Reviewerly/exHarmony huggingface dataset:
Enhancing in-tree-based clustering via distance ensemble and kernelization

Recently, we have proposed a novel physically-inspired method, called the Nearest Descent (ND), which plays the role of organizing all the samples into an effective Graph, called the in-tree. Due to its effective characteristics, this in-tree proves very suitable for data clustering. Nevertheless, this in-tree-based clustering still has some non-trivial limitations in terms of robustness, capability, etc. In this study, we first propose a distance-ensemble-based framework for the in-tree-based clustering, which proves a very convenient way to overcome the robustness limitation in our previous in-tree-based clustering. To enhance the capability of the in-tree-based clustering in handling extremely linearly-inseparable clusters, we kernelize the proposed ensemble-based clustering via the so-called kernel trick. As a result, the improved in-tree-based clustering method achieves high robustness and accuracy on diverse challenging synthetic and real-world datasets, showing a certain degree of practical value.
Efficient Conversion Prediction in E-Commerce Applications with Unsupervised Learning

Unsupervised machine learning became a ubiquitous method appearing in E-commerce solutions that strive to provide personalized recommendations for their users. Most of those solutions embrace collaborative filtering (CF) to predict conversions, which are the beneficial user events, such as a purchase. Traditionally, the predictions were made based on rating data. However, e-commerce users seldom leave ratings. Instead, we must rely on user events, such as viewing an item or adding it to the cart. The event-based approach seems counter-intuitive, for the reason that the operation time of recommender systems increases exponentially with the increase of data-points.One of the main contributions of this paper is the UX value function. It reduces all events between an item and a user to a single user experience number, which also depends on the sequentiality of the events. We present a method to calculate this number in linear time. Then we use a deep neural network to predict the likelihood of conversions based on this number to prove the practical solvability of the problem in a scalable manner, with a relatively fast learning speed and good prediction accuracy. We have conducted an extensive experimental analysis on Kechinov’s ‘eCommerce Events History in Cosmetics Shop’ dataset, containing 8,738,120 user events. The results of those experiments prove the efficiency and applicability of the developed approach.
Digital Processing and Automatic Picking of Seismic Signals

In this research, we have implemented a numerical algorithm to identify the seismic wave parameters: P phase, S phase, amplitude, period and seismic signal duration. We have used the Matlab programming language and the digital signal processing toolbox to conduct the automatic picking of seismic signal parameters. As a mathematical tool, we have used the discrete Fourier transform to study the frequency components of the signal and the Hilbert transform to obtain the envelope signal. The Butterworth filter was used to bandpass the seismic signal, in case of a noisy signal. We have implemented a graphical user interface in Matlab to pick the seismic signals (with options of automatic picking and manual picking). In this way, the routine of picking a great set of seismic signals is easier. The results of the automatic algorithm agree very well with that of a human operator for a Signal/Noise ratio greater than 50, according to the sensitivity test, with an accuracy of around [Formula: see text]0.05[Formula: see text]s.
Analysis of a Telescoped Orogenic Gold System: Insights from the Fosterville Deposit

Abstract The Fosterville gold (Au) deposit is hosted in the Bendigo zone within the western Lachlan orogen, southeast Australia, and contains three distinct mineralization styles: (1) refractory Au in fine-grained arsenopyrite and arsenian pyrite disseminated throughout metasedimentary rocks near brittle faults, (2) visible Au hosted in fault-controlled quartz-carbonate veins associated with stibnite mineralization, and (3) vein-hosted visible Au with little or no associated stibnite. Refractory Au mineralization is found throughout the deposit, whereas visible Au ± stibnite occurs deeper in the system (&amp;gt;800-m depth from surface). Thus, Fosterville provides a unique opportunity to study a telescoped orogenic Au system that changes mineralization style as a function of depth. Microscopy, neutron tomography, nanoscale secondary ion mass spectrometry, and field observations have been conducted to investigate mineralogical and structural controls on the various styles of Au mineralization. These observations are used as the foundation for reactive mass transport geochemical modeling using HCh software. Results are considered in the context of an evolving mineral system over the formation history of the deposit, and relative timing of mineralization is inferred. Two alternatives for the genesis of such a system include the following: (1) metal deposition was controlled by ongoing physicochemical changes at a very shallow level in the crust in one evolving mineralization stage or (2) two or three deposits formed in the same location, with each different style of mineralization representing a separate period of fluid infiltration, each potentially tens of millions of years apart. Based on careful observations, microanalysis, and thermodynamic modeling, we suggest that the latter is more likely. Therefore, we suggest that Fosterville is to be recognized as a telescoped orogenic Au system, where relatively high temperature mineralization and alteration assemblages were overprinted vertically by later, lower-temperature assemblages.
An Algorithm of Query Expansion for Chinese EMR Retrieval by Improving Expansion Term Weights and Retrieval Scores

