Reviewerly/exHarmony huggingface dataset:
Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification

In this letter, we investigate fine-grained classification of arbitrary-oriented ships in very high resolution optical imagery using Bag of Word model with Spatial Pyramid (SP-BoW). Given that based on ‘spatial pyramid’ of the histogram of local features, the final feature vectors not only count the multiplicity of ‘words’ but also represent their spatial topology. We attempt to improve the performance of this model by introducing augmented data for training phase. Our aim is to make the dataset big enough to be able to capture holistic variation of ship orientation. Three data augmentation operations are used including random rotate by an angle of modulo 90°, random flip-left-right, and random flip-top-bottom. Through this procedure, our trained SP-BoW model is able to get better generalization. The proposed approach is validated on the High-Resolution Ship Collections 2016 (HRSC2016) ship dataset. The results indicate that training on augmented data can significantly improve the performance of SP-BoW. Beside, compared to other state-of-the-art convolutional neural network-based approaches, the approach proposed in this research has yielded competitive results and could make it a good baseline for evaluating more sophisticated CNN architecture in the future.
Face expression recognition using Local Gabor Binary Pattern Three Orthogonal Planes (LGBP-TOP) and Support Vector Machine (SVM) method

The video expression recognition system has been created before using the Local Gabor Binary Pattern Three Orthogonal Planes (LGBP-TOP) extraction method and the Support Vector Machine (SVM) classifi-cation method. However, the recognizable facial expressions use the entire area of the face image, while the expression can be recognized from the change of face fiducial point on the eyes and lips only. In this study, the introduction of facial expressions was developed using LGBP-TOP and SVM methods by focusing on facial and lip images only. Therefore, an algorithm is needed to extract the eye and lip area of the face ima-ge using 3x3 blocks and 4x4 blocks, which will then be used as input on the LGBP-TOP method. After the image of the eyes and lips extracted its features, the extraction results are classified using the SVM method. The results obtained is the recognition of facial expressions using the eye and lip area get 80% accuracy and better than using the entire area of the face, eye area only, and lip area only.
Cross Domain Model Compression by Structurally Weight Sharing

Regular model compression methods focus on RGB input. While cross domain tasks demand more DNN models, each domain often needs its own model. Consequently, for such tasks, the storage cost, memory footprint and computation cost increase dramatically compared to single RGB input. Moreover, the distinct appearance and special structure in cross domain tasks make it difficult to directly apply regular compression methods on it. In this paper, thus, we propose a new robust cross domain model compression method. Specifically, the proposed method compress cross domain models by structurally weight sharing, which is achieved by regularizing the models with graph embedding at training time. Due to the channel wise weights sharing, the proposed method can reduce computation cost without specially designed algorithm. In the experiments, the proposed method achieves state of the art results on two diverse tasks: action recognition and RGB-D scene recognition.
C3-Flow

Existing approaches to neural network compression have failed to holistically address algorithmic (training accuracy) and computational (inference performance) demands of real-world systems, particularly on resource-constrained devices. We present C3-Flow, a new approach adding non-uniformity to low-rank approximations and designed specifically to enable highly-efficient computation on common hardware architectures while retaining more accuracy than competing methods. Evaluation on two state-of-the-art acoustic models (versus existing work, empirical limit study approaches, and hand-tuned models) demonstrates up to 60% lower error. Finally, we show that our co-design approach achieves up to 14X inference speedup across three Haswell- and Broadwell-based platforms.
Rate Allocation for Bayer-Pattern Image Compression with JPEG XS

Sensors in digital cameras use a technology called "Bayer Patterns'" allowing color photography with a planar arrangements of sensor elements. Alternating arrangements of red, green and blue filter masks on top of a rectangular grid of sensor elements allow capturing of color information, but also require a de-mosaicing algorithm to reconstruct a full-resolution color image from the sensor data. In high-speed applications, or applications where the system design requires low-latency, low-complexity compression, the JPEG~XS standard of the JPEG committee offers an elegant solution to compress Bayer pattern images close to the sensor, and to transmit the compressed data over a lower bandwidth connection while maintaining visually lossless quality. This paper discusses algorithms, and in particular modifications to the rate allocation of JPEG XS to enable compression of Bayer pattern sensor data. The main contribution of this work is a modified algorithm to compute band gains and band priorities that ensures PSNR-optimality of the demosaiced image rather the Bayer pattern itself.
Towards Improved Saudi Dialectal Arabic Stemming

