Reviewerly/exHarmony huggingface dataset:
Fast deep parallel residual network for accurate super resolution image processing

Recently, Convolutional Network (CNN) has become an excellent solution to solve single image super resolution (SISR) problems. In this paper, a novel Deep Parallel Residual Network (DPRN) has been proposed which has been proved to be a fast and efficient algorithm to solve SISR problems using residual learning. It demonstrated significant advantages in accuracy and speed factors than other approaches with its deeper convolutional network structure, more accurate image reconstruction and real-time model execution. The proposed approach categorizes layers in branches and increases the number of layers to 35 with parallel local residual learning. Also, it adapts the Adam optimizer which can make the proposed system to achieve faster training speed and better image quality. Our experiments have been conducted using standard benchmark datasets such as Set5, Set14, BSDS100 and Urban100 and compared with current state-of-the-art approaches. Our results show that the proposed DPRN can provide higher super resolution quality with real time execution (27.18 fps average) when compare with different state-of-the-art algorithms.
Mining a new biometrics to improve the accuracy of keystroke dynamics-based authentication system on free-text

Related works for applying keystroke dynamics (KD) on free text identification indicated that applying KD can improve the accuracy of personal authentication on free text. As the result, this paper proposes a new biometrics, i.e., the keystroke clusters map (KC-Map), by clustering users’ keystrokes in order to effectively enhance the accuracy of personal authentication in free text. Since KC-Map is conducted via clustering, it is not suitable for traditional classifiers. In order to tackle this problem, the paper further proposes a keystroke clusters map similarity classifier (KCMS classifier). Experimental results positively show that the proposed KC-Map and KCMS classifier can efficiently improve the accuracy of personal authentication on free text with up to 1.27 times. In addition, one of the huge disadvantages on the current approaches in free text identification is that users are generally required to be trained for several months. Longer training time makes free text identification more impractical. Another motivation of this paper is to explore whether it is possible to shorten the training time into an acceptable range. Experimental results show that, to achieve relatively fair identification accuracy, users only need to carry out about 20 min for training.
An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.
Design and Implementation of Classroom Attendance System Based on Video Face Recognition

Classroom attendance, as an indispensable part of teaching activities, plays a very important role in classroom teaching. Classroom attendance can effectively supervise students to attend classes on time and ensure the quality of classroom teaching. However, the current classroom attendance is mainly achieved by the way of teacher's name-calling, which will cause a lot of waste of classroom time. This paper presents a classroom attendance system based on video face recognition technology. The system uses a camera installed in the classroom to obtain classroom video information. For the collected video information, it is first divided into a frame of static pictures, and from the pictures, several pictures with clear face and better light are selected for face recognition, and finally the recognition results are aggregated and merged. In order to solve the influence of the location of the camera on the recognition results, the system can be combined with the platform control system to control the rotation and focusing of the camera through the platform, and further improve the recognition accuracy.
Reversible image reconstruction for reversible data hiding in encrypted images

Reversible data hiding in encrypted image (RDHEI) can protect the privacy of original image content and enable data owner manages the encrypted image by reversible embedding additional data. In this paper, we propose a novel RDHEI framework based on reversible image reconstruction (RIR). Different from all the previous RDHEI methods that reversible data hiding (RDH) phase are unsatisfactory as the encrypted images hardly contain redundancy space, RIR rearranges the original image to construct a redundancy image and the meaningful information of the original image will become invisible at the same time. This rearranged image will be used as encrypted image, so RDH into this encrypted image becomes very easy and more additional data can be embedded. With RIR, general RDH algorithms for plaintext domain can be employed to realize a RDHEI scheme. Experimental results show that the performance of the proposed RIR based RDHEI schemes outperform the previous similar algorithms.
Geospatial relation captioning for high-spatial-resolution images by using an attention-based neural network

High-spatial-resolution (HSR) remote sensing images serve as carriers of geographic information. Exploring geo-objects and their geospatial relations is fundamental in understanding HSR remote sensing images. To this end, this study proposes an intelligent semantic understanding method for HSR remote sensing images via geospatial relation captions. Firstly, we propose a method of geospatial relation expression to convey the topological, directional and distance relations of geo-objects in HSR images. Secondly, on the basis of images and their geospatial relation captions, an image dataset is constructed for model training. Finally, geospatial relation captioning is implemented for HSR images by using an attention-based deep neural network model. Experimental results demonstrate that the proposed captioning method can effectively provide geospatial semantics for HSR image understanding.
A refined method for computing bibliographic coupling strengths

We propose a new method for computing the bibliographic coupling strength between two documents. This new method is based on the TF-IDF formula from the field of information retrieval. It is shown that this formula is a valid alternative for the original formula introduced by Kessler and is, from a probabilistic point of view, a correction of the Vladutz-Cook formula. We further define a cosine based similarity formula generalizing the Sen-Gan coupling angle formula.
Degree 2 is Complete for the Round-Complexity of Malicious MPC

