Reviewerly/exHarmony huggingface dataset:
InfiniteWalk

The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networks. Recent work of Qiu et al. (2018) provides a closed-form expression for the DeepWalk objective, obviating the need for sampling for small datasets and improving accuracy. In these methods, the "window size" T within which words or nodes are considered to co-occur is a key hyperparameter. We study the objective in the limit as T goes to infinity, which allows us to simplify the expression of Qiu et al. We prove that this limiting objective corresponds to factoring a simple transformation of the pseudoinverse of the graph Laplacian, linking DeepWalk to extensive prior work in spectral graph embeddings. Further, we show that by a applying a simple nonlinear entrywise transformation to this pseudoinverse, we recover a good approximation of the finite-T objective and embeddings that are competitive with those from DeepWalk and other skip-gram methods in multi-label classification. Surprisingly, we find that even simple binary thresholding of the Laplacian pseudoinverse is often competitive, suggesting that the core advancement of recent methods is a nonlinearity on top of the classical spectral embedding approach.
An Empirical Analysis of Backward Compatibility in Machine Learning Systems

In many applications of machine learning (ML), updates are performed with the goal of enhancing model performance. However, current practices for updating models rely solely on isolated, aggregate performance analyses, overlooking important dependencies, expectations, and needs in real-world deployments. We consider how updates, intended to improve ML models, can introduce new errors that can significantly affect downstream systems and users. For example, updates in models used in cloud-based classification services, such as image recognition, can cause unexpected erroneous behavior in systems that make calls to the services. Prior work has shown the importance of "backward compatibility" for maintaining human trust. We study challenges with backward compatibility across different ML architectures and datasets, focusing on common settings including data shifts with structured noise and ML employed in inferential pipelines. Our results show that (i) compatibility issues arise even without data shift due to optimization stochasticity, (ii) training on large-scale noisy datasets often results in significant decreases in backward compatibility even when model accuracy increases, and (iii) distributions of incompatible points align with noise bias, motivating the need for compatibility aware de-noising and robustness methods.
A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are called bags, and the points inside each bag are called instances. Considering the case with two classes of instances (positive and negative) and inspired by a well-established instance-space support vector machine (SVM) model, we propose to extend to MIL classification the proximal SVM (PSVM) technique that has revealed very effective for supervised learning, especially in terms of computational time. In particular, our approach is based on a new instance-space model that exploits the benefits coming from both SVM (better accuracy) and PSVM (computational efficiency) paradigms. Starting from the standard MIL assumption, such a model is aimed at generating a hyperplane placed in the middle between two parallel hyperplanes: the first one is a proximal hyperplane that clusters the instances of the positive bags, while the second one constitutes a supporting hyperplane for the instances of the negative bags. Numerical results are presented on a set of MIL test data sets drawn from the literature.
Unifying Functional User Interface Design Principles

Many designers and User Interface (UI) educators discuss principles to be followed when designing the functional aspects of a UI. However, many UI principles have been proposed, scattered in scientific papers and teaching books. Some principles are different, and many are somehow similar or overlapping with others. This makes it very difficult to comprehend where to pay attention to when designing a UI. In this paper, we perform a systematic literature review to first identify the most relevant authors in the domain of functional UI design principles. Focusing on the three most cited works of these authors, we extracted 257 principles. We next analyzed all these principles, unified their variants, and, considering their scientific influence, finally derived a shorter and core selection of 36 principles. This core selection provides educators and UI designers with a clear path to teach, evaluate, learn and improve the UI functional design.
Cloud-based Livestock Monitoring System Using RFID and Blockchain Technology

Food security and quality has become an increasingly concerned issue around the world. Recent food falsification cases have caused vast public health risk and economic damage while eroding consumers’ trust. Ever more prevalent is the danger of infectious disease among livestock that may cause serious economic damage while adding uncertainty to the world’s food supply. Animal-human transmitted diseases infect humans and are the culprits for the 2003 SARS, 2009 H1N1 Avian Flu, and 2019 Coronavirus outbreaks. Livestock monitoring is a promising approach in disease warning, authenticating food supply chain and tracking sources of contamination in the production cycle. One way of addressing the traceability issue and ensuring accountability is by utilizing blockchain technology as a ledger. This paper proposes a novel method to apply blockchain technology to ensure traceability and authenticity in the livestock supply chain, hence making the livestock farming process more scientific and trustworthy.
Optimising Sample Preparation and Calibrations in EDXRF for Quantitative Soil Analysis

