Reviewerly/exHarmony huggingface dataset:
Indian Languages Corpus for Speech Recognition

Robust Speech Recognition System for various languages have transcended beyond research labs to commercial products. It has been possible owing to the major developments in the area of machine learning, especially deep learning. However, development of advanced speech recognition systems could be leveraged only with the availability of specially curetted speech data. Such systems having usable quality are yet to be developed for most of the Indian languages. The present paper describes the design and development of a standard speech corpora which can be used for developing general purpose ASR systems and benchmarking them. This database has been developed for Indian languages namely Hindi, Bengali and Indian English. The corpus design incorporates important parameters such as phonetic coverage and distribution. The data was recorded by 1500 speakers in each language by male and female speakers of different age groups in varying environments. The data was recorded on a server using online recording system and transcribed using semi-automatic tools. The paper describes the corpus designing methodology, challenges faced and approach adopted to overcome them. The whole process of designing speech database has been generic enough to be used for other languages as well.
User Identity Authentication and Identification Based on Multi-Factor Behavior Features

Behavior-based identity authentication has been of research interests due to its low cost and the fact that such authentication factors cannot easily copied nor stolen. However, single behavior feature authentication usuall cannot gaurantee sufficient accuracy. In this paper, a multi-factor behavior features identity authentication method is proposed by combining factors of mouse, keystroke and address. We apply support vector machine by using the information divergence of features as the weight of the linear kernel function to solve quantification's problem of features influence degree on authentication. The tailored support vector machine is applied to multi-factor behavior features to identity authentication. Our experimental results show that the improved support vector machine is better than both the linear and Gaussian kernel support vector machines for identity authentication in accuracy. The proppsed method can be applied to large volume users' identity authentication cases where their behavior features are likely to be similar.
SmartSO: Chinese Character and Stroke Order Recognition With Smartwatch

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO, which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO, and extensive experiments confirm its effectiveness and robustness.
Assembly Motion Recognition Framework Using Only Images

This work proposes a method for recognizing and segmenting assembly tasks into single motions. First, using a motion capture system based on pose estimation from multiple points, we obtain a time series data of the human's motion during an assembly task (motion data). We use an object detector algorithm to determine the assembly parts and tools that the user (human) is grasping. Then, we divide (segment) the assembly motion based on the change of the manipulated object and the velocity of the hand. We carry out the motion recognition of the segmented motion data by using several Hidden Markov Models (HMMs) that represent the actions that can be executed with the manipulated object(s). We recorded the assembly motion of an airplane toy done by two experts for training the HMMs and recorded the assembly motion of five subjects to verify the validity of the proposed method.
Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years. Most of existing methods are grouped into physical prior based and non-physical data-driven based categories. However, image dehazing is a challenging ill-conditioned and inherently ambiguous problem. Due to random distribution and concentration of haze, color distortion and excessive brightness often happen in physical prior based methods. Defects on high-frequency details’ recovery are not solved well in non-physical data-driven methods. Therefore, to overcome these obstacles, in this paper, we have proposed an effective progressive back-traced dehazing network based on multi-resolution recurrent reconstruction strategies. A kind of irregular multi-scale convolution module is proposed to extract fine-grain local structures. And a kind of multi-resolution residual fusion module is proposed to progressively reconstruct intermediate haze-free images. We have compared our method with several popular state-of-the-art methods on public RESIDE and 2018 NTIRE Dehazing datasets. The experiment results demonstrate that our method could restore satisfactory high-frequency textures and high-fidelity colors. Related source code and parameters will be distributed on Github for further study.
A-ECN Minimizing Queue Length for Datacenter Networks

