of non-LUS stutter products when analyzing DNA profiles, specifically mixtures with potential low-level contributors.
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume

Dense prediction in medical volume provides enriched guidance for clinical analysis. CNN backbones have met bottleneck due to lack of long-range dependencies and global context modeling power. Recent works proposed to combine vision transformer with CNN, due to its strong global capture ability and learning capability. However, most works are limited to simply applying pure transformer with several fatal flaws (i.e., lack of inductive bias, heavy computation and little consideration for 3D data). Therefore, designing an elegant and efficient vision transformer learner for dense prediction in medical volume is promising and challenging. In this paper, we propose a novel 3D Shuffle-Mixer network of a new Local Vision Transformer-MLP paradigm for medical dense prediction. In our network, a local vision transformer block is utilized to shuffle and learn spatial context from full-view slices of rearranged volume, a residual axial-MLP is designed to mix and capture remaining volume context in a slice-aware manner, and a MLP view aggregator is employed to project the learned full-view rich context to the volume feature in a view-aware manner. Moreover, an Adaptive Scaled Enhanced Shortcut is proposed for local vision transformer to enhance feature along spatial and channel dimensions adaptively, and a CrossMerge is proposed to skip-connect the multi-scale feature appropriately in the pyramid architecture. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical dense prediction methods.
Single-shot N-step phase measurement profilometry

A single-shot N-step phase measurement profilometry (single-shot N-PMP) is proposed. In the traditional N-step PMP, N (N > 2) frames of phase-shifting sinusoidal gratings are needed to be projected onto the measured object. The corresponding N frames of phase-shifting deformed patterns modulated by the object are needed to be captured. And the larger the N is, the higher the measuring accuracy will be. In the proposed method, only one sinusoidal grating is needed to be projected and only one corresponding deformed pattern is captured. The proposed method can efficiently avoid direct filtering in the spectrum of the deformed pattern because of the secondary modulation in computer. The proposed method is an innovative single-shot, three-dimensional (3D) measurement with the highest accuracy among the single-shot 3D measurements so far for inheriting of the traditional optional N-step PMP. And the optional larger N can result in higher accuracy while the single-shot feature can be always guaranteed both the static and real-time 3D measurements. Furthermore, the mathematical model of the proposed method is more concise compared with the traditional PMP for the phase just modulated by the measured object itself can be directly solved without two phase resolutions and two phase unwrappings for both the reference plane and the measured object. The simulations and experimental results show the feasibility and validity of the proposed method.
Artificial Intelligence Technology in the Development of a Mobile Application for Higher Education Institution Information Portal

The article describes the technology of artificial intelligence for the mobile application of the information portal of higher education institution. Artificial intelligence technology is integrated into the university mobile application. A mobile application and an algorithm for user interaction with the mobile application with artificial intelligence are developed. The proposed idea is used in a mobile application of the GPA YALTA Portal.
Getting the Most out of Virtual Reality: Evaluating Short Breaks to Reduce Cybersickness and Cognitive Aftereffects

Considering the importance of getting the most out of virtual reality (VR) training, we evaluated the use of breaks to reduce virtual reality aftereffects in head-mounted displays. In a crossover design, we compared a 50-minute no-break exposure with an interrupted 50-minute exposure with four 10-minute breaks. Cybersickness, re-action times, workload and spatial learning were measured. Despite device manufacturers recommending breaks to reduce sickness, we found no evidence that breaks can mitigate aftereffects. Introducing breaks may also impact performance and logistical costs for training.
Teaching the Development of Information Web Portals Using ASP.NET Technology

The article describes a method for teaching future IT specialists in the 09.03.03 "Applied Informatics" field of training to develop an information portal using ASP.NET technology. The algorithm of actions for working in the Microsoft Visual Studio 2019 Community programming environment to develop an information portal, which allows IT students to acquire professional skills in a structured way, has been developed. The proposed algorithm of actions was used in the development of the GPA Yalta Portal.
The Impact of Partial Balance of Imbalanced Dataset on Classification Performance

The imbalance of network data seriously affects the classification performance of algorithms. Most studies have only used a rough description of data imbalance with less exploration of the specific factors affecting classification performance, which has resulted in difficulty putting forward targeted solutions. In this paper, we find that the impact of medium categories on classification performance cannot be ignored, and therefore propose the concept of partial balance, consisting of Class Number of Partial Balance (β) and Balance Degree of Partial Samples (μ). Combined with Global Slope (α), a parameterized model is established to describe the difference of imbalanced datasets. Experiments are performed on the Moore Dataset and CICIDS 2017 Dataset. The experiment’s results on Random Forest, Decision Tree and Deep Neural Network show increasing α is a conducive step in the performance improvement of minority classes and overall classes. When β of dominant categories increases, that of inferior classes decreases, which results in a decrease in the average performance of minority classes. The lower μ is, the closer the sample size of medium classes is to the minority classes, and the better the average performance is. Based on the conclusions, we propose and verify some basic strategies by various classical algorithms.
Global or Local: Constructing Personalized Click Models for Web Search

Click models are widely used for user simulation, relevance inference, and evaluation in Web search. Most existing click models implicitly assume that users’ relevance judgment and behavior patterns are homogeneous. However, previous studies have shown that different users interact with search engines in rather different ways. Therefore, a unified click model can hardly capture the heterogeneity in users’ click behavior. To shed light on this research question, we propose a Click Model Personalization framework (CMP) that adaptively selects from global and local models for individual users. Different adaptive strategies are designed to personalize click behavior modeling only for specific users and queries. We also reveal that capturing personalized behavior patterns is more important than modeling personalized relevance assessments in constructing personalized click models. To evaluate the performance of the proposed CMP framework, we build a large-scale practical Personalized Web Search (PWS) dataset, which consists of the search logs of 1,249 users from a commercial search engine over six months. Experimental results show that the proposed CMP framework achieves significant performance improvements than the non-personalized click models in click prediction.
Coarse-to-Fine Sparse Sequential Recommendation

Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% [email protected] on average.
Suspicious Region Segmentation Using Deep Features in Breast Cancer Mammogram Images

One of the most challenging processes in the image processing field is segmentation, especially region of the breast and Pectoral Muscle segmentation in mammogram images due to the presence of artifacts, homogeneity between the Region of Interest and pectoral muscle, also because of low contrast along the boundary of breast region. Moreover, similarities in texture between the texture of Region of Interest and pectoral muscle and irregularities in Region of Interest. The main objective of this work is to propose a model-based deep convolutional neural network feature to segment the Region of Interest from the pectoral muscle. The pre-processing stage has been employed in the first step. Morphological operations with Otsu&#x0027;s thresholding are used to remove artifacts and labels in images. After that, Wavelet Transform has been exploited to remove noise, and histogram equalization is employed to ameliorate image contrast as a last step of pre-processing. Then, Artificial Neural Network and Support Vector Machine classifiers are trained based on deep convolutional neural network features to estimate the Region of Interest from the pectoral muscle. This work was evaluated by employing 322 mammogram images from the mammographic image analysis society (mini-MIAS). Accordingly, the Region of Interest segmentation method achieved an accuracy of 95.34&#x0025;.
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning

Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Particularly, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.