Reviewerly/exHarmony huggingface dataset:
Cross-domain Network Traffic Classification Using Unsupervised Domain Adaptation

Traffic classification greatly contributes to traffic billing, traffic engineering, malware detection and so forth. Motivated by the recent advents in machine learning (ML) including deep learning, the statistical-based method has achieved a satisfactory performance. Despite that, a pre-trained model is insufficient to tackle a diverse and uncertain network which is rarely considered yet. The underlying problem is that the traffic used during training (source domain) could be very different from the test traffic (target domain) and meanwhile there are not sufficient data to cover the traffic from different domains. Traffic classification is expected to tackle the cross-domain network traffic. By using unsupervised domain adaptation, we propose a novel framework to classify cross-domain traffic for the above scenario, which is the first attempt to tackle this problem. Specifically, a feature transformation module is added to the current classification task to adapt both marginal distribution and the conditional distribution of training data and new unlabeled data. After that, transformed data are used to retrain the classifier to boost the accuracy of traffic classification when statistical features are changed by varied network conditions. To prove the universality of our approach, we evaluate the proposed framework based on five common models. The experiment results demonstrate that our approach achieves accuracy over 86% for cross-domain traffic classification.
Person Search by Queried Description in Vietnamese Natural Language

Nowadays, surveillance camera systems are widely deployed today from public places to private houses. This leads to huge image databases. Recent years have witnessed a significant improvement of surveillance video analysis, especially for person detection and tracking. However, finding the interested person in these databases is still very challenging issue. A majority of the existing person search methods bases on the assumption that the example image of the person of interest is available. This assumption is however not always satisfied in practical situations. Therefore, recently, person search by using query of natural language description has attracted the attention of researchers. However, they mainly dedicate to queried descriptions in English. In this paper, we propose a person search method with query of Vietnamese natural language. For this, Gated Neural Attention - Recurrent Neural Network (GNA-RNN) is employed to learn the affinity from pairs of description and image and then to estimate the similarity between query and images in the database. To evaluate the effectiveness of the proposed method, extensive experiments have performed in two datasets: CUHK-PEDES with description translated in Vietnamese and our own collected dataset named VnPersonSearch. The promising experimental results show the great potential of the proposed method.
Image Similarity Check of Nailfold Capillary by Template Matching

Objective: This study was to propose a continuous observation method of same nailfold capillaries for the early diagnosis of secondary Raynauds phenomenon through the image processing. Methods: After checking the amount of blurring, a panorama image, which enabled us to understand the whole condition of nailfold capillaries and decide the target area for the continuous observation, was made by captured images with the microscope. After the targeted area was automatically detected, the image similarity check between the previous and the current captured capillary images was evaluated in the real-time through the template matching. Simultaneously, the edge detection allowed us to evaluate the targeted area with coordinates of capillaries as quantitative method. Discussion: Because the observation of continuous same nailfold capillaries enabled us to help the early diagnosis of systemic sclerosis, it was discussed that the low-cost proposed method based on the software approach had the clinical efficacy in rheumatic diseases.
Nexus Between Money Laundering and Sustainable Development Goals

Money laundering is a hot debate discussion among policymakers, as money laundering usually arises due to theft of money or other illegal activity. Such criminal activities damage every stakeholder of the economic cycle, whether it is trade, productivity, or contribution of the financial sector itself. Due to the fact money laundering makes the industrial growth process very slow and undercuts economic activities, which are essential for the development. This chapter explores the nexus between money laundering as a threat to a sustainable development goal from different angles. The discussion reveals that money laundering negatively impacts economic growth, and the fundamental pillar of sustainable development is economic growth. So can we achieve sustainable economic growth and development without controlling money laundering? The authors conclude it is not possible.
Scene Text Detection and Localization using Fully Convolutional Network

