Reviewerly/exHarmony huggingface dataset:
Dynamic Faceted Search for Technical Support Exploiting Induced Knowledge

IT support is a vital and integral part of technology adoption. Conventionally, IT support service providers heavily rely on human effort and expertise to respond to user queries. Given the cost-benefit and 24 $$\times $$ 7 availability for answering user questions, Virtual Assistants (VA) are highly applicable in the technical support domain. In this paper, we describe a novel methodology for building interactive virtual assistants for IT support using Dynamic Faceted Search (DFS). Given a question, dynamic facets are generated automatically, enabling the user to refine and narrow down their intent. To do so we leverage knowledge automatically induced from textual content and existing Semantic Web resources such as Wikidata. Such knowledge is then used to dynamically generate facets interactively based on the user’s responses as shown in the demo video ( https://ibm.box.com/v/iswc2020-dfs ). The experiments on two real-world datasets in the IT support domain show the effectiveness of DFS in refining the user’s queries and efficiently identifying possible solutions to their technical problems.
Hydrogeochemistry in the Yukon-Tanana upland region of east-central Alaska: Possible exploration tool for porphyry-style deposits

A hydrogeochemical study using high resolution ICP-MS was undertaken at the Taurus and other porphyry Cu–Mo(-Au) occurrences and Ag–Au–Cu (+/− Pb, Zn) occurrences with epithermal-style characteristics in the Yukon-Tanana upland region of eastern Alaska. Surface water samples were collected from 30 sites on creeks that drain known deposits and occurrences and surrounding presumably unmineralized areas. Water samples for the entire ~9 km length of McCord Creek, which drains the Taurus deposit, and those from streams draining the areas at and near the Bluff and Dennison porphyry occurrences have high conductivity values (492–1250 μS/cm) and consistently high concentrations of B (3–250 μg/L), Co (2.3–42 μg/L), Mn (339–4750 μg/L), Re (0.012–0.1 μg/L), and SO42− (>200 mg/L), all of which are well above the median value for this data set and significantly greater than concentrations in water samples from the unmineralized areas. These are the best pathfinder elements specifically for porphyry style deposits because most of them are not anomalous in waters near epithermal occurrences. Copper concentrations are high (up to 115 μg/L) in some low-pH water samples from McCord Creek and drainages around Bluff, and a few near neutral pH waters have high molybdenum (>1 μg/L), but neither element is consistently anomalous in close vicinity to the porphyry occurrences, possibly due to a metal-poor, sulfide-poor leached cap (average of ~50 m) that overlies supergene and hypogene mineralized zones and is the dominant rock at surface. High concentrations of Bi and/or As occur in many waters associated with mineralized areas, particularly the Bluff and Dennison occurrences. In general, the element associations related to porphyry deposits reflect the deposit mineralogy, as well as size of the footprint related to alteration and mineralization.
Creating a technical architecture framework for m-voting application

South Africa's general election of 2019 posed many challenges, some of which threatened the credibility of its result, and the entire election process. Some of the challenges were multiple voting, many hours of queuing, which led to fatigue, and a significant decline in the number of voters owing to the limited timeframe. These factors heighten the need for m-voting as an alternative or complementary method of voting in the country. Owing to similar challenges, many studies have been conducted, which consequently focused on development and strengthening of security features of e-voting and m-voting applications. However, many countries, including South Africa are not deploying the applications because of their complex nature, such as not being able to trace and track its components. The complexity is attributed to the lack of an architecture framework. This paper presents a technical architecture framework, to support and guide the development and implementation of m-voting applications, to possibly eradicate complexity and enhance the electoral process in South Africa. The outcome of this study was based on findings from analysis of existing materials and semi-structured interviews, in which the hermeneutics approach was employed.
Fixed Point Sets of Digital Curves and Digital Surfaces

Given a digital image (or digital object) (X,k), we address some unsolved problems related to the study of fixed point sets of k-continuous self-maps of (X,k) from the viewpoints of digital curve and digital surface theory. Consider two simple closed k-curves with li elements in Zn, i∈{1,2},l1⪈l2≥4. After initially formulating an alignment of fixed point sets of a digital wedge of these curves, we prove that perfectness of it depends on the numbers li,i∈{1,2}, instead of the k-adjacency. Furthermore, given digital k-surfaces, we also study an alignment of fixed point sets of digital k-surfaces and digital wedges of them. Finally, given a digital image which is not perfect, we explore a certain condition that makes it perfect. In this paper, each digital image (X,k) is assumed to be k-connected and X♯≥2 unless stated otherwise.
Neural Networks for Detecting Irrelevant Questions During Visual Question Answering

Visual question answering (VQA) is a task to produce correct answers to questions about images. When given an irrelevant question to an image, existing models for VQA will still produce an answer rather than predict that the question is irrelevant. This situation shows that current VQA models do not truly understand images and questions. On the other hand, producing answers for irrelevant questions can be misleading in real-world application scenarios. To tackle this problem, we hypothesize that the abilities required for detecting irrelevant questions are similar to those required for answering questions. Based on this hypothesis, we study what performance a state-of-the-art VQA network can achieve when trained on irrelevant question detection. Then, we analyze the influences of reasoning and relational modeling on the task of irrelevant question detection. Our experimental results indicate that a VQA network trained on an irrelevant question detection dataset outperforms existing state-of-the-art methods by a big margin on the task of irrelevant question detection. Ablation studies show that explicit reasoning and relational modeling benefits irrelevant question detection. At last, we investigate a straight-forward idea of integrating the ability to detect irrelevant questions into VQA models by joint training with extended VQA data containing irrelevant cases. The results suggest that joint training has a negative impact on the model’s performance on the VQA task, while the accuracy on relevance detection is maintained. In this paper we claim that an efficient neural network designed for VQA can achieve high accuracy on detecting relevance, however integrating the ability to detect relevance into a VQA model by joint training will lead to degradation of performance on the VQA task.
Spatio-Temporal Self-Attention Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation, which aims to recommend next POIs that the user will likely visit in the near future, has become essential in Location-based Social Networks (LBSNs). Various Recurrent Neural Network (RNN) based sequential models have been proposed for next POI recommendation and achieved state-of-the-art performance, however RNN is difficult to parallelize which limits its efficiency. Recently, Self-Attention Network (SAN), which is purely based on the self-attention mechanism instead of recurrent modules, improves both performance and efficiency in various sequential tasks. However, none of the existing self-attention networks consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Self-Attention Network (STSAN), which combines self-attention mechanisms with spatio-temporal patterns of users’ check-in history. Specifically, time-specific weight matrices and distance-specific weight matrices through a decay function are used to model the spatio-temporal influence of POI pairs. Moreover, we introduce a simple but effective way to dynamically measure the importances of spatial and temporal weights to capture users’ spatio-temporal preferences. Finally, we evaluate the proposed model using two real-world LBSN datasets, and the experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.
An Improved Method Multi-View Group Recommender System (IMVGRS)

Today, one of the users' issues on the web is finding their desired information from a massive amount of data. Recommender systems aid users in making decisions and choosing their suitable items by personalizing the contents for users by their interest. In the past, most of the researches has been done on individual recommender systems. But now, attention has been drawn to group recommender systems. For this reason, this paper tried to improve a group recommender system. In this article, an Improved Multi-View Group Recommender System (IMVGRS) has been proposed. This multi-view group recommender system recommends to a group of the user from two standpoints of user preferences (ratings) and social connection (trust). First, the dimension of the data has been reduced with the Singular-Value Decomposition (SVD) method. Second, the system has been clustered with the complete linkage method. Experimental results, show the effectiveness of the proposed improved method.
Privacy preservation auction in a dynamic social network

Summary The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent methods even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers' information has not known by the seller, current mechanism still cannot preserve the information such as prices. In this paper, we propose a novel mechanism which modeled the auction process in online social network and preserved users' privacy by using differential privacy mechanism. Our mechanism can successfully process the auction and at the same time preserve clients' price information from neighbors. We achieved these by adding Laplace noise for its valuation and the number of valuation seller received in the auction process. We also formulate this mechanism on the real network to show the feasibility and effective of the proposed mechanism.
A Secure IoT Data Communication and Sharing Framework Based on Permissioned Blockchain

Rapid development of internet and communication throughout the world has made IoT one of the key components of fourth industrial revolution. IoT has its impact on various aspects of our life and has remarkable contributions to world economics. Integrated with different technologies, IoT has been expanded to many more applications thus making direct interaction with our personal and corporate life. Security of these devices has been a major issue. But problems arise with on-demand security solution as devices are provided with low processing and energy resources. Heavy and expensive systems cannot be applied to IoT devices. Addressing these problems, in this paper we propose a novel blockchain based framework for IoT device data sharing and communication. We implemented a prototype of our proposed framework using Hyperledger Fabric and evaluated its performance. We show that our proposed solution is efficient for IoT devices and different security requirements such as confidentiality, availability and integrity of data are met.
Leveraging VR/AR/MR and AI as Innovative Educational Practices for “iGeneration” Students

The use of virtual reality (VR), augmented reality (AR), mixed reality (MR), immersive experiences, and artificial intelligence technologies are more focused on innovative education technologies. Studies show that the use of these innovative technologies helps to improve the quality of education. It is essential to promote these new innovative techniques, which show an immense prominence in the improvement of education technologies. So, this chapter shows how these innovative technologies combined with games are a more effective way of transforming our education from a good old traditional way of teaching and learning to a tech savvy way of teaching. Since the students are not the same, and there has been a lot of evolution over the years, it is important for everyone in the education sector to rethink the methods of teaching and learning and the use of incorporating new education technologies to enhance learning.
Toothbrushing data and analysis of its potential use in human activity recognition applications

In this paper, we describe and analyze a time-series dataset from toothbrushing activity using brush-attached and wearable sensors. The data was collected from 17 participants when they brushed their teeth over one week in 5 different locations. The dataset consists of 62 toothbrushing sessions for each of the brush-attached and wearable sensor approaches, using both electric and manual brushes. The average duration of each session is 2 minutes. One sensor device was attached to the handle of the brush while the other was worn by the participants as a wrist-watch. We collected the data from a 3-axis accelerometer and a 3-axis gyroscope at a 200 Hz sampling rate. Most of the data has been labelled. We investigated the characteristics of the data using spectral analysis and performed a pre-processing pipeline in order to generate features used to train a Support Vector Machine Classifier. We were able to identify which part of the jaw was being brushed with 98.6% accuracy.
A Text Classification Method Based on the Merge-LSTM-CNN Model

Abstract An MLCNN (Merge-LSTM-CNN) based text classification model combining a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is proposed due to CNN’s deficiency in obtaining context dependency in text and feature loss of the deep neural network in terms of text character extraction. First, the vector representation of input text is realized through word embedding, then full-text semantics are integrated by extracting the local features of the text through a three-layer CNN. Meanwhile, LSTM is used to store the features of historical information in the text to obtain its context-related semantics. Second, input vectors are integrated with the outputs of CNN in each layer respectively to allow the reuse of original features.
RETRACTED: Enterprise financial cost management platform based on FPGA and neural network

At present, the domestic costs of most construction companies are relatively scattered with the cost data of various business agents. Unless it is controlled by an experienced manager, decision-makers cannot have the real-time dynamic cost of a project. In the information age, it is of vital importance to use the information to control the cost of construction projects dynamically. Cost management and the establishment of an information platform are ways to control the platform integrated cost data, operators, computer software and hardware, and corresponding method information, and its core is cost data information. The place for financial cost analysis and decision making is a conceptually rich field where information is a commercial product which is complicated, extensive, and invaluable. In this model, first,a set of extracts from the macro-credit feature space is designed and then, FPGA and neural network (FPGA, NN) models for credit evaluation is built based on these indicators, eventually it is applied scientifically, and reasonably, practically. Several state credit metrics are randomly selected. Our model shows applications that are both practical and competent. Using this model, authorities can analyze local credit conditions, allowing investors to make wise decisions to invest while saving on operating and credit costs. Most importantly, this model can help impulsive local government leaders, businesses, and even everyone to enhance competitiveness and capacities of attractive regions, thereby foster a good atmosphere for a credit culture.
TracKenzan: Digital Flower Arrangement Using Trackpad and Stylus Pen

We propose “TracKenzan,” a training system for Ikebana (Japanese flower arrangement) in a 3D computer graphics space using a trackpad and a stylus pen. Ikebana includes a Kenzan, a flat metal base with hundreds of upward-pointing pins to hold flowers and branches in place. In the proposed system, a trackpad represents the Kenzan, and a stylus pen equipped with a 3D tracker resembles each flower stem. The users build the flower arrangement by selecting virtual flowers with adjustable length, whose position and orientation correspond to those of the pen, and then place the flowers by pressing the desired position on the trackpad. By using the trackpad and pen resembling the Kenzan and flower, respectively, TracKenzan provides an intuitive and straightforward interface for users to practice Ikebana. We present the system implementation and evaluation tests conducted by 11 beginner and 7 experienced users, as well as one Ikebana teacher. They verified the system usability and capabilities as a learning tool for Ikebana.
Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the- art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.
Fall-detection on a wearable micro controller using machine learning algorithms

Wearables providing fall detection can provide faster emergency services for elderly, yet privacy concerns limit acceptance of this technology. In this work, we evaluate a machine learning algorithm, called Bosnai, for embedded edge devices to detect falls. The prototype is Arduino based and can be integrated into fabrics for clothes, belts, or other accessories. The fall detection is performed offline on the device. We used data from public datasets of movement and fall events to train a tree-based machine learning model. We evaluated different combinations of prepossessed parameters as input features for the learning algorithm. The learned model is transferred to the microcontroller and can classify the sensor data offline but in real-time. We evaluate the performance of our device by performing intensive test runs with the prototype. The microcontroller is extremely limited in terms of memory capacity and computing performance, which only allows a limited number of features for learning. For this reason, it is especially important to preprocess the raw accelerator data and select the right features for training and inference. Our results show that the best performance (approx. 94.2 % accuracy) is achieved when we choose absolute acceleration and variance as features, with a sampling rate of 20 Hz and a recording window of 3s, as this system is the most robust against external interference.
Layered Neighborhood Expansion for Incremental Multiple Graph Matching

Graph matching has been a fundamental problem in computer vision and pattern recognition, for its practical flexibility as well as NP hardness challenge. Though the matching between two graphs and among multiple graphs have been intensively studied in literature, the online setting for incremental matching of a stream of graphs has been rarely considered. In this paper, we treat the graphs as graphs on a super-graph, and propose a novel breadth first search based method for expanding the neighborhood on the super-graph for a new coming graph, such that the matching with the new graph can be efficiently performed within the constructed neighborhood. Then depth first search is performed to update the overall pairwise matchings. Moreover, we show our approach can also be readily used in the batch mode setting, by adaptively determining the order of coming graph batch for matching, still under the neighborhood expansion based incremental matching framework. Experiments on both online and offline matching of graph collections show our approach’s state-of-the-art accuracy and efficiency.
Automatic Traffic Monitoring Using Images from Road Camera

The article presents an algorithm for visual inspection of traffic intensity. At first, the acquisition process of video material from a road camera is described. Then the algorithm for processing and analyzing images from the recorded video material is presented. Software was prepared in MATLAB environment. Algorithm tests were conducted in real conditions, at different times of the day, different atmospheric conditions and different levels of traffic intensity. Test results show that in good working conditions the vehicle counting accuracy was 95.6%. When the sun shined in the camera lens the counting accuracy decreased to 87.2%. The smallest accuracy 83.2% was noted for traffic jams.
Performance Benchmarking of NewSQL Databases with Yahoo Cloud Serving Benchmark

Selecting a NewSQL database product is an important process. Like any other successful database management system technology, the product selected today begins to define the legacy of the future. There are many different parameters that can be used to evaluate the NewSQL database alternatives, and there is no single most-correct process for conducting such an evaluation. Not only are there many possible pertinent evaluation criteria, but there also is typically a degree of uncertainty about the requirements and characteristics of the intended application environment. This paper focuses on three NewSQL databases (viz., VoltDB, MemSQL and NuoDB), with emphasis on evaluating their performance. The evaluation is done based on Yahoo Cloud Serving Benchmark.
An Analysis of Early Use of Deep Learning Terms in Natural Language Processing

In this paper, we present the preliminary results on the analysis of deep learning terms used for natural language processing (NLP) tasks. We propose a statistical analysis of papers published from 2012 to 2015 in the main ACL conferences. Our aim is investigating which DL term, and consequently which DL method, is mostly used for each specific NLP task, since its introduction in the field. In order to do this, our first contribution is the development of two terminological lists, referring respectively to DL methods for text analysis and NLP tasks. The list of deep learning terms contains 41 terms and acronyms, as well as a NLP term list contains 145 terms and acronyms. From our corpus, the frequencies of various terms have been extracted with respect to the ACL conference and the publication year. After the preliminary data analysis, we decided to restrict the extraction process to abstract texts. We applied multivariate techniques called correspondence analysis in order to visualize and evaluate the joint behavior of our variables.