Reviewerly/exHarmony huggingface dataset:
Diagnostic accuracy of triple test in breast pathologies of women above 20 years of age

Background: This study is to establish the diagnostic accuracy of triple test in assessing breast pathologies in women above 20 years of age taking their histopathological report as standard.Patients and Methods: In our analytical study, we included women presenting with a complaint of breast lump or change in breast texture in an age group of above 20 years. Systematic clinical examination was done followed by mammography and finally fine-needle aspiration cytology (FNAC) for tissue sampling. Lesions were considered triple test positive, if lesions were FNAC positive and any one of the remaining two modalities also gave a positive (malignant) interpretation, supporting FNAC, but each of three components must be negative for labeling triple test as negative. Postoperatively, cumulative results were compared with histopathology reports and statistical parameters such as specificity, sensitivity, positive predictive value, negative predictive value, and accuracy of triple test were calculated.Results: We have obtained 100% sensitivity using triple test in all age groups when each element was interpreted as malignant and 100% specificity (P < 0.001) when each element was interpreted as benign with diagnostic accuracy of almost 100% in concordant cases. It was recommended that in cases, where all three modalities are not in agreement for benign pathology and in FNAC positive cases where other two parameters are not in agreement, and lesion is interpreted as suspicious on triple test, the nature of the lesion must be ascertained by excision biopsy.Conclusion: Triple test of breast pathologies is a reliable method and allows detection of breast pathologies in an effective manner, and undue delay in treatment can be minimized by using this modality in limited resource country.
Innovation Finance Beyond Bitcoin

Over the past two decades, the diffusion of technological innovations introduced to the finance industry has been inconceivable. Internet, at the end of the 20th century, brought e-commerce, later e-payments, and more recently, e-money. Such innovations in digital world increase the impact on the business world, and so might do cryptocurrencies, currently spreading out across the globe. To this end, this chapter builds up an across-the-board synthesis of current investment trends and analysis, aiming to lead a way forward for research on this uncharted breed of alternative finance assets looming anew.
Similarity Calculation Based on Pass Regions in Soccer Videos

This paper discusses a similarity calculation between soccer scenes in soccer videos. In the soccer games, pass regions, which are parts of the soccer court that include some pass courses are very important elements of each tactic in various scenes. Therefore, by visualizing the pass regions on soccer court in soccer videos, many audiences can easily understand tactics and game situations. Also, since the visualized pass regions describe tactics of soccer games, tactically similar scenes include similar pass regions each other. Thus, the similarity of soccer scenes is obtained by utilizing estimated pass regions in soccer videos. In this paper, we discuss the similarity calculation for soccer scenes. Specifically, the similarity of soccer scenes is compared which are calculated from a Deep Neural Network(DNN)-based visual feature of visualized pass regions, a DNN-based visual feature of original soccer image and Active Net-based feature of estimated pass regions.
Ammeter Inspection With Densely Connected Object Detection Network

It is very important to understand the safety status of the electric metering devices in our daily life. This paper proposes a method based on deep learning to detect and evaluate electric metering devices. The detection Convolutional Neural Network (CNN) model is designed with the end-to-end architecture of You Only Look Once version 3(YOLOv3), but the backbone network is substituted with Densnet201 based on the dense connection idea. The detection objects include ammeter components of various sizes. The experimental results show that the proposed method can effectively detect the electric metering device in real time.
Development of An Effective Cryptographic Algorithm Using Random Matrix Shared Key

Data security refers to protect digital privacy measures that are applied to prevent unauthorized access to computers, databases and websites. Confidentiality is one of the most important part of security and the principle of confidentiality ensures only the sender and the intended recipients have access to the contents of a message. There are a number of algorithms using encryption and decryption procedure to achieve security measures for data and information. Each algorithm has different method. For encrypting and decrypting large number of data sets, most of the algorithms are complex and some existing algorithms are vulnerable to cryptanalysis attacks. The main objective of our research work is to build a simple algorithm which can encrypt and decrypt large number of data sets and is free from maximum cryptanalysis attack to provide confidentiality. In this research paper, an effective symmetric cryptographic algorithm is proposed where the algorithm procedures are much simpler to encrypt or decrypt large number of data sets. Security level is higher because poly-alphabetic substitution mapping method, translation and transposition techniques are used.
Real-Time Online Multi-Object Tracking

