Reviewerly/exHarmony huggingface dataset:
Suggestion Mining from Online Reviews usingRandom Multimodel Deep Learning

In this paper, we propose a new deep learning based method for suggestion mining. The major challenges of suggesion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multimodel Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.
A Survey of Various Effective Codec Implementation Methods with Different Real Time Applications

A codec is a device which helps to encode or decode digital data signals, it encodes a data stream or encrypted form and a decoder operates a reverse process of encoder. This codec's are applied in signal editing, video editing; signal conferencing, video conferencing etc. The codec's have two major principles like pulse code modulation method and delta modulation method. In this paper, we proposed a survey of various effective codec implementation and performance analyzing methods with different applications. The various effective codec's are implemented such as CELP codec, MELP codec, SPEEX speech codec, and OPUS speech codec. A CELP codec is code excited linear prediction method which helps to increase bit detection rate, high efficient process in speech coding, and peak quality in low bit rates. A CELP codec is to be applied in real time advanced technologies such as automatic watermarking process, communication field, Zigbee applications, filtering process, power spectrum analyzing fields, quantization methods, Gaussian processing fields, ARM processors etc. A MELP is a mixed excitation liner prediction method which helps to eliminate the voicing error obtains in multiple state excitation method. A SPEEX method helps to analyze speech quality and bit rate and an OPUS is high flexible speech codec technique which plays a vital role in real time applications. These methods are implemented and performances are analyzed based on different real world applications.
Blockchain and other Distributed Ledger Technologies: Where is the Accounting?

In a recent survey of academic research, Fintech related topics, broadly classified as crypto-currency studies, were by far the most researched topics in the social sciences. However, we have observed that, perhaps surprisingly, even though crypto-currencies rely on a distributed accounting ledger technology, relatively few of those studies were conducted by accounting academics. While some of the features of a system like Bitcoin do not necessarily rely on a traditional accounting knowledge, this knowledge is key in designing effective real-world distributed systems. Building on a founda- tional framework developed by Risius and Spohrer (2017), we provide support for their hypothesis that to date, research in this area has been predominantly of a somewhat narrow focus (i.e., based upon exploiting existing programming solutions without adequately considering the fundamental needs of users). This is particularly reflected by the abundance of Bitcoin-like crypto-currency code-bases with little or no place for business applications. We suggest that this may severely limit an appreciation of the relevance and applicability of decentralized systems, and how they may support value creation and improved governance. We provide supporting arguments for this statement by considering four applied classes of problems where a blockchain/distributed ledger can add value without requiring a crypto-currency to be an integral part of the functioning system. We note that each class of problem has been viewed previously as part of accounting issues within the legacy centralized ledger systems paradigm. We show how accounting knowledge is still relevant in the shift from centralized to decentralized ledger systems. We advance the debate on the develop- ment of (crypto-currency free) value-creating distributed ledger systems by showing that applying accounting knowledge in this area has potentially a much wider impact than that currently being applied in areas limited to auditing and operations management. We develop a typology for general distributed ledger design which assists potential users to understand the wide range of choices when developing such systems.
Security and Encryption at Modern Databases

Data is a most valuable part of most of nowadays system. A lot of hackers and criminals are trying to steal this data all the time. Due to that data should also be the best protected part of every company's systems. We would like our systems to be impenetrable, but that is not possible. If we want to protect the data, in case our system is compromised, we need to use encryption. This article describes traditional ways of database encryption, modern concept of securing data and some possible concepts how to secure the data using encryption. All of these approaches are discussed from high-level point of view to show their impact on security of entire system.
Mining Temporal Fuzzy Utility Itemsets by Tree Structure