Query expansion (QE) has been widely used in electronic medical record (EMR) retrieval for assisted diagnosis and clinical research. However, existing QE algorithms haven't achieved satisfactory performance in Chinese EMR retrieval, and one noticeable problem is that the weights of expansion terms and retrieval scores have unreasonable factors for lack of the solid consideration of clinical needs. Here we propose an algorithm of QE for Chinese EMR retrieval by improving expansion term weights and retrieval scores. First, the weights of expansion terms are assigned with semantic similarities, category weights and co-occurrence frequencies between expansion terms and multiple query terms. Then the retrieval scores calculated by expansion terms are limited to reduce the query drift caused by high-frequency expansion terms. Experiment results show that our method gets a 33.3% increase in the precision at top 10, a 90.4% increase in the recall, and a 13.2% increase in MAP compared with four baselines. It proves that our improvement scheme can ensure the accuracy of expansion term weights and decrease the query drift caused by QE, which substantially improves the performance of Chinese EMR retrieval.
Optimal environment localization

Quantum channels model many physical processes. For this reason, hypothesis testing between quantum channels is a fundamental task in quantum information theory. Here we consider the paradigmatic case of channel position finding, where the aim is to determine the position of a target quantum channel within a sequence of background channels. We explore this model in the setting of bosonic systems, considering Gaussian channels with the same transmissivity (or gain) but different levels of environmental noise. Thus the goal of the problem becomes detecting the position of a target environment among a number of identical background environments, all acting on an input multi-mode system. We derive bounds for the ultimate error probability affecting this multi-ary discrimination problem and find an analytic condition for quantum advantage over protocols involving classical input states. We also design an explicit protocol that gives numerical bounds on the ultimate error probability and often achieves quantum advantage. Finally, we consider direct applications of the model for tasks of thermal imaging (finding a warmer pixel in a colder background) and quantum communication (for localizing a different level of noise in a sequence of lines or a frequency spectrum).
Terrain generalization with line integral convolution

Line integral convolution is a technique originally developed for visualizing vector fields, such as wind or water directions, that places densely packed lines following the direction of movement. Geisthövel and Hurni adapted line integral convolution to terrain generalization in 2018. Their method successfully removes details and retains sharp mountain ridges; it is particularly suited for creating generalized shaded relief. This paper extends line integral convolution generalization with a series of enhancements to reduce spurious artifacts, accentuate mountain ridges, control the level of detail in mountain slopes, and preserve sharp transitions to flat areas. The enhanced line integral convolution generalization effectively removes excessive terrain details without changing the position of terrain features. Sharp mountain ridgelines are accentuated, and transitions to flat waterbodies and valley bottoms are preserved. Shaded relief imagery derived from generalized elevation models is visually pleasing and resembles manually produced shaded relief.
Linking Ontological Classes and Archaeological Forms

Archaeological studies are a trans-disciplinary endeavor, where a number of different scientists collaborate to get a reasonable account of material artefacts, through the various phases of recovery, analysis, and, recently, also exhibition. A large amount of digital data support the whole process, and there is a high value of keeping the coherence of the information and knowledge contributed by each discipline. The paper introduces a modular computational ontology, which is in use in a comprehensive archaeological project, Beyond Archaeology. The ontology provides the information structure to all the phases of the project, from the excavation phase, to the archaeometric analyses, up to the design and the implementation of the exhibition. The computational ontology is compliant with CIDOC-CRM reference model and introduces a number of novel properties and classes to link the description of the archaeological world with the forms traditionally used by the archaeologists to record the excavation and data about findings on the field and in the lab. The forms are implemented through a CMS structured site, for the creation of a data base, that is also filled with multimedia items that are to be employed in interpretation and exhibition, respectively.
Investigation of the Effect of MFCC Variation on the Convolutional Neural Network-based Speech Classification