In recent years, object detection technology has been continuously developed, and the tracking-by-detection strategy has gradually become the main method of multi-object tracking. Based on detection, the accuracy of the multi-object tracking depends on the detection results to a large extent. However, in many practical applications, especially the case of complex scenes and crowded objects, the detection results are usually inaccurate. In this paper, a joint detection and tracking framework is proposed with a unified confidence scoring function to evaluate tracks confidence and complement low confidence detections with high confidence tracks. In this way, detections and tracks can be combined organically and achieved complementarity. High confidence detection results can prevent long-term tracking drift, while high confidence tracking prediction can deal with false detection and missed detection caused by occlusion during object interaction. Moreover, we trained the ReID appearance feature with higher identification capabilities on the large-scale person re-identification datasets, which has higher identification capability. Extensive experiments are conducted on MOT17 benchmarks to demonstrate the real-time and advanced performance of our tracker.
Improvement of Self Localization Method Based on Frequency Modulated LED Blinking and Omnidirectional Camera

This paper describes the improvement of our proposed self-localization method utilizing LEDs and an omnidirectional camera. In our previous framework, there were some image and signal processes which need high calculation cost. For this reason, the framework could not achieve real-time position estimation. In this paper, we applied machine learning to reduce the calculation cost, especially for extracting the LED pixels from a captured video image in shorter time.
Entangling Problem Hamiltonian for Adiabatic Quantum Computation

Adiabatic quantum computation starts from embedding a computational problem into a Hamiltonian whose ground state encodes the solution to the problem. This problem Hamiltonian is normally chosen to be diagonal in the computational basis, that is a product basis for qubits. We demonstrate that, in fact, a problem Hamiltonian can always be chosen to be non-diagonal in the computational basis. To be more precise, we show how to construct a problem Hamiltonian in such a way that all its excited states are entangled with respect to the qubit tensor product structure, while the ground state is still of the product form and encodes the solution to the problem. We conjecture that such entangling problem Hamiltonians might evade the many-body localization bottleneck of the adiabatic quantum computation and thus improve its performance.
Virtual Hand Position Correction Algorithm Based on Virtual Hand Interaction

The interaction based on virtual hand is the main way of interaction in virtual reality. However, the three-dimensional visual angle of the virtual scene makes the objects in the interactive scene appear near large and far small. The objects near the edge of the visual area are visually stretched and deformed. The relative position of the virtual hand and the real hand deviates seriously, and the immersion sense of virtual real interaction is greatly reduced. In order to solve this problem, a vision correction algorithm based on the position of virtual hand is proposed. According to the position of virtual hand in the scene, the position of virtual hand is corrected to make it coincide with the position of real hand in space and perspective. This method corrects the sense of dislocation when users interact, improves the interaction authenticity experience, and improves the success rate of interaction to a certain extent. The experimental results show that the position of virtual hand is corrected, the user experience is improved, and the interaction efficiency is also improved.
Ar-Math: Augmented Reality Technology Applied for Education

Currently, technology opens the doors to a new generation of methodologies in the teaching of various subjects. "Serious games" are a tool that allows students to learn while playing, which has several cognitive and social benefits. Augmented reality by its sensory element becomes a tool suitable for application in serious games for young ages. This paper presents a serious game for teaching numerical place value for children up to 6 years, we called as AR-Math.
Supervised Aspect Category Detection of Co-occurrence Data using Conditional Random Fields

Customer's sentiments on products and services are recognized by the internet through consumer reviews. But, to make a purchase decision one cannot identify all the data and make a fair evaluation of a product or resource. Hence, all reviews are summarized using a text processing framework. In this paper, we work on identifying the aspect categories in the review sentences. We have built Conditional Random Field based system for category detection and summarization to achieve the Aspect Based Sentiment Analysis(ABSA) task on Restaurant review dataset. Implicit aspect in product review sentences is a difficult task. In the proposed system explicit as well as implicit aspects are extracted to identify the aspect category.
Challenges and Solutions in Recommender Systems