Recently, Explicit Congestion Notification (ECN) has been leveraged by DCTCP and its successive protocols to reduce the latency in datacenters. However, we find that DCTCP may induce the queue underflow and high latency in multi-queue scenarios and even single-queue environments. Our analysis concludes that the problem is caused by improper ECN marking thresholds of DCTCP. In this paper, an Adaptive ECN (A-ECN) marking scheme is proposed to enhance the performance of the ECN marking scheme of DCTCP.A-ECN is designed to minimize queue length as well as keeping high link utilization. More importantly, A-ECN can adaptively adjust ECN marking thresholds in different scenarios to achieve good generality. Therefore, network operators can directly deploy A-ECN in various environments regardless of underlying bandwidth, packet schedulers and the number of concurrent flows. At last, the testbed evaluation results show that A-ECN can minimize queue length and maintain high throughput. The flow completion times (FCT) for short flows could be reduced by up to 40% compared to the ECN marking scheme of DCTCP.
Alteration patterns linked to high-grade gold mineralization at the Wattle Dam deposit, Western Australia

Sharp physio-chemical gradients occur in Archean gold systems, yet they are often poorly defined. These physio-chemical gradients can provide windows into fluids that deposit high-grade gold mineralization. Recognizing different alteration styles and fluid-types in mineral systems helps define processes that led to the deposition of high-grade gold. Here we report on the architecture and chemical gradients across the komatiite-hosted Wattle Dam gold deposit in the eastern Yilgarn Craton, Western Australia, which is one of Australia’s highest grade-gold deposits. The deposit notably contains millimeter- to centimeter-thick gold-only veins with an absence of quartz. Whole rock geochemistry shows that high-grade gold veins are hosted in komatiite flow-margins facies with olivine orthocumulate komatiites in the footwall, interbedded graphitic shale close to the lode and komatiite basalt in the hangingwall. The komatiite flow-margin facies that hosts high-grade gold veins have been pervasively altered by stage 1 biotite-actinolite. Stage 2 gold veins cross-cut the biotite-actinolite altered komatiite and these veins contain thin (<100 µm) selvages of sodicgedrite, riebeckite, aegirine and ullmannite ± sulfates (barite and anhydrite) ± magnetite. Millimeter-thick gold-only veins are associated with the dissolution of zircon and magnetite, and precipitation of catapleiite in veins. These textures are consistent with the contention that fluids mobilized Zr and Fe and were highly reactive. Arsenic- and Sb-bearing alteration in the form of cobaltite, arsenopyrite, nickeline, gersdorffite, breithauptite and ullmannite occur as wide halos (up to 300 m) around the main lode. These As and Sb bearing-phases form in both the hangingwall komatiite basalt unit and footwall olivine orthocumulate komatiites. Arsenides and antinomides contain inclusions of Au and can be directly linked to mineralization. The diverse array of mineral assemblages associated with high-grade and low-grade gold mineralization indicates sharp chemical gradients at the time of gold mineralization. The mobility of Zr and Fe at the time of mineralization in the core of the deposit points towards the presence of alkaline fluids, whereas peripheral zones are associated with minerals associated with the near-neutral H2O-CO2 fluids, commonly linked with Archean gold systems. The generation of carbonate-undersaturated alkaline fluids in the high-grade core of the Wattle Dam deposit, may be explained by phase-separation or a CO2-poor alkaline fluid source. Either way these alkaline fluids played a critical role in mobilizing and concentrating very high amounts of gold into veins.
Students’ Attendance Monitoring through the Face Recognition

This paper describes experimental software solution for monitoring of students' attendance through the face detection and recognition in the video. The analysis of existing face detection and recognition algorithms was carried out, and the design of the system solution is described, implementing the previously analyzed algorithms. The Viola-Jones algorithm and HOG are used to detect the face in the video. To recognize an identity of a student, the convolutional neural network (NN) is utilized. The system was tested in the real university environment on selected courses. Once the face recognition is finished, the attendance list of present people may be generated. The system then provides additional visual verification of recognized faces to the lecturer (administrator).
Network Traffic Images: A Deep Learning Approach to the Challenge of Internet Traffic Classification