Arabic is a highly inflected language with a complex morphological structure compared to English, which makes the development of stemming mechanisms difficult. Stemming is even more challenging for dialectal Arabic, as it does not follow standard Arabic grammar rules for term conflation and attaching affixes to words. In this paper, we propose a new stemmer that integrates two techniques to stem Saudi words: The Information Science Research Institute (ISRI) Arabic stemmer and a rule-based stemmer developed in-house. Our stemming approach was applied to a corpus collected from Saudi dialect tweets and demonstrated improved stemming accuracy.
Printed Arabic Text Database for Automatic Recognition Systems

Document image analysis and recognition are important topics in artificial intelligence as they are necessary for the retrieval of documents. Hence, the availability of a database with good script samples is a key requirement for machine-learning processes. Good printed text databases exist for Latin languages. However, there is a lack of databases with Arabic samples. This paper presents a new comprehensive database called PATD (Printed Arabic Text Database), which contains eight hundred and ten images scanned in grayscale format and different resolutions, leading to two thousand and nine hundred and fifty-four images (smartphone-captured images) under varying capture conditions (blurred, at different angles and in different light). It is based on ten newspapers created with different structures, and an open-vocabulary, multi-font, multi-size and multi-style text. The database is described in detail and it is intended for the research community.
Reference-based local color distribution transformation method and its application to image integration

We propose a reference-based filtering method that transforms the local color distribution of individual image patches. With our method, the color of a reference image is transformed by patch-wise color transformation so that it comes close to a noisy input image. Our method can be regarded as an extended version of reference-based filtering methods, such as guided image filtering and our previously proposed linear local color distribution transformation (LCDT). The main contribution is to enable more flexible transformation, which is required in some applications, e.g., flash/no-flash image integration and high dynamic range (HDR) image generation. We also propose an efficient framework for HDR image generation which is particularly suitable for generation of dark scenes without loss of image contrast. Experimental results show that the proposed quadratic LCDT achieves more robust color transformation, even in flash/no-flash image integration, and it was confirmed that the proposed framework based on quadratic LCDT is effective in HDR image generation.
STT-ANGIE: Asynchronous True Random Number GEnerator Using STT-MTJ

The Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ) is an emerging memory technology whose interesting stochastic behavior might benefit security applications. In this paper, we leverage this stochastic behavior to construct a true random number generator (TRNG), the basic module in the process of encryption key generation. Our proposed TRNG operates asynchronously and thus can use small and fast STT MTJ devices. As such, it can be embedded in low-power and low-frequency devices without loss of entropy. We evaluate the proposed TRNG using a numerical simulation, solving the Landau-Lifshitz-Gilbert (LLG) equation system of the STT-MTJ devices. Design considerations, attack analysis, and process variation are discussed and evaluated. The evaluation shows that our solution is robust to process variation, achieving a Shannon-entropy generating rate between 99.7Mbps and 127.8Mbps for 90% of the instances.
BudayaKB

Cultural heritage is the root of national identities, and contributes to tourism, economics, industry, and business. Digital preservation of cultural heritage is therefore crucial, particularly in a form that is easily processable by machines. Available cultural heritage information on Web sources (e.g., Wikipedia) is presented in multiple formats, such as free-form text, lists, and tables. Such formats, however, lack structures and links to other information sources. The provision of cultural heritage information as a knowledge base, that is both structured and linked, would pave new ways for consuming such information. In this paper, we propose an approach to extract entities of cultural heritage from diverse formats (i.e., text, lists, and tables), and to construct a knowledge base of cultural heritage entities, called BudayaKB, using RDF data model that provides an integrated, format-independent view. Our extraction approach follows on the observation that cultural heritage entities are often written down either with common noun descriptors (e.g., Jiwa temple) or hypernym-hyponym sentence patterns (e.g., ... Acehnese traditional weapons such as Rencong...). We evaluate our approach to Indonesian Wikipedia, and achieve a precision of 84% for extracting Indonesian cultural heritage entities. The extracted entities are then imported and linked to the Wikidata KB, allowing greater interoperability of cultural heritage information. BudayaKB is openly available at https://budayakb.cs.ui.ac.id/.
Fast Computing the Algebraic Degree of Boolean Functions

