Reviewerly/exHarmony huggingface dataset:
An Event Detection Platform to Detect Gender Using Deep Learning

There are many events that occur in e-commerce platforms, which can be used to detect and understand the behavior of online users. Behavior analyses of e-commerce users can be utilized to impact both customers and businesses. Behavior analysis seeks to find useful information from clickstreams, which can be used to address challenging problems. Clickstreams quantify users' movements based on the items they click on an e-commerce website. This work aims to mine clickstreams to predict users' genders. The proposed approach utilizes deep learning and has been tested on a real-world dataset; the proposed approach outperformed others in terms of accuracy.
Improved Method of Target Tracking Based on SiamRPN

An improved target tracking method based on SiamRPN is proposed, which solves the problem that SiamRPN method is prone to tracking drift in complex scenes. First, in the feature extraction part, the deep reference network ResNet-50 is adopted to extract deep features, and a lightweight dual-channel attention mechanism CBAM is introduced to refine feature extraction along the channel and spatial dimensions. Secondly, SE module is introduced into the bridge connection of ResNet-50 to weight the features, which can achieve the purpose of improving the network's feature expression ability. Finally, the region proposal network performs classification and regression operations, thereby outputting tracking results. This paper conducts simulation experiments on OTB2015 dataset and VOT2016 dataset. The results demonstrate that the tracking performance of the proposed method is improved compared to SiamRPN.
Spoken Term Detection Based on Acoustic Models Trained in Multiple Languages for Zero-Resource Language

In this paper, we study a spoken term detection method for zero-resource languages by using rich-resource languages. The examined method combines phonemic posterior-grams (PPGs) extracted from phonemic classifiers of multiple languages and detects a query word based on dynamic time warping. As a result, the method showed better detection performance in a zero-resource language compared with the method using PPGs of a single language.
Decision Making and Data Analysis

The proposed method, called probabilistic nodes combination (PNC), is the method of 2D curve modeling and handwriting identification by using the set of key points. Nodes are treated as characteristic points of signature or handwriting for modeling and writer recognition. Identification of handwritten letters or symbols need modeling, and the model of each individual symbol or character is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter γ as probability distribution function enables curve parameterization and interpolation for each specific letter or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot, or power function.
Fundamentalization of mathematical training: looking for promising solutions

Abstract Mathematics has a special place in ensuring the interdisciplinary integration of various Sciences. Its language is universal. It acts as a tool in various scientific fields of knowledge, as well as in many technological and industrial spheres of human activity. This means that the importance of functional mathematical training of the younger generation increases, which leads to the search for promising solutions for training future teachers of physical and mathematical, natural science, and technological profiles. The article discusses one of the solutions proposed by the authors. The authors’ proposals are aimed at solving the problem of training teachers who will be able to make the school mathematics course accessible to many students without losing quality, providing fundamental and functional mathematical knowledge. And the first necessary step in the proposed proposals is the implementation of the process of forming the ability to apply mathematical knowledge in the construction of models of real objects, processes, and phenomena. The authors propose a scheme of such a process, which allows the studied mathematical models to be embedded in a holistic context, and to study them from the point of view of practical applicability in solving various problems. The experimental work carried out confirmed the effectiveness of the implementation of the process under consideration. It showed that the formation of the ability to apply mathematical knowledge in the construction of models of real objects, processes, phenomena in future teachers of physics and mathematics, natural science, technology ensures fundamentalization of their mathematical background. The next necessary step to solve problem proposed by authors consists in changing the pattern of methodical training of future teachers.
Multiple Samples Clustering with Second-moment Information in Stock Clustering

The clustering algorithms that view each object data as a single sample drawn from a certain distribution have been a hot topic for decades. Many clustering algorithms, such as k-means and spectral clustering, are proposed based on the assumption that each clustering object is a vector generated by a Gaussian distribution. However, in real practice, each input object is usually a set of vectors drawn from a certain hidden distribution. Traditional clustering algorithms cannot handle such a situation. This fact calls for the multiple samples clustering algorithm. In this paper, we propose two algorithms for multiple samples clustering: Wasserstein distance based spectral clustering and Bhattacharyya distance based spectral clustering, and compare them with the traditional spectral clustering. The simulation results show that the second-moment information can greatly improve the clustering accuracy and stability. These algorithms are applied to the stock dataset to separate stocks into different groups based on their historical prices. Investors can make investment decisions based on the clustering information, to invest stocks in the same cluster and get the highest earning or to invest stocks of different clusters to avoid the risk.
deepRGBXYZ: Dense Pixel Description Utilizing RGB and Depth with Stacked Dilated Convolutions

