Reviewerly/exHarmony huggingface dataset:
Domain Name Detection and Classification Using Deep Neural Networks

The malware families uses Domain Generated Algorithms (DGA) to generate and register different domains to connect to the command and Control server. To improve the automated analysis of DGA-based malware, we have developed an analysis system for detection and classification of DGA’s. In this paper we proposed to take a string of characters as input given in the domain names and classify them as either benign or malicious domain name using deep learning architectures such as Long Short Term Memory (LSTM) and Bidirectional LSTM. We have used the data set given by shared task on Detecting Malicious Domain names (DMD 2018). We have developed a system for both binary and multiclass classification task to detect the malicious domain names. We have observed that the proposed model for binary classification performed better than multiclass classification.
ACS‐HCA: An Access Control Scheme Under Hierarchical Cryptography Architecture

Binding access policies to data, Ciphertext-policy attribute-based encryption (CP-ABE) enables data access control to be independent from a certain application and lets users face data directly. It is regarded as one of the most suitable access control methods in cloud storage system and gets the attention of extensive researches. In those researches, Hierarchical cryptography architecture (HCA) is often applied to improve the efficiency of the system. There exist two open issues: illegal leakage of symmetric keys and low efficiency of revocation of an attribute of a user. We propose an Access control scheme under Hierarchical cryptography architecture (ACS-HCA). In this scheme, key derivation mechanism and forward derivation function are used to avoid the leakage of symmetric keys, All-or-Nothing transform is used to prevent the illegal reuse of symmetric keys, and attribute revocation is realized without re-issuing other users' private keys. Analyses and simulations demonstrate that our scheme sustains less encrypting cost on each owner and less decrypting cost on each user, but gain high efficiency in revocation of an attribute of a user.
OUR JOURNEY: DESIGNING AND UTILISING A TOOL TO SUPPORT STUDENTS TO REPRESENT THEIR STUDY JOURNEYS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 3140-3147ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0826Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Spatio-Temporal Action Detection in Untrimmed Videos by Using Multimodal Features and Region Proposals

This paper proposes a novel deep neural network model for solving the spatio-temporal-action-detection problem, by localizing all multiple-action regions and classifying the corresponding actions in an untrimmed video. The proposed model uses a spatio-temporal region proposal method to effectively detect multiple-action regions. First, in the temporal region proposal, anchor boxes were generated by targeting regions expected to potentially contain actions. Unlike the conventional temporal region proposal methods, the proposed method uses a complementary two-stage method to effectively detect the temporal regions of the respective actions occurring asynchronously. In addition, to detect a principal agent performing an action among the people appearing in a video, the spatial region proposal process was used. Further, coarse-level features contain comprehensive information of the whole video and have been frequently used in conventional action-detection studies. However, they cannot provide detailed information of each person performing an action in a video. In order to overcome the limitation of coarse-level features, the proposed model additionally learns fine-level features from the proposed action tubes in the video. Various experiments conducted using the LIRIS-HARL and UCF-10 datasets confirm the high performance and effectiveness of the proposed deep neural network model.
LSTM Neural Networks for Transfer Learning in Online Moderation of Abuse Context

Recently, the impact of offensive language and derogatory speech to online discourse, motivated social media platforms to research effective moderation tools that safeguard internet access. However, automatically distilling and flagging inappropriate conversations for abuse remains a difficult and time consuming task. In this work, we propose an LSTM based neural model that transfers learning from a platform domain with a relatively large dataset to a domain much resource constraint, and improves the target performance of classifying toxic comments. Our model is pretrained on personal attack comments retrieved from a subset of discussions on Wikipedia, and tested to identify hate speech on annotated Twitter tweets. We achieved an F1 measure of 0.77, approaching performance of the in-domain model and outperforming out-domain baseline by about nine percentage points, without counseling the provided labels.
Gold Price Forecasting Based on Projection Pursuit and Neural Network

