Reviewerly/exHarmony huggingface dataset:
Understanding Bitcoin Price Prediction Trends under Various Hyperparameter Configurations

Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.
HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem

Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO problems is the Wireless Sensor Network (WSN) coverage optimization problem, which plays an important role in Internet of Things (IoT). A novel hybrid algorithm is proposed, named hybrid particle swarm butterfly algorithm (HPSBA), by combining their strengths of particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), for solving this problem. Significantly, the value of individual scent intensity should be non-negative without consideration of the basic BOA, which is calculated with absolute value of the proposed HPSBA. Moreover, the performance of the HPSBA is comprehensively compared with the fundamental BOA, numerous potential BOA variants, and tried-and-true algorithms, for solving the twenty-six commonly used benchmark functions. The results show that HPSBA has a competitive overall performance. Finally, when compared to PSO, BOA, and MBOA, HPSBA is used to solve the node coverage optimization problem in WSN. The experimental results demonstrate that the HPSBA optimized coverage has a higher coverage rate, which effectively reduces node redundancy and extends WSN survival time.
Evaluating Augmented Reality Based Remote Collaboration: A Contextualized Approach

AugmentedAugmented Reality (AR) has tremendous capacity to assist scenarios of remoteRemote collaborationCollaboration in the context of IndustryIndustry 4.0, where distributed teamTeams-members with different expertise need to achieve shared targets. Solutions using AR enable integrationIntegration of virtual and real-world content, empowering individuals with a common ground that improves understanding of the problem and its context, as well as knowledge retention. Given the field novelty, most efforts have been dedicated to proposing novel technologies to support the basic needs of collaborationCollaboration. However, as the field evolves, evaluation must also be addressed, moving beyond the technologicalTechnological aspects to better understand its purpose to the collaborativeCollaborative process. Nevertheless, evaluating such contexts is a challenging goal given the number of characteristics that may impactImpact collaborationCollaboration. Furthermore, existing literature suggests evaluation methods still take advantage on single-user methods, not suitable to collaborativeCollaborative scenarios. As such, evaluation of such activities needs to advance into more contextualizedContextualized perspectives, considering different members actions and how they relate with the technology being used. Also, the environment and how distinct factors affect the process of decision-making. In this vein, a conceptualConceptual framework for AR-based remoteRemote scenarios is proposed for supporting more contextualizedContextualized evaluations. It encompasses identification of important dimensions, fostering an additional overview of the added value of AR to the nuances of the collaborativeCollaborative effort.
Demystifying BERT: System Design Implications

Transfer learning in natural language processing (NLP) uses increasingly large models that tackle challenging problems. Consequently, these applications are driving the requirements of future systems. To this end, we study the computationally and time-intensive training phase of NLP models and identify how its algorithmic behavior can guide future accelerator design. We focus on BERT (Bi-directional Encoder Representations from Transformer), one of the most popular Transformer-based NLP models, and identify key operations which are worthy of attention in accelerator design. In particular, we focus on the manifestation, size, and arithmetic behavior of these operations which remain constant irrespective of hardware choice. Our results show that although computations which manifest as matrix multiplications dominate BERT’s execution, they have considerable heterogeneity. Furthermore, we characterize memory-intensive computations which also feature prominently in BERT but have received less attention. To capture future Transformer trends, we also show and discuss implications of these behaviors as networks get larger. Moreover, we study the impact of key training techniques like distributed training, check-pointing, and mixed-precision training. Finally, our analysis identifies holistic solutions to optimize systems for BERT-like models and we further demonstrate how enhancing compute-intensive accelerators with near-memory compute can help accelerate Transformer networks.
Is voice really persuasive? The influence of modality in virtual assistant interactions and two alternative explanations