Early methods for text detection in scene images have obtained encouraging achievements over several benchmarks. Nevertheless, they commonly drop when working with challenging situations, although implemented by using deep learning designs. In this research, we introduce a robust yet simple pipeline that produces accurate and fast text detection in natural scene images. The pipeline quickly predicts text of random orientations in full-color images, discarding redundant intermediate stages, with a single neural network. The proposed method experimented on ICDAR 2015 and MSRA-TD500 datasets and an advantage in detecting text from natural scene images efficiently for assisting the visually impaired.
Blockchain for Efficient Public Key Infrastructure and Fault-Tolerant Distributed Consensus

This chapter consists of two major works that apply blockchain technology for improving security and trust in cyber systems. First, a cloud-based public key infrastructure (PKI) utilizing blockchain technology model is described. Enhancements over past models include the use of blockchains to establish persistent access to certificate data and certificate revocation lists. By completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts, the proposed method yields a sizable performance boost while decreasing the attack surface. Second, we propose a dynamic runtime sharding of Tendermint, which is an in-development state machine replication algorithm that uses the blockchain model to provide Byzantine-fault tolerance. We call this variation Randition. We incorporate recent research from blockchain consensus and replicated state machine partitioning to allow Randition users to shard their blockchain, which has greatly improved write performance at a slight cost of some Byzantine fault tolerance.
FriendNet Backdoor

Deep neural networks (DNNs) provide good performance in image recognition, speech recognition and pattern analysis. However, DNNs are vulnerable to backdoor attacks. Backdoor attacks allow attackers to proactively access training data of DNNs to train additional malicious data, including the specific trigger. In normal times, DNNs correctly classify the normal data, but the malicious data with the specific trigger trained by attackers can cause misclassification of DNNs. For example, if an attacker sets up a road sign that includes a specific trigger, an autonomous vehicle equipped with a DNN may misidentify the road sign and cause an accident. Thus, an attacker can use a backdoor attack to threaten the DNN at any time. However, this backdoor attack can be useful in certain situations, such as in military situations. Since there is a mixture of enemy and friendly force in the military situations, it is necessary to cause misclassification of the enemy equipment and classification of the friendly equipment. Therefore, it is necessary to make backdoor attacks that are correctly recognized by friendly equipment and misrecognized by the enemy equipment. In this paper, we propose a friendnet backdoor that is correctly recognized by friendly classifier and misclassified by the enemy classifier. This method additionally trains the friendly and enemy classifier with the proposed data, including the specific trigger that is correctly recognized by friendly classifier and misclassified by enemy classifier. We used MNIST and Fashion-MNIST as experimental datasets and Tensorflow as a machine learning library. Experimental results show that the proposed method in MNIST and Fashion-MNIST has 100% attack success rate of the enemy classifier and the 99.21% and 92.3% accuracy of the friendly classifier, respectively.
Development of a DSP Microcontroller-Based Fuzzy Logic Controller for Heliostat Orientation Control

This paper describes the design and implementation of a heliostat orientation control system based on a low-cost microcontroller. The proposed system uses a fuzzy logic controller (FLC) with the Center of Sums defuzzification method embedded on a dsPIC33EP256MU806 Digital Signal Processor (DSP), in order to modify the orientation of a heliostat by controlling the angular position of two DC motors connected to the axes of the heliostat. The FLC is compared to a traditional Proportional-Integral-Derivative (PID) controller to evaluate the performance of the system. Both the FLC and PID controller were designed for the position control of the heliostat DC motors at no load, and then they were implemented in the orientation control of the heliostat using the same controller parameters. The experimental results show that the FLC has a better performance and flexibility than a traditional PID controller in the orientation control of a heliostat.
Optimization Process for Removing of Copper ions from Groundwater of Iraq Using Watermelon Shells as Natural Adsorbent