Gold price has significant nonlinear, time-varying, many influence factors and more difficult to determine. In order to improve the precision of forecast gold price, this paper proposes a gold price combination forecasting model based on pursuit algorithm and neural network. Firstly, projection pursuit algorithm is used to choose the influence factors, and then the selected impact factors are used as BP neural network input variables to learn and establish gold price forecast model, finally forecast performance is tested by simulation experiments. The test results show that combined model can well depict the gold price trend, simplify network structure, accelerate the network learning speed, and improve the prediction accuracy of gold price. It provides a new forecast method for gold price.
Research on Intelligent Retrieval System for agricultural information resources based on ontology

With the rapid development of computer technology and Internet, agricultural information resources are exploding. The traditional information retrieval based on keyword matching lacks semantic analysis ability and semantic extension ability, resulting in a large number of missed and false detection of retrieval resources and low retrieval efficiency. Based on the framework of Scientific and Technological Knowledge Organization (STKOS), this paper constructs the ontology of agricultural domain and applies domain ontology in semantic annotation, query extension and result sorting. On this basis, the paper constructs an intelligent retrieval system of agricultural information resources based on domain ontology, in order to improve the retrieval efficiency of agricultural information resources and improve user satisfaction.
Pruning the AST with Hunks to Speed up Tree Differencing

Inefficiency is a problem in tree-differencing approaches. As textual-differencing approaches are highly efficient and the hunks returned by them reflect the line range of the modified code, we propose a novel approach to prune the AST with hunks. We define the pruning strategies at the declaration level and the statement level, respectively. And, we have designed an algorithm to implement the strategies. Furthermore, we have integrated the algorithm to Change Distiller and GumTree. Through an evaluation on four open source projects, the results show that the approach is very effective in reducing the number of nodes and shortening the running time. On average, with declaration-level pruning, the number of nodes in the two tools is reduced by at least 64%. With statement-level pruning, the number of nodes in both tools is reduced by at least 74%. By using the declaration-level pruning and the statement-level pruning, GumTree's runtime is reduced by at least 70% and 75%, respectively.
Discovering Periodic Itemsets Using Novel Periodicity Measures

Discovering periodic patterns in a customer transaction database is the task of identifying itemsets (sets of items or values) that periodically appear in a sequence of transactions.Numerous methods can identify patterns exhibiting a periodic behavior.Nonetheless, a problem of these traditional approaches is that the concept of periodic behavior is defined very strictly.Indeed, a pattern is considered to be periodic if the amount of time or number of transactions between all pairs of its consecutive occurrences is less than a fixed maxP er (maximum periodicity) threshold.As a result, a pattern can be eliminated by a traditional algorithm for mining periodic patterns even if all of its periods but one respect the maxP er constraint.Consequently, many patterns that are almost always periodic are not presented to the user.But these patterns could be considered as interesting as they generally appear periodically.To address this issue, this paper suggests to use three measures to identify periodic patterns.These measures are named average, maximum and minimum periodicity, respectively.They are each designed to evaluate a different aspect of the periodic behavior of patterns.By using them together in a novel algorithm called Periodic Frequent Pattern Miner, more flexibility is given to users to select patterns meeting specific periodic requirements.The designed algorithm has been evaluated on several datasets.Results show that the proposed solution is scalable, efficient, and can identify a small sets of patterns compared to the Eclat algorithm for mining all frequent patterns in a database.
Parallel Mining of Top-k High Utility Itemsets in Spark In-Memory Computing Architecture

Top-k high utility itemset (abbr. Top-k HUI) mining aims at efficiently mining k itemsets having the highest utility without setting the minimum utility thresholds. Although some studies have been conducted on top-k HUI mining recently, they mainly focus on centralized databases and are not scalable for big data environments. To address the above issues, this paper proposes a novel framework for parallel mining of top-k high utility itemsets in big data. Besides, a new algorithm called PKU (Parallel Top-K High Utility Itemset Mining) is proposed for parallel mining of top-k HUIs on Spark in-memory platform. It adopts MapReduce architecture to divide the whole mining task into several independent subtasks, and takes good use of Spark in-memory computing technology for efficiently processing data in parallel. Moreover, several novel strategies are also proposed for pruning the redundant candidates such that the execution time and memory usage in the mining process are reduced greatly. The proposed PKU algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results on both real and synthetic datasets show that PKU has good scalability and performance on large datasets with outperforming several benchmarking algorithms.
Sparseout: Controlling Sparsity in Deep Networks

Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dropout-based regularization. In this work, we propose Sparseout a simple and efficient variant of Dropout that can be used to control the sparsity of the activations in a neural network. We theoretically prove that Sparseout is equivalent to an $$L_q$$ penalty on the features of a generalized linear model and that Dropout is a special case of Sparseout for neural networks. We empirically demonstrate that Sparseout is computationally inexpensive and is able to control the desired level of sparsity in the activations. We evaluated Sparseout on image classification and language modelling tasks to see the effect of sparsity on these tasks. We found that sparsity of the activations is favorable for language modelling performance while image classification benefits from denser activations. Sparseout provides a way to investigate sparsity in state-of-the-art deep learning models. Source code for Sparseout could be found at https://github.com/najeebkhan/sparseout .
Machine Learning Based Predictive Action on Categorical Non-Sequential Data

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.
Kerdock Codes Determine Unitary 2-Designs

The non-linear binary Kerdock codes are known to be Gray images of certain extended cyclic codes of length $N = 2^m$ over $\mathbb{Z}_4$. We show that exponentiating these $\mathbb{Z}_4$-valued codewords by $\imath \triangleq \sqrt{-1}$ produces stabilizer states, that are quantum states obtained using only Clifford unitaries. These states are also the common eigenvectors of commuting Hermitian matrices forming maximal commutative subgroups (MCS) of the Pauli group. We use this quantum description to simplify the derivation of the classical weight distribution of Kerdock codes. Next, we organize the stabilizer states to form $N+1$ mutually unbiased bases and prove that automorphisms of the Kerdock code permute their corresponding MCS, thereby forming a subgroup of the Clifford group. When represented as symplectic matrices, this subgroup is isomorphic to the projective special linear group PSL($2,N$). We show that this automorphism group acts transitively on the Pauli matrices, which implies that the ensemble is Pauli mixing and hence forms a unitary $2$-design. The Kerdock design described here was originally discovered by Cleve et al. (arXiv:1501.04592), but the connection to classical codes is new which simplifies its description and translation to circuits significantly. Sampling from the design is straightforward, the translation to circuits uses only Clifford gates, and the process does not require ancillary qubits. Finally, we also develop algorithms for optimizing the synthesis of unitary $2$-designs on encoded qubits, i.e., to construct logical unitary $2$-designs. Software implementations are available at https://github.com/nrenga/symplectic-arxiv18a, which we use to provide empirical gate complexities for up to $16$ qubits.
Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

How can I deliver defect-free software? Can I achieve more with less resources? How can I reduce time, effort, and cost involved in developing software? Software defect prediction is an important area of research which can significantly help the software development teams grappling with these questions in an effective way. A small increase in prediction accuracy will go a long way in helping software development teams improve their efficiency. In this paper, we have proposed a framework which uses PCA for dimensionality reduction and Naïve Bayes classification algorithm for building the prediction model. We have used seven projects from NASA Metrics Data Program for conducting experiments. We have seen an average increase of 10.3% in prediction accuracy when the learning algorithm is applied with the key features extracted from the datasets.
High-dimensional similarity searches using query driven dynamic quantization and distributed indexing

The concept of similarity is used as the basis for many data exploration and data mining tasks. Nearest neighbor (NN) queries identify the most similar items, or in terms of distance the closest points to a query point. Similarity is traditionally characterized using a distance function between multi-dimensional feature vectors. However, when the data is high-dimensional, traditional distance functions fail to significantly distinguish between the closest and furthest points, as few dissimilar dimensions dominate the distance function. Localized similarity functions, i.e. functions that only consider dimensions close to the query, quantize each dimension independently and only compute similarity for the dimensions where the query and the points fall into the same bin. These quantizations are query-agnostic and there is potential to improve accuracy when a query-dependent quantization is used. In this work we propose a query dependent equi-depth (QED) on-the-fly quantization method to improve high-dimensional similarity searches. The quantization is done for each dimension at query time and localized scores are generated for the closest p fraction of the points while a constant penalty is applied for the rest of the points. QED not only improves the quality of the distancemetric,but also improves query time performance by filtering out non relevant data. We propose a distributed indexing and query algorithm to efficiently compute QED. Our experimental results show improvements in classification accuracy as well as query performance up to one order of magnitude faster than Manhattan-based sequential scan NN queries over datasets with hundreds of dimensions. Furthermore, similarity searches with QED show linear or better scalability in relation to the number of dimensions, and the number of compute nodes.
Image Enhancement Based on Fractional Calculus and Genetic Algorithm