Energy-dispersive X-ray fluorescence spectrometry (EDXRF) is a rapid and inexpensive method for soil analysis; however, analytical results are influenced by particle size effects and spectral interferences. The objective of this study was to optimise sample preparation and calibrations to improve the accuracy of EDXRF for soil tests. Methods of sample preparation were compared by calculating the recoveries of 13 elements in four International Soil-Analytical Exchange (ISE) standards prepared as loose powder (LP), pressed pellet (PP), and pressed pellet with wax binder (PPB). A matching library (ML) was created and evaluated against the fundamental parameters (FP) calibration using 20 ISE standards. Additionally, EDXRF results of 41 tillage soils were compared with Inductively coupled plasma optical emission spectrometry (ICP-OES) results. The PPB had most recoveries within the acceptable range of 80–120%; conversely, PP yielded the poorest element recoveries. For the calibration, the ML provided better recoveries of Ni, Pb, Cu, Mg, S, P, and Cr; however, for Zn, and Mn, it had the opposite effect. Furthermore, EDXRF results compared with ICP-OES separated by soil texture class for Al, K, Mn, and Fe. In conclusion, the EDXRF is suitable for quantifying both trace elements and macronutrients in contaminated soils and has the potential to provide screening or prediction of soil texture in agriculture.
Attentive Composite Residual Network for Robust Rain Removal from Single Images

In rainy conditions, imaging devices often capture degraded and blurry images. Most existing works on this problem focus on rain streak removal, but these approaches cannot handle the various types of rain found in images. In this paper, we propose a robust rain removal method for use with single images using an attentive composite residual network. We put forth a single-to-dual encoder-decoder structure, which consists of an attentive net that identifies regions containing rain components during encoding, followed by a dual-channel architecture which recovers the background and detail components of the identified regions during decoding. For the detail subnet, we designed a novel building block, namely a composite residual block (CRB), by constructing multiple residual connections among Res2Net modules. Additionally, we designed another attentive-CRB for the attentive net that uses a squeeze-and-excitation (SE)-Res2Net module, to build a channel-wise attention mechanism. We show that such a deep network can be trained end-to-end from rainy images and that it outperforms the previous state-of-the-art methods on datasets containing different types of rainy images. Experimental results also demonstrate the proposed model's superiority over the competitor's on real rain-affected images, recovering visually clean images and retaining good detail.
Text Augmentation Using BERT for Image Captioning

Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number of impressive results have been achieved. The typical models are based on a neural networks, including convolutional ones for encoding images and recurrent ones for decoding them into text. More than that, attention mechanism and transformers are actively used for boosting performance. However, even the best models have a limit in their quality with a lack of data. In order to generate a variety of descriptions of objects in different situations you need a large training set. The current commonly used datasets although rather large in terms of number of images are quite small in terms of the number of different captions per one image. We expanded the training dataset using text augmentation methods. Methods include augmentation with synonyms as a baseline and the state-of-the-art language model called Bidirectional Encoder Representations from Transformers (BERT). As a result, models that were trained on a datasets augmented show better results than that models trained on a dataset without augmentation.
Text-conditioned Transformer for automatic pronunciation error detection

Automatic pronunciation error detection (APED) plays an important role in the domain of language learning. As for the previous ASR-based APED methods, the decoded results need to be aligned with the target text so that the errors can be found out. However, since the decoding process and the alignment process are independent, the prior knowledge about the target text is not fully utilized. In this paper, we propose to use the target text as an extra condition for the Transformer backbone to handle the APED task. The proposed method can output the error states with consideration of the relationship between the input speech and the target text in a fully end-to-end fashion.Meanwhile, as the prior target text is used as a condition for the decoder input, the Transformer works in a feed-forward manner instead of autoregressive in the inference stage, which can significantly boost the speed in the actual deployment. We set the ASR-based Transformer as the baseline APED model and conduct several experiments on the L2-Arctic dataset. The results demonstrate that our approach can obtain 8.4\% relative improvement on the $F_1$ score metric.
Bitcoin: An Axiomatic Approach and an Impossibility Theorem

Bitcoin’s main innovation lies in allowing a decentralized system that relies on anonymous, profit-driven miners who can freely join the system. We formalize these properties in three axioms: anonymity of miners, no incentives for miners to consolidate, and no incentive to assuming multiple fake identities. This novel axiomatic formalization allows us to characterize what other protocols are feasible: every protocol with these properties must have the same reward scheme as Bitcoin. This implies an impossibility result for risk-averse miners. Furthermore, any protocol either gives up on some degree of decentralization or its reward scheme is equivalent to Bitcoin’s. (JEL D82, E42, O33)
Real-time Conversion of Sign Language to Text and Speech

This paper presents an analysis of the performance of different techniques that have been used for the conversion of sign language to text/speech format. Using the best possible method after analysis, an android application is developed that can convert real-time ASL (American Sign Language) signs to text/speech.
Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers' abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.
Integrating elaboration likelihood model and herd theory in information security message persuasiveness

Determining how to design persuasive information security messages to increase employees' engagement in protection-motivated behaviors is an important organizational objective. Information security messages that are argument-based have been shown to effectively elicit employees' protection-motivated behaviors. The mechanism by which cue-based information security messages persuade employees to engage in protection-motivated behaviors is not clear. This paper applies the lenses of the elaboration likelihood model (ELM) and herd theory to propose a comprehensive conceptual model which explains how security argument persuasiveness and popularity cues influence individuals' cognitive attitude and herd mentality and subsequently influence employees' protection-motivated behaviors. The results have implications for designing effective information security messages for organizational information security management.
Ensemble Belief Rule-Based Model for complex system classification and prediction