Here we consider an approach for fast computing the algebraic degree of Boolean functions. It combines fast computing the ANF (known as ANF transform) and thereafter the algebraic degree by using the weight-lexicographic order (WLO) of the vectors of the n-dimensional Boolean cube. Byte-wise and bitwise versions of a search based on the WLO and their implementations are discussed. They are compared with the usual exhaustive search applied in computing the algebraic degree. For Boolean functions of n variables, the bitwise implementation of the search by WLO has total time complexity $$O(n.2^n)$$ . When such a function is given by its truth table vector and its algebraic degree is computed by the bitwise versions of the algorithms discussed, the total time complexity is $$\varTheta ((9n-2).2^{n-7})=\varTheta (n.2^n)$$ . All algorithms discussed have time complexities of the same type, but with big differences in the constants hidden in the $$\varTheta $$ -notation. The experimental results after numerous tests confirm the theoretical results—the running times of the bitwise implementation are dozens of times better than the running times of the byte-wise algorithms.
The DOMJudge Based Online Judge System with Plagiarism Detection

There were many online judge systems which were created to automatically judge programming code. Among the development of online judge systems, the open-source online judge systems are also developing gradually, like for example Sharif Online Judge and DOMjudge. This research modified the DOMjudge by creating additional functions and improved the graphic user interface of DOMjudge in the user-friendly way to students and teachers. Besides, the research also developed a plagiarism detection system, which helps teachers to find out who cheated during the contest. This paper showed that proposed preprocessing and using k-Grams, Hashing and Suffix Array to compare two source codes is better than conventional methods.
An Improved Video Steganography: Using Random Key-Dependent

Steganography is defined as the art of hiding secret data in a non-secret digital carrier called cover media. Trading delicate data without assurance against intruders that may intrude on this data is a lethal. In this manner, transmitting delicate information and privileged insights must not rely on upon just the current communications channels insurance advancements. Likewise should make more strides towards information insurance. This article proposes an improved approach for video steganography. The improvement made by searching for exact matching between the secret text and the video frames RGB channels and Random Key -Dependent Data, achieving steganography performance criteria, invisibility, payload/capacity and robustness.
A Single Program Multiple Data Algorithm for Feature Selection

Feature selection is a critical component in data science and has been the topic of research for many years. Advances in hardware and the availability of better multiprocessing platforms have enabled parallel computing to reach very high levels of performance. Minimum Redundancy Maximum Relevance (mRMR) is a powerful feature selection technique used in many applications. In this paper, we present a novel optimized Single Program Multiple Data (SPMD) approach to implement the mRMR algorithm with synchronous computation, optimum load balancing and greater speedup than task-parallel approaches. The experimental results presented using multiple synthesized datasets prove the efficiency and scalability of the proposed technique over original mRMR.
SparkJNI: A Toolchain for Hardware Accelerated Big Data Apache Spark

The JVM (Java virtual machine) is the cornerstone in most big data frameworks, focusing on automatic memory management and enabling high-productivity languages. Aside from the performance overhead induced by JVM languages (e.g., Java, Scala, etc.), big data frameworks, including Spark, also restrict code execution to general purpose processors (CPUs), while HPC clusters readily include dedicated accelerators for achieving their high performance. In this paper, we analyze the state-of-the-art developments in the field of heterogeneously accelerated Spark, and we propose SparkJNI, a framework for JNI accelerated Spark. The design provides two main components. First, it enables a seamless utilization of native CPU code, in addition to integration of GPU as well as FPGA accelerators. Secondly, SparkJNI enables accelerated execution through native code integration by automatically generating C++ code wrappers for easy code development by the programmer. This makes it non-disruptive to the Java programmer, while allowing great flexibility for native code development. Results of running a number of benchmarks show insignificant JNI-induced overhead in access time and bandwidth, with speedups of up to 12x for compute-intensive kernels (such as convolution), in comparison to pure Java Spark implementations. Last, a DNA analysis algorithm (Pair-HMM) is implemented in Spark and integrated with FPGAs, targeting cluster deployments, with benchmark results showing an overall speedup of ~ 2.7x over state-of-the art CPU optimizations. The result of the presented work, along with the SparkJNI framework are publicly available on GitHub for open-source usage and development.
Research in fractured digital spaces

The internet is a medium for research, a place for the exchange of drugs and knowledge, and a method for governing and surveilling drug users. Opportunities for drug research with and in digital spaces are expanding, using internet mediated methods such as online surveys, web scraping, and research with web communities and users of cryptomarkets and apps. As the sphere of social data grows, so does the degree to which data itself is a product of fractured, governed, privatised set of spaces. Research often has to work with these structural aspects and researchers have to be aware of the structural mediation of their data. The opportunities for research also demand that researchers consider the validity of traditional scientific hierarchies such as the assumed superiority of probability sampling, and parse the naturally occurring taxonomies that are produced by the systems they research. A positive development has been the growth of internet focused researchers who operate in and outwith the academy and who are creating an independent research infrastructure with potential for a democratic research politics.
A2A: Attention to Attention Reasoning for Movie Question Answering