We show, via a non-interactive reduction, that the existence of a secure multi-party computation (MPC) protocol for degree-2 functions implies the existence of a protocol with the same round complexity for general functions. Thus showing that when considering the round complexity of MPC, it is sufficient to consider very simple functions. Our completeness theorem applies in various settings: information theoretic and computational, fully malicious and malicious with various types of aborts. In fact, we give a master theorem from which all individual settings follow as direct corollaries. Our basic transformation does not require any additional assumptions and incurs communication and computation blow-up which is polynomial in the number of players and in $$S,2^D$$ , where S, D are the circuit size and depth of the function to be computed. Using one-way functions as an additional assumption, the exponential dependence on the depth can be removed. As a consequence, we are able to push the envelope on the state of the art in various settings of MPC, including the following cases. Technically, our non-interactive reduction draws from the encoding method of Applebaum, Brakerski and Tsabary (TCC 2018). We extend these methods to ones that can be meaningfully analyzed even in the presence of malicious adversaries.
Private Blockchain Network for IoT Device Firmware Integrity Verification and Update

There are various possible mechanisms for updating potentially vulnerable and exploitable software and firmware on Internet connected devices. Due to their well-known benefits, delta updates have become a common way of updating software. Recently, several authors proposed the use of blockchain technology to update software and firmware. While both delta updates and blockchain technology are now used in different areas, this paper studies the feasibility of combining the two technologies for firmware updates on resource constrained IoT devices such as Wi-Fi smart plugs and sensors. The paper identifies the scenarios where delta updates may not work and proposes a private blockchain network-based IoT device firmware integrity verification and update mechanism. The proposed private blockchain mechanism for integrity verification utilizes a tamper-proof blockchain server. The proposed solution aims to enhance firmware update performance.
PID-Like Adaptive Fuzzy Controller Design Based on Absolute Stability Criterion

This paper describes a method of PID-like adaptive fuzzy controller design for a linear time-invariant single-input and single-output dynamic plant. The plant transfer function is assumed to be partially known. The controller operates in the direct adaptation mode with the output feedback and a nonlinear reference model which provides better closed-loop response parameters than the system with a PID linear controller. The closed-loop stability is guaranteed during the adaptation process. The fuzzy controller is assumed to be a nonlinearity in a bounded sector and is defined by highly interpretable fuzzy rules. The absolute stability criterion for the system, containing PID-like fuzzy controller, is delivered. A design procedure that generalizes and significantly extends results reported so far in the literature and considerably automates the searching process of a nonlinear adaptive fuzzy controller is provided.
Towards Robust Compressed Convolutional Neural Networks

Recent studies on robustness of Convolutional Neural Network (CNN) shows that CNNs are highly vulnerable towards adversarial attacks. Meanwhile, smaller sized CNN models with no significant accuracy loss are being introduced to mobile devices. However, only the accuracy on standard datasets is reported along with such research. The wide deployment of smaller models on millions of mobile devices stresses importance of their robustness. In this research, we study how robust such models are with respect to state-of-the-art compression techniques such as quantization. Our contributions include: (1) insights to achieve smaller models and robust models (2) a compression framework which is adversarial-aware. In the former, we discovered that compressed models are naturally more robust than compact models. This provides an incentive to perform compression rather than designing compact models. Additionally, the latter provides benefits of increased accuracy and higher compression rate, up to 90×.
First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.
Deeply supervised model for click-through rate prediction in sponsored search

In sponsored search it is critical to match ads that are relevant to a query and to accurately predict their likelihood of being clicked. Commercial search engines typically use machine learning models for both query-ad relevance matching and click-through-rate (CTR) prediction. However, matching models are based on the similarity between a query and an ad, ignoring the fact that a retrieved ad may not attract clicks, while click models rely on click history, limiting their use for new queries and ads. We propose a deeply supervised architecture that jointly learns the semantic embeddings of a query and an ad as well as their corresponding CTR. We also propose a novel cohort negative sampling technique for learning implicit negative signals. We trained the proposed architecture using one billion query-ad pairs from a major commercial web search engine. This architecture improves the best-performing baseline deep neural architectures by 2% of AUC for CTR prediction and by statistically significant 0.5% of NDCG for query-ad matching.
Seeing possibilities for action: Orienting and exploratory behaviors in VR

This study aims to apply the concept of affordances (J. Gibson, 1979) to the context of immersive virtual reality (VR). As a first step, this study investigates users' visual orienting and exploratory behaviors while they navigate a virtual house indexed by their gaze on virtual objects, viewing time, and time spent in different rooms in the environment. A content analysis of participants' (N = 22) video recordings of VR exploration was conducted based on second-by-second time frame. Results indicate that participants tend to orient to the virtual objects when they display novel or unusual information. Also, users’ exploratory behaviors were focused on the virtual objects that are related to available actions of the given virtual environment.
Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts

Abstract Text readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts. In view of this, this study proposes a latent-semantic-analysis (LSA)-constructed hierarchical conceptual space that can be used to train a readability model to accurately assess domain-specific texts. Compared with a baseline reference using a traditional model, the new model improves by 13.88% to achieve 68.98% of accuracy when leveling social science texts, and by 24.61% to achieve 73.96% of accuracy when assessing natural science texts. We then combine the readability features developed for the current study with general linguistic features, and the accuracy of leveling social science texts improves by an even higher degree of 31.58% to achieve 86.68%, and that of natural science texts by 26.56% to achieve 75.91%. These results indicate that the readability features developed in this study can be used both to train a readability model for leveling domain-specific texts and also in combination with the more common linguistic features to enhance the efficacy of the model. Future research can expand the generalizability of the model by assessing texts from different fields and grade levels using the proposed method, thus enhancing the practical applications of this new method.
Cryptographic Technology Blockchain and Its Applications

The use of modern cryptotechnology “Blockchain” for acceleration, transparency and decentralization of financial transactions and as a promising digital payment instruments and cryptocurrencies (e.g. Bitcoin, Monero, and Ethereum) has become an important trend since approximately 2000. This article focuses the problem of using blockchain technologies and their networked cryptographic applications and apps. The influencing factors and sources of Blockchain cryptotechnology were discussed, the comparison of centralized bank systems vs. decentralized systems was carried out, the mining process for cryptographic currencies, the concept of a public ledger, the validation principles PoW and PoS are represented, and profitability of cryptographic currencies was analyzed. Furthermore, important applications of Blockchain cryptotechnology were shown (such as Smart Contracting, Bletchley) as well as the accompanying risks, their advantages and disadvantages were discussed. In addition, the malicious applications were discussed such as the ransomware (extortion Trojans). Finally, the potential and future perspectives of Blockchain cryptotechnology for real business applications were assessed.
Matrix Algebra Framework for Portable, Scalable and Efficient Query Engines for RDF Graphs

Existing query engines for RDF graphs follow one of two design paradigms: relational or graph-based. We explore sparse matrix algebra as a third paradigm and propose MAGiQ: a framework for implementing SPARQL query engines that are portable on various hardware architectures, scalable over thousands of compute nodes, and efficient for very large RDF datasets. MAGiQ represents the RDF graph as a sparse matrix and defines a domain-specific language of algebraic operations. SPARQL queries are translated into matrix algebra programs that are oblivious to the underlying computing infrastructure. Existing matrix algebra libraries, optimized for each particular architecture, are called to execute the program and handle the performance issues. We present three case studies of matrix algebra back-end libraries: SuiteSparse, MATLAB, and CombBLAS; we demonstrate how MAGiQ can effortlessly be ported on a variety of architectures such as Intel CPUs, NVIDIA GPUs, and Cray XC40 supercomputers. Our experiments on large-scale real and synthetic datasets show that MAGiQ performs comparably to or better than existing specialized SPARQL query engines for data-intensive queries, scales to very large computing infrastructures, and handles datasets with up to 512 billion triples.
An Improved Initialization Based Histogram of K-Mean Clustering Algorithm for Hyperchromatic Nucleus Segmentation in Breast Carcinoma Histopathological Images

Mitotic count assessment in breast carcinoma can be a considerable challenge especially when involve with algorithm development. The challenges lie within the hyperchromatic nucleus segmentation that served as a fundamental block in mitotic count assessment. In this study, we proposed an improved initialization based histogram of K-Mean clustering algorithm for hyperchromatic nucleus segmentation in breast carcinoma histopathological images. The focus is to segment the hyperchromatic nucleus from the background using K-Mean clustering algorithm. Conventional initialization method for K-Mean clustering was improved by establishing a relationship between the hyperchromatic nucleus and the intensity histogram. 75 images captured from 15 histopathological slides were used as dataset. The overall Sensitivity in ground truth segmentation of the proposed method was found to have a percentage of 100.0%. The values of precision (AreaPre) and sensitivity (AreaSen) in mitotic cells area segmentation were found to be promising with percentages of 95.2% and 89.2%, respectively. The promising results perhaps could be used to enhance performance of the true mitotic cell detection.
Enriching Word Embeddings for Patent Retrieval with Global Context

The training and use of word embeddings for information retrieval has recently gained considerable attention, showing competitive performance across various domains. In this study, we explore the use of word embeddings for patent retrieval, a challenging domain, especially for methods based on distributional semantics. We hypothesize that the previously reported limited effectiveness of semantic approaches, and in particular word embeddings (word2vec Skip-gram) in this domain, is due to inherent constraints on the (short) window context that is too narrow for the model to capture the full complexity of the patent domain. To address this limitation, we jointly draw from local and global contexts for embedding learning. We do this in two ways: (1) adapting the Skip-gram model's vectors using global retrofitting (2) filtering word similarities using global context. We measure patent retrieval performance using BM25 and LM Extended Translation models and observe significant improvements over three baselines.
Evaluation of Throughput Prediction for Adaptive Bitrate Control Using Trace-Based Emulation

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE).In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering.Throughput prediction plays an important role in helping select the proper video bitrate dynamically.In this paper, we studied the influence of throughput prediction on adaptive video streaming.Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically.However, this is difficult in academic research.Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments.The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming.The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods.However, throughput prediction alone is not enough to ensure high QoE.To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection.By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used.The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.