Reviewerly/exHarmony huggingface dataset:
Case-Based Reasoning – Methods, Techniques, and Applications

Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR is seen as a method for problem solving and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. We will review in this paper the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are the similarity, memory organization, CBR learning, and case-base maintenance. Then we will review based on applications the open problems that need to be solved. The applications we are focusing on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.
Social-Based Collaborative Recommendation: Bees Swarm Optimization Based Clustering Approach

This paper focuses on the recommendation of items in social networks, through which the social information is formalized and combined with the collaborative filtering algorithm using an optimized clustering method. In this approach, users are clustered from the views of both user similarity and trust relationships. A Bees Swarm optimization algorithm is designed to optimize the clustering process and therefore recommend the most appropriate items to a given user. Extensive experiments have been conducted, using the well-known Epinions dataset, to demonstrate the effectiveness of the proposed approach compared to the traditional recommendation algorithms.
Jointly Aligning Millions of Images With Deep Penalised Reconstruction Congealing

Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e.g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction. Several joint image alignment and congealing techniques have been proposed to tackle this problem, but robustness to initialisation, ability to scale to large datasets, and alignment accuracy seem to hamper their wide applicability. To overcome these limitations, we propose an unsupervised joint alignment method leveraging a densely fused spatial transformer network to estimate the warping parameters for each image and a low-capacity auto-encoder whose reconstruction error is used as an auxiliary measure of joint alignment. Experimental results on digits from multiple versions of MNIST (i.e., original, perturbed, affNIST and infiMNIST) and faces from LFW, show that our approach is capable of aligning millions of images with high accuracy and robustness to different levels and types of perturbation. Moreover, qualitative and quantitative results suggest that the proposed method outperforms state-of-the-art approaches both in terms of alignment quality and robustness to initialisation.
A Simple Survey for Supply Chain Finance Risk Management with Applications of Blockchain

Supply chain finance (SCF) is an important solution that optimizes cash flow in order to lower financing costs and improve business efficiency especially for small and medium-sized enterprises (SMEs). Risk management is the essential requirement of SCF. In recent years, the digital economy is developing rapidly worldwide and holds huge potential for entrepreneurs and SMEs. In the digital economy scenario, digitalization of supply chains is also becoming increasingly dynamic. Blockchain technology is regarded as a potential means of digitalization for supply chains and could play an important role in supply chain finance risk management. This paper first reviews the literature of supply chain finance risk management and provides some disadvantages of traditional supply chain finance risk management. Then we survey the new perspective for supply chain finance risk management based on blockchain technology. In particular, blockchain can increase the information transparency of the supply chain, thereby reducing the credit risk of SMEs financing and the operational risk in SCF. Categorization and analysis of the literature, it provides an important perspective for future research of supply chain finance risk management based on blockchain technology in the era of digital economy.
Hierarchical Visual Relationship Detection

Acting as a bridge between vision and language, visual relationship detection (VRD) aims to represent objects and their interactions in an image with several relationship triplets. Nevertheless, the conventional VRD task shows little consideration for the penalization of incorrect relationship predictions, which in turn undermines its support for image understanding applications. In this paper, we propose a novel VRD task named hierarchical visual relationship detection (HVRD), which encourages predictions with abstract yet compatible relationship triplets when the confidence level of the specific image content is relatively low. Meanwhile, HVRD can handle the inevitable ambiguity of groundtruth annotation in VRD. Based on this, we propose a HVRD method, consisting of hierarchical object detection and hierarchical predicate detection. It can effectively detect the hierarchical visual relationships by exploiting both object concept hierarchy and predicate concept hierarchy with order embedding. We also propose the first datasets for HVRD evaluation, H-VRD and H-VG, by expanding the relationship category spaces of VRD and VG datasets to hierarchical ones respectively. The experimental results show that our method is superior to the state-of-the-art baselines.
Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

We read with great interest the recent publication by Hekler et al. [1]. The authors compared analysis of histology images from melanomas and nevi by deep learning with assessment by 11 humans. The results are interesting; however, we have considerable concerns regarding the title, study design and the conclusions made.
Modata -- Improving Dota 2 Experience and Spectatorship through Tangible Gameplay Visualization