Recommender Systems play a huge part in most of our lives today. A large portion of today’s digital customers rely on such programs to shape their usage of online markets. The main objective of such systems is to build relationships between the products and its users and to help them make the best decisions depending on their needs. There are 4 main types of Recommender Systems that follow different methods in order to satisfy user preferences by filtering through data in an efficient manner. Content-based filtering systems suggest items based on attributes they share with other similar items. Collaborative filtering systems analyze the past behaviour of the customer and recommend items they might find interesting. Demographic filtering uses pre-existing and already compiled data about the behaviour of the customer based on population statistics and uses it to create a list of recommendations and the Hybrid filtering system is a combination of all these systems. Essentially, recommender systems filter through large amounts of data to give the user personalized results. This paper explains and analyzes the Filtering Systems in depth and delves into the challenges these systems face in order to produce accurate suggestions.
Examination on the Trend of Researches on Absorptive Capacity using Text Mining Technique

Background/Objectives: This paper is investigated the trend of researches on absorptive capacity through search of Scopus Database by using text mining technique for academic theses related to absorptive capacity.Methods/Statistical analysis: This study used Text Mining Technique, discovering hidden meanings in big data such as extraction of information from large volume of documents or finding linkages, classification, clustering and summarization by using the method of NLP.Findings: As a result of analysis on the keywords related to absorptive capacity, most frequently used words were ‘capacity’ followed by ‘absorptive’, ‘knowledge’, ‘firm’, ‘innovation’, 'study', 'performance', 'research', 'results', 'model', 'effect', 'development' and 'technology' in descending order. Absorptive capacity is the very core of current convergence era, many studies are expected to be conducted related to absorptive capacity in the digital transformation era.Improvements/Applications: The trend of researches on absorptive capacity and related studies in the leading academic thesis database, Scopus, and drew out and analyzed 20 keywords.In the future, related researches will be continuously conducted as well. Keywords: Big Data, Absorptive Capacity, Text mining, Trend, Scopus, R.
Automatic Distortion Type Recognition for Stereoscopic Images

Stereoscopic image quality evaluation and enhancement are facing more challenges than its 2D counterparts. The use of stereoscopic/3D imaging is rapidly increasing. Stereo images could be afflicted by different types of distortion. For the development of stereoscopic image quality evaluation and enhancement algorithms, a no-reference distortion classification model has been proposed. Disparity/depth map is constructed and utilized as a source of information. Gradient map variance is extracted as feature from disparity and stereo image. Following feature extraction, a machine learning based on Support Vector Machine (SVM) has been employed to learn and identify the distortion type. The model is trained and used to classify the most common types of distortions. The benchmark database LIVE 3D has been used to test and evaluate the model. Testing results of the proposed classifier have shown reliability and good accuracy on five types of distortions.
A Study on the Operational Status of NCS-based Curriculum in Refrigeration Area of Fisheries &amp; Maritime High School

The purpose of this paper is to analyze the actual condition of NCS-based curriculum in refrigeration of fisheries and marine field. To this end, the curriculums among the three schools was compared and analyzed. In addition, a focus group interview was conducted on five refrigeration teachers and analyzed using the Giorgi method. The results of the study were as follows. First, the schools of the present study successfully organized occupation and life subjects and allocated a lot of time to practical classes. Second, the problems of operating the NCS-based curriculum were classified into curriculum, evaluation, teacher competence, and policy of Ministry of Education. While most of participants negatively perceived the organization and operation of curriculum, level of competence unit or learning module, and difference between the existing curriculum and NCS-based curriculum, they showed a positive attitude toward the evaluation by performance evaluation only. In addition, they recognized the need for training on the teacher"s competence. In the aspects of government support, they perceived to need organizing class research community at the national level, supplying more teachers, decreasing busywork, and securing equipment required in class.
Determining the maximum length of logical rules in a classifier and visual comparison of results

Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as the classifier search method, helping the technique of applied machine learning. In this context, the knowledge representation in form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction and is based on the idea of sequential covering. We introduce genetic programming as the search method for classification-rules. From this approach, we have given results on subjects as maximum rule length, number of rules needed in a classifier and the rule intersection problem. The experiments developed on benchmark clinical data resulted in a methodology to follow in the learning method evaluation. Moreover, the results achieved compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.•The method is based on genetic programming techniques to find rules holding each class in a dataset.•The method is approached to solve the problem of rule intersection from different classes.•The method states the maximum length of a rule to generalize.
A Proposed Speech Scrambling based on Haar Transform and Permutation