This paper presents the Attention to Attention (A2A) reasoning mechanism to address the challenging task of movie question answering (MQA). By focusing on the various aspects of attention cues, we establish the technique of attention propagation to uncover latent but useful information to the underlying QA task. In addition, the proposed A2A reasoning seamlessly leads to effective fusion of different representation modalities about the data, and also can be conveniently constructed with popular neural network architectures. To tackle the out-of-vocabulary issue caused by the diverse language usages in nowadays movies, we adopt the GloVe mapping as a teacher model and establish a new and flexible word embedding based on character n-grams learning. Our method is evaluated on the MovieQA benchmark dataset and achieves the state-of-the-art accuracy for the “Video+Subtitles” entry.
Wireless Wearable for Sign Language Translator Device with Android-Based App

Sign language translator device is an assistive device that helps hearing impaired and normal people communicate with each other. The device can be either a camera vision-based or wearable glove-based system that translates hand gestures into text or voice. However, vision-based devices are susceptible to low light conditions and expensive. In contrast, glove-based devices are popular and low cost, but many designs utilized cable system that limits hand motions. The main focus of this work is to develop a wireless wearable sign language translator device, so that free-hand motions can be achieved. An Android-based smartphone application (app) has been developed to receive and display translated gestures from the wearable device in text form. The usage of smartphone app significantly reduces development cost, simplifies the design and improves the ease-of-use for hearing impaired communities. In this article, the developed hardware, circuit diagrams, app development as well as experimental results are presented.
Suitability of features of deep convolutional neural networks for modeling somatosensory information processing

Deep Learning (DL) has recently led to great excitement and success in AI and attracted further attention due to the fact that the features extracted in the early cortical layers have properties similar to those of real neurons in the primary visual cortex. Understanding cortical mechanisms of sensory information processing is important for improving DL systems as well as for developing more realistic simulations of cortical systems. Just like representing speech in timefrequency domain makes it possible to use DL systems trained on images for speech recognition, simulating tactile information processing can also be modeled and studied using DL. Tactile stimulators, such as those invented by Cortical Metrics, are efficient tools for quantitative sensory testing. To model the features extracted by tactile information processing in the somatosensory cortex more accurately, we propose a novel unsupervised DL method using transfer learning and the principle of contextual guidance. Our approach helps describe the goal of sensory coding in early cortical areas because low-level stimulus features that would be behaviorally useful as building blocks enabling the construction of high-level behaviorally significant features have to rely on some principles, such as contextual guidance and transfer-utility, applicable to any sensory modality (whether visual, auditory, or tactile). We show that the emergent features offer higher performance accuracy than AlexNet on the Caltech-101 dataset and on classification of textures resembling tactile stimuli S1 somatosensory area processes. Our computational modeling approach can help improve (i) Cortical Metrics approaches, (ii) sensorimotor cortical models, and (iii) deep hybrids of unsupervised and supervised networks.
Tracking using state estimation: A brief introduction

In this brief introduction to tracking using state estimation, we compare two techniques. We compare the performance of the well known Kaiman filter to that of a simple receding horizon filter, a less well known technique. Our goal is to introduce the less well known family of techniques and show some of its advantages and disadvantages relative to Kalman-filter-based techniques.
KEcruiser: A novel control flow protection for kernel extensions

Vulnerable kernel extensions are severe threats to the security of modern operating systems. Due to lack of protection mechanism in the kernel space, the kernel extension exploitation could take over the entire operating system’s control. To enhance security and reliability of kernel extensions, many solutions mainly rely on adding the kernel isolation mechanisms to confine the execution behaviors of kernel extensions. However, previous methods suffer from limitations in terms of compatibility and performance cost. To address these issues, we present KEcruiser, a novel control flow protection mechanism for kernel extensions. The basic idea of our approach is to monitor the control flow of a kernel extension and then identify the abnormal execution behavior during run-time. Based on the recent hardware feature, our system can collect the kernel control flow information efficiently. By leveraging the virtualization technology, our security monitor is deployed outside of the target VM so that the kernel control flow can be checked securely. To ensure the monitoring correctness and concurrency, we make use of Lamport’s ring buffer algorithm. Our system is compatible with the existing commodity operating system, and it can protect the running kernel extensions transparently. The experiments show that KEcruiser can effectively identify control flow violation occurred in kernel extensions with small performance cost.