This paper deals with the step-by-step implementation of a Bengali speech classification model with the variations of Mel-Frequency Cepstral Coefficient (MFCC). A deep convolutional neural network is developed for the classification purpose. Bengali vowels are the representative of the lower MFCC variational group, and words are the higher variational group. The necessary dataset derived from the vowel and word speech tokens was utilized for the analysis of the convolutional neural network. Then the model was trained and validated for both of the isolated vowels and words separately for the same number of data. The performance was measured according to four different metrics- loss, accuracy, confusion matrix, and cross-validation score. In all these cases, the performance of vowel recognition has been found superior to word recognition. The reason behind this performance variation has been studied and found that it is mostly related to the dynamic variation of the vocal tract between vowels and words at the time of speaking. MFCC is chosen as the feature of interest for classification purposes. The variation of MFCC for vowels and words have been compared and found that MFCCs of words have more variation than the vowels. As a consequence, it is concluded that the dynamic variation of the vocal tract is inversely related to the performance of recognition.
Spineless Data Centers

In enterprises, CDNs, and increasingly in edge computing, most data centers have moderate scale. Recent research has developed designs such as expander graphs that are highly efficient compared to large-scale, 3-tier Clos networks, but moderate-scale data centers need to be constructed with standard hardware and protocols familiar to network engineers, and are overwhelmingly built with a leaf-spine architecture. This paper explores whether the performance efficiency that is known to be theoretically possible at large scale can be realized in a practical way for the common leaf-spine data center. First, we find that more efficient topologies indeed exist at moderate scale, showing through simulation and analysis that much of the benefit comes from choosing a 'flat' network that uses one type of switch rather than having separate roles for leafs and spines; indeed, even a simple ring-based topology outperforms leaf-spine for a wide range of traffic scenarios. Second, we design and prototype an efficient routing scheme for flat networks that uses entirely standard hardware and protocols. Our work opens new research directions in topology and routing design that can have significant impact for the most common data centers.
On the Impact of Multi-language Development in Machine Learning Frameworks

The role of machine learning frameworks in software applications has exploded in recent years. Similar to non-machine learning frameworks, those frameworks need to evolve to incorporate new features, optimizations, etc., yet their evolution is impacted by the interdisciplinary development teams needed to develop them: scientists and developers. One concrete way in which this shows is through the use of multiple programming languages in their code base, enabling the scientists to write optimized low-level code while developers can integrate the latter into a robust framework. Since multi-language code bases have been shown to impact the development process, this paper empirically compares ten large open-source multi-language machine learning frameworks and ten large open-source multi-language traditional systems in terms of the volume of pull requests, their acceptance ratio i.e., the percentage of accepted pull requests among all the received pull requests, review process duration i.e., period taken to accept or reject a pull request, and bug-proneness. We find that multi-language pull request contributions present a challenge for both machine learning and traditional systems. Our main findings show that in both machine learning and traditional systems, multi-language pull requests are likely to be less accepted than mono-language pull requests; it also takes longer for both multi- and mono-language pull requests to be rejected than accepted. Machine learning frameworks take longer to accept/reject a multi-language pull request than traditional systems. Finally, we find that mono-language pull requests in machine learning frameworks are more bug-prone than traditional systems.
Twitter Moral Stance Classification Using Long Short-Term Memory Networks

In Natural Language Processing, stance detection is the computational task of deciding whether a piece of text expresses a favourable or unfavourable attitude (or stance) towards a given topic. Stance detection may be divided into two subtasks: deciding whether a piece of text conveys any stance towards the target topic and, once we have established that the text does convey a stance, determining its polarity (e.g., favourable or unfavourable) towards the target. Both tasks - hereby called stance recognition and (stance) polarity classification - are the focus of the present work. Taking as a basis a corpus of 13.7k tweets in the Brazilian Portuguese language, and which conveys stances towards five moral issues (abortion legislation, death penalty, drug legalisation, lowering of criminal age, and racial quotas at universities), we compare a number of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) models for stance recognition and polarity classification. In doing so, the two tasks are addressed both independently and as a joint model. Results suggest that the use of BiLSTM models with attention mechanism outperform the alternatives under consideration, and pave the way for more comprehensive studies in this domain.
Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs

Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanism.
Hyper-hybrid entanglement, indistinguishability, and two-particle entanglement swapping

Hyper-hybrid entanglement for two indistinguishable bosons has been recently proposed by Li et al. [Y. Li, M. Gessner, W. Li, and A. Smerzi, Phys. Rev. Lett. 120, 050404 (2018)]. In the current paper, we show that this entanglement exists for two indistinguishable fermions also. Next, we establish two no-go results: no hyper-hybrid entanglement for two distinguishable particles, and no unit fidelity quantum teleportation using indistinguishable particles. If either of these is possible, then the no-signaling principle would be violated. While several earlier works have attempted extending many results on distinguishable particles to indistinguishable ones, and vice versa, the above two no-go results establish a nontrivial separation between the two domains. Finally, we propose an efficient entanglement swapping using only two indistinguishable particles, whereas a minimum number of either three distinguishable or four indistinguishable particles is necessary for existing protocols.
Generating Videos of Zero-Shot Compositions of Actions and Objects

Human activity videos involve rich, varied interactions between people and objects. In this paper we develop methods for generating such videos – making progress toward addressing the important, open problem of video generation in complex scenes. In particular, we introduce the task of generating human-object interaction videos in a zero-shot compositional setting, i.e., generating videos for action-object compositions that are unseen during training, having seen the target action and target object separately. This setting is particularly important for generalization in human activity video generation, obviating the need to observe every possible action-object combination in training and thus avoiding the combinatorial explosion involved in modeling complex scenes. To generate human-object interaction videos, we propose a novel adversarial framework HOI-GAN which includes multiple discriminators focusing on different aspects of a video. To demonstrate the effectiveness of our proposed framework, we perform extensive quantitative and qualitative evaluation on two challenging datasets: EPIC-Kitchens and 20BN-Something-Something v2.
Demonstrating a Modular Construction Toolkit for Interactive Textile Applications

We present a toolkit for the DIY production of interactive textile prototypes and smart fashion. The toolkit is easily accessible and requires only an iron for implementation. The key to our approach is the use of flexible PCBs and textile conductor paths fused together by a heat-activated adhesive in a single process step. The toolkit offers a variety of applications, such as the realization of prototypes, the production of small series, applications in the DIY community and within the field of digital education. In the following, we present our technology and provide insights into the application of the toolkit.
Big data outlier detection model based on improved density peak algorithm

Outlier detection is an important branch of data mining. This paper proposes an advanced fast density peak outlier detection algorithm based on the characteristics of big data. The algorithm is an outlier detection method based on the improved density peak clustering algorithm. This paper improves the original algorithm. From the perspective of outlier detection, although it is a clustering idea, it avoids the clustering process, reduces the time complexity of the cluster-based outlier detection algorithm, and absorbs. The outlier detection based on neighbors is not sensitive to data dimensions and other advantages. In the power industry, outlier detection can be used in areas such as grid fault detection, equipment fault detection, and power abnormality detection. The simulation experiment of outlier detection based on the daily load curve of single and multiple transformers in a certain province shows that the improved algorithm can effectively detect outliers in the data.
Unboxing empathy: reflecting on architectural design for maternal health

Co-design aims to bring designers and end-users together to improve the quality of design projects. In this paper, we study how the distance between designers and users can be reduced with an empathic approach particularly in settings where it is significant. By investigating various approaches on empathy in design and architecture, we were able to retrospectively understand different aspects of the design process of a maternity ward project in which we were involved. Engaging a theoretical clarification of empathy as a multi-dimensional concept made it possible to empirically explicate diverse difficulties that designers face when trying to employ empathy as a guiding philosophy in their work. As a result, we identified three registers of empathy on a varying scale of depth that can be integrated in the design process. Our work shows that various registers of empathy can complement each other or be utilised in different circumstances where one form might be more appropriate than another. By presenting these registers, we seek to unbox the different views on empathy and draw attention to the potential of empathic engagement when aiming for depth in a project.