The challenge of network traffic classification exists at the heart of many networking related tasks aimed at improving the overall user experience and usability of the internet. Current techniques, such as deep packet inspection, depend heavily on interaction by network administrators and engineers to maintain up to date stores of application network signatures and the infrastructure required to utilize them effectively. In this paper, we introduce Network Traffic Images, a 2-dimensional (2D) formulation of a stream of packet header lengths, which enable us to employ deep convolutional neural networks for network traffic classification. Five different network traffic image orientation mappings are carefully designed to deduce the best way to transform the 1-dimensional packet-subflow into a 2D image. Two different mapping strategies, one packet-relative and the other time-relative, are experimented with to map the packets of a packet flow to the pixels in the image. Experiments shows that high classification accuracy can be achieved with minimal manual effort using network traffic images in deep learning.
An Arabic Dialects Dictionary Using Word Embeddings

The dialectical Arabic and the Modern Standard Arabic lacks sufficient standardized language resources to enable the tasks of Arabic language processing, despite it being an active research area. This work addresses this issue by firstly highlighting the steps and the issues related to building a multi Arabic dialect corpus using web data from blogs and social media platforms (i.e. Facebook, Twitter, etc.). This is to create a vectorized dictionary for the crawled data using the word Embeddings. In other terms, the goal of this article is to build an updated multi-dialect data set, and then, to extract an annotated corpus from it.
Logical Acquisition in the Forensic Investigation Process of Android Smartphones based on Agent using Open Source Software

Abstract Smartphones have an architecture with integrated components. Maintain the process of procuring digital goods on smartphones when asking for special assistance. According to the National Institute of Standards and Technology the process of procuring digital evidence on mobile devices There are 5 levels of acquisition: Manual Extraction, Logical Extraction, Hex Dumping / JTAG, Chip-Off and Micro Read. Which means buying digital evidence using logical acquisition methods or logical extraction still approved in SNI ISO 27037: 2014 also supports the process of procuring logistical permits in Special requirements, limited space. Dengen uses the live procurement method for logical acquisition on smartphones using AFLogical OSE can be obtained digital proof in the form of Contact List, Call Log, SMS and MMS. The use of digital proof Larons can be obtained through basic data from the application installed on the smartphone.
Crime Anatomization Using QGIS

With a population of approximately more than hundred lakh, the Mumbai city is witnessing an alarming increase in the rate of cognisable crimes year after year. It is the need of the hour, for the police in their role to catch criminals that they remain convincingly ahead in the race between lawbreakers and law enforcers. One of the paramount concerns of the police is how to enhance investigative effectiveness and to analyse the increasing number of crimes. The proposed research work creates a user-friendly interface to map crime using QGIS, visualize and analyze and predict crime incident patterns and trends using clustering algorithms such as K-Means, Agglomerative and predictive algorithm such as SVM and Random Forest.
What Is Face Recognition Technology?

Face recognition technology is one of several biometric tools or modalities used for person identification and verification. Broadly, it is mainly a monitoring and security technology designed to facilitate or control access used by governments, law enforcement agencies, and commerce. It has many other uses already in play, and many others are on the horizon. The chapter begins with a discussion of the technology and is followed with an overview of a few large-scale utilisers of FRT.
Substructure similarity search for engineering service-based systems

With the broad application of service-oriented architecture in service-oriented software engineering, service-based systems (SBSs) are becoming ever more widely used. As a result, the selection of appropriate component services destined to fulfill the functional requirements becomes a critical challenge for successfully building SBSs, especially when the pre-specified SBS plan involves a complicated structure. Because building an exact SBS is often too restrictive, a similarity search for complex functional requirements becomes an essential operation that must be efficiently supported. We thus investigate in this work the substructure similarity search problem of building a SBS. To solve this new research problem, we propose a feature-based method, called the substructure similarity search for service-based systems (5S), to help users find similar SBS solutions by progressively relaxing a SBS plan. The 5S approach models each SBS as a set of features and transforms the task of relaxation of a SBS into the maximum allowed missing features, which can filter many SBSs directly without costly structure comparisons. 5S thus opens a new paradigm for efficient SBS engineering that shortens the build cycle. Finally, we discuss a series of experiments using real-world Web service datasets that demonstrate the effectiveness and efficiency of the proposed approach.
Cross-lingual Information Retrieval: application and Challenges for Indian Languages