More complicated than fuzzy data mining, temporal fuzzy utility data mining takes into account the temporal factor of transactions, purchased quantities, item profits, and linguistic terms. In this paper, a tree structure modified from the frequent-pattern tree is designed and a mining algorithm based on it was proposed to extract high temporal fuzzy utility patterns from transactional datasets with the temporal property. The method requires two-phase processing to find all high temporal fuzzy utility itemsets. Experimental results show that the proposed algorithm performs better than the Apriori-based mining algorithm.
Security analysis of a random number generator based on a chaotic hyperjerk system

This paper analyzes the security of a random number generator (RNG) based on a 4-D chaotic hyperjerk system. An attack system is designed to reveal the security weaknesses of the proposed chaotic RNG. Knowing the structure of the RNG and observing one of the state variables of the chaotic system, convergence between attack and target systems is demonstrated by applying linear continuous coupling in master-slave synchronization scheme. Output bit sequence of the chaotic RNG is identically reproduced. The feasibility of the attack system is verified through numerical simulations. In this paper, a specific continuous-time chaos-based RNG is targeted as a case study. However, the cryptanalysis method presented in this paper is applicable to any continuous-time or discrete-time chaos-based RNGs. Therefore, this study highlights the security vulnerabilities of chaos-based RNGs and underlines that deterministic chaos itself cannot be considered as an entropy source for generation of random numbers.
QoS Based Optimal Resource Allocation in Service Composition for Heterogeneous Devices

Service-Oriented Computing(SOC) rapidly gains importance in the industry to built complex web service applications. The web service plays a very important role in SOC. Service composition is the process of adding services to the existing one using functional attributes of multiple services rapidly and effectively. One of the critical issues in the composition is non-consideration of Quality of service (QoS) parameters which can lead to inaccurate and low performing composite service. Next to selection tasks in the composition process is to allocate resource to selected services in composite service is also crucial. Resource allocation in the composition is the process of allocating component service to selected candidate service for each and every candidate services in composite service. In the cloud environment, component service holds physical computational resource or infrastructural resource. This paper, present mapping of resource allocation problem in composition to 0/1 Multi-dimensional knapsack problem which is NP-hard computational problem. The work in the paper provides the solution to the resource allocation problem with the help of a dynamic programming strategy. The computational simulation was carried out and revealed that the dynamic programming strategy as not suitable for a large number of services considered during the allocation process. Further development of the solutions for such problems should be attempted by applying various heuristics and meta-heuristics approaches. However, after analysis of these approaches, the work suggests to study and apply meta-heuristics to address this type of problems for an optimal solution with the feasibility.
Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.
Coarse-to-Fine Deep Orientation Estimator for Local Image Matching

Convolutional neural networks (CNNs) have become a mainstream method for keypoint matching in addition to image recognition, object detection, and semantic segmentation. Learned Invariant Feature Transform (LIFT) is pioneering method based on CNN. It performs keypoint detection, orientation estimation, and feature description in a single network. Among these processes, the orientation estimation is needed to obtain invariance for rotation changes. However, unlike the feature point detector and feature descriptor, the orientation estimator has not been considered important for accurate keypoint matching or been well researched even after LIFT is proposed. In this paper, we propose a novel coarse-to-fine orientation estimator that improves matching accuracy. First, the coarse orientation estimator estimates orientations to make the rotation error as small as possible even if large rotation changes exist between an image pair. Second, the fine orientation estimator further improves matching accuracy with the orientation estimated by the coarse orientation estimator. By using the proposed two-stage CNNs, we can accurately estimate orientations improving matching performance. The experimental results with the HPatches benchmark show that our method can achieve a more accurate precision-recall curve than single CNN-based orientation estimators.
Inheritance Coding with Gagné-Based Learning Hierarchy Approach to Developing Mathematics Skills Assessment Systems