Purpose Virtual assistants are increasingly used for persuasive purposes, employing the different modalities of voice and text (or a combination of the two). In this study, the authors compare the persuasiveness of voice-and text-based virtual assistants. The authors argue for perceived human-likeness and cognitive load as underlying mechanisms that can explain why voice- and text-based assistants differ in their persuasive potential by suppressing the activation of consumers' persuasion knowledge. Design/methodology/approach A pre-registered online-experiment ( n = 450) implemented a text-based and two voice-based (with and without interaction history displayed in text) virtual assistants. Findings Findings show that, contrary to expectations, a text-based assistant is perceived as more human-like compared to a voice-based assistant (regardless of whether the interaction history is displayed), which in turn positively influences brand attitudes and purchase intention. The authors also find that voice as a communication modality can increase persuasion knowledge by being cognitively more demanding in comparison to text. Practical implications Simply using voice as a presumably human cue might not suffice to give virtual assistants a human-like appeal. For the development of virtual assistants, it might be beneficial to actively engage consumers to increase awareness of persuasion. Originality/value The current study adds to the emergent research stream considering virtual assistants in explicitly exploring modality differences between voice and text (and a combination of the two) and provides insights into the effects of persuasion coming from virtual assistants.
Machine Learning-Based Security Pattern Recognition Techniques for Code Developers

Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are complex and matching different and variate skills, most applications are launched intrinsically vulnerable. We have advanced our research for a security scanner able to use automated learning techniques based on machine learning algorithms to recognize patterns of security weaknesses in source code. To make the scanner independent on the programming language, the source code is converted to a vectorial representation using natural language processing methods, which are able to retain semantical traits of the original code and at the same time to reduce the dependency on the lexical structure of the program. The security flaws detection performance is in the ranges accepted by software security professionals (recall &gt; 0.94) even when vulnerable samples are very low represented in the dataset (e.g., less than 4% vulnerable code for a specific CWE in the dataset). No significant change or adaptation is needed to change the source code language under scrutiny. We apply this approach on detecting Common Weaknesses Enumeration (CWE) vulnerabilities in datasets provided by NIST (Test suites–NIST Software Assurance Reference Dataset).
Speaker Representation Learning via Contrastive Loss with Maximal Speaker Separability

A great challenge in speaker representation learning using deep models is to design learning objectives that can enhance the discrimination of unseen speakers under unseen domains. This work proposes a supervised contrastive learning objective to learn a speaker embedding space by effectively leveraging the label information in the training data. In such a space, utterance pairs spoken by the same or similar speakers will stay close, while utterance pairs spoken by different speakers will be far apart. For each training speaker, we perform random data augmentation on their utterances to form positive pairs, and utterances from different speakers form negative pairs. To maxi-mize speaker separability in the embedding space, we incorporate the additive angular-margin loss into the contrastive learning objective. Experimental results on CN-Celeb show that this new learning objective can cause ECAPA-TDNN to find an embedding space that exhibits great speaker discrimination. The contrastive learning objective is easy to implement, and we provide PyTorch code at https://github.com/shanmon110/AAMSupCon.
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites

Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resolution method for such multi-exposure sequences, a problem that has received very little attention in the literature. The proposed method can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times. Furthermore, it can be trained end-to-end with self-supervision, without requiring ground truth high resolution frames, which makes it especially suited to handle real data. Central to our method are three key contributions: i) a base-detail decomposition for handling errors in the exposure times, ii) a noise-level-aware feature encoding for improved fusion of frames with varying signal-to-noise ratio and iii) a permutation invariant fusion strategy by temporal pooling operators. We evaluate the proposed method on synthetic and real data and show that it outperforms by a significant margin existing single-exposure approaches that we adapted to the multi-exposure case.
EXPLORING ELECTROCOAGULATION THROUGH DATA ANALYSIS AND TEXT MINING PERSPECTIVES