Abstract People are completely dependent on groundwater for drinking and irrigation in distant Iraq. In many places, copper ions are one of the primary pollutants found in extra quantities in groundwater. The current work uses watermelon shell for removing of copper ions from groundwater. An initial concentration of 10 mg / L and an adsorbent particle size of 1 mm were used to examine the working parameters: PH, dosage, and contact time using STATISTICA and WinQSB software. The optimization technique was set to predict the optimum conditions. However, the efficiency of the watermelon shell was more than 80% and the maximum capacity for adsorption was 9.57 mg / g under optimum conditions such as: 8 for PH, 1 g / L for adsorbent dosage and 2.5 hours for contact time. The general findings verified that the shell of watermelon is an outstanding and alternative adsorbent to remove Cu(II) from groundwater of Iraq.
Analyzing Restricted Boltzmann Machine Neural Network for Building Recommender Systems

Deep learning becomes a useful technology in many fields for solving many problems with high ratio of user satisfaction as well playing a big role as alternative smart techniques for handling big data problems. While recommender system one of the hottest fields that deep learning achieves huge success in case of predication the user needs based on their preferences. Restricted Boltzmann machine (RBM neural network) used for building recommender system with various types of the recommender. This paper analyzes the factors (Batch size, and number of epochs) that affect RBM to build a such recommender while shows the errors and time consumption as analysis factors on a dataset with 1 million movie rating.
Job failure prediction in Hadoop based on log file analysis

Hadoop is a popular framework based on MapReduce programming model to allow for distributed processing of large datasets across clusters with various number of computer nodes. Just like any dynamic computational environment, Hadoop has some problems and one of which is unsuccessful execution of MapReduce jobs. Job failures can cause significant resource wasting, performance deterioration, and user dissatisfaction. Therefore, a proactive and predictive management approach could be very useful in Hadoop systems. In this paper, we try to predict the futurity of MapReduce jobs in OpenCloud Hadoop cluster by using its log files. OpenCloud is a research cluster managed by CMU’s Parallel Data Lab which uses Hadoop to process big data. We first tried to study the log files and analyze the relationship between the jobs, resources, and workload characteristics and the failures in order to discover the effective features for the prediction process. After recognizing the job failure patterns, some popular machine learning algorithms are deployed to predict the success/failure status of the jobs before they start to execute. Eventually, we compared the learning methods and showed that the C5.0 algorithm had the best results with an accuracy of 91.37%, a recall of 74.43%, and a precision of 80.31%.
On the rate of convergence of image classifiers based on convolutional neural networks

Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is analyzed. Under suitable assumptions on the smoothness and structure of a posteriori probability, the rate of convergence is shown which is independent of the dimension of the image. This proves that in image classification, it is possible to circumvent the curse of dimensionality by convolutional neural networks. Furthermore, the obtained result gives an indication why convolutional neural networks are able to outperform the standard feedforward neural networks in image classification. Our classifiers are compared with various other classification methods using simulated data. Furthermore, the performance of our estimates is also tested on real images.
A unified model for accelerating unsupervised iterative re‐ranking algorithms

Summary Despite the continuous advances in image retrieval technologies, performing effective and efficient content‐based searches remains a challenging task. Unsupervised iterative re‐ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re‐ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re‐ranking algorithms creates the need for exploiting efficiency vs effectiveness trade‐offs. In this article, we introduce a class of unsupervised iterative re‐ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL‐Sim, Contextual Re‐ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0×, 16.1×, 3.3×, and 7.1× for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems.
Clearing the Transcription Hurdle in Dialect Corpus Building: The Corpus of Southern Dutch Dialects as Case Study

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other, related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve.
A Backpressure Mitigation Scheme in Distributed Stream Processing Engines

There is a significant rise in the adaptation of streaming applications in the past decade by individuals researchers and organizations in both industry and academia. These applications are all based on the modern data stream processing systems that implement resource allocation and management in order to provide an uninterrupted track of queries over incoming input distributed data streams. More than a few stream processing engines exists to handle these distributed streaming applications. These distributed applications have open challenges like backpressure. In this paper, we introduce a backpressure mitigation mechanism for the distributed stream processing systems. The proposed backpressure mitigation technique is a generic one and is feasible to be implemented on top of a number of popular streaming frameworks. We use Flink as a testbed for this work and use its available APIs.
Educational ecosystems for Information Science: The case of the University of Pisa