Recently, interest in streaming and esports has flourished, placing increased emphasis on the spectator experience. We argue that by escaping the boundaries of the screen and incorporating a physical aspect, another stimulatory and social dimension, which heightens the interaction with the data and positively contributes to spectatorship, can be added. This paper reports on the design of Modata, a tangible visualization of Dota 2 matches that projects gameplay data onto a tangible representation of the Dota map. We report on the design process of Modata, explore the preferred type of interaction, and report on first user feedback collected through surveys and hands-on experience with a prototype of the concept. Results indicate that most (88%) of the asked participants see value in augmenting the game's experience by visualizing in-game data and translating it into the physical world.
Assessing the Impact of Visual Design on the Interpretation of Aggregated Playtesting Data Visualization

Making effective use of data generated from players interacting with games (often via playtesting to improve game quality) is a challenging task since the datasets are often mixed and very large. To address this, various visualization techniques have been introduced to help game developers cope with the data. However, there is a gap in research concerning the impact of different visual designs on the interpretation of gameplay data. In this paper, we propose four alternative visual designs of an aggregated visualization and assess how professional game developers interpret the data differently due to the changes in the visual designs. Our results provide an understanding and a supporting argument about the impact of the visual properties transparency and shading (both positive and negative) on the interpretation of the represented data. This is an important contribution to the field of Games User Research given the current move towards data-informed decision making and the popularity of data visualizations.
Fuzzy Clustering of Incomplete Data by Means of Similarity Measures

The current standing in the scope of Data Mining considers clustering to be one of the most useful and widely used tools. Multiple real-world datasets usually contain drops/gaps in the data due to various reasons. The currently known approaches are highly efficient only in those cases when original datasets do not change their volumes during the analysis. However, up-to-date problems mostly deal with sequential online data processing. On the other hand, there is no prior knowledge on which feature vectors contain overlooks. In this manuscript, the challenge of possibilistic and probabilistic online clustering approaches for processing incomplete data is solved through similarity measures of a specific kind which are capable of either loosening outliers' influence or repressing them.
A Systematic Review on Mobile Technology-Assisted English Learning

Mobile English language learning has drawn global attention. This study systematically examined the literature in the recent eleven years. It visualized the general trend of the number of related publications in a decade; discussed the attitudes of teachers and learners toward mobile device assisted English learning; explored the effect of motivation on mobile device assisted English learning, discussed the influence of mobile device assisted English learning on output and input English language skills; presented the influence of mobile device assisted English learning on vocabulary knowledge acquisition; demonstrated the factors influencing the effectiveness of mobile English language learning, showed the general learning outcomes in mobile device assisted English learning; together with the disadvantages of mobile technology-assisted English learning and co-citations of publications. When designing mobile devices, designers could include and optimize learning functions based on the findings.
Embedded Deep Inference in Practice

With increased focus on in situ analytics, artificial intelligence (AI) algorithms are getting deployed on embedded devices at the network edge. Growing popularity of Deep Learning (DL) and inference largely due to minimization of feature engineering, availability of pre-trained models and fine-tunable datasets especially in image and video analytics, have made these de-facto standard. However, the embedded systems employing these models are often resource constrained and fail to handle scenarios where arrival rate and input data volume increase over a given time period. This has a direct effect on the storage and network usage of such devices, rendering the traditional strategies of input buffering and network offloading ineffective. This paper investigates the use of dynamic layer-wise partitioning and partial execution of DL inference phase to enable inelastic embedded systems to support varying sensing rates and large data volume. The proposed partial execution scheme and partitioning algorithm perform better than standard frame-wise inference methods, when evaluated using workloads of few popular CNNs used in standard object detection models.
CBConv: Service for Automatic Conversion of Chinese Characters into Braille with High Accuracy

The conversion of Chinese Characters into Braille faces the challenges of Braille word segmentation, pronunciation determination, and tone marking. In this paper, we present the CBConv service for automatic conversion of Chinese characters into Braille with high accuracy. The service supports two modes: real-time conversion of Chinese strings and asynchronous conversion of documents. To address the challenges described above, an end-to-end deep learning framework is proposed, which can perform pronunciation determination, word segmentation and tone marking simultaneously using a deep neural network. Accuracy evaluation and user study are conducted, which demonstrate the usability of the proposed system.
Creation of Multiple Conceptual Models from User Stories – A Natural Language Processing Approach

