Reviewerly/exHarmony huggingface dataset:
Multi-Facet Recommender Networks with Spherical Optimization

Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interactions among users and items. Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation. To capture the multiple facets of user preferences and item properties while resolving their potential conflicts, we propose the novel framework of Multi-fAcet Recommender networks with Spherical optimization (MARS). By designing a cross-facet similarity measurement, we project users and items into multiple metric spaces for fine-grained representation learning, and compare them only in the proper spaces. Furthermore, we devise a spherical optimization strategy to enhance the effectiveness and robustness of the multi-facet recommendation framework. Extensive experiments on six real-world benchmark datasets show drastic performance gains brought by MARS, which constantly achieves up to 40\% improvements over the state-of-the-art baselines regarding both HR and nDCG metrics.
A Latent Class Analysis for Item Demand Based on Temperature Difference and Store Characteristics

In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by external factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to express the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis experiment using an actual data set, we show the usefulness of the proposed model by extracting items that are influenced by weather conditions.
Query2Prod2Vec Grounded Word Embeddings for eCommerce

We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature. Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.
NRCP-Miner: Towards the Discovery of Non-redundant Co-location Patterns

Co-location pattern mining, which refers to discovering neighboring spatial features in geographic space, is an interesting and important task in spatial data mining. However, in practice, the usefulness of prevalent (interesting) co-location patterns generated by traditional frameworks is strongly limited by their huge amount, which may affect the user’s following decisions. To address this issue, in this demonstration, we present a novel schema, named NRCP-Miner, aiming at the redundancy reduction for prevalent co-location patterns, i.e., discovering non-redundant co-location patterns by utilizing the spatial distribution information of co-location instances. NRCP-Miner can effectively remove the redundant patterns contained in prevalent co-location patterns, thus furtherly assists the user to make the following decisions. We evaluated the efficiency of NRCP-Miner compared with related state-of-the-art approaches.
Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model

The Artificial Intelligence field is under the spotlight as of its wide use and efficiency in solving real world problems. As of this decade, a notable rise in the amounts of data collected, which were made available to the public, is witnessed. This allowed the emergence of many research problems among which working with short texts and their different challenges. In this paper, we propose the collapsed Gibbs Sampling algorithm for the generalized Dirichlet Multinomial Mixture model for short text clustering (GSDMM). The proposed approach has been evaluated on the Google News dataset. Our approach proved to be more efficient than the related-works and succeeded into overcoming the common challenges that come with short texts.
Assessing the Effectiveness of Smartphone Usage to Interact with Learning Materials in Independent Learning Outside of Classrooms among Undergraduate Students

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.
Knowledge Engineering the Web

This paper focuses on the largest source of human knowledge: The Web. It presents the state of the art and patented technologies on search engine, automatic organization of webpages, and knowledge-based automatic webpage summarization. For the patented search engine technology, it describes new methods to present search results to the users and through browsers to allow the users to customize and organize webpages. For the patented classification technology, it describes new methods to automatically organize webpages into categories. For the knowledge-based summarization technology, it presents new technics for computers to "read" webpages and then to "write" a summary by creating new sentences to describe the contents of the webpages. These search engine, classification, and summarization technologies build a strong framework for knowledge engineering the Web.
A NEXT GENERATION CLASSROOM, AS CREATED BY THE NEXT GENERATION

P. Lane 1, R. Lafferty2 1Grand Valley State University/UNAN Managua (UNITED STATES) 2CEO/Chief Innovation Officer, Fusion Innovation Group (UNITED STATES)
Survey of Query correction for Thai business-oriented information retrieval

The importance of effective Thai information retrieval (IR) increases as more businesses in Thailand undergo digital transformation. However, previous research on Thai IR systems has mainly focused on web search engines. This study will focus on using query correction to reduce user errors to improve Thai IR. Experiments are conducted on our business-oriented Thai IR task (bTIR). Our investigation presented three notable findings. First, cognitive errors are less of an issue in a business setting. Thus, homophones correction methods provide very little to no benefit for bTIR. Second, approximation based spelling correction methods can significantly reduce search performance. Thus, partial matching on a full dictionary, such as symmetric delete indexing (SymSpell), should be preferred over non-optimal search methods. Third, we introduce a re-ranking algorithm for query corrector, which features multiple sub-correctors (e.g., ThaiQCor 2.0), which results in better performance across multiple configurations.
Utilizing landsat-8 sensor operational land image data for hydrothermal alteration mapping within Anka Schist Belt, northwestern Nigeria