In this paper, the original speech will be scrambling using a hybrid structure of Wavelet- Haar transform and Permutation. The Haar transforms were applied on the speech file after dividing it into frames with 256-samples size. In the first case, the sound frame was passed through a low pass filter and high pass filter at the first level of the analysis, after that in the second level, the low pass filter coefficients input again in a series of low and high pass. In the second case, the sound frame was passed through a low pass filter and high pass filter in the first level of the analysis. The second level, the coefficients of high pass filter passed through a series of low and high pass filters. While for the third case, each frame was converted to a two-dimensional matrix; a 2D-haar transform was applied to the matrix and then returned to the frame with one-dimension. All three cases applying the permutation process at the end of the Wavelet -Haar transform, to offer higher possible security. And all three cases simulated in system with noise channel of multi (SNR) values, and then the security and quality were measured by (SSNR).
Topology-Aware Job Scheduling for Machine Learning Cluster

Parameter Server (PS) has been widely used to train a large amount of data on multiple machines in parallel. In parameter server, a critical problem is how to effectively schedule multiple training jobs to minimize the job completion time. Some existing work has proposed methods of setting the number of concurrent workers. However, they do not effectively consider the topology of GPU placement which affects the efficiency of communication. This paper proposes a novel resource-to-time model based on the number of workers and the topology of GPU placement. According to the model, we propose an algorithm called TOPO-PS particularly for topology problem in parameter servers. The algorithm achieves the placement strategy based on graph mapping algorithm. Evaluation under various algorithms evidences the superiority of our algorithm. TOPO-PS yields shorter job completion, by up to 53.48% of that of FIFO and 88.77% of OASIS.
Accuracy Enhancement of GPS for Tracking Multiple Drones Based on MCMC Particle Filter

GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).
Knowledge-Based Multimodal Intention Active Perception Algorithm Research

With the aging of the population, more and more old people need to live alone at home. In order to improve the robot's ability to understand the user's intention in the home environment, this paper proposes a knowledge-based multimodal intention active perception algorithm. The robot actively perceives the user's intention to interact before the user issues interactive commands. Finally, the algorithm was verified on Pepper robot. On the basis of knowledge, this algorithm can actively understand the user's intention by integrating multimodal information. First of all, visual intention inference and auditory intention inference are obtained by combining database and expert knowledge base. Then, the fusion results are obtained by using the multimodal intention active perception algorithm. The robot initiatively initiates interactions to request user feedback and perform corresponding actions. Experimental results show that the proposed multimode algorithm is superior to the single-mode algorithm. It improves the efficiency of human-computer interaction and reduces the burden of users, and is well received by test users.
Kesantunan Berbahasa Dalam Debat Pilkada DKI Jakarta 2017

The purpose in this study is to describe compliance and violations in the Regional Head Election Debate DKI Jakarta 2017. The method used is descriptive. The results of this study on data on the use of the language politeness in the regional head election debate DKI Jakarta as many as 5 data that comply with the maxim of politeness principle and 55 data that violated the maxim of politeness principle. As for the details of the maxim of politeness compliance, namely the award of 3 utterances, the maxim of simplicity of 1 utterance, and the maxim of agreement of 1 utterance, and the details of maxim of violation of the principle of politeness in language are wisdom found 29 utterances, maxim of appreciation of 11 utterances, maxim of simplicity of 9 utterances, and maxim of agreement of 6 utterances. Obedience to the principle of politeness in language that is not contained in the Jakarta 2017 election debate is a maxim of generosity, a maxim of wisdom, and a maxim of sympathy, while violations of politeness maxim of language not found in this study are generosity and maxim of sympathy.Keyword: politeness of language, debate
WatchOUT: A Practical Secure Pedestrian Warning System Based on Smartphones

Nowadays, smartphones have become indispensable for people and many users may browse information on their smartphones during walking. This raises severe concerns about pedestrian safety when distracted users inadvertently walks from the sidewalk onto the street. In this paper, we present WatchOut, a convenient pedestrian alerting system based on fine-grained step classification (e.g., flat, up and down the ramp) and event detection (e.g., entering the street, turn) in an urban environment. In contrast to existing systems that rely on shoe-mounted sensors, Watchout leverages rich sensors on the smartphone without requiring any additional hardwares. With carefully designed realtime data processing and classification algorithms, WatchOut can achieve a high detection accuracy. We develop a fully-functional Android application of WatchOut with user-friendly interface that will issue timely alerts to warn users of unsafe conditions. Extensive experiments with 23 volunteers in real pedestrian environment have confirmed the effectiveness of WatchOut, which achieves a comparable detection accuracy as the shoe-mounted system with off-the-shelf smartphones.