While Agile methodologies are used in software development, researchers have identified many issues related to requirements engineering in Agile approaches. Some of these issues relate to ambiguity in user stories, which is a widely-used requirements specification mechanism in Agile methodologies. This research proposes the use of conceptual models while developing user stories. We posit that the use of conceptual models helps reducing ambiguity in user stories. An important aspect of our research is the creation of an algorithm for automatic generation of such models while developing the user stories.
Named Entity Recognition in Chinese Electronic Medical Record Using Attention Mechanism

In order to improve the accuracy of the NER (Named entity recognition) model on the Chinese EMR (Electronic Medical Record), we propose an effective deep learning model based on the attention mechanism and bootstrapping method to identify entity and relationship on the Chinese EMR. For the attention-based deep learning model, we first convert words into word vectors and input them into Bi-directional Long Short-Term Memory (BiLSTM). Then we adopt attention mechanism to assign different attention weights on hidden layer states at different words, thus the semantic features can focus on the words related to entity vocabulary, making up for the deficiencies of the BiLSTM model. Moreover, we employ the transformation matrix in Conditional Random Field (CRF) to solve the labeling bias problem in BiLSTM, thus we can obtain a global optimal tagging for the input sentences. Next, we use the bootstrapping methods to expand the labeled EMR corpus for overcoming the scarcity of the EMR corpus. Finally, the extensive experimental evaluation on Chinese EMR shows the superiority of the proposed approach under multiple evaluation criteria.
Survey of Hand Gesture Recognition Systems

Abstract Recognition of human gestures is an important subject in computer science, especially in computer vision and sign language. It aims at interpreting human gestures by mathematical models. Gestures originate from different parts of the human body, but the most common ones emerge from the hand or face. Gestures recognition is a method to enable computers to understand and interpret the language of the human body in the best way possible and to build a bridge between humans and machines from uncomplicated user interfaces that have been command-line to graphical user interfaces GUI, so far they limit the common input on the keyboard and mouse. In this paper, we have reviewed and analyzed several methods of recognition for hand gestures including, Artificial Neural Networks (ANN), Histogram based feature, a Fuzzy Clustering algorithm, Hidden Markov Model (HMM), Condensation algorithm and Finite-State Machine (FSM).
Supporting Journalistic News Angles with Models and Analogies

News angles are approaches to content presentation in journalism, where the journalist chooses which facts of an event to present. The News Angler project investigates how to computationally support the creation and selection of original news angles for a news event based on information from big data sources. At least two creative approaches are possible. One is to maintain a library of well-known news angles represented in a suitable modeling language, matching published reports on a current event to news angles in order to identify possible angles that have not yet been used. A second approach is not to represent news angles explicitly, instead matching the current event with previous events, and transferring angles from past to present reports by similarity and analogy. Both approaches are described and technologies needed to proceed in either direction are discussed.
Information Granularity With the Self-Emergence Mechanism for Event Detection in WSN-Based Tunnel Health Monitoring

Because of its rapid deployment, low cost, self-organization, node distribution and other characteristics, the wireless sensor networks (WSNs) are very suitable for various types of monitoring systems. Event detection using sensor data is the purpose of the monitoring system. To build the bridge between data and phenomena, this work proposes an information granularity. The information granularity characterizes the health state of each monitoring ring in the tunnel by qualitative description and quantitative representation. Through building the tree augmented naïve Bayesian-based qualitative classifier based on the minimum risk, a qualitative description method of the information granularity is proposed, which is taken as a preliminary judgment of disasters. Inspired by vague set theory, a quantitative representation method of the information granularity is also presented, which analyzes disaster phenomena in detail. To realize the multiscale online monitoring of the entire tunnel state, a kind of self-emergence mechanism of the information granularity is also described. The information granularity is formed gradually through distributed computing during information transmission, which characterizes the event autonomously. The experimental results show that the information granularity realizes rapid, accurate and multiscale representation of tunnel health status.
Improved Density Peaks Clustering Based on Shared-Neighbors of Local Cores for Manifold Data Sets