Image enhancement is an interesting topic in the image processing area. In this work, image enhancement with fractional-order derivative and genetic algorithm is proposed. Fractional-order derivative possesses a non-local property, which is helpful to find the fine edges of the image. In this paper, firstly, fractional-order partial differences are computed in forward x-direction, backward x-direction, forward y-direction, and backward y-direction. These differences are represented based on discrete Fourier transform (DFT). Finally, genetic algorithm (GA) is applied for the fractional-order selection to get optimum results and the fractional-order is chosen in the range from 0 to 1. The experimental results give the superiority of the proposed algorithm than the traditional methods.
Heralds of Corporate Irresponsible Behavior: Using Partial Least Square Approach of Structural Equation Modeling

This study consolidates internal and external viewpoints to look at the heralds of corporate social irresponsible behavior. As far as internal elements are concerned, we concentrate on the degree to which a firm joins corporate social responsibility (CSR) into its corporate strategy and the level to which companies commit resources into R&amp;D for consumer oriented product development. As for external determinants, we concentrate on market sector costs forces and government agencies responsible for local governance on corrupt practices. The hypothesis we developed for the study are tested, utilizing unique survey data of 120 companies spanning a period of five years. The outcome demonstrates that consolidating corporate social responsibility into business strategy and putting resources into R&amp;D both foresee less socially irresponsible behaviors by companies. Conversely, pressures from costs and corrupt practices within governmental setting associates directly with incremental probability of irresponsible social and behaviors of companies in Ghana.
Towards Influence of Chunk Size Variation on Video Streaming in Wireless Networks

In recent years, the growth in popularity of mobile video streaming services is unbroken. There are tremendous demands for video streaming over wireless networks. Currently, most video streaming is over HTTP. Up to now, HTTP-based adaptive video streaming is standardized as DASH, where a client-side video player can dynamically pick the bitrate level according to the perceived network conditions. Actually, not only the available bandwidth drastically varies due to wireless network properties, but also the chunk sizes in the same bitrate level significantly fluctuate, which also influences the bitrate adaptation. However, existing bitrate adaptation algorithms mostly focus on available bandwidth but do not involve chunk size variation, leading to performance losses. In this paper, we theoretically analyze the influence of chunk size variation on bitrate adaptation performance in wireless networks. Based on DASH system features, we build a general model describing playback buffer evolution. Applying stochastic theories, we respectively analyze the influence of the chunk size variation on rebuffering probability, average bitrate, and bitrate switching interval. Furthermore, based on theoretical insights, we provide several suggestions for algorithm designing and rate encoding, and also design a simple bitrate adaptation algorithm. Extensive simulations verify our insights, suggestions, and designed algorithm effectiveness.
A Novel Memory-Scheduling Strategy for Large Convolutional Neural Network on Memory-Limited Devices

Recently, machine learning, especially deep learning, has been a core algorithm to be widely used in many fields such as natural language processing, speech recognition, object recognition, and so on. At the same time, another trend is that more and more applications are moved to wearable and mobile devices. However, traditional deep learning methods such as convolutional neural network (CNN) and its variants consume a lot of memory resources. In this case, these powerful deep learning methods are difficult to apply on mobile memory-limited platforms. In order to solve this problem, we present a novel memory-management strategy called mmCNN in this paper. With the help of this method, we can easily deploy a trained large-size CNN on any memory size platform such as GPU, FPGA, or memory-limited mobile devices. In our experiments, we run a feed-forward CNN process in some extremely small memory sizes (as low as 5 MB) on a GPU platform. The result shows that our method saves more than 98% memory compared to a traditional CNN algorithm and further saves more than 90% compared to the state-of-the-art related work “vDNNs” (virtualized deep neural networks). Our work in this paper improves the computing scalability of lightweight applications and breaks the memory bottleneck of using deep learning method on memory-limited devices.