This study developed an inheritance coding with Gagné-based learning hierarchy approach to building systems for assessing mathematics skills and diagnosing student learning problems. The proposed Gagné-based learning hierarchy approach combines Gagné learning hierarchy theory with an inheritance coding approach. First, Gagné learning hierarchy theory is used to generate test questions and learning path diagrams for a skills assessment system. To assess learning achievement, an inheritance coding approach is used to encode the test questions according to learning hierarchy paths. The analysis, design, development, implementation, and evaluation design model is used throughout the process of developing the assessment system. Statistical analyses of the test questions for assessing student learning achievement included expert validity, internal reliability, test–retest reliability, and parallel-form reliability. System performance questionnaires were also designed to survey the opinions of the students regarding the mathematics skills assessment system. The internal reliability of the overall questionnaire was also calculated. The experimental practical application of the assessment system, developed by the Gagné-based learning hierarchy approach, showed that it can accurately diagnose student learning barriers and provide learning suggestions for students and teachers.
A Short Survey on Population-Based Incremental Learning Algorithm

Population-Based Incremental Learning (PBIL) algorithm is an evolutionary algorithm technique that belongs to the class of Estimation of Distribution Algorithms (EDAs) which has been proposed as an extension of Genetic Algorithms (GAs). It combines aspects of simple Genetic Algorithms (GAs) and competitive learning. PBIL has been used recently to solve many optimization problems in engineering and other field with promising results. However, this algorithm is not well-known by the larger community of researchers. This paper presents a short survey on the standard PBIL algorithm and the many variants that have been proposed.
LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node will be represented as a feature vector. However, due to the detachment of the embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection, network alignment or information diffusion. In this paper, we propose to study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, we propose a novel network embedding framework, namely “application oriented network Embedding” (LATTE). In LATTE, the heterogeneous network structure can be applied to compute the node “diffusive proximity” scores, which capture both the local and global network structures. Based on these computed scores, LATTE learns the network representation feature vectors by extending the autoencoder model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets with community detection as an example task, and the experimental results have demonstrated the outstanding performance of LATTE. Experiental results on other tasks are provided in the full-version of this paper at [16].
A New LSB Attack on Special-Structured RSA Primes

Asymmetric key cryptosystem is a vital element in securing our communication in cyberspace. It encrypts our transmitting data and authenticates the originality and integrity of the data. The Rivest–Shamir–Adleman (RSA) cryptosystem is highly regarded as one of the most deployed public-key cryptosystem today. Previous attacks on the cryptosystem focus on the effort to weaken the hardness of integer factorization problem, embedded in the RSA modulus, N = p q . The adversary used several assumptions to enable the attacks. For examples, p and q which satisfy Pollard’s weak primes structures and partial knowledge of least significant bits (LSBs) of p and q can cause N to be factored in polynomial time, thus breaking the security of RSA. In this paper, we heavily utilized both assumptions. First, we assume that p and q satisfy specific structures where p = a m + r p and q = b m + r q for a , b are positive integers and m is a positive even number. Second, we assume that the bits of r p and r q are the known LSBs of p and q respectively. In our analysis, we have successfully factored N in polynomial time using both assumptions. We also counted the number of primes that are affected by our attack. Based on the result, it may poses a great danger to the users of RSA if no countermeasure being developed to resist our attack.
Revisiting Text and Knowledge Graph Joint Embeddings: The Amount of Shared Information Matters!

Jointly learning embeddings from text and a Knowledge Graph benefits both word and entity/relation embeddings by taking advantage of both large-scale unstructured content (text) and high-quality structured data (the Knowledge Graph). Current techniques leverage anchors to associate entities in the Knowledge Graph to corresponding words in the text corpus; these anchors are then used to generate additional learning samples during the embedding learning process. However, we show in this paper that such techniques yield suboptimal results, as they fail to control the amount of shared information between the two data sources during the joint learning process. Moreover, the additional learning samples often incur significant computational overhead. Aiming at releasing the power of such joint embeddings, we propose JOINER, a new joint text and Knowledge Graph embedding method using regularization. JOINER not only preserves co-occurrence between words in a text corpus and relations between entities in a Knowledge Graph, it also provides the flexibility to control the amount of information shared between the two data sources via regularization. Our method does not generate additional learning samples, which makes it computationally efficient. Our extensive empirical evaluation on real datasets shows the superiority of JOINER across different evaluation tasks, including analogical reasoning, link prediction, and relation extraction. Compared to state-of-the-art techniques generating additional learning samples from a set of anchors, our method yields better results (with up to 4.3% absolute improvement) and significantly less computational overhead (76% less learning time overhead).
Improving Speech-Based End-of-Turn Detection Via Cross-Modal Representation Learning with Punctuated Text Data