A novel clustering algorithm by fast search and find of density peaks (DP) was proposed in Science, 2014.It has attracted much attention from researchers.It can easily select clusters centers with decision graph.However, it cannot be used to cluster manifold data sets as the existing distance measurement is not suitable to evaluate the dissimilarity between objects on manifold structure.Some researchers use graph-based distance to measure the dissimilarity between objects on manifold clusters, but computing the graph-based distance on the original data set is time consuming.An improved density peaks clustering algorithm based on shared-neighbors between local cores, SLORE-DP, is proposed in this paper.First, it finds local cores to represent the data set and redefines the graph-based distance between local cores with sharedneighbors-based distance.Then natural neighbor-based density and the new defined graph-based distance are used to construct decision graph on local cores and DP algorithm is employed to cluster local cores.Finally, the remaining points are assigned to the same cluster as their local cores belong to.Since we use the new defined graph-based distance to estimate the dissimilarity between local cores, SLORE-DP can be used to cluster manifold data sets and at the same time it only calculates the shortest path between local cores, which greatly reduces the running time of the algorithm.We do experiments on several synthetic data sets containing manifold clusters and several real data sets from UCI.The results show that SLORE-DP is more effective and efficient than other algorithms when clustering manifold data sets.
An experiential learning cybersecurity project for multiple STEM undergraduates

The human element is often regarded as the weakest link in cybersecurity, yet education efforts focus primarily on the technical aspects of cybersecurity and downplay the relevance of the human factor. One way to exploit this human vulnerability is through social engineering, in which cybercriminals utilize persuasion and manipulation to get targets to reveal private information. This paper shares an educator's efforts to engage undergraduate students from multiple STEM disciplines in a hands-on social engineering project across the Fall 2017 and Spring 2018 semesters. This activity was designed using the experiential learning framework that promotes “learning by doing.” The authors share the experiences and challenges of both the students and the educator in conducting this learning exercise. In addition, this paper details aspects of designing multidisciplinary hands-on projects that consider meeting university ethics standards; generating relevant project instructions and rubrics; engaging in and promoting cross-disciplinary STEM dialog; and evaluating student engagement and learning.
Transmission Line Fault Identification Based on BP Neural Network

Aiming at the nonlinear characteristics of fault signs and fault characteristics of high-voltage transmission lines, combined with fault classification methods, the BP neural network is used to identify various faults of HVDC transmission lines. In this paper, a transmission line fault identification system based on BP network is designed, which mainly relies on a series of real-time sequence information generated in the accident environment to analyze the type of fault occurrence. The simulation of PSCAD proves that the BP neural network can be effectively applied to the fault identification of transmission lines. Through the collection and analysis of existing data, the power system failure is finally determined.
30 Years Later

It is arguable that academics working in accessibility want to improve lives. Last year marked 30 years since the first accessible computing publication [46] on supporting people with Color Vision Deficiency (CVD - commonly called colorblindness), making it a great time to review the CVD academic research that has been completed since then and assess its broad availability to the general population. In this paper, we identify seven central themes in the CVD academic literature, and employ a novel technique (PDI - Persona Driven Inquiry) to explore if and how that literature is available to the general public. Subject to some limitations, we found that this research is indeed available.
The use of reflectance spectroscopy and the prehnite spectral index to target gold mineralization at the Bonfim polymetallic skarn deposit, Seridó Mobile Belt, Borborema Province, Brazil

The Seridó Mobile Belt hosts numerous mineralized skarns in the Borborema Province, northeastern Brazil. The Bonfim W-Mo-Au-Bi-Te skarn occurs within a Neoproterozoic metasedimentary sequence (Seridó Group), within marble and schist lenses, or at the contacts between both. An ASD-FieldSpec™ 3 High-Resolution spectrometer was used to characterize the mineralogy of a representative drill core interval (181 m) from the Bonfim deposit, in order to generate the spectral characterization to define the gold mineralization. Skarns are characterized by the presence of diopside, actinolite-tremolite, clinozoisite-zoisite, muscovite and prehnite. Prehnite and epidote constitute the main components of the calc-silicate assemblage associated with late-stage gold mineralization. The diagnostic absorption feature of prehnite at 1478 nm (OH) can be used to identify its presence, and the prehnite index proposed in this case study (a reflectance ratio of 1465 nm/1478 nm) allows highlighting the prehnite enriched sections along the core. Geochemical data show a positive correlation between gold and the presence of a prehnite absorption at 1478 nm. The results suggest that the prehnite spectral signature can be used as a reliable and efficient (rapid) indicator for gold skarns, perhaps under geologically similar circumstances to those at Bonfim.