This study is a bibliometric analysis of electrocoagulation with data analysis and text mining aspects.Related research was conducted to take a picture of the current state of electrocoagulation in the literature to find out the less-used wastewater, electrode, and pollutants types, to discover the common words used in article titles, to understand how many pages an average article has, to figure out if electrocoagulation has passed its prime time and to provide helpful information to the researchers in developing of their research strategies.The first part of the study was the statistical analysis of the raw data.Some valuable information such as cited reference count, publication year, number of pages, and times cited -all databases have been revealed with density plots.Then a word cloud approach was used to inspect the abstracts, the titles, and the keywords.Afterward, the abstracts were classed into two, using word embedding and a k-means algorithm.Descriptive statistics, word cloud, and sentiment analysis were performed for each cluster.Finally, a classification process was conducted depending on research areas with the decision tree algorithm.The decision tree method could not classify the data set sufficiently depending on whether the abstracts of the papers were not compatible with the research area classes or because there were too many research area categories.
Keyword Extraction from Short Texts with a Text-to-Text Transfer Transformer

The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short text passages. The evaluation is carried out on the new Polish Open Science Metadata Corpus (POSMAC), which is released with this paper: a collection of 216,214 abstracts of scientific publications compiled in the CURLICAT project. We compare the results obtained by four different methods, i.e. plT5kw, extremeText, TermoPL, KeyBERT and conclude that the plT5kw model yields particularly promising results for both frequent and sparsely represented keywords. Furthermore, a plT5kw keyword generation model trained on the POSMAC also seems to produce highly useful results in cross-domain text labelling scenarios. We discuss the performance of the model on news stories and phone-based dialog transcripts which represent text genres and domains extrinsic to the dataset of scientific abstracts. Finally, we also attempt to characterize the challenges of evaluating a text-to-text model on both intrinsic and extrinsic keyword extraction.
Toward Vehicle Occupant-Invariant Models for Activity Characterization

With the advent of self-driving cars and the push by large companies into fully driverless transportation services, monitoring passenger behaviour in vehicles is becoming increasingly important for several reasons, such as ensuring safety and comfort.Although several human action recognition (HAR) methods have been proposed, developing a true HAR system remains a very challenging task.If the dataset used to train a model contains a small number of actors, the model can become biased towards these actors and their unique characteristics.This can cause the model to generalise poorly when confronted with new actors performing the same actions.This limitation is particularly acute when developing models to characterise the activities of vehicle occupants, for which data sets are short and scarce.In this study, we describe and evaluate three different methods that aim to address this actor bias and assess their performance in detecting in-vehicle violence.These methods work by removing specific information about the actor from the model's features during training or by using data that is independent of the actor, such as information about body posture.The experimental results show improvements over the baseline model when evaluated with real data.
Human Action Recognition Using Hybrid Deep Evolving Neural Networks

Human action recognition can be applied in a multitude of fully diversified domains such as active large-scale surveillance, threat detection, personal safety in hazardous environments, human assistance, health monitoring, and intelligent robotics. Owing to its high demands in real-world applications, it has drawn significant attention. In this research, we propose hybrid deep neural networks, i.e. Convolutional Long Short-Term Memory (ConvLSTM) Networks, Long-term Recurrent Convolutional Networks (LRCN), for tackling video action classification. In particular, for the LRCN model, different CNN encoder architectures such as VGG16, ResNet50, DenseNet121 and MobileNet, as well as several Long Short-Term Memory (LSTM) variant decoder architectures, such as LSTM, bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), are used for spatial-temporal feature extraction to test model performance. We adopt diverse experimental settings including using different numbers of frames per video and learning configurations to optimize performance. The empirical results indicate the superiority of MobileNet in combination with a BiLSTM network over other hybrid network settings for the action classification using the UCF50 dataset. Owing to the lightweight MobileNet encoder, this LRCN model also achieves a better trade-off between performance and training and inference computational costs, while outperforming existing state-of-the-art methods.
Algorithm Aversion: Theory and Evidence from Robo-Advice