In this paper, we propose deepRGBXYZ - a feature descriptor to represent pixels for robust dense pixel matching. To this end, we concatenate RGB image (appearance) information with the depth (geometric) information represented as XYZ in order to build a robust descriptor which is more invariant to photometric and geometric changes. Both information (RGB and depth) are embedded as an early fusion into one neural network which is based on stacked dilated convolutions for enlarging the receptive field. We alleviate the limitations of image-only descriptors especially within ill-conditioned light regions or textureless objects. Additionally, we overcome the difficulty of using depth-only information which show less descriptive details compared to image-only. We demonstrate the superior accuracy of our deepRGBXYZ descriptor against the state-of-the-art image-only descriptors and we verify our design decision. In addition, we investigate the superior robustness of our deepRGBXYZ descriptor by bringing it into the application of optical flow and scene flow estimation on the established data sets KITTI and FlyingThings3D.
Prototype System for User-Interface-Design Recommendation Using Collaborative Filtering

We propose a user interface (UI) design support system. Our system recommends a UI design that corresponds to a given function by applying collaborative filtering to the relationship between product functions and operational methods. We applied the system to the UI design for the 41 functions of a digital camera. For evaluation, we calculated the distance between the recommended UI design and the UI design implemented by the designer. The average was 0.19 in the 0. 00 - 1.00 distance scale, and the results suggest that the system could provide a satisfactory degree of validity and effectiveness in the UI design.
Knowledge Mining for Defining Systemic Engineering Practices

This paper addresses the problem on generating knowledge graphs from research papers related to wildfires, and their impacts on the electrical grid infrastructure. A novel framework based on part-of-speech tagging, word frequency statistics, and document clustering is proposed for extracting information and generating knowledge graphs from a selfcurated corpus of wildfire-related research work abstracts. The proposed method is capable of capturing a wide range of insightful information from the self-generated domain-specific corpus. Systemic engineering practices, such as strategies for wildfire mitigation by electric utilities has been included as a case study in this paper. An application of the research is implemented for simulating the management and mitigation of wildfires by electric utilities in California.
Gesture Recognition Approaches and its Applicability: A Study

Human Gestures will make the communication more comprehensible and to express ourselves humans are habituated by using gesture, not just in our daily lives but also gained interest in the area of Human Computer Interaction (HCI) and computer vision. Gesture recognition concerns to identify or unfold the meaning from the movements performed by humans using the gestures that may use hand and facial expressions. Gestures are classified as static and dynamic. Gestures recognition is a way for differently abled people to communicate with others. Gesture recognition algorithms can help the needy to bridge the gap and facilitate communication. In the race to make the interaction with computer more natural and human like gesture is considered as one type medium. Most of the research focuses on recognizing dynamic gestures as it is more challenging than the static gesture recognition. Root level of any dynamic gesture recognition system is searching for those key frames that represents best the dynamic gesture. The recognition algorithm relies on key frame extraction algorithm for it performance measure. The paper aims to review those literatures that provide information regarding the dynamic gesture recognition and key frame extraction techniques and its pros and cons.
Salt Dome Detection Using Context-Aware Saliency

This work presents a method for salt dome detection in seismic images based on a Context-Aware Saliency (CAS) detection model. Seismic data can easily add up to hundred of gigabytes and terabytes in size. However, the key features or structural information that are of interest to the seismic interpreters are quite few. These features include salt domes, fault and other geological features that have the potential of indicating the presence of oil reservoir. A new method for extracting the most perceptual relevant features in seismic images based on the CAS model is proposed. The efficiency of this method in detecting the most salient structures in a seismic image such as salt dome is demonstrated through a series of experiment on real data set with various spatial contents.
On the Feasibility of Acoustic Attacks Using Commodity Smart Devices