This paper presents a novel training method for speech-based end-of-turn detection for which not only manually annotated speech data sets but also punctuated text data sets are utilized. The speech-based end-of-turn detection estimates whether a target speaker's utterance is ended or not using speech information. In previous studies, the speech-based end-of-turn detection models were trained using only speech data sets that contained manually annotated end-of-turn labels. However, since the amounts of annotated speech data sets are often limited, the end-of-turn detection models were unable to correctly handle a wide variety of speech patterns. In order to mitigate the data scarcity problem, our key idea is to leverage punctuated text data sets for building more effective speech-based end-of-turn detection. Therefore, the proposed method introduces cross-modal representation learning to construct a speech encoder and a text encoder that can map speech and text with the same lexical information into similar vector representations. This enables us to train speech-based end-of-turn detection models from the punctuated text data sets by tackling text-based sentence boundary detection. In experiments on contact center calls, we show that speech-based end-of-turn detection models using hierarchical recurrent neural networks can be improved through the use of punctuated text data sets.
Secure parameterized pattern matching

Parameterized pattern matching (PPM) is the problem of matching between two given parameterized strings over two constant and parameter alphabets. PPM has special applications in software maintenance, information retrieval, computational biology, and so on. In some applications of PPM, preserving the privacy of the involved parties is essential. For example, a researcher holding an amino acid pattern needs to receive the parameterized matched locations of his/her input with the patterns in a biological database while the database owner has to obtain no information about the matching results and the pattern. In this paper, we define this problem as secure PPM (SPPM), present a scheme to resolve it in the semi-honest and malicious adversarial models, and prove the security of the proposed scheme in the universal composability (UC) framework. The proposed scheme supports wildcard and approximate PPM, too. We evaluate the security and performance of the proposed scheme. The proposed scheme is experimentally evaluated on a case of secure ribonucleic acid (RNA) search over the RNAcentral dataset. Implementation results show that the proposed scheme is secure and efficient for preserving privacy in contexts where PPM is applicable.
Augmented reality for narrow area navigation in jaw surgery: Modified tracking by detection volume subtraction algorithm

Abstract Background and Aim Jaw surgery based on augmented reality (AR) still has limitations in terms of navigating narrow areas. Surgeons need to avoid nerves, vessels, and teeth in their entirety, not just root canals. Inaccurate positioning of the surgical instrument may lead to positional or navigational errors and can result in cut blood vessels, nerve channels, or root canals. This research aims to decrease the positional error during surgery and improve navigational accuracy by reducing the positional error. Methodology The proposed 2D/3D system tracks the surgical instrument, consisting of the shaft and the cutting element, each part being assigned a different feature description. In the case of the 3D position estimation, the input vector is composed of image descriptors of the instrument and the output value consists of 3D coordinates of the cutter. Results Sample results from a jawbone—maxillary and mandibular jaw—demonstrate that the positional error is reduced. The system, thus, led to an improvement in alignment of the video accuracy by 0.25 to 0.35 mm from 0.40 to 0.55 mm and a decrease in processing time of 11 to 14 frames per second (fps) against 8 to 12 fps of existing solutions. Conclusion The proposed system is focused on overlaying only on the area to be operated on. Thus, this AR‐based study contributes to accuracy in navigation of the deeper anatomical corridors through increased accuracy in positioning of surgical instruments.