In today's digital era, accessing the web contents in the local regional languages is essential for the developing nations. Cross-Lingual Information Retrieval (CLIR) system add more complexity in terms of average precision as a part of the Indian languages compared to English text documents. CLIR refers to the retrieving data where the query and/or documents are in different languages. The poor performance is due to variation in term and ambiguity in lexical resources. This paper presents the challenges, issues that come along the way when developing CLIR applications which can be used for improving access ratio in local/regional languages and reviews some of the recent research methods in domain of cross lingual retrieval with current innovative directions.
A Hybrid Solution for Confidential Data Transfer Using PKI, Modified AES Algorithm and Image as a Secret Key

Nowadays the provision of online services by government or business organizations has become a standard and necessary operation. Transferring data including the confidential or sensitive information via Internet or insecure network and exchange of them is also increased day by day. As a result, confidential information leakage and cyber threats are also heightened. Confidential information trading became one of the most profitable businesses. Encrypting the data is a solution to secure the data from being exposed. In this paper, we would like to propose a solution for the secure transfer of data using symmetric encryption, asymmetric encryption technologies and Key Generation Server as a mixed hybrid solution. A Symmetric encryption, modified AES algorithm, is used to encrypt data. Digital certificate is used both for data encryption and digital signing to assure data integrity. Key generation server is used to generate the second secret key from the publicly recognized information of a person and this key is used as a second secret key in the modified AES. The proposed hybrid solution can be utilized in any applications that require high confidentiality, integrity of data and non-repudiation.
Efficient and Robust Pseudo-Labeling for Unsupervised Domain Adaptation

Unsupervised domain adaptation is to transfer knowledge from an annotated source domain to a fully-unlabeled target domain. The conventional methods consider the data which exceed a certain threshold of confidence as pseudo-labeled data for the target domain, and thus choosing the appropriate threshold affects the target performance. In this paper, we propose a new confidence-based weighting scheme for obtaining pseudo-labels and an adaptive threshold adjustment strategy to provide sufficient and accurate pseudo-labels throughout the training. To be precise, our confidence-based weighting scheme generates pseudo-labels to have a different contribution based on the confidence, which makes the performance less sensitive to the threshold. Also, the proposed adaptive threshold adjustment method chooses the threshold according to the degree of adaptation of a network to the target domain, and thus obviates the need for an exhaustive search for the appropriate threshold. Experimental results on a digit classification task show that the proposed methods efficiently utilizes the pseudo-labels to preserve sufficiency and accuracy.
Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.
Student postures and gestures recognition system for adaptive learning improvement

Adaptive learning is an educational method that uses computers to coordinate interaction with student and provide customized learning resources and activities to meet each student's unique needs that are a trend. In order to adjust the content and teaching methods appropriate for each learner, it is necessary to track the attitude of students with the content and methods being implemented. One of the ways of detecting student's attitudes is through the gestures and posture of student in the classroom.In this study, we propose to develop a model to monitor and identify students' postures and gestures during class time to help assess the level of student participation in the content and teaching methods of teachers. From there, it is possible to give advice to trainers and training managers in adjusting the subject content, teaching methods suitable for each specific object.
Coordinated and Hindsight Resources Allocation in Distributed Computing

In this work, we consider heuristic algorithms for parallel jobs execution and efficient resources allocation in heterogeneous computing environments. Existing modern job-flow execution features and realities impose many restrictions for the resources allocation procedures. Emerging virtual organizations and incorporated economic scheduling models allow users and resource owners to compete for suitable allocations based on market principles and fair scheduling policies. Subject to these features a special dynamic programming scheme is proposed to select resources depending on how they fit a particular job execution duration. Hindsight approach makes it possible to select between several different scenarios obtained with the same base scheduling procedure. Based on a conservative backfilling scheduling procedure we study how different resources allocation heuristics affect integral job-flow scheduling characteristics in a dedicated simulation environment.