Belief Rule-Based (BRB) model has been widely used for complex system classification and prediction. However, excessive antecedent attributes will cause the combinatorial explosion problem, which restricts the applicability of the BRB model to high-dimensional problems. In this paper, we propose an Ensemble-BRB model with the use of the bagging framework to downsize the belief rule base and avoid the combinatorial explosion problem. The kernel of the Ensemble-BRB model is to generate several weak BRBs orderly, each of which only consists of a subset of antecedent attributes. Different combination methods can be used to integrate these weak BRBs coherently for classification and prediction respectively. Four benchmark problems are tested to validate the efficiency of the proposed Ensemble-BRB model in classification, and a real case on the health index prediction of engines proves the feasibility of the Ensemble-BRB model in prediction. The results on both classification and prediction show that the Ensemble-BRB model can effectively downsize the BRB as well as reach a high modeling accuracy.
Learning to rank developers for bug report assignment

Bug assignment is a burden for projects receiving many bug reports. To automate the process of assigning bug reports to the appropriate developers, several studies have relied on combining natural language processing and information retrieval techniques to extract two categories of features. One of these categories targets developers who have fixed similar bugs before, and the other determines developers working on source files similar to the description of the bug. Commit messages represent another rich source for profiling developer expertise as the language used in commit messages is closer to that used in bug reports. In this work, we propose a more enhanced profiling of developers through their commits, which are captured in a new set of features that we combine with features used in previous studies. More precisely, we propose an adaptive ranking approach that takes as input a given bug report and ranks the top developers who are most suitable to fix it. This approach learns from the history of previously fixed bugs to profile developers in terms of their expertise. With respect to a given bug report, the ranking score of each developer is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. Our model was evaluated using around 22,000 bug reports, exported from four large scale open-source Java projects. Results show that our model significantly outperformed two recent state-of-the-art methods in recommending the suitable developer to handle a certain bug report. Specifically, the percentage of recommending a developer within the top 5 ranked developers correctly was over 80% for both the Eclipse UI Platform and Birt projects.
Gender Classification using Twitter Text Data

Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.
Attention-adaptive and deformable convolutional modules for dynamic scene deblurring

We investigate two aspects of network architecture design for dynamic scene deblurring: (1) Learning blur characteristics and their location in dynamic scenes, which corresponds to learning what and where to attend in the channel and spatial axes, respectively. In this regard, we design an attention-adaptive module (AAM), the innovation of which is that it adaptively determines the arrangement of channel and spatial attention modules (i.e., sequentially or in parallel). Ablation experiments verified the effectiveness of the AAM by incorporating it into existing deblurring convolutional neural network (CNN) architectures. (2) Intuitively, geometric variations are widely observed in objects in dynamic scenes because different spatial regions are blurred by different motion kernels. However, owing to the fixed geometric structures in their modules, regular CNNs fail to adapt to these variations. Accordingly, we propose a deformable convolutional module (DCM) to handle geometric variations. Preliminary experiments demonstrated that incorporating the AAM and DCM into existing deblurring models can significantly improve performance. Moreover, it was empirically verified that an encoder–decoder ResBlock network incorporating the proposed modules compares favorably with state-of-the-art methods.
<scp>AI</scp> in digital forensics: Ontology engineering for cybercrime investigations

Abstract In parallel with the exponentially growing number of computing devices and IoT networks, the data storage and processing requirements of digital forensics are also increasing. Therefore, automation is highly desired in this field, yet not readily available, and many challenges remain, ranging from unstructured forensic data derived from diverse sources to a lack of semantics defined for digital forensic investigation concepts. By formally describing digital forensic concepts and properties, purpose‐designed ontologies enable integrity checking via automated reasoning and facilitate anomaly detection for the chain of custody in digital forensic investigations. This article provides a review of these ontologies, and investigates their applicability in the automation of processing traces of digital evidence. This article is categorized under: Digital and Multimedia Science &gt; Artificial Intelligence Digital and Multimedia Science &gt; Cybercrime Investigation Digital and Multimedia Science &gt; Cyber Threat Intelligence
Fuzzy Set-Based Isolation Forest

One of the main challenges is the analysis of large data sets, in particular those containing various types of data, such as time, place, image, and those assuming categorical values. This type of data may contain numerous outliers. Despite the continuous development of data analysis, many methods can be effectively improved, in particular through the use of efficient solutions based on fuzzy set technologies. In this paper, we analyze the improvement of a well-known method, i.e. Isolation Forest, for which we introduce an innovative modification, referred to as the Fuzzy Set-Based Isolation Forest.
Staged text clustering algorithm based on K-means and hierarchical agglomeration clustering

In order to achieve better clustering, a staged hybrid clustering algorithm SC-KH (Staged text clustering algorithm based on K-means and HAC) is proposed to overcome the shortcomings of low accuracy of K-means algorithm and high time complexity of hierarchical agglomeration clustering algorithm. The algorithm divides the clustering process into two stages: splitting and merging. The K-means algorithm is used in the splitting stage, and the k value and the initial clustering center are provided using the Canopy algorithm. The hierarchical agglomeration clustering algorithm is used in the merging stage. The experimental results show that the performance of the improved SC-KH algorithm is better than that of K-means and hierarchical agglomeration, and the clustering quality is improved.