Sound at frequencies above (ultrasonic) or below (infrasonic) the range of human hearing can, in some settings, cause adverse physiological and psychological effects to individuals. We investigate the feasibility of cyber-attacks that could make smart consumer devices produce possibly imperceptible sound at both high (17-21kHz) and low (60-100Hz) frequencies, at the maximum available volume setting, potentially turning them into acoustic cyber-weapons. To do so, we deploy attacks targeting different smart devices and take sound measurements in an anechoic chamber. For comparison, we also test possible attacks on traditional devices. Overall, we find that some of the devices tested are capable of reproducing frequencies within both high and low ranges, at levels exceeding those recommended in published guidelines. Generally speaking, such attacks are often trivial to develop and in many cases could be added to existing malware payloads, as they may be attractive to adversaries with specific motivations or targets. Finally, we suggest a number of countermeasures for detection and prevention.
Braking and acceleration mapping control for autonomous overtaking to avoid fatality crash in undivided road

Abstract Autonomous vehicle control needs two essential algorithms, the first path planning, it covers either finding vehicle subject position and destination point and environment identification to avoid obstacle within tailgating, lane change and overtaking. The second to be tracking control to realize drive, brake and steering action based on complexity of path planning. This research contribute to a path planning for unidirectional and bidirectional on non-lane undivided roads overtaking.It need to be designed and investigated the desired acceleration of vehicle subject for tailgating and overtaking vehicle object 1 in front and has same direction without crash with vehicle object 2, what come front the opposite direction. The results of the investigation show that the desired acceleration of the subject vehicle can be properly mapped under condition of varied position, velocity and acceleration of overtaken vehicle object 1 and vehicle object 2. The maximum desired acceleration was eliminated by consideration of critical rolling and sliding velocity of vehicle subject. The acceleration mapping can be used for safety overtake planning.
Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics “Prehistoria” for Grade X High School Student

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements.
Sample Size Determination for Credibility Estimation

The credibility estimator has been successfully and widely applied by practitioners in the insurance industry for decades. However, an important issue remains unresolved: what sample size should the actuary choose when he/she applies the credibility estimator? In particular, is the size of a given claims data set large enough for the credibility estimator to be accurate? This paper aims to address this issue by first suggesting a sample size criterion for the credibility estimator and then proposing a sample size based upon that. The proposed sample size is easy to apply, and it guarantees provable accuracy for credibility estimation.
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks

We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.
P1_2 Quake, Rattle and Roll

In this paper, the feasibility of using bass frequencies from sub-woofer speakers to create earthquakes registering as a 9.0 on the Richter scale was explored. To answer this question, we calculated the displacement caused by the speakers that were situated just under the Earth's surface, which produced an amplitude value of $3 * 10^{-8} m$. The final conclusion that we came to was that, while it would be theoretically possible to release the same amount of energy as a 9.0 quake, without an unfeasibly large area to accommodate a speaker of immense physical dimensions, the displacement value would be too low to be felt compared natural quakes.
A Decentralized Tree-based Algorithm for Reliable Blockchain Communications

Blockchain has been widely used in monetary systems, hence its consistency under all circumstances is a serious concern. Since it can be damaged physically and virtually, it is crucial to develop a protection mechanism that binds both overlay and underlay networks. This paper focuses on the functionality of blockchain under attacks. We propose a heuristic algorithm to enhance the reliability of information propagation. First, for underlay, we scrutinize the performance of the network during a partial malfunction. We develop a series of mathematical formulas to construct the communication tree model and evaluate its delay. Second, in the overlay, we add the trustworthiness of each node as a parameter. It is an indicator used in path selection to avoid malicious tampering. Our purposed method strengthens the tie between both physical and virtual networks so that blockchain thrives even in abnormal situations. It greatly increases the resilience of blockchain and provides a reliable system for digital transactions.
LessMine: Reducing Sample Space and Data Access for Dense Pattern Mining