Exploring for mineral deposits within the Anka Schist Belt involves the use of traditional geological techniques such as geochemical and geophysical studies that are very expensive and time consuming. There is therefore need for a better alternative that will provide accurate and reliable information with cost effective and time efficient solution. This effort seeks to explore the potential of remotely sensed digital data in highlighting mineralized zones through hydrothermal alteration studies. Landsat 8 OLI data covering the investigated area was used to detect and map locations of hydrothermal alterations. Image processing methods used were spectral enhancement, false colour composites, band rationing and Principal Component Analysis. Results of false colour composites of band 5: 7: 3 highlighted generally locations of hydrothermal alterations. Band ratios of 4/2, 6/7 and 6/5 revealed the presence of ferric iron minerals, clay rich minerals and ferrous minerals respectively. Principal Components (PCs) of two sets of images (2, 4, 5, 7 H-image and 2,5,6,7 F-image) depicting iron-oxide and hydroxyl mineral deposits as bright pixels were generated. Colour composite of H, F and H+F images enhanced the location of the mineral deposits, by showing areas of mineralization in dark blue (Fe rich), bright yellow (clay rich) and white (Fe and clay rich areas) pixels. Field coordinates of mining locations were superimposed on the remote sensing generated mineral map. The results were found to be in tune. This study recommends the use of remote sensing and geospatial technology in mineral studies through hydrothermal alteration within the basement complex rocks of Nigeria.
Optimisation and application research of ant colony algorithm in vehicle routing problem

In this paper, an improved ant colony algorithm based on ant system is proposed in order to solve vehicle routing problem. When choosing the path, the 2-opt method is used to explore the reasonable selection of the parameters of the algorithm for vehicle routing problem taking the path savings among customers as heuristic information. The performance of ant colony algorithm is affected by the information heuristic factor α, expectation heuristic factor β and pheromone volatile factor ρ. The method breaks through empirically setting the ant colony algorithm parameter values. By calculation, the optimal parameters of the ant colony algorithm in solving the vehicle routing problem are: α ε [1.0, 1.7], β ε [4.5, 8.5], ρ ε [0.5, 0.6]. At last, an exploration is established to find the optimal solution by combining three parameters and the ant colony algorithm will have a better effect in the actual optimisation problem.
A Guidebook to Arbitrating Disputes Involving Blockchains and Smart Agreements

Blockchain‐based distributed ledgers, referred to here as Blockchain Ledgers, provide an immutable, secure, and tamper‐evident alternative to conventional transactional modalities, one which also yields enhanced accountability, traceability, and transparency.
Extensive-dynamic-range and high-resolution surface profiling with phase-sensitive spectral-domain white-light interferometry

We demonstrate a Fourier-transform-based method for extensive-dynamic-range and high-resolution surface profiling using phase-sensitive spectral-domain white-light interferometry. By combining the frequency and phase of interference fringes, this method is capable of displacement measurement with nanometer-scale resolution and a dynamic range up to several millimeters. The performance of the method is demonstrated by surface profiling of a coin, gauge blocks, and a cell-phone circuit board.
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization

Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN. Most of the existing efforts speed up some steps at the cost of a significant slowdown of other steps or sacrificing the support of non-differentiable search metrics. The unbalanced reduction in the time spent per step limits the total search time reduction, and the inability to support non-differentiable search metrics limits the performance of discovered DNNs. In this paper, we present NetAdaptV2 with three innovations to better balance the time spent for each step while supporting non-differentiable search metrics. First, we propose channel-level bypass connections that merge network depth and layer width into a single search dimension to reduce the time for training and evaluating sampled DNNs. Second, ordered dropout is proposed to train multiple DNNs in a single forward-backward pass to decrease the time for training a super-network. Third, we propose the multi-layer coordinate descent optimizer that considers the interplay of multiple layers in each iteration of optimization to improve the performance of discovered DNNs while supporting non-differentiable search metrics. With these innovations, NetAdaptV2 reduces the total search time by up to $5.8\times$ on ImageNet and $2.4\times$ on NYU Depth V2, respectively, and discovers DNNs with better accuracy-latency/accuracy-MAC trade-offs than state-of-the-art NAS works. Moreover, the discovered DNN outperforms NAS-discovered MobileNetV3 by 1.8% higher top-1 accuracy with the same latency. The project website is http://netadapt.mit.edu.
Blockchain Based Electronic Health Record System

Electronic Medical Records serve to be a crucial form of personal information. It’s necessary to maintain the confidentiality and integrity of an EMR. This problem is naturally solved by implementing a block-chain based health care system. Each transaction record serves to be an EMR for each patient visit. The system implemented links doctors, patients and health bureaus for record sharing, review and remote care administration. This implementation is only used by the hospital nodes of the health-care system. This system uses consortium block-chain, making EMR access role-specific. It also eases retrieval of specific records confined to the nodes requesting the EMR.
URL Classification based on Active Learning Approach

Web address URL fractions are commonly used in building URL detection techniques. However, the problem of training efficiency is still unsolved despite different models proposed in the literature. As training machine learning models require large quantities of training data and labeling data is time-consuming besides being expensive, using active learning models can be a powerful tool to overcome this situation. This paper investigated the efficiency of active learning using query-based committee strategy. An experiment was carried out; active learning outperforms state-of-the-art methods such as classical machine learning algorithms in terms of performance.
"I'm Not Mad": Commonsense Implications of Negation and Contradiction

Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., "I'm mad at you"), humans can reason about the varying shades of contradictory statements ranging from straightforward negations ("I'm not mad at you") to commonsense contradictions ("I'm happy"). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in nontrivial ways. For example, while "I'm mad" implies "I'm unhappy about something," negating the premise (i.e., "I'm not mad") does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION1, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.
Asymptotically ideal Chinese remainder theorem ‐based secret sharing schemes for multilevel and compartmented access structures

Multilevel and compartmented access structures are two important classes of access structures where participants are grouped into levels/compartments with different degrees of trust and privileges. The construction of secret sharing schemes for such access structures has been the attention of researchers for a long time. Two main approaches have been taken so far, one of them is based on polynomial interpolation and the other one is based on the Chinese Remainder Theorem (CRT). In this article the first asymptotically ideal CRT-based secret sharing schemes for (disjunctive, conjunctive) multilevel and compartmented access structures are proposed. Our approach is compositional and it is based on a variant of the Asmuth-Bloom secret sharing scheme where some participants may have public shares. Based on this, the proposed secret sharing schemes for multilevel and compartmented access structures are asymptotically ideal if and only if they are based on 1-compact sequences of co-primes. Possible applications for secret image and multi-secret sharing are pointed-out.
Selection of a Suitable Agile Methodology – Case Study

Lean manufacturing and lean thinking, in general, have accompanied especially manufacturing companies for many decades.This is not different when developing software.Agile development methodologies are currently among the most used methods in software development and their supply is relatively wide.At the same time, the selection of a suitable method is fundamental, not only for timely delivery but mainly for the optimal functionality of the entire final solution.The agile methodology is mainly focused on value for the customer.This paper is focused on a case study of selecting a suitable agile methodology for a selected medium-sized company.The selection criteria were based on a discussion with the key employees, and then the weights for the particular criteria were determined using a questionnaire survey.The attributes of the selected methodologies were evaluated by experts from the selected company.Saaty's method was chosen for multicriteria decision-making.Subsequently, a decision support system (Criterium Decision Plus) was used to select a suitable methodology from the selection of the most used agile methods.
MLDS: A Dataset for Weight-Space Analysis of Neural Networks

Neural networks are powerful models that solve a variety of complex real-world problems. However, the stochastic nature of training and large number of parameters in a typical neural model makes them difficult to evaluate via inspection. Research shows this opacity can hide latent undesirable behavior, be it from poorly representative training data or via malicious intent to subvert the behavior of the network, and that this behavior is difficult to detect via traditional indirect evaluation criteria such as loss. Therefore, it is time to explore direct ways to evaluate a trained neural model via its structure and weights. In this paper we present MLDS, a new dataset consisting of thousands of trained neural networks with carefully controlled parameters and generated via a global volunteer-based distributed computing platform. This dataset enables new insights into both model-to-model and model-to-training-data relationships. We use this dataset to show clustering of models in weight-space with identical training data and meaningful divergence in weight-space with even a small change to the training data, suggesting that weight-space analysis is a viable and effective alternative to loss for evaluating neural networks.
The Development of an Online Database for Historical Archiving of Primary Sources

Historical primary sources serve as an important historical facts specially for sociologist and historian it helps them to retrieve data easily. It is very difficult to do research, data gathering specially If the data has not been stored in a cabinet or any repository properly, especially if it is being a decade or a century ago. This study will help store all the primary sources in a central database using digital technology, this approach the sociologist and historian can retrieve it easily.
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Natural-language prompts have recently been used to coax pretrained language models into performing other AI tasks, using a fill-in-the-blank paradigm (Petroni et al., 2019) or a few-shot extrapolation paradigm (Brown et al., 2020). For example, language models retain factual knowledge from their training corpora that can be extracted by asking them to "fill in the blank" in a sentential prompt. However, where does this prompt come from? We explore the idea of learning prompts by gradient descent -- either fine-tuning prompts taken from previous work, or starting from random initialization. Our prompts consist of "soft words," i.e., continuous vectors that are not necessarily word type embeddings from the language model. Furthermore, for each task, we optimize a mixture of prompts, learning which prompts are most effective and how to ensemble them. Across multiple English LMs and tasks, our approach hugely outperforms previous methods, showing that the implicit factual knowledge in language models was previously underestimated. Moreover, this knowledge is cheap to elicit: random initialization is nearly as good as informed initialization.
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets

Federated Generative Adversarial Network (FedGAN) is a communication-efficient approach to train a GAN across distributed clients without clients having to share their sensitive training data. In this paper, we experimentally show that FedGAN generates biased data points under non-independent-and-identically-distributed (non-iid) settings. Also, we propose Bias-Free FedGAN, an approach to generate bias-free synthetic datasets using FedGAN. Our approach generates metadata at the aggregator using the models received from clients and retrains the federated model to achieve bias-free results for image synthesis. Bias-Free FedGAN has the same communication cost as that of FedGAN. Experimental results on image datasets (MNIST and FashionMNIST) validate our claims.