Algorithms hold great potential to lower costs and democratize access to a wide variety of consumer services. How do humans interact with algorithms, and what are the major barriers to algorithmic adoption? We answer these questions using a structural model applied to unique data that captures interactions between human clients and “hybrid” robo-advisors that offer different levels and standards of human counseling to complement algorithmic investment. The model features three dimensions of investors’ algorithm aversion, all of which can be influenced by human advice, namely: a per-period disutility of dealing with the algorithm, pessimism about the algorithm’s ability to manage assets, and uncertainty about the algorithm’s performance. We estimate the model’s parameters using quasi-random variation in the matching of clients with human advisors generated by mechanical allocation rules. We find evidence that algorithm aversion is mainly driven by ongoing disutility and uncertainty and that human advice is important in retaining investors in robo-advice during market downturns.
ColorFormer: Image Colorization via Color Memory Assisted Hybrid-Attention Transformer

Automatic image colorization is a challenging task that attracts a lot of research interest. Previous methods employing deep neural networks have produced impressive results. However, these colorization images are still unsatisfactory and far from practical applications. The reason is that semantic consistency and color richness are two key elements ignored by existing methods. In this work, we propose an automatic image colorization method via color memory assisted hybrid-attention transformer, namely ColorFormer. Our network consists of a transformer-based encoder and a color memory decoder. The core module of the encoder is our proposed global-local hybrid attention operation, which improves the ability to capture global receptive field dependencies. With the strong power to model contextual semantic information of grayscale image in different scenes, our network can produce semantic-consistent colorization results. In decoder part, we design a color memory module which stores various semantic-color mapping for image-adaptive queries. The queried color priors are used as reference to help the decoder produce more vivid and diverse results. Experimental results show that our method can generate more realistic and semantically matched color images compared with state-of-the-art methods. Moreover, owing to the proposed end-to-end architecture, the inference speed reaches 40 FPS on a V100 GPU, which meets the real-time requirement.
Sequential Fusion of Multi-view Video Frames for 3D Scene Generation

3D scene understanding and generation are to reconstruct the layout of the scene and each object from an RGB image, estimate its semantic type in 3D space and generate a 3D scene. At present, the 3D scene generation algorithm based on deep learning mainly recovers the 3D scene from a single image. Due to the complexity of the real environment, the information provided by a single image is limited, and there are problems such as the lack of single-view information and the occlusion of objects in the scene. In response to the above problems, we propose a 3D scene generation framework SGMT, which realizes multi-view position information fusion and reconstructs the 3D scene from multi-view video time series data to compensate for the missing object position in existing methods. We demonstrated the effectiveness of multi-view scene generation of SGMT on the UrbanScene3D and SUNRGBD dataset and studied the influence of SGCN and joint fine-tuning. In addition, we further explored the transfer ability of the SGMT between datasets and discussed future improvements.
An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation

Target detection in synthetic aperture radar (SAR) images has a wide range of applications in military and civilian fields. However, for engineering applications involving edge deployment, it is difficult to find a suitable balance of accuracy and speed for anchor-based SAR image target detection algorithms. Thus, an anchor-free detection algorithm for SAR ship targets with deep saliency representation, called SRDet, is proposed in this paper to improve SAR ship detection performance against complex backgrounds. First, we design a data enhancement method considering semantic relationships. Second, the state-of-the-art anchor-free target detection framework CenterNet2 is used as a benchmark, and a new feature-enhancing lightweight backbone, called LWBackbone, is designed to reduce the number of model parameters while effectively extracting the salient features of SAR targets. Additionally, a new mixed-domain attention mechanism, called CNAM, is proposed to effectively suppress interference from complex land backgrounds and highlight the target area. Finally, we construct a receptive-field-enhanced detection head module, called RFEHead, to improve the multiscale perception performance of the detection head. Experimental results based on three large-scale SAR target detection datasets, SSDD, HRSID and SAR-ship-dataset, show that our algorithm achieves a better balance between ship target detection accuracy and speed and exhibits excellent generalization performance.
Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.