Interdisciplinarity is becoming increasingly important in education. With the rapidly evolving job market, an interdisciplinary education can prepare students for the flexibility and broad knowledge base required to adapt. At the University of Pisa, we recognized the value of an interdisciplinary e ducational environment during our participation in the European project EINFOSE, where we harmonized the entry requirements for master programs in Information Science. Prior to this project, we had been building study programs in Digital Humanities and Data Science, whose intersection organically nurtured a diverse learning space. Through this lens, we will reflect on the obstacles constituted by disciplinary barriers and stress the importance of a flexible and open ‘ecosystem’ for education. These conclusions will be supported by data analysis on the careers of our students over the last eight years.
Detecting similarity in color images based on perceptual image hash algorithm

Abstract Due to the ever-increasing digitization, the authentication of digital media content is becoming more and more important. Authentication, in general, means deciding whether a digital media is authentic or not, that is, if it matches a given original image. The authentication depends heavily on the type of the digital media, in other words, it is important that every single bit exactly matches the original digital media. Cryptographic hash functions are adequate for such tasks; in this case, a robust hashing is a technology as a change tolerant alternative to cryptographic hashes. Since normal cryptographic hashing methods are error prone to image processing techniques, perceptual hashing is a promising solution to image content authentication. However, conventional image hash algorithms only offer a limited authentication level for overall protection of the content. When intensity components of the color image are used, it is meant that the image is converted to only hashing functions image to produce strong adaptive hashes and that is lead to inadequate recognition abilities. In this paper, a hash function for color images has been introduced and it is considered robust against global changes inside the image contents. A good discrimination achievement has been obtained since it takes all constituents of the color images into account. Firstly, the input image is resized or re-scaled into a fixed size that is predefined earlier. Secondly, converting the RGB image to HSI and Y C b C r color spaces, respectively. The purpose of this process is to extract local color features of the RGB image. Thereafter, the Y C b C r image is divided into blocks for computing mean and variance of each block component. Finally, the hash values for both source and attack images are calculated using Euclidian distances formula to measure the difference between them. Experiments are lead to validate the efficiency of the suggested perceptual robustness hash function. Different image operations; brightness, darkness and rotating; were used to test the robustness of the image hash functions. Besides, different evaluated metrics, such as Mean Square Error (MSE) and Peak to Signal to Noise Ratio (PSNR), were used to measure the performance of the hash function. The obtained results from the perceptual image hash algorithm matched up to 98% between the source image and their corresponding attack image.
Classifying Human Activity and Smartphone Holding Mode Using Accelerometer and Gyroscope

Recently, there has been an increased demand for understanding the behavior and position of people within large-scale indoor facilities, such as customers within a department store. Current behavior and positioning analysis systems, however, require many sensing devices distributed across the area (e.g. cameras, Wi-Fi access points, and Bluetooth low-energy beacons) and can therefore be expensive. In this paper, we propose an initial low-cost system to determine a person's activity using only smartphone sensors (accelerometer and gyroscope). We succeeded in classifying three activities (sitting/standing/walking) and six modes (swing/handling/calling/pocket/handbag/backpack), with 83.2% accuracy using a LSTM network.
Method of analyzing of antivirus errors when audit provides

In this paper are given mean and types of audit, probabilities of transitions between the functioning states of information of protection tools with discrete work time. Also, in this work are given four types of antivirus errors with possible situations in the performance of means of discrete work time. Performance cases of antivirus software are described in the form of directed Graf. By four types of antivirus errors is calculated average time of operation without breaking, intensity of appearing of error, average time of backup and intensity of backup of error.