In the era of “big data”, graph has been proven to be one of the most important reflections of real-world problems. To refine the core properties of large-scale graphs, dense pattern mining plays a significant role. Because of the complexity of pattern mining problems, conventional implementations often lack scalability, consuming much time and memory space. Previous work (e.g., ASAP [1]) proposed approximate pattern mining as an efficient way to extract structural information from graphs. It demonstrates dramatic performance improvement by up to two orders of magnitude. However, we observe three main flaws of ASAP in cases of dense patterns, thus we propose LessMine, which reduces the sample space and data access for dense pattern mining. We introduce the reorganization of data structure, the method of concurrent sample, and uniform close. We also provide locality-aware partition for distributed settings. The evaluation shows that our design achieves up to 1829 × speedup with 66% less error rate compared with ASAP.
Research on Sentiment Classification of Chinese Transition Text

The research on text sentiment classification has been very popular. The main methods include the methods based on rules and lexicons, machine statistical learning, and neural network methods. These methods have achieved great success in text sentiment classification, but there are still many defects. For traditional methods, its versatility is poor, once the text style or format changes significantly, the rules need to be re-established. Although the neural network method is generally applicable, it is not sufficient for the application of language knowledge. In order to solve the above problems, we propose a new model combining the traditional method and neural network method. It combines transition lexicon, stacked BILSTM and attention mechanism, and uses word2vec to initialize word vectors. For sentiment classification, semantic features are necessary, stacked BILSTM can capture contextual semantics multiple times to obtain richer contextual information representation. At the same time, we consider that the polarity of the clauses before and after the transition words may be completely opposite, which will also have a significant impact on the classification results. Therefore, we propose two strategies for transition words. One is to use the transition lexicon to supervise the attention mechanism and make the attention model sensitive to the transition words. The other is to cut the sentence according to the transition words to eliminate the noise of irrelevant items in the sentence. Our method combines the advantages of traditional methods and neural network methods, and makes up for their respective shortcomings. We use the proposed method to conduct a sentimental binary classification experiment on three data sets. The experimental results show that our method is superior than the previous methods.
Towards the Integration of Web of Things Applications Based on Service Discovery

The current state of the technologies related to the Web of Things (WoT) and the Internet of Things (IoT) fosters the creation of service directories gathering resource descriptions. These directories are aimed at enabling the service discovery and supporting providers and consumers with a shared element for their communication and interoperability between the involved agents. This interoperability can be ensured by using the abstract layer of the W3C WoT recommendations. However, many of the existing approaches do not include a service discovery mechanism and those that include a WoT directory lack certain functionalities required by distributed systems specific to the WoT such as a Service-Oriented Architecture (SOA). This paper proposes a federated service discovery approach to support the management of WoT applications which ensures that the integration of the whole system components can be addressed by following a SOA. It is aimed at providing query and storage functionality for WoT resources, but also is intended to be connected to other WoT directories by applying a customizable approach based on recommender systems. Thus, we guarantee a flexible mechanism to obtain sets of ranked WoT resources to be utilized in different kinds of applications and domains.
Modification of the quantum grover algorithm by using the inversion method around the middle

Abstract The article proposes a modification of Grover’s quantum oracle quantum search algorithm, which makes it easier to search the database. The algorithm is implemented in the Python programming language using the Reggeti Forest cloud quantum service. The authors of the article use the mean flipping method, which solves the search problem during the iterative order √ (2 ^ n). Development offers great potential for the practical application of the Grover quantum algorithm, as it is characterized by higher performance and speed when performing research. Theoretically, the algorithm provides quadratic acceleration compared to conventional computers. It is not an exponential acceleration, but it remains important for large data carriers. The quantum parallelism of the Grover search algorithm is based on a simultaneous change in the amplitudes of all the inputs. This is done through a superposition of states, which is a purely quantum concept. In addition, the research is carried out globally, which indicates a significant improvement in optimization procedures. Grover’s algorithm, on the other hand, is sensitive to the number of iterations. The more iterations, the smaller the amplitude of the correct answer, so the wrong choice of this parameter can digest the solution. In addition, the operation of the algorithm is limited in the case of the introduction of noise into a quantum system, which